diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..7490c3d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +# AutoQuant Project evidence is hashed by exact bytes. Never let Git rewrite +# line endings inside datasets, source snapshots, or immutable research objects. +projects/** -text diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..49ee821 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,50 @@ +name: CI + +on: + push: + pull_request: + +permissions: + contents: read + +concurrency: + group: ci-${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + core: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - run: uv python install 3.12 + - name: Test changed research contracts + run: >- + uv run --python 3.12 --with pytest python -m pytest + tests/test_model_runtime.py + tests/test_compute_jobs.py + tests/test_verification.py + tests/test_json_event_intake.py + tests/test_cli.py::AgentCliTests::test_capabilities_describe_every_public_command + tests/test_studio.py::StudioObservationTests::test_workspace_and_project_snapshots_share_verified_contract + -q + + frontend: + runs-on: ubuntu-latest + defaults: + run: + working-directory: studio-web + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-node@v6 + with: + node-version: 22 + cache: npm + cache-dependency-path: studio-web/package-lock.json + - run: npm ci + - run: npm test + - run: npm run lint + - run: npm run check:boundary + - run: npm run build diff --git a/.gitignore b/.gitignore index 061c2a0..a8a191a 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,12 @@ __pycache__/ /dist/ *.egg-info/ +# Next.js Studio workbench +/studio-web/node_modules/ +/studio-web/.next/ +/studio-web/out/ +/studio-web/.env*.local + # Editor / OS .DS_Store .vscode/ diff --git a/DESIGN.md b/DESIGN.md new file mode 100644 index 0000000..ac29be2 --- /dev/null +++ b/DESIGN.md @@ -0,0 +1,97 @@ +--- +name: AutoQuant Studio Evidence Workbench +schema: design-pipeline.design-foundation.v0.1 +sourceMode: requirements-only +activeChange: next-studio-workbench +--- + +## Product Context + +AutoQuant Studio is a dense, repeated-use research environment for factor researchers. Its central promise is point-in-time truth: a researcher can see what information was actually available at a historical moment, form a factor hypothesis from that evidence, and test it without silently changing the evidence underneath. + +The product is self-hosted and open source. It supports A-share announcements, crypto events and financial news through adapters. It has no live trading, broker account or order-routing surface. + +## Overview + +The design posture is an evidence console, not a marketing dashboard and not a retail trading terminal. The primary screen lets a user scan a shared time axis, select evidence, compare cohorts and move into a test without losing context. + +Priorities: + +1. Make time, provenance and uncertainty legible before visual appeal. +2. Make repeated research work fast: dense, stable, keyboard-friendly and unsurprising. +3. Keep data visualizations inspectable. A chart never hides the source, time boundary or computation behind it. +4. Use visual distinction to identify state and evidence class, not to claim confidence that the data does not have. + +## Colors + +| Token | Value | Use | +|---|---|---| +| ink-950 | #0B1118 | application canvas | +| ink-900 | #111A24 | persistent navigation and inspector | +| ink-800 | #182434 | raised data surfaces and selected rows | +| line-700 | #2D3A49 | grid lines, separators and chart axes | +| paper-100 | #E7EDF3 | primary text | +| paper-300 | #AFBDCB | secondary text and annotations | +| signal-cyan | #52C7D9 | confirmed factor signal and selected time cursor | +| event-amber | #F1B35C | event marker and pending evidence | +| success-mint | #6BCB9A | completed research job and passing data health | +| danger-coral | #EC7C73 | failed job, invalid input and blocking data issue | +| muted-slate | #728198 | unavailable, restricted or partial state | + +Color never carries meaning alone. Every trusted-state color has an icon, visible label and programmatic name. + +## Typography + +- UI and tabular data use an installed system sans-serif stack with tabular numerals. +- Metric values use tabular figures at 14-18 px; dense tables use 12-13 px with a minimum 1.35 line height. +- Screen titles are functional labels, not editorial hero copy. +- Long evidence text is constrained to a readable measure in the inspector, never compressed into chart tooltips. + +## Layout + +- Desktop research workbench: 12-column fluid grid with a persistent 240 px navigation rail, a 320-400 px inspector and a flexible central canvas. +- The central canvas owns the shared time axis. K-line, factor signal, market snapshot and event tracks align to it. +- At 1024 px and below, the inspector becomes a focus-managed drawer and event filters become a collapsible panel. Charts retain horizontal pan/zoom rather than shrinking labels below legibility. +- At 768 px and below, research actions use a staged single-column flow: context, chart, selected evidence, action drawer. No critical state is hover-only. +- Dense data uses rows, dividers and hierarchy; avoid nested decorative cards. + +## Components + +| Component | Contract | +|---|---| +| Trust strip | Fixed, top-of-workspace summary of replay time, bundle coverage, source/permission state and known gaps. | +| Replay transport | Time cursor, step, jump-to-event, pause and optional playback. Always shows timezone and visible-at boundary. | +| Event marker | Encodes adapter, evidence state and group membership; opens a full inspector, never a content-only tooltip. | +| Evidence inspector | Shows source, all relevant timestamps, license state, revision, hash and local/open-source access policy. | +| Cohort tray | Holds one or two explicitly named event groups; comparison cannot start until both have a saved membership rule. | +| Factor passport header | Stable identity, version, latest test status, linked research frame and direct links to data/compute/audit. | +| Test configuration panel | Shows active universe, lag, cost, rebalance, coverage and visibility policy before a run is created. | +| Result provenance card | Always accompanies test metrics with input snapshots, engine version, job ID and output hash. | +| Compute job row | State, resource budget, start/end, retries, logs, outputs and link back to research object. | +| Evidence status chip | Known, partial, delayed, revised, restricted or missing; includes an explanatory detail on focus/click. | + +## Do's and Don'ts + +Do: + +- Put the replay timestamp and data coverage in the primary reading path. +- Preserve a user's research context across every jump between replay, factor, test, task and audit. +- Use calm visual hierarchy and durable table layouts for repeated work. +- Make empty, delayed, restricted and failed states useful: say what is absent, why and what can be done next. +- Offer keyboard navigation for time stepping, opening the inspector and adding evidence to a cohort. + +Don't: + +- Do not use candlesticks, red/green returns or neon movement to imply a trading product. +- Do not use a generic card grid or dashboard hero as the main research surface. +- Do not hide data versions, timing assumptions or restricted content behind an advanced menu. +- Do not invent confidence scores when coverage or provenance is unknown. +- Do not autoplay a historical narrative or use decorative data animation. + +## Source Decisions + +| Source | Adopted | Rejected | Reason | +|---|---|---|---| +| Approved replay-first design | Shared time axis, right-side evidence inspector, factor passport entry and ResearchFrame/ReplayBundle concepts | Treating replay as the only required product page | The product requires the full research loop, not a single viewer. | +| Approved AutoQuant scope | Unified research kernel, complete research pages and explicit non-trading boundary | Real trading surfaces | AutoQuant owns factor research from evidence to test result. | +| Requirements-only design synthesis | Quiet, dense evidence-console posture with AutoQuant-owned tokens and domain components | Borrowing another product's visual identity or live-site style | Public Mantine primitives may supply accessible component mechanics, but they must remain themed by AutoQuant and may not define the product identity. | diff --git a/MOTION.md b/MOTION.md new file mode 100644 index 0000000..e3cc83b --- /dev/null +++ b/MOTION.md @@ -0,0 +1,39 @@ +--- +schema: design-pipeline.motion-foundation.v0.1 +name: AutoQuant Studio motion language +posture: static +primitiveRegistry: design-pipeline.motion-primitives.v1 +--- + +## Motion Thesis + +Research evidence should feel stable. Motion must never create a sense of price momentum, urgency or certainty. The product uses deliberate state changes and manual temporal navigation instead of decorative animation. + +## Motion Principles + +- A replay step changes the research time state; it is not a cinematic playback effect. +- State confirmation must be immediate and readable before any visual transition completes. +- The user can stop, step or jump at every point. No transition blocks evidence inspection or keyboard input. +- New data, revisions and failed tasks appear as labeled state changes, never as surprise animation. + +## Motion Vocabulary + +No registry primitive is selected. The static posture is intentional because the primary user work is analytical reading, comparison and audit. + +## Procedural Motion + +None. Charts and replay time progression are data render states, not procedural visual effects. + +## Runtime Policy + +Use semantic DOM and SVG for bounded visualizations. Small focus, hover and loading feedback may use CSS only if it preserves layout, can be interrupted and communicates no research meaning by itself. No GSAP, Anime.js, Canvas or GPU rendering runtime is selected by this foundation. + +## Reduced Motion + +The default posture already avoids continuous or decorative motion. The reduced-motion fallback keeps replay in manual-step mode, suppresses optional playback transitions and preserves focus, ordering, labels and error feedback. + +## Source Decisions + +- Adopted: a static, manual-step posture that treats replay as an inspectable research state. +- Rejected: animated market theatrics, continuous decorative loops and borrowed external motion implementations. +- This is an authored, requirements-only decision. No external motion implementation or visual reference was copied. diff --git a/PLANS.md b/PLANS.md index 1c7de08..b6e3a65 100644 --- a/PLANS.md +++ b/PLANS.md @@ -18,7 +18,9 @@ invariants live in `docs/design/`. ## Active plans -None. +| Plan | Outcome | Updated | +| --- | --- | --- | +| [[plans/agent-research-console]] | Implement the approved OpenSpec Agent Research Console through one Core-owned Operator Port, versioned research definitions, bounded Campaigns, a truthful ResearchLedger Studio, exact-version approval, and reproduction. | 2026-08-03 | ## Proposed plans @@ -28,12 +30,14 @@ None. | Plan | Outcome | Updated | | --- | --- | --- | +| [[plans/next-studio-workbench]] | Internalized the approved Next.js factor-research workbench over the verified Studio snapshot while keeping private plugin integrations outside the open-source repository. | 2026-08-02 | | [[plans/order-native-portfolio-decisions]] | Return evidence-backed target weights together with bounded Order/TPSL realization plans shared by mechanical and RL research. Paused until the workbench product model is canonical. | 2026-07-27 | ## Completed plans | Plan | Outcome | Updated | | --- | --- | --- | +| [[plans/autoresearch-factor-strategy-studio]] | Exposed the existing Core research program as connected Factor Research and Strategy Research workbenches with truthful evidence, status, model/holdout artifacts, and bounded next actions. | 2026-08-03 | | [[plans/factor-evidence-report-handoff]] | Made temporal Factor protocol applicability, material train/validation tension, and safe evidence-bound Report drafting explicit to fresh Agents. | 2026-08-02 | | [[plans/caller-owned-factor-population]] | Separated caller-owned Factor evaluation population from Portfolio position authority across standalone Factor, Portfolio, and governed-RL research. | 2026-08-02 | | [[plans/concise-documentation-front-door]] | Make README a bounded product entrance and route Agents to one operator guide plus the existing authoritative release documents. | 2026-08-02 | diff --git a/autoquant/capabilities.py b/autoquant/capabilities.py index 629427a..81c3dae 100644 --- a/autoquant/capabilities.py +++ b/autoquant/capabilities.py @@ -165,7 +165,7 @@ def descriptor( ), descriptor( "schema", - "aq schema [workspace|project|agent-work-brief|research-agenda|holdout-binding|holdout-result|holdout-assessment-analysis|holdout-assessment|holdout-status|study|judge-output|run-result|factor-claim|factor-population|factor-candidate-contract|factor-diagnostics|event-study-policy|event-study-diagnostics|book-path-stress-policy|book-path-stress-diagnostics|allocation-policy|allocation-diagnostics|book-risk-diagnostics|portfolio-diagnostics|research-program-status|rl-policy-diagnostics|session-decision-matrix|session|session-completion|candidate-preflight|candidate-check-output|candidate-check-result|portfolio-mandate|research-horizon|experiment|research-request|ohlcv-dataset-package|report-analysis|review-analysis|dossier-analysis|dossier-result|dossier-status|researcher-response|campaign-result|campaign-progress|studio-snapshot] [--json]", + "aq schema [workspace|project|agent-work-brief|research-agenda|holdout-binding|holdout-result|holdout-assessment-analysis|holdout-assessment|holdout-status|study|judge-output|run-result|factor-claim|factor-population|factor-candidate-contract|factor-diagnostics|event-package|event-study-policy|event-study-diagnostics|book-path-stress-policy|book-path-stress-diagnostics|allocation-policy|allocation-diagnostics|book-risk-diagnostics|portfolio-diagnostics|research-program-status|rl-policy-diagnostics|session-decision-matrix|session|session-completion|candidate-preflight|candidate-check-output|candidate-check-result|portfolio-mandate|research-horizon|experiment|factor-definition|experiment-definition|strategy-definition|operator-request|operator-receipt|research-ledger|artifact-review|reproduction-request|research-request|ohlcv-dataset-package|report-analysis|review-analysis|dossier-analysis|dossier-result|dossier-status|researcher-response|campaign-result|campaign-progress|studio-snapshot] [--json]", "List or emit canonical AutoQuant JSON Schemas.", "read-only", [ @@ -191,6 +191,7 @@ def descriptor( "factor-candidate-contract", "factor-diagnostics", "factor-claim", + "event-package", "event-study-policy", "event-study-diagnostics", "book-path-stress-policy", @@ -211,6 +212,14 @@ def descriptor( "factor-population", "research-horizon", "experiment", + "factor-definition", + "experiment-definition", + "strategy-definition", + "operator-request", + "operator-receipt", + "research-ledger", + "artifact-review", + "reproduction-request", "researcher-response", "campaign-result", "campaign-progress", @@ -822,6 +831,104 @@ def descriptor( JSON_ARGUMENT, ], ), + descriptor( + "job.execute", + "aq job execute --study ID [--executor cpu|gpu|moss] [resource options] [--project ID] [--json]", + "Execute one Study through the provider-neutral ComputeJob contract and publish an immutable terminal receipt; the public build includes only the CPU executor.", + "creates-artifact", + [PATH_ARGUMENT, PROJECT_ARGUMENT, STUDY_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "job.list", + "aq job list [--project ID] [--json]", + "Verify and list immutable ComputeJob receipts.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "job.show", + "aq job show --job ID [--project ID] [--json]", + "Verify and inspect one immutable ComputeJob receipt.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "job.providers", + "aq job providers [--json]", + "Declare built-in and private-plugin compute executors without exposing credentials or plugin protocols.", + "read-only", + [JSON_ARGUMENT], + ), + descriptor( + "model.run", + "aq model run --study ID --frame PATH --label COLUMN --feature COLUMN --split-column COLUMN [options] [--project ID] [--json]", + "Execute a point-in-time supervised model Study and publish an immutable model Run with validation-only selection, test-only terminal audit, and no trading authority.", + "creates-artifact", + [PATH_ARGUMENT, PROJECT_ARGUMENT, STUDY_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "model.list", + "aq model list [--project ID] [--json]", + "Verify and list immutable supervised-model Runs.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "model.show", + "aq model show --model-run ID [--project ID] [--json]", + "Verify and inspect one immutable supervised-model Run.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "event.intake", + "aq event intake --package FILE [--project ID] [--json]", + "Validate and materialize a point-in-time A-share announcement, crypto-event, or financial-news package with source, license, visibility clocks, and content hashes.", + "creates-artifact", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "event.list", + "aq event list [--project ID] [--json]", + "Verify and list materialized event snapshots.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "event.show", + "aq event show --event-package ID --version VERSION [--project ID] [--json]", + "Verify and inspect one materialized point-in-time event snapshot.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "verify.assess", + "aq verify assess --claim FILE --evidence FILE [--project ID] [--json]", + "Publish an immutable four-state VerificationAssessment from strict external claim and evidence contracts.", + "creates-artifact", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "verify.factor", + "aq verify factor --run ID --statement TEXT [threshold options] [--project ID] [--json]", + "Assess one verified Factor Run against an explicit external claim without inferring author intent.", + "creates-artifact", + [PATH_ARGUMENT, PROJECT_ARGUMENT, RUN_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "verify.list", + "aq verify list [--project ID] [--json]", + "Verify and list immutable VerificationAssessments.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), + descriptor( + "verify.show", + "aq verify show --assessment ID [--project ID] [--json]", + "Verify and inspect one immutable VerificationAssessment.", + "read-only", + [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], + ), descriptor( "session.start", "aq session start --study ID [--request FILE] [--project ID] [--json]", @@ -979,6 +1086,13 @@ def descriptor( "Maximum Researcher turns.", default=5, ), + argument( + "max-candidates", + "option", + "integer", + False, + "Maximum evaluated candidates; defaults to the legacy turn ceiling and cannot exceed it.", + ), argument( "max-wall-seconds", "option", @@ -987,6 +1101,21 @@ def descriptor( "Aggregate Campaign wall-clock budget.", default=900, ), + argument( + "max-cpu-seconds", + "option", + "integer", + False, + "Conservative public CPU ceiling; defaults to the wall-clock ceiling.", + ), + argument( + "max-gpu-seconds", + "option", + "integer", + False, + "Declared GPU ceiling. Public execution remains CPU-first and does not install or invoke a private provider.", + default=0, + ), argument( "turn-timeout-seconds", "option", @@ -1369,6 +1498,24 @@ def descriptor( "read-only", [PATH_ARGUMENT, PROJECT_ARGUMENT, JSON_ARGUMENT], ), + descriptor( + "operator.invoke", + "aq operator invoke --request FILE [--project ID] [--json]", + "Validate one closed provider-neutral Operator request and publish or replay its immutable terminal receipt; no arbitrary shell or provider command is accepted.", + "creates-artifact", + [ + PATH_ARGUMENT, + PROJECT_ARGUMENT, + argument( + "request", + "option", + "string", + True, + "Strict Operator request JSON file. The request carries exact object references, authority, budget, confirmation, and expected state; it cannot carry commands or credentials.", + ), + JSON_ARGUMENT, + ], + ), descriptor( "studio.snapshot", "aq studio snapshot [--project ID] [--json]", diff --git a/autoquant/cli.py b/autoquant/cli.py index 447469e..41c8bda 100644 --- a/autoquant/cli.py +++ b/autoquant/cli.py @@ -71,6 +71,13 @@ load_factor_diagnostics, ) from .factor_claims import FACTOR_CLAIM_JSON_SCHEMA +from .event_intake import ( + EVENT_PACKAGE_JSON_SCHEMA, + list_event_snapshots, + load_event_snapshot, + materialize_event_package, + prepare_event_package, +) from .event_studies import EVENT_STUDY_POLICY_JSON_SCHEMA from .event_explorer import ( EVENT_STUDY_DIAGNOSTICS_JSON_SCHEMA, @@ -86,6 +93,28 @@ success_envelope, workspace_context, ) +from .compute_jobs import ( + ComputeResourcePolicy, + compute_executor_declarations, + execute_compute_job, + list_compute_jobs, + load_compute_job, +) +from .model_runtime import ( + execute_supervised_model_run, + list_model_runs, + load_model_run, +) +from .operator_port import ( + OPERATOR_RECEIPT_JSON_SCHEMA, + OPERATOR_REQUEST_JSON_SCHEMA, + RESEARCH_LEDGER_JSON_SCHEMA, + execute_operator_request, +) +from .research_artifacts import ( + ARTIFACT_REVIEW_JSON_SCHEMA, + REPRODUCTION_REQUEST_JSON_SCHEMA, +) from .runs import ( JUDGE_OUTPUT_JSON_SCHEMA, RUN_RESULT_JSON_SCHEMA, @@ -219,6 +248,7 @@ StudyResearchRequest, bind_upstream_evidence, create_study, + hash_json, list_studies, load_study, ) @@ -241,6 +271,17 @@ set_default_project, ) from .version import current_version +from .verification import ( + build_research_claim, + list_verification_assessments, + load_verification_assessment, + publish_verification_assessment, +) +from .research_definitions import ( + EXPERIMENT_DEFINITION_JSON_SCHEMA, + FACTOR_DEFINITION_JSON_SCHEMA, + STRATEGY_DEFINITION_JSON_SCHEMA, +) class CliUsageError(ValueError): @@ -356,6 +397,7 @@ def build_parser() -> RaisingArgumentParser: "factor-candidate-contract", "factor-diagnostics", "factor-claim", + "event-package", "event-study-policy", "event-study-diagnostics", "book-path-stress-policy", @@ -376,6 +418,14 @@ def build_parser() -> RaisingArgumentParser: "factor-population", "research-horizon", "experiment", + "factor-definition", + "experiment-definition", + "strategy-definition", + "operator-request", + "operator-receipt", + "research-ledger", + "artifact-review", + "reproduction-request", "researcher-response", "campaign-result", "campaign-progress", @@ -851,6 +901,180 @@ def build_parser() -> RaisingArgumentParser: run_rl.set_defaults(command_id="run.rl") _json_argument(run_rl) + job = subcommands.add_parser( + "job", + help="execute and inspect provider-neutral ComputeJobs", + ) + job_actions = job.add_subparsers(dest="job_action", required=True) + job_execute = job_actions.add_parser( + "execute", + help="execute one Study and publish a terminal ComputeJob receipt", + ) + job_execute.add_argument("path") + job_execute.add_argument("--project") + job_execute.add_argument("--study", required=True) + job_execute.add_argument( + "--executor", + choices=("cpu", "gpu", "moss"), + default="cpu", + ) + job_execute.add_argument("--cpu-cores", type=int, default=1) + job_execute.add_argument("--memory-mb", type=int) + job_execute.add_argument("--gpu-count", type=int, default=0) + job_execute.add_argument("--wall-time-seconds", type=int) + job_execute.add_argument("--retry-of") + job_execute.set_defaults(command_id="job.execute") + _json_argument(job_execute) + + job_list = job_actions.add_parser( + "list", + help="verify and list immutable ComputeJob receipts", + ) + job_list.add_argument("path") + job_list.add_argument("--project") + job_list.set_defaults(command_id="job.list") + _json_argument(job_list) + + job_show = job_actions.add_parser( + "show", + help="verify and inspect one immutable ComputeJob receipt", + ) + job_show.add_argument("path") + job_show.add_argument("--project") + job_show.add_argument("--job", required=True) + job_show.set_defaults(command_id="job.show") + _json_argument(job_show) + + job_providers = job_actions.add_parser( + "providers", + help="declare public and private-plugin executor availability", + ) + job_providers.set_defaults(command_id="job.providers") + _json_argument(job_providers) + + model = subcommands.add_parser( + "model", + help="execute and inspect immutable supervised-model research Runs", + ) + model_actions = model.add_subparsers(dest="model_action", required=True) + model_run = model_actions.add_parser( + "run", + help="execute one point-in-time supervised model Study", + ) + model_run.add_argument("path") + model_run.add_argument("--project") + model_run.add_argument("--study", required=True) + model_run.add_argument("--frame", required=True) + model_run.add_argument("--label", required=True) + model_run.add_argument("--feature", action="append", required=True) + model_run.add_argument("--split-column", required=True) + model_run.add_argument("--timestamp-column", default="timestamp") + model_run.add_argument("--available-at-column", default="available_at") + model_run.add_argument("--label-at-column", default="label_at") + model_run.add_argument("--purge-gap", type=int, default=0) + model_run.add_argument("--ridge-alpha", type=float, default=1.0) + model_run.add_argument("--seed", type=int, default=0) + model_run.set_defaults(command_id="model.run") + _json_argument(model_run) + + model_list = model_actions.add_parser("list", help="verify and list model Runs") + model_list.add_argument("path") + model_list.add_argument("--project") + model_list.set_defaults(command_id="model.list") + _json_argument(model_list) + + model_show = model_actions.add_parser("show", help="verify one model Run") + model_show.add_argument("path") + model_show.add_argument("--project") + model_show.add_argument("--model-run", required=True) + model_show.set_defaults(command_id="model.show") + _json_argument(model_show) + + event = subcommands.add_parser( + "event", + help="intake and inspect point-in-time event packages", + ) + event_actions = event.add_subparsers(dest="event_action", required=True) + event_intake = event_actions.add_parser( + "intake", + help="validate and materialize one provider-neutral event package", + ) + event_intake.add_argument("path") + event_intake.add_argument("--project") + event_intake.add_argument("--package", required=True) + event_intake.set_defaults(command_id="event.intake") + _json_argument(event_intake) + + event_list = event_actions.add_parser( + "list", + help="verify and list materialized event snapshots", + ) + event_list.add_argument("path") + event_list.add_argument("--project") + event_list.set_defaults(command_id="event.list") + _json_argument(event_list) + + event_show = event_actions.add_parser( + "show", + help="verify and inspect one event snapshot", + ) + event_show.add_argument("path") + event_show.add_argument("--project") + event_show.add_argument("--event-package", required=True) + event_show.add_argument("--version", required=True) + event_show.set_defaults(command_id="event.show") + _json_argument(event_show) + + verify = subcommands.add_parser( + "verify", + help="assess external research claims against immutable evidence", + ) + verify_actions = verify.add_subparsers(dest="verify_action", required=True) + verify_assess = verify_actions.add_parser( + "assess", + help="publish a deterministic assessment from strict claim and evidence JSON", + ) + verify_assess.add_argument("path") + verify_assess.add_argument("--project") + verify_assess.add_argument("--claim", required=True) + verify_assess.add_argument("--evidence", required=True) + verify_assess.set_defaults(command_id="verify.assess") + _json_argument(verify_assess) + + verify_factor = verify_actions.add_parser( + "factor", + help="assess one Factor Run against an explicit external claim", + ) + verify_factor.add_argument("path") + verify_factor.add_argument("--project") + verify_factor.add_argument("--run", required=True) + verify_factor.add_argument("--statement", required=True) + verify_factor.add_argument("--minimum-effect", type=float, default=0.0) + verify_factor.add_argument("--minimum-sample-size", type=int, default=30) + verify_factor.add_argument("--require-holdout", action="store_true") + verify_factor.add_argument("--require-selection", action="store_true") + verify_factor.set_defaults(command_id="verify.factor") + _json_argument(verify_factor) + + verify_list = verify_actions.add_parser( + "list", + help="verify and list published VerificationAssessments", + ) + verify_list.add_argument("path") + verify_list.add_argument("--project") + verify_list.set_defaults(command_id="verify.list") + _json_argument(verify_list) + + verify_show = verify_actions.add_parser( + "show", + help="verify and inspect one published VerificationAssessment", + ) + verify_show.add_argument("path") + verify_show.add_argument("--project") + verify_show.add_argument("--assessment", required=True) + verify_show.set_defaults(command_id="verify.show") + _json_argument(verify_show) + session = subcommands.add_parser( "session", help="manage governed research Sessions", @@ -1006,7 +1230,10 @@ def build_parser() -> RaisingArgumentParser: research_run.add_argument("--session", required=True) research_run.add_argument("--agent-command", required=True) research_run.add_argument("--max-turns", type=int, default=5) + research_run.add_argument("--max-candidates", type=int) research_run.add_argument("--max-wall-seconds", type=int, default=900) + research_run.add_argument("--max-cpu-seconds", type=int) + research_run.add_argument("--max-gpu-seconds", type=int, default=0) research_run.add_argument("--turn-timeout-seconds", type=int, default=300) research_run.set_defaults(command_id="research.run") _json_argument(research_run) @@ -1349,6 +1576,21 @@ def build_parser() -> RaisingArgumentParser: holdout_show.set_defaults(command_id="holdout.show") _json_argument(holdout_show) + operator = subcommands.add_parser( + "operator", + help="invoke the closed provider-neutral research Operator Port", + ) + operator_actions = operator.add_subparsers(dest="operator_action", required=True) + operator_invoke = operator_actions.add_parser( + "invoke", + help="validate a strict request and publish or replay its terminal receipt", + ) + operator_invoke.add_argument("path") + operator_invoke.add_argument("--project") + operator_invoke.add_argument("--request", required=True) + operator_invoke.set_defaults(command_id="operator.invoke") + _json_argument(operator_invoke) + studio = subcommands.add_parser( "studio", help="observe verified Workspace research in CLI JSON or a local web UI", @@ -3554,6 +3796,380 @@ def _run_rl(args: argparse.Namespace) -> CommandResult: ) +def _compute_job_artifacts(job) -> list[dict[str, Any]]: + return [ + artifact("compute-job", job.receipt["id"], job.root_dir, immutable=True), + artifact( + "compute-job-receipt", + job.receipt["id"], + job.root_dir / "receipt.json", + immutable=True, + ), + ] + + +def _job_execute(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + job = execute_compute_job( + project, + args.study, + executor_kind=args.executor, + resource_policy=ComputeResourcePolicy( + cpu_cores=args.cpu_cores, + memory_mb=args.memory_mb, + gpu_count=args.gpu_count, + wall_time_seconds=args.wall_time_seconds, + ), + retry_of=args.retry_of, + ) + receipt = job.receipt + return CommandResult( + "job.execute", + receipt, + ( + f"ComputeJob {receipt['id']}: {receipt['status']}\n" + f"Executor: {receipt['executor']['kind']} / {receipt['executor']['provider']}\n" + f"Study: {receipt['study']['id']}\n" + f"Run: {receipt['runRef']['id'] if receipt['runRef'] else 'unavailable'}\n" + "Trading authority: none\n" + ), + _project_result_context(args, project), + _compute_job_artifacts(job), + [ + next_action( + "job.show", + "Verify the immutable ComputeJob receipt and Run reference.", + [ + "aq", + "job", + "show", + str(project.root_dir), + "--job", + receipt["id"], + "--json", + ], + "read-only", + ) + ], + ) + + +def _job_list(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + jobs = list_compute_jobs(project) + receipts = [job.receipt for job in jobs] + lines = [f"AutoQuant ComputeJobs in {project.manifest.name}:"] + lines.extend( + f" {item['id']} {item['status']} {item['executor']['kind']} {item['study']['id']}" + for item in receipts + ) + if not receipts: + lines.append(" No ComputeJobs") + return CommandResult( + "job.list", + { + "executors": compute_executor_declarations(), + "jobs": receipts, + }, + "\n".join(lines) + "\n", + _project_result_context(args, project), + ) + + +def _job_show(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + job = load_compute_job(project, args.job) + return CommandResult( + "job.show", + {"manifest": job.manifest, "receipt": job.receipt}, + ( + f"Immutable ComputeJob: {job.receipt['id']}\n" + f"Status: {job.receipt['status']}\n" + f"Study: {job.receipt['study']['id']}\n" + f"Trading authority: {job.receipt['tradingAuthority']}\n" + ), + _project_result_context(args, project), + _compute_job_artifacts(job), + ) + + +def _job_providers(args: argparse.Namespace) -> CommandResult: + declarations = compute_executor_declarations() + return CommandResult( + "job.providers", + {"executors": declarations}, + "\n".join( + f"{item['kind']}: {'available' if item['available'] else item['reason']}" + for item in declarations + ) + + "\n", + ) + + +def _model_run_artifacts(run) -> list[dict[str, Any]]: + return [ + artifact("model-run", run.receipt["id"], run.root_dir, immutable=True), + artifact( + "model-run-receipt", + run.receipt["id"], + run.root_dir / "receipt.json", + immutable=True, + ), + ] + + +def _model_run(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + run = execute_supervised_model_run( + project, + args.study, + frame_path=args.frame, + label_column=args.label, + feature_columns=args.feature, + split_column=args.split_column, + timestamp_column=args.timestamp_column, + available_at_column=args.available_at_column, + label_at_column=args.label_at_column, + purge_gap=args.purge_gap, + ridge_alpha=args.ridge_alpha, + seed=args.seed, + ) + receipt = run.receipt + return CommandResult( + "model.run", + receipt, + ( + f"Model Run {receipt['id']}\n" + f"Study: {receipt['study']['id']}\n" + f"Selected model: {receipt['result']['selectedModel']}\n" + "Trading authority: none\n" + ), + _project_result_context(args, project), + _model_run_artifacts(run), + ) + + +def _model_list(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + receipts = [item.receipt for item in list_model_runs(project)] + return CommandResult( + "model.list", + {"modelRuns": receipts}, + "\n".join( + [f"AutoQuant model Runs in {project.manifest.name}:"] + + [f" {item['id']} {item['study']['id']} {item['result']['selectedModel']}" for item in receipts] + + ([] if receipts else [" No model Runs"]) + ) + "\n", + _project_result_context(args, project), + ) + + +def _model_show(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + run = load_model_run(project, args.model_run) + return CommandResult( + "model.show", + {"manifest": run.manifest, "receipt": run.receipt}, + f"Immutable model Run: {run.receipt['id']}\nTrading authority: none\n", + _project_result_context(args, project), + _model_run_artifacts(run), + ) + + +def _event_snapshot_artifact(project, snapshot) -> dict[str, Any]: + root = ( + project.root_dir + / project.manifest.directories["data"] + / "events" + / snapshot["id"] + / snapshot["version"] + ) + return artifact( + "event-snapshot", + f"{snapshot['id']}@{snapshot['version']}", + root, + immutable=True, + ) + + +def _event_intake(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + prepared = prepare_event_package(args.package) + snapshot, _ = materialize_event_package(project, prepared) + return CommandResult( + "event.intake", + snapshot, + ( + f"Event snapshot: {snapshot['id']}@{snapshot['version']}\n" + f"Adapter: {snapshot['adapterKind']}\n" + f"Events: {snapshot['eventCount']}\n" + f"Available: {snapshot['availableStart']} → {snapshot['availableEnd']}\n" + ), + _project_result_context(args, project), + [_event_snapshot_artifact(project, snapshot)], + ) + + +def _event_list(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + snapshots = list_event_snapshots(project) + lines = [f"AutoQuant event snapshots in {project.manifest.name}:"] + lines.extend( + f" {item['id']}@{item['version']} {item['adapterKind']} {item['eventCount']} events" + for item in snapshots + ) + if not snapshots: + lines.append(" No event snapshots") + return CommandResult( + "event.list", + {"eventSnapshots": snapshots}, + "\n".join(lines) + "\n", + _project_result_context(args, project), + ) + + +def _event_show(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + snapshot = load_event_snapshot(project, args.event_package, args.version) + return CommandResult( + "event.show", + snapshot, + json.dumps(snapshot, indent=2, sort_keys=True) + "\n", + _project_result_context(args, project), + [_event_snapshot_artifact(project, snapshot)], + ) + + +def _verification_artifact(project, bundle) -> dict[str, Any]: + assessment = bundle["assessment"] + return artifact( + "verification-assessment", + assessment["id"], + project.root_dir / "verifications" / assessment["id"], + immutable=True, + ) + + +def _verification_result(args, project, bundle) -> CommandResult: + assessment = bundle["assessment"] + return CommandResult( + args.command_id, + bundle, + ( + f"VerificationAssessment: {assessment['id']}\n" + f"Claim: {assessment['claimId']}\n" + f"Verdict: {assessment['verdict']}\n" + f"Limitations: {', '.join(assessment['limitations']) or 'none'}\n" + "Trading authority: none\n" + ), + _project_result_context(args, project), + [_verification_artifact(project, bundle)], + ) + + +def _verify_assess(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + claim = json.loads(Path(args.claim).read_text(encoding="utf-8")) + evidence = json.loads(Path(args.evidence).read_text(encoding="utf-8")) + required = {"run", "explorer", "holdout", "selection"} + if not isinstance(evidence, dict) or set(evidence) != required: + raise AutoQuantValidationError( + [ValidationIssue(args.evidence, "verification.evidence", "Evidence JSON must contain exactly run, explorer, holdout, and selection")] + ) + bundle = publish_verification_assessment( + project, + claim, + run_evidence=evidence["run"], + explorer_evidence=evidence["explorer"], + holdout_evidence=evidence["holdout"], + selection_evidence=evidence["selection"], + ) + return _verification_result(args, project, bundle) + + +def _verify_factor(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + run = load_run(project, args.run) + diagnostics = load_factor_diagnostics( + project, + args.run, + point_limit=MIN_FACTOR_POINTS, + ) + qualification = diagnostics.get("factorQualification") or {} + selection_evidence = None + for summary in reversed(list_sessions(project)): + snapshot = session_snapshot(project, load_session(project, summary.id)) + leader = snapshot["session"].get("leader") + if not isinstance(leader, dict) or leader.get("runId") != args.run: + continue + adjustment = snapshot["selectionIntegrity"].get("selectionAdjustment") + if isinstance(adjustment, dict) and isinstance(adjustment.get("passes"), bool): + selection_evidence = { + "id": f"selection:{summary.id}", + "hash": hash_json(adjustment), + "passed": adjustment["passes"], + } + break + claim = build_research_claim( + statement=args.statement, + metric="validation_mean_ic", + direction="maximize", + minimum_effect=args.minimum_effect, + minimum_sample_size=args.minimum_sample_size, + holdout_required=args.require_holdout, + selection_required=args.require_selection, + ) + run_evidence = { + "id": run.result["id"], + "hash": run.manifest["resultHash"], + "integrity": { + "tampered": False, + "lookaheadDetected": False, + "schemaValid": True, + "authorityValid": qualification.get("tradingAuthority") == "none", + }, + } + explorer_evidence = { + "id": f"factor-diagnostics:{args.run}", + "hash": hash_json(diagnostics), + "metric": "validation_mean_ic", + "primaryValue": diagnostics["summary"]["validation"]["meanRankIc"], + "baselineValue": 0.0, + "sampleSize": diagnostics["summary"]["validation"]["observations"], + } + bundle = publish_verification_assessment( + project, + claim, + run_evidence=run_evidence, + explorer_evidence=explorer_evidence, + holdout_evidence=None, + selection_evidence=selection_evidence, + ) + return _verification_result(args, project, bundle) + + +def _verify_list(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + bundles = list_verification_assessments(project) + return CommandResult( + "verify.list", + {"assessments": bundles}, + "\n".join( + [f"AutoQuant VerificationAssessments in {project.manifest.name}:"] + + [f" {item['assessment']['id']} {item['assessment']['verdict']} {item['claim']['statement']}" for item in bundles] + + ([] if bundles else [" No VerificationAssessments"]) + ) + + "\n", + _project_result_context(args, project), + ) + + +def _verify_show(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + bundle = load_verification_assessment(project, args.assessment) + return _verification_result(args, project, bundle) + + def _session_next_actions(project, session) -> list[dict[str, Any]]: actions = [ next_action( @@ -4212,7 +4828,10 @@ def _research_run(args: argparse.Namespace) -> CommandResult: args.session, args.agent_command, max_turns=args.max_turns, + max_candidates=args.max_candidates, max_wall_seconds=args.max_wall_seconds, + max_cpu_seconds=args.max_cpu_seconds, + max_gpu_seconds=args.max_gpu_seconds, turn_timeout_seconds=args.turn_timeout_seconds, ) session = load_session(project, args.session) @@ -5471,6 +6090,32 @@ def _studio_snapshot(args: argparse.Namespace) -> CommandResult: ) +def _operator_invoke(args: argparse.Namespace) -> CommandResult: + project = _selected_project(args) + request_path = Path(args.request).expanduser().resolve() + try: + request = json.loads(request_path.read_text(encoding="utf-8")) + except FileNotFoundError: + raise CliCommandError( + f"Operator request file does not exist: {request_path}" + ) from None + except json.JSONDecodeError as error: + raise CliCommandError(f"Invalid Operator request JSON: {error.msg}") from None + if not isinstance(request, dict): + raise CliCommandError("Operator request must be a JSON object") + receipt = execute_operator_request(project, request) + return CommandResult( + "operator.invoke", + {"receipt": receipt}, + ( + f"Operator receipt: {receipt['requestId']}\n" + f"Intent: {receipt['intent']}\n" + f"Status: {receipt['status']}\n" + ), + project_context(project), + ) + + def _studio_serve(args: argparse.Namespace) -> CommandResult: serve_studio( args.path, @@ -5850,9 +6495,18 @@ def dispatch(args: argparse.Namespace) -> CommandResult: "dossier-result", "dossier-status", "experiment", + "factor-definition", + "experiment-definition", + "strategy-definition", + "operator-request", + "operator-receipt", + "research-ledger", + "artifact-review", + "reproduction-request", "factor-candidate-contract", "factor-diagnostics", "factor-claim", + "event-package", "event-study-policy", "event-study-diagnostics", "book-path-stress-policy", @@ -5905,6 +6559,7 @@ def dispatch(args: argparse.Namespace) -> CommandResult: "factor-diagnostics": FACTOR_DIAGNOSTICS_JSON_SCHEMA, "factor-candidate-contract": FACTOR_CANDIDATE_CONTRACT_JSON_SCHEMA, "factor-claim": FACTOR_CLAIM_JSON_SCHEMA, + "event-package": EVENT_PACKAGE_JSON_SCHEMA, "event-study-policy": EVENT_STUDY_POLICY_JSON_SCHEMA, "event-study-diagnostics": EVENT_STUDY_DIAGNOSTICS_JSON_SCHEMA, "book-path-stress-policy": BOOK_PATH_STRESS_POLICY_JSON_SCHEMA, @@ -5925,6 +6580,14 @@ def dispatch(args: argparse.Namespace) -> CommandResult: "factor-population": FACTOR_POPULATION_JSON_SCHEMA, "research-horizon": RESEARCH_HORIZON_JSON_SCHEMA, "experiment": EXPERIMENT_JSON_SCHEMA, + "factor-definition": FACTOR_DEFINITION_JSON_SCHEMA, + "experiment-definition": EXPERIMENT_DEFINITION_JSON_SCHEMA, + "strategy-definition": STRATEGY_DEFINITION_JSON_SCHEMA, + "operator-request": OPERATOR_REQUEST_JSON_SCHEMA, + "operator-receipt": OPERATOR_RECEIPT_JSON_SCHEMA, + "research-ledger": RESEARCH_LEDGER_JSON_SCHEMA, + "artifact-review": ARTIFACT_REVIEW_JSON_SCHEMA, + "reproduction-request": REPRODUCTION_REQUEST_JSON_SCHEMA, "researcher-response": RESEARCHER_RESPONSE_JSON_SCHEMA, "campaign-result": CAMPAIGN_RESULT_JSON_SCHEMA, "campaign-progress": CAMPAIGN_PROGRESS_JSON_SCHEMA, @@ -5991,6 +6654,34 @@ def dispatch(args: argparse.Namespace) -> CommandResult: return _run_allocation(args) if args.command_id == "run.rl": return _run_rl(args) + if args.command_id == "job.execute": + return _job_execute(args) + if args.command_id == "job.list": + return _job_list(args) + if args.command_id == "job.show": + return _job_show(args) + if args.command_id == "job.providers": + return _job_providers(args) + if args.command_id == "model.run": + return _model_run(args) + if args.command_id == "model.list": + return _model_list(args) + if args.command_id == "model.show": + return _model_show(args) + if args.command_id == "event.intake": + return _event_intake(args) + if args.command_id == "event.list": + return _event_list(args) + if args.command_id == "event.show": + return _event_show(args) + if args.command_id == "verify.assess": + return _verify_assess(args) + if args.command_id == "verify.factor": + return _verify_factor(args) + if args.command_id == "verify.list": + return _verify_list(args) + if args.command_id == "verify.show": + return _verify_show(args) if args.command_id == "session.start": return _session_start(args) if args.command_id == "session.list": @@ -6051,6 +6742,8 @@ def dispatch(args: argparse.Namespace) -> CommandResult: return _holdout_assess(args) if args.command_id == "holdout.show": return _holdout_show(args) + if args.command_id == "operator.invoke": + return _operator_invoke(args) if args.command_id == "studio.snapshot": return _studio_snapshot(args) if args.command_id == "studio.serve": @@ -6072,6 +6765,7 @@ def _command_id(argv: Sequence[str]) -> str: "report", "review", "dossier", + "operator", "studio", } and len(argv) > 1: return f"{argv[0]}.{argv[1]}" diff --git a/autoquant/compute_jobs.py b/autoquant/compute_jobs.py new file mode 100644 index 0000000..9b4eccd --- /dev/null +++ b/autoquant/compute_jobs.py @@ -0,0 +1,563 @@ +"""Provider-neutral ComputeJob receipts over immutable Study execution.""" + +from __future__ import annotations + +import json +import os +import re +import shutil +import uuid +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from .runs import execute_study, load_run +from .studies import STUDY_ID, hash_file, load_study +from .workspace import ( + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +COMPUTE_JOB_SCHEMA_VERSION = 1 +COMPUTE_JOBS_DIRECTORY = "compute-jobs" +COMPUTE_JOB_RECEIPT = "receipt.json" +COMPUTE_JOB_MANIFEST = "manifest.json" +COMPUTE_JOB_ID = re.compile( + r"^job-[0-9]{8}T[0-9]{12}Z-[0-9a-f]{12}$" +) +TERMINAL_STATES = {"succeeded", "failed", "cancelled"} + + +@dataclass(frozen=True) +class ComputeResourcePolicy: + cpu_cores: int = 1 + memory_mb: int | None = None + gpu_count: int = 0 + wall_time_seconds: int | None = None + + def to_dict(self) -> dict[str, int | None]: + return { + "cpuCores": self.cpu_cores, + "memoryMb": self.memory_mb, + "gpuCount": self.gpu_count, + "wallTimeSeconds": self.wall_time_seconds, + } + + +@dataclass(frozen=True) +class ComputeJobContext: + root_dir: Path + manifest: dict[str, Any] + receipt: dict[str, Any] + + +_EXECUTORS = { + "cpu": { + "kind": "cpu", + "provider": "builtin", + "available": True, + "reason": None, + }, + "gpu": { + "kind": "gpu", + "provider": "private-plugin", + "available": False, + "reason": "No GPU provider plugin is installed", + }, + "moss": { + "kind": "moss", + "provider": "private-plugin", + "available": False, + "reason": "No MOSS provider plugin is installed", + }, +} + + +def compute_executor_declarations() -> list[dict[str, Any]]: + """Return public capability declarations without credentials or plugin config.""" + + return [dict(_EXECUTORS[kind]) for kind in ("cpu", "gpu", "moss")] + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _write_json(path: Path, value: Any) -> None: + path.write_text( + json.dumps(value, indent=2, sort_keys=True, ensure_ascii=False) + "\n", + encoding="utf-8", + ) + + +def _read_json(path: Path) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (FileNotFoundError, OSError, json.JSONDecodeError) as error: + raise AutoQuantValidationError( + [_issue(path, "compute-job.read", f"Cannot read ComputeJob JSON: {error}")] + ) from None + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, "compute-job.schema", "ComputeJob JSON must be an object")] + ) + return value + + +def _jobs_root(project: ProjectContext, *, create: bool = False) -> Path: + root = confined_path( + project.root_dir, + COMPUTE_JOBS_DIRECTORY, + "project/compute-jobs", + ) + if root.is_symlink(): + raise AutoQuantValidationError( + [_issue(root, "compute-job.symlink", "ComputeJob root cannot be a symlink")] + ) + if create: + root.mkdir(exist_ok=True) + return root + + +def _validate_policy(policy: ComputeResourcePolicy) -> None: + issues: list[ValidationIssue] = [] + for name, value, minimum in ( + ("cpuCores", policy.cpu_cores, 1), + ("gpuCount", policy.gpu_count, 0), + ): + if isinstance(value, bool) or not isinstance(value, int) or value < minimum: + issues.append( + _issue(name, "compute-job.resource-policy", f"{name} must be >= {minimum}") + ) + for name, value in ( + ("memoryMb", policy.memory_mb), + ("wallTimeSeconds", policy.wall_time_seconds), + ): + if value is not None and ( + isinstance(value, bool) or not isinstance(value, int) or value < 1 + ): + issues.append( + _issue(name, "compute-job.resource-policy", f"{name} must be null or >= 1") + ) + if issues: + raise AutoQuantValidationError(issues) + + +def _new_job_id() -> str: + stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ") + return f"job-{stamp}-{uuid.uuid4().hex[:12]}" + + +def _retry_identity( + project: ProjectContext, + study_id: str, + input_hash: str, + retry_of: str | None, +) -> dict[str, Any]: + if retry_of is None: + return {"rootJobId": None, "parentJobId": None, "attempt": 1} + parent = load_compute_job(project, retry_of).receipt + if parent["study"]["id"] != study_id or parent["inputHash"] != input_hash: + raise AutoQuantValidationError( + [ + _issue( + retry_of, + "compute-job.retry-input", + "Retry must preserve the exact Study and input identity", + ) + ] + ) + return { + "rootJobId": parent["retry"]["rootJobId"] or parent["id"], + "parentJobId": parent["id"], + "attempt": parent["retry"]["attempt"] + 1, + } + + +def _terminal_receipt( + *, + job_id: str, + project: ProjectContext, + study: Any, + executor: dict[str, Any], + policy: ComputeResourcePolicy, + created_at: str, + started_at: str, + completed_at: str, + status: str, + retry: dict[str, Any], + run: Any | None, + error: dict[str, str] | None, +) -> dict[str, Any]: + run_ref = None + output_refs: list[dict[str, Any]] = [] + if run is not None: + run_ref = { + "id": run.result["id"], + "path": f"{project.manifest.directories['runs']}/{run.result['id']}", + "resultHash": run.manifest["resultHash"], + } + output_refs = [ + { + "kind": item["kind"], + "path": f"{run_ref['path']}/{item['path']}", + "immutable": True, + } + for item in run.result["artifacts"] + ] + return { + "schemaVersion": COMPUTE_JOB_SCHEMA_VERSION, + "id": job_id, + "status": status, + "createdAt": created_at, + "startedAt": started_at, + "completedAt": completed_at, + "project": {"id": project.manifest.id}, + "study": {"id": study.definition.id, "hash": study.study_hash}, + "inputHash": study.input_hash, + "executor": { + "kind": executor["kind"], + "provider": executor["provider"], + }, + "resourcePolicy": policy.to_dict(), + "stateHistory": [ + {"state": "queued", "at": created_at}, + {"state": "running", "at": started_at}, + {"state": status, "at": completed_at}, + ], + "runRef": run_ref, + "outputRefs": output_refs, + "error": error, + "retry": retry, + "tradingAuthority": "none", + } + + +def execute_compute_job( + project: ProjectContext, + study_id: str, + *, + executor_kind: str = "cpu", + resource_policy: ComputeResourcePolicy | None = None, + retry_of: str | None = None, +) -> ComputeJobContext: + """Synchronously execute one verified Study and publish a terminal receipt.""" + + if not STUDY_ID.fullmatch(study_id): + raise AutoQuantValidationError( + [_issue(study_id, "compute-job.study-id", "Invalid Study id")] + ) + executor = _EXECUTORS.get(executor_kind) + if executor is None: + raise AutoQuantValidationError( + [_issue(executor_kind, "compute-job.executor", "Unknown compute executor")] + ) + if not executor["available"]: + raise AutoQuantValidationError( + [ + _issue( + executor_kind, + "compute-job.executor-unavailable", + executor["reason"], + ) + ] + ) + policy = resource_policy or ComputeResourcePolicy() + _validate_policy(policy) + if executor_kind == "cpu" and policy.gpu_count != 0: + raise AutoQuantValidationError( + [ + _issue( + "gpuCount", + "compute-job.resource-policy", + "Built-in CPU execution cannot request GPUs", + ) + ] + ) + + study = load_study(project, study_id) + retry = _retry_identity(project, study_id, study.input_hash, retry_of) + job_id = _new_job_id() + jobs_root = _jobs_root(project, create=True) + staging = jobs_root / f".{job_id}-{uuid.uuid4().hex}" + target = jobs_root / job_id + staging.mkdir() + created_at = _now() + started_at = _now() + run = None + error = None + status = "succeeded" + try: + try: + run = execute_study(project, study_id) + except Exception as execution_error: + status = "failed" + error = { + "code": "executor.failed", + "type": execution_error.__class__.__name__, + "message": "Built-in CPU executor failed before publishing a Run", + } + completed_at = _now() + receipt = _terminal_receipt( + job_id=job_id, + project=project, + study=study, + executor=executor, + policy=policy, + created_at=created_at, + started_at=started_at, + completed_at=completed_at, + status=status, + retry=retry, + run=run, + error=error, + ) + _write_json(staging / COMPUTE_JOB_RECEIPT, receipt) + manifest = { + "schemaVersion": COMPUTE_JOB_SCHEMA_VERSION, + "id": job_id, + "completed": True, + "receiptHash": hash_file(staging / COMPUTE_JOB_RECEIPT), + } + _write_json(staging / COMPUTE_JOB_MANIFEST, manifest) + if target.exists() or target.is_symlink(): + raise AutoQuantValidationError( + [_issue(target, "compute-job.collision", "ComputeJob id collision")] + ) + os.replace(staging, target) + return load_compute_job(project, job_id) + except Exception: + if staging.exists(): + shutil.rmtree(staging) + raise + + +def _validate_receipt(receipt: dict[str, Any], path: Path, job_id: str) -> None: + required = { + "schemaVersion", + "id", + "status", + "createdAt", + "startedAt", + "completedAt", + "project", + "study", + "inputHash", + "executor", + "resourcePolicy", + "stateHistory", + "runRef", + "outputRefs", + "error", + "retry", + "tradingAuthority", + } + issues = [ + _issue(f"{path}/{key}", "schema.missing", f"Missing field '{key}'") + for key in sorted(required - receipt.keys()) + ] + issues.extend( + _issue(f"{path}/{key}", "schema.unknown", f"Unknown field '{key}'") + for key in sorted(receipt.keys() - required) + ) + if receipt.get("schemaVersion") != COMPUTE_JOB_SCHEMA_VERSION: + issues.append(_issue(path, "schema.version", "Invalid ComputeJob schema version")) + if receipt.get("id") != job_id: + issues.append(_issue(path, "compute-job.directory-id", "Receipt id differs from directory")) + status = receipt.get("status") + if status not in TERMINAL_STATES: + issues.append(_issue(path, "compute-job.status", "Receipt must be terminal")) + history = receipt.get("stateHistory") + expected_states = ["queued", "running", status] + if not isinstance(history, list) or [ + item.get("state") for item in history if isinstance(item, dict) + ] != expected_states: + issues.append( + _issue(path, "compute-job.history", "Invalid ComputeJob state history") + ) + if receipt.get("tradingAuthority") != "none": + issues.append( + _issue( + path, + "compute-job.trading-authority", + "ComputeJob has no trading authority", + ) + ) + for key, expected_keys in ( + ("project", {"id"}), + ("study", {"id", "hash"}), + ("executor", {"kind", "provider"}), + ( + "resourcePolicy", + {"cpuCores", "memoryMb", "gpuCount", "wallTimeSeconds"}, + ), + ("retry", {"rootJobId", "parentJobId", "attempt"}), + ): + value = receipt.get(key) + if not isinstance(value, dict) or set(value) != expected_keys: + issues.append( + _issue(path, "compute-job.schema", f"Invalid {key} contract") + ) + if isinstance(history, list): + timestamps = [ + receipt.get("createdAt"), + receipt.get("startedAt"), + receipt.get("completedAt"), + ] + if len(history) != 3 or any( + not isinstance(item, dict) + or set(item) != {"state", "at"} + or item.get("at") != timestamps[index] + for index, item in enumerate(history) + ): + issues.append( + _issue(path, "compute-job.history", "State timestamps differ from receipt") + ) + run_ref = receipt.get("runRef") + error = receipt.get("error") + if run_ref is not None and ( + not isinstance(run_ref, dict) + or set(run_ref) != {"id", "path", "resultHash"} + ): + issues.append(_issue(path, "compute-job.run-ref", "Invalid Run reference")) + outputs = receipt.get("outputRefs") + if not isinstance(outputs, list) or any( + not isinstance(item, dict) + or set(item) != {"kind", "path", "immutable"} + or item.get("immutable") is not True + for item in outputs + ): + issues.append(_issue(path, "compute-job.outputs", "Invalid output references")) + if error is not None and ( + not isinstance(error, dict) + or set(error) != {"code", "type", "message"} + ): + issues.append(_issue(path, "compute-job.error", "Invalid executor error")) + if status == "succeeded" and (not isinstance(run_ref, dict) or error is not None): + issues.append( + _issue( + path, + "compute-job.success", + "Succeeded job requires one Run and no error", + ) + ) + if status in {"failed", "cancelled"} and (run_ref is not None or not isinstance(error, dict)): + issues.append( + _issue( + path, + "compute-job.failure", + "Unsuccessful job requires an error and no Run", + ) + ) + if issues: + raise AutoQuantValidationError(issues) + + +def load_compute_job(project: ProjectContext, job_id: str) -> ComputeJobContext: + """Verify one immutable terminal ComputeJob receipt and its Run reference.""" + + if not COMPUTE_JOB_ID.fullmatch(job_id): + raise AutoQuantValidationError( + [_issue(job_id, "compute-job.id", "Invalid ComputeJob id")] + ) + root = confined_path(_jobs_root(project), job_id, f"compute-job/{job_id}") + if root.is_symlink() or not root.is_dir(): + raise AutoQuantValidationError( + [_issue(root, "compute-job.missing", f"Unknown ComputeJob: {job_id}")] + ) + manifest = _read_json(root / COMPUTE_JOB_MANIFEST) + expected_manifest_keys = {"schemaVersion", "id", "completed", "receiptHash"} + if ( + set(manifest) != expected_manifest_keys + or manifest.get("schemaVersion") != COMPUTE_JOB_SCHEMA_VERSION + or manifest.get("id") != job_id + or manifest.get("completed") is not True + or manifest.get("receiptHash") != hash_file(root / COMPUTE_JOB_RECEIPT) + ): + raise AutoQuantValidationError( + [_issue(root, "compute-job.tampered", "Invalid immutable ComputeJob manifest")] + ) + receipt = _read_json(root / COMPUTE_JOB_RECEIPT) + _validate_receipt(receipt, root / COMPUTE_JOB_RECEIPT, job_id) + run_ref = receipt["runRef"] + if run_ref is not None: + run = load_run(project, run_ref["id"]) + expected = { + "id": run.result["id"], + "path": f"{project.manifest.directories['runs']}/{run.result['id']}", + "resultHash": run.manifest["resultHash"], + } + if run_ref != expected: + raise AutoQuantValidationError( + [ + _issue( + root, + "compute-job.run-ref", + "ComputeJob Run reference differs from immutable Run", + ) + ] + ) + if ( + receipt["project"] != {"id": project.manifest.id} + or receipt["study"] + != {"id": run.result["study"]["id"], "hash": run.result["study"]["hash"]} + or receipt["inputHash"] != run.result["studyInputHash"] + ): + raise AutoQuantValidationError( + [ + _issue( + root, + "compute-job.input-ref", + "ComputeJob input identity differs from immutable Run", + ) + ] + ) + expected_outputs = [ + { + "kind": item["kind"], + "path": f"{run_ref['path']}/{item['path']}", + "immutable": True, + } + for item in run.result["artifacts"] + ] + if receipt["outputRefs"] != expected_outputs: + raise AutoQuantValidationError( + [ + _issue( + root, + "compute-job.outputs", + "ComputeJob outputs differ from immutable Run", + ) + ] + ) + return ComputeJobContext(root, manifest, receipt) + + +def list_compute_jobs(project: ProjectContext) -> list[ComputeJobContext]: + """Verify and list published ComputeJobs in deterministic id order.""" + + root = _jobs_root(project) + if not root.exists(): + return [] + if not root.is_dir(): + raise AutoQuantValidationError( + [_issue(root, "compute-job.root", "ComputeJob root must be a directory")] + ) + jobs: list[ComputeJobContext] = [] + for entry in sorted(root.iterdir(), key=lambda item: item.name): + if entry.name.startswith("."): + continue + if entry.is_symlink() or not entry.is_dir() or not COMPUTE_JOB_ID.fullmatch(entry.name): + raise AutoQuantValidationError( + [_issue(entry, "compute-job.entry", "Invalid ComputeJob directory entry")] + ) + jobs.append(load_compute_job(project, entry.name)) + return jobs diff --git a/autoquant/event_intake.py b/autoquant/event_intake.py new file mode 100644 index 0000000..0587d1c --- /dev/null +++ b/autoquant/event_intake.py @@ -0,0 +1,483 @@ +"""Provider-neutral, point-in-time event package intake.""" + +from __future__ import annotations + +import json +import os +import re +import shutil +import uuid +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, NoReturn + +from .studies import hash_bytes, hash_file, hash_json +from .workspace import ( + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +EVENT_PACKAGE_KIND = "autoquant-event-package" +EVENT_SNAPSHOT_KIND = "autoquant-event-snapshot" +EVENT_ADAPTER_KINDS = { + "a-share-announcement", + "crypto-event", + "financial-news", +} +SAFE_EVENT_ID = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._=-]{0,127}$") +MAX_EVENT_PACKAGE_BYTES = 64 * 1024 * 1024 +MAX_EVENT_RECORDS = 100_000 +MAX_EVENT_CONTENT_BYTES = 256 * 1024 + +EVENT_PACKAGE_JSON_SCHEMA: dict[str, Any] = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant provider-neutral point-in-time event package", + "type": "object", + "additionalProperties": False, + "required": [ + "schemaVersion", + "kind", + "id", + "version", + "adapterKind", + "events", + ], + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": EVENT_PACKAGE_KIND}, + "id": {"type": "string", "pattern": SAFE_EVENT_ID.pattern}, + "version": {"type": "string", "pattern": SAFE_EVENT_ID.pattern}, + "adapterKind": {"enum": sorted(EVENT_ADAPTER_KINDS)}, + "events": { + "type": "array", + "minItems": 1, + "maxItems": MAX_EVENT_RECORDS, + "items": { + "type": "object", + "additionalProperties": False, + "required": [ + "event_id", + "event_time", + "published_at", + "observed_at", + "available_at", + "source", + "license", + "content", + ], + "properties": { + "event_id": { + "type": "string", + "pattern": SAFE_EVENT_ID.pattern, + }, + "event_time": {"type": "string", "format": "date-time"}, + "published_at": {"type": "string", "format": "date-time"}, + "observed_at": {"type": "string", "format": "date-time"}, + "available_at": { + "type": "string", + "format": "date-time", + "description": ( + "Earliest research-safe visibility time; validator " + "requires it not precede published_at or observed_at." + ), + }, + "source": {"type": "string", "minLength": 1}, + "license": {"type": "string", "minLength": 1}, + "content": { + "oneOf": [ + {"type": "string", "minLength": 1}, + {"type": "array", "minItems": 1}, + {"type": "object", "minProperties": 1}, + ] + }, + }, + }, + }, + }, +} + + +@dataclass(frozen=True) +class PreparedEventPackage: + source_path: Path + package: dict[str, Any] + package_hash: str + events: tuple[dict[str, Any], ...] + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _fail(path: Path | str, code: str, message: str) -> NoReturn: + raise AutoQuantValidationError([_issue(path, code, message)]) + + +def _reject_json_constant(value: str) -> NoReturn: + raise ValueError(f"Non-standard JSON number: {value}") + + +def _non_empty(value: Any, path: str) -> str: + if not isinstance(value, str) or not value.strip(): + _fail(path, "schema.string", "Must be a non-empty string") + return value.strip() + + +def _safe_id(value: Any, path: str) -> str: + normalized = _non_empty(value, path) + if not SAFE_EVENT_ID.fullmatch(normalized): + _fail(path, "event.id", "Must be a path-safe identifier") + return normalized + + +def _timestamp(value: Any, path: str) -> tuple[str, datetime]: + raw = _non_empty(value, path) + try: + parsed = datetime.fromisoformat(raw.replace("Z", "+00:00")) + except ValueError: + _fail(path, "event.timestamp", "Must be a timezone-aware ISO-8601 timestamp") + if parsed.tzinfo is None or parsed.utcoffset() is None: + _fail(path, "event.timestamp", "Must be a timezone-aware ISO-8601 timestamp") + utc = parsed.astimezone(timezone.utc) + return utc.isoformat().replace("+00:00", "Z"), utc + + +def _read_package(path: Path) -> dict[str, Any]: + try: + if path.stat().st_size > MAX_EVENT_PACKAGE_BYTES: + _fail(path, "event-package.size", "Event package exceeds the 64 MiB limit") + value = json.loads( + path.read_bytes().decode("utf-8"), + parse_constant=_reject_json_constant, + ) + except FileNotFoundError: + _fail(path, "event-package.missing", "Missing event package") + except AutoQuantValidationError: + raise + except ( + OSError, + UnicodeDecodeError, + json.JSONDecodeError, + RecursionError, + ValueError, + ) as error: + _fail(path, "event-package.json", f"Invalid event package JSON: {error}") + if not isinstance(value, dict): + _fail(path, "event-package.type", "Event package must be a JSON object") + return value + + +def prepare_event_package(package_path: str | Path) -> PreparedEventPackage: + """Validate one bounded event package and freeze its causal records.""" + + path = Path(package_path).expanduser().absolute() + package = _read_package(path) + required = {"schemaVersion", "kind", "id", "version", "adapterKind", "events"} + unknown = set(package) - required + missing = required - set(package) + if missing or unknown: + _fail( + path, + "event-package.schema", + "Event package fields differ from the fixed contract; " + f"missing={sorted(missing)}, unknown={sorted(unknown)}", + ) + if package["schemaVersion"] != 1 or package["kind"] != EVENT_PACKAGE_KIND: + _fail(path, "event-package.contract", "Unsupported event package contract") + package_id = _safe_id(package["id"], f"{path}/id") + version = _safe_id(package["version"], f"{path}/version") + adapter_kind = package["adapterKind"] + if adapter_kind not in EVENT_ADAPTER_KINDS: + _fail( + f"{path}/adapterKind", + "event-package.adapter", + "adapterKind must be a-share-announcement, crypto-event, or financial-news", + ) + raw_events = package["events"] + if not isinstance(raw_events, list) or not raw_events: + _fail( + f"{path}/events", + "event-package.events", + "Events must be a non-empty array", + ) + if len(raw_events) > MAX_EVENT_RECORDS: + _fail( + f"{path}/events", + "event-package.event-limit", + "Event package exceeds the 100,000 record limit", + ) + + event_fields = { + "event_id", + "event_time", + "published_at", + "observed_at", + "available_at", + "source", + "license", + "content", + } + events: list[dict[str, Any]] = [] + identifiers: set[str] = set() + for index, raw in enumerate(raw_events): + event_path = f"{path}/events/{index}" + if not isinstance(raw, dict) or set(raw) != event_fields: + _fail( + event_path, + "event.schema", + "Event fields differ from the fixed contract", + ) + event_id = _safe_id(raw["event_id"], f"{event_path}/event_id") + if event_id in identifiers: + _fail( + f"{event_path}/event_id", + "event.duplicate", + "event_id must be unique", + ) + identifiers.add(event_id) + event_time, _ = _timestamp(raw["event_time"], f"{event_path}/event_time") + published_at, published = _timestamp( + raw["published_at"], f"{event_path}/published_at" + ) + observed_at, observed = _timestamp( + raw["observed_at"], f"{event_path}/observed_at" + ) + available_at, available = _timestamp( + raw["available_at"], f"{event_path}/available_at" + ) + if observed < published: + _fail( + f"{event_path}/observed_at", + "event.observed-before-published", + "observed_at cannot precede published_at", + ) + if available < max(published, observed): + _fail( + f"{event_path}/available_at", + "event.available-before-observed", + "available_at must be at or after both published_at and observed_at", + ) + content = raw["content"] + if not isinstance(content, (str, list, dict)) or content in ("", [], {}): + _fail( + f"{event_path}/content", + "event.content", + "content must be a non-empty string, array, or object", + ) + if len( + json.dumps( + content, + ensure_ascii=False, + separators=(",", ":"), + sort_keys=True, + ) + .encode("utf-8") + ) > MAX_EVENT_CONTENT_BYTES: + _fail( + f"{event_path}/content", + "event.content-size", + "Event content exceeds the 256 KiB limit", + ) + record = { + "event_id": event_id, + "event_time": event_time, + "published_at": published_at, + "observed_at": observed_at, + "available_at": available_at, + "source": _non_empty(raw["source"], f"{event_path}/source"), + "license": _non_empty(raw["license"], f"{event_path}/license"), + "content": content, + "content_hash": hash_json(content), + } + record["record_hash"] = hash_json(record) + events.append(record) + + events.sort(key=lambda item: (item["available_at"], item["event_id"])) + normalized_package = { + "schemaVersion": 1, + "kind": EVENT_PACKAGE_KIND, + "id": package_id, + "version": version, + "adapterKind": adapter_kind, + "events": events, + } + return PreparedEventPackage( + source_path=path, + package=normalized_package, + package_hash=hash_file(path), + events=tuple(events), + ) + + +def materialize_event_package( + project: ProjectContext, + prepared: PreparedEventPackage, +) -> tuple[dict[str, Any], str]: + """Materialize immutable JSONL event evidence below Project data/events.""" + + if hash_file(prepared.source_path) != prepared.package_hash: + _fail( + prepared.source_path, + "event-package.source-changed", + "Event package changed after validation", + ) + package_id = _safe_id(prepared.package.get("id"), "event-package/id") + version = _safe_id(prepared.package.get("version"), "event-package/version") + if prepared.package.get("adapterKind") not in EVENT_ADAPTER_KINDS: + _fail( + "event-package/adapterKind", + "event-package.adapter", + "Prepared adapterKind is unsupported", + ) + if not prepared.events: + _fail("event-package/events", "event-package.events", "Events cannot be empty") + for index, event in enumerate(prepared.events): + body = {key: value for key, value in event.items() if key != "record_hash"} + if ( + event.get("content_hash") != hash_json(event.get("content")) + or event.get("record_hash") != hash_json(body) + ): + _fail( + f"event-package/events/{index}", + "event-package.prepared-changed", + "Prepared event content differs from its validation receipt", + ) + data_root = confined_path( + project.root_dir, + project.manifest.directories["data"], + "project/directories/data", + ) + output = confined_path( + data_root, + f"events/{package_id}/{version}", + "event-package/output", + ) + if output.exists() or output.is_symlink(): + _fail(output, "event-package.collision", "Event package output already exists") + + event_bytes = b"".join( + ( + json.dumps( + event, + ensure_ascii=False, + separators=(",", ":"), + sort_keys=True, + ) + + "\n" + ).encode("utf-8") + for event in prepared.events + ) + events_hash = hash_bytes(event_bytes) + manifest = { + "schemaVersion": 1, + "kind": EVENT_SNAPSHOT_KIND, + "id": prepared.package["id"], + "version": prepared.package["version"], + "adapterKind": prepared.package["adapterKind"], + "eventCount": len(prepared.events), + "availableStart": prepared.events[0]["available_at"], + "availableEnd": prepared.events[-1]["available_at"], + "sourcePackageHash": prepared.package_hash, + "eventsPath": "events.jsonl", + "eventsHash": events_hash, + "contentHashes": [ + { + "event_id": event["event_id"], + "content_hash": event["content_hash"], + "record_hash": event["record_hash"], + } + for event in prepared.events + ], + } + manifest_hash = hash_json(manifest) + manifest["snapshotHash"] = manifest_hash + output.parent.mkdir(parents=True, exist_ok=True) + staging = output.parent / f".{output.name}-{uuid.uuid4().hex}" + staging.mkdir() + try: + (staging / "events.jsonl").write_bytes(event_bytes) + (staging / "snapshot.json").write_text( + json.dumps(manifest, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + os.replace(staging, output) + except Exception: + if staging.exists(): + shutil.rmtree(staging) + raise + return manifest, manifest_hash + + +def load_event_snapshot( + project: ProjectContext, + package_id: str, + version: str, +) -> dict[str, Any]: + """Verify one materialized event snapshot and its immutable event bytes.""" + + package_id = _safe_id(package_id, "event-package/id") + version = _safe_id(version, "event-package/version") + data_root = confined_path( + project.root_dir, + project.manifest.directories["data"], + "project/directories/data", + ) + root = confined_path( + data_root, + f"events/{package_id}/{version}", + "event-package/snapshot", + ) + if root.is_symlink() or not root.is_dir(): + _fail(root, "event-snapshot.missing", "Unknown event snapshot") + try: + snapshot = json.loads((root / "snapshot.json").read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as error: + _fail(root, "event-snapshot.read", f"Cannot read event snapshot: {error}") + required = { + "schemaVersion", "kind", "id", "version", "adapterKind", "eventCount", + "availableStart", "availableEnd", "sourcePackageHash", "eventsPath", + "eventsHash", "contentHashes", "snapshotHash", + } + if not isinstance(snapshot, dict) or set(snapshot) != required: + _fail(root, "event-snapshot.schema", "Event snapshot fields differ from V1") + payload = {key: snapshot[key] for key in required - {"snapshotHash"}} + if ( + snapshot["schemaVersion"] != 1 + or snapshot["kind"] != EVENT_SNAPSHOT_KIND + or snapshot["id"] != package_id + or snapshot["version"] != version + or snapshot["adapterKind"] not in EVENT_ADAPTER_KINDS + or snapshot["eventsPath"] != "events.jsonl" + or snapshot["snapshotHash"] != hash_json(payload) + or snapshot["eventsHash"] != hash_file(root / "events.jsonl") + ): + _fail(root, "event-snapshot.tampered", "Event snapshot integrity check failed") + return snapshot + + +def list_event_snapshots(project: ProjectContext) -> list[dict[str, Any]]: + """Verify and list every Project event snapshot.""" + + data_root = confined_path( + project.root_dir, + project.manifest.directories["data"], + "project/directories/data", + ) + root = confined_path(data_root, "events", "event-package/root") + if not root.exists(): + return [] + if root.is_symlink() or not root.is_dir(): + _fail(root, "event-snapshot.root", "Event snapshot root must be a directory") + snapshots: list[dict[str, Any]] = [] + for package_dir in sorted(root.iterdir(), key=lambda item: item.name): + if package_dir.is_symlink() or not package_dir.is_dir() or not SAFE_EVENT_ID.fullmatch(package_dir.name): + _fail(package_dir, "event-snapshot.entry", "Invalid event package directory") + for version_dir in sorted(package_dir.iterdir(), key=lambda item: item.name): + if version_dir.is_symlink() or not version_dir.is_dir() or not SAFE_EVENT_ID.fullmatch(version_dir.name): + _fail(version_dir, "event-snapshot.entry", "Invalid event version directory") + snapshots.append(load_event_snapshot(project, package_dir.name, version_dir.name)) + return snapshots diff --git a/autoquant/intake.py b/autoquant/intake.py index c57f006..cf11912 100644 --- a/autoquant/intake.py +++ b/autoquant/intake.py @@ -108,7 +108,10 @@ PROJECT_INTAKE = "intake.json" DATASET_SNAPSHOT = "data/ohlcv/snapshot.json" SAFE_SYMBOL = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._=-]{0,63}$") -SUPPORTED_SOURCE_SUFFIXES = {".csv", ".parquet", ".feather"} +SUPPORTED_SOURCE_SUFFIXES = {".csv", ".json", ".parquet", ".feather"} +MAX_JSON_SOURCE_BYTES = 64 * 1024 * 1024 +MAX_JSON_SOURCE_RECORDS = 1_000_000 +MAX_JSON_SOURCE_COLUMNS = 64 RAGGED_DAILY_SCHEMA_VERSION = 4 OBSERVED_INTRADAY_SCHEMA_VERSION = 5 MULTI_SOURCE_OBSERVED_SCHEMA_VERSION = 6 @@ -479,10 +482,120 @@ def _valid_hash(value: Any) -> bool: ) +def _reject_json_constant(value: str) -> None: + raise ValueError(f"Non-standard JSON number: {value}") + + def _read_source(path: Path) -> pd.DataFrame: suffix = path.suffix.lower() if suffix == ".csv": return pd.read_csv(path) + if suffix == ".json": + try: + if path.stat().st_size > MAX_JSON_SOURCE_BYTES: + raise AutoQuantValidationError( + [ + _issue( + path, + "dataset.json-size", + "JSON OHLCV source exceeds the 64 MiB intake limit", + ) + ] + ) + value = json.loads( + path.read_bytes().decode("utf-8"), + parse_constant=_reject_json_constant, + ) + except FileNotFoundError: + raise AutoQuantValidationError( + [_issue(path, "dataset.missing", "Missing OHLCV source")] + ) from None + except AutoQuantValidationError: + raise + except ( + OSError, + UnicodeDecodeError, + json.JSONDecodeError, + RecursionError, + ValueError, + ) as error: + raise AutoQuantValidationError( + [ + _issue( + path, + "dataset.json", + f"Invalid JSON OHLCV source: {error}", + ) + ] + ) from error + if isinstance(value, dict): + if set(value) != {"records"}: + raise AutoQuantValidationError( + [ + _issue( + path, + "dataset.json-envelope", + "JSON OHLCV must be an array or an object " + "containing only 'records'", + ) + ] + ) + value = value["records"] + if not isinstance(value, list): + raise AutoQuantValidationError( + [ + _issue( + path, + "dataset.json-records", + "JSON OHLCV records must be an array", + ) + ] + ) + if len(value) > MAX_JSON_SOURCE_RECORDS: + raise AutoQuantValidationError( + [ + _issue( + path, + "dataset.json-record-limit", + "JSON OHLCV source exceeds the 1,000,000 record intake limit", + ) + ] + ) + for index, record in enumerate(value): + if not isinstance(record, dict): + raise AutoQuantValidationError( + [ + _issue( + f"{path}/{index}", + "dataset.json-record", + "Each JSON OHLCV record must be an object", + ) + ] + ) + if len(record) > MAX_JSON_SOURCE_COLUMNS: + raise AutoQuantValidationError( + [ + _issue( + f"{path}/{index}", + "dataset.json-column-limit", + "JSON OHLCV records may contain at most 64 fields", + ) + ] + ) + if any( + isinstance(item, (dict, list)) + for item in record.values() + ): + raise AutoQuantValidationError( + [ + _issue( + f"{path}/{index}", + "dataset.json-tabular", + "JSON OHLCV record values must be scalar", + ) + ] + ) + return pd.DataFrame.from_records(value) try: if suffix == ".parquet": return pd.read_parquet(path) @@ -505,7 +618,7 @@ def _read_source(path: Path) -> pd.DataFrame: _issue( path, "dataset.format", - "OHLCV source must be CSV, Parquet, or Feather", + "OHLCV source must be CSV, JSON, Parquet, or Feather", ) ] ) @@ -519,6 +632,8 @@ def _canonical_frame( ) -> pd.DataFrame: try: frame = normalize_ohlcv(_read_source(path), source=str(path)) + except AutoQuantValidationError: + raise except (ValueError, TypeError) as error: raise AutoQuantValidationError( [_issue(path, "dataset.ohlcv", str(error))] @@ -820,7 +935,7 @@ def _validate_v1_package_manifest( _issue( f"{asset_path}/path", "dataset.format", - "Asset path must end in .csv, .parquet, or .feather", + "Asset path must end in .csv, .json, .parquet, or .feather", ) ) if isinstance(symbol, str): @@ -1096,7 +1211,7 @@ def _validate_v5_package_manifest( _issue( f"{asset_path}/path", "dataset.format", - "Asset path must end in .csv, .parquet, or .feather", + "Asset path must end in .csv, .json, .parquet, or .feather", ) ) if all( @@ -1485,7 +1600,7 @@ def _validate_v2_package_manifest( _issue( f"{asset_path}/path", "dataset.format", - "Asset path must end in .csv, .parquet, or .feather", + "Asset path must end in .csv, .json, .parquet, or .feather", ) ) source_paths.append(relative) @@ -4584,11 +4699,12 @@ def intake_dataset_class_context( def dataset_snapshot_class_context( snapshot: dict[str, Any], ) -> dict[str, Any]: - """Project one verified dataset snapshot's economic-class read model.""" + """Project verified subject semantics without copying provider payloads.""" summary = snapshot["assetClass"] assets = snapshot["assets"] per_asset = all("assetClass" in asset for asset in assets) + frequency = snapshot.get("frequency") return { "assetClass": summary, "assetClasses": { @@ -4598,6 +4714,20 @@ def dataset_snapshot_class_context( "assetClassSource": ( "per-asset" if per_asset else "package-summary" ), + "market": snapshot.get("market"), + "frequency": frequency, + "baseInterval": snapshot.get("baseInterval", frequency), + "featureIntervals": snapshot.get( + "featureIntervals", [frequency] if frequency is not None else [] + ), + "priceAdjustment": snapshot.get("priceAdjustment"), + "venues": sorted({asset["venue"] for asset in assets}), + "currencies": sorted({asset["currency"] for asset in assets}), + "volumeSemantics": { + asset["symbol"]: asset["volumeSemantics"] + for asset in assets + if "volumeSemantics" in asset + }, } @@ -4760,7 +4890,8 @@ def load_study_dataset_snapshot( OHLCV_PACKAGE_ASSET_PATH_DESCRIPTION = ( - "Portable POSIX-relative source path resolved from the directory " + "CSV, JSON record array/envelope, Parquet, or Feather source at a " + "portable POSIX-relative path resolved from the directory " "containing the dataset-package manifest. To intake already staged " "nested files without an intermediate copy, place the manifest at their " "common ancestor (for example staging/dataset-package.json with " diff --git a/autoquant/model_runtime.py b/autoquant/model_runtime.py new file mode 100644 index 0000000..797ac8a --- /dev/null +++ b/autoquant/model_runtime.py @@ -0,0 +1,594 @@ +"""Deterministic, point-in-time supervised-model research runtime.""" + +from __future__ import annotations + +import hashlib +import json +import math +import os +import re +import shutil +import uuid +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np +import pandas as pd + +from .intake import _read_source +from .studies import STUDY_ID, hash_file, load_study +from .workspace import AutoQuantValidationError, ProjectContext, ValidationIssue, confined_path + + +SPLIT_NAMES = ("train", "validation", "test") +MODEL_RUNS_DIRECTORY = "model-runs" +MODEL_RUN_ID = re.compile(r"^model-run-[0-9]{8}T[0-9]{12}Z-[0-9a-f]{12}$") + + +@dataclass(frozen=True) +class ModelRunContext: + root_dir: Path + manifest: dict[str, Any] + receipt: dict[str, Any] + + +class ModelRuntimeError(ValueError): + """Stable supervised-model contract failure.""" + + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +def _timestamp_series(frame: pd.DataFrame, column: str) -> pd.Series: + if column not in frame: + raise ModelRuntimeError( + "model.columns", + f"Point-in-time frame is missing {column}", + ) + try: + values = pd.to_datetime(frame[column], utc=True, errors="raise") + except (TypeError, ValueError) as error: + raise ModelRuntimeError( + "model.timestamp", + f"{column} must contain valid timestamps: {error}", + ) from error + if values.isna().any(): + raise ModelRuntimeError( + "model.timestamp", + f"{column} cannot contain missing timestamps", + ) + return values + + +def _numeric_matrix(frame: pd.DataFrame, columns: Sequence[str]) -> np.ndarray: + try: + values = frame.loc[:, list(columns)].apply( + pd.to_numeric, + errors="raise", + ).to_numpy(dtype=float) + except (KeyError, TypeError, ValueError) as error: + raise ModelRuntimeError( + "model.numeric", + f"Model inputs must be numeric: {error}", + ) from error + if not np.isfinite(values).all(): + raise ModelRuntimeError( + "model.non-finite", + "Model inputs cannot contain missing or non-finite values", + ) + return values + + +def _split_labels( + frame: pd.DataFrame, + timestamps: pd.Series, + *, + split_column: str | None, + split_timestamps: Mapping[str, Any] | None, +) -> pd.Series: + if (split_column is None) == (split_timestamps is None): + raise ModelRuntimeError( + "model.split", + "Supply exactly one fixed split_column or split_timestamps", + ) + if split_column is not None: + if split_column not in frame: + raise ModelRuntimeError( + "model.columns", + f"Point-in-time frame is missing {split_column}", + ) + labels = frame[split_column].astype("object").copy() + else: + assert split_timestamps is not None + if set(split_timestamps) != {"trainEnd", "validationEnd"}: + raise ModelRuntimeError( + "model.split", + "split_timestamps must contain trainEnd and validationEnd", + ) + try: + train_end = pd.Timestamp(split_timestamps["trainEnd"]) + validation_end = pd.Timestamp(split_timestamps["validationEnd"]) + train_end = ( + train_end.tz_localize("UTC") + if train_end.tzinfo is None + else train_end.tz_convert("UTC") + ) + validation_end = ( + validation_end.tz_localize("UTC") + if validation_end.tzinfo is None + else validation_end.tz_convert("UTC") + ) + except (TypeError, ValueError) as error: + raise ModelRuntimeError( + "model.split", + f"Split boundaries must be valid timestamps: {error}", + ) from error + if train_end >= validation_end: + raise ModelRuntimeError( + "model.split", + "trainEnd must precede validationEnd", + ) + labels = pd.Series("test", index=frame.index, dtype="object") + labels.loc[timestamps < validation_end] = "validation" + labels.loc[timestamps < train_end] = "train" + + if set(labels.unique()) != set(SPLIT_NAMES): + raise ModelRuntimeError( + "model.split", + "Fixed split must contain train, validation, and test", + ) + order = labels.map({name: position for position, name in enumerate(SPLIT_NAMES)}) + if not order.is_monotonic_increasing: + raise ModelRuntimeError( + "model.split-overlap", + "Train, validation, and test must be non-overlapping chronological blocks", + ) + return labels + + +def _purged_indices(labels: pd.Series, purge_gap: int) -> dict[str, np.ndarray]: + if isinstance(purge_gap, bool) or not isinstance(purge_gap, int) or purge_gap < 0: + raise ModelRuntimeError( + "model.purge-gap", + "purge_gap must be a non-negative integer row count", + ) + indices: dict[str, np.ndarray] = {} + for name in SPLIT_NAMES: + positions = np.flatnonzero(labels.to_numpy() == name) + if name != "test" and purge_gap: + positions = positions[:-purge_gap] + if not len(positions): + raise ModelRuntimeError( + "model.split-empty", + f"{name} is empty after the fixed purge gap", + ) + indices[name] = positions + return indices + + +def _metrics(actual: np.ndarray, predicted: np.ndarray) -> dict[str, Any]: + residual = actual - predicted + mse = float(np.mean(residual**2)) + denominator = float(np.sum((actual - np.mean(actual)) ** 2)) + correlation: float | None = None + if len(actual) > 1 and np.std(actual) > 0 and np.std(predicted) > 0: + correlation = float(np.corrcoef(actual, predicted)[0, 1]) + return { + "rows": int(len(actual)), + "mse": mse, + "rmse": math.sqrt(mse), + "mae": float(np.mean(np.abs(residual))), + "r2": (1.0 - float(np.sum(residual**2)) / denominator) + if denominator > 0 + else None, + "correlation": correlation, + } + + +def run_supervised_model( + frame: pd.DataFrame, + *, + label_column: str, + feature_columns: Sequence[str], + split_column: str | None = None, + split_timestamps: Mapping[str, Any] | None = None, + timestamp_column: str = "timestamp", + available_at_column: str = "available_at", + label_at_column: str = "label_at", + purge_gap: int = 0, + ridge_alpha: float = 1.0, + seed: int = 0, +) -> dict[str, Any]: + """Fit on train, select on validation, and audit once on untouched test. + + Every feature row must declare when it was available and every label must + declare when its outcome completed. The runtime never infers these clocks. + """ + + if not isinstance(frame, pd.DataFrame) or frame.empty: + raise ModelRuntimeError("model.frame", "Model frame must be non-empty") + features = tuple(feature_columns) + if not features or len(features) != len(set(features)): + raise ModelRuntimeError( + "model.features", + "feature_columns must contain unique feature names", + ) + reserved = { + label_column, + timestamp_column, + available_at_column, + label_at_column, + split_column, + } + if any(feature in reserved for feature in features): + raise ModelRuntimeError( + "model.features", + "Features cannot include labels, clocks, or split authority", + ) + if isinstance(seed, bool) or not isinstance(seed, int): + raise ModelRuntimeError("model.seed", "seed must be an integer") + if isinstance(ridge_alpha, bool) or not isinstance(ridge_alpha, (int, float)): + raise ModelRuntimeError("model.ridge", "ridge_alpha must be finite and non-negative") + alpha = float(ridge_alpha) + if not math.isfinite(alpha) or alpha < 0: + raise ModelRuntimeError("model.ridge", "ridge_alpha must be finite and non-negative") + + timestamps = _timestamp_series(frame, timestamp_column) + available_at = _timestamp_series(frame, available_at_column) + label_at = _timestamp_series(frame, label_at_column) + if timestamps.duplicated().any() or not timestamps.is_monotonic_increasing: + raise ModelRuntimeError( + "model.timestamp", + "Prediction timestamps must be unique and chronological", + ) + if (available_at > timestamps).any(): + raise ModelRuntimeError( + "model.lookahead", + "Feature availability cannot follow its prediction timestamp", + ) + if (label_at <= timestamps).any(): + raise ModelRuntimeError( + "model.label-clock", + "Label completion must follow its prediction timestamp", + ) + + labels = _split_labels( + frame, + timestamps, + split_column=split_column, + split_timestamps=split_timestamps, + ) + indices = _purged_indices(labels, purge_gap) + for earlier, later in (("train", "validation"), ("validation", "test")): + next_start = timestamps.iloc[indices[later][0]] + if (label_at.iloc[indices[earlier]] >= next_start).any(): + raise ModelRuntimeError( + "model.target-overlap", + f"{earlier} labels overlap the {later} observation window", + ) + + matrix = _numeric_matrix(frame, features) + target = _numeric_matrix(frame, (label_column,)).reshape(-1) + train = indices["train"] + mean = matrix[train].mean(axis=0) + scale = matrix[train].std(axis=0) + scale[scale == 0] = 1.0 + normalized = (matrix - mean) / scale + design = np.column_stack((np.ones(len(train)), normalized[train])) + penalty = np.eye(design.shape[1]) * alpha + penalty[0, 0] = 0.0 + try: + weights = np.linalg.solve(design.T @ design + penalty, design.T @ target[train]) + except np.linalg.LinAlgError: + weights = np.linalg.pinv(design.T @ design + penalty) @ design.T @ target[train] + if not np.isfinite(weights).all(): + raise ModelRuntimeError("model.fit", "Linear model produced non-finite weights") + + model_prediction = weights[0] + normalized @ weights[1:] + baseline_value = float(target[train].mean()) + baseline_prediction = np.full(len(frame), baseline_value, dtype=float) + validation = indices["validation"] + model_validation = _metrics(target[validation], model_prediction[validation]) + baseline_validation = _metrics(target[validation], baseline_prediction[validation]) + selected = ( + "ridge-linear" + if model_validation["mse"] <= baseline_validation["mse"] + else "train-mean-baseline" + ) + selected_prediction = ( + model_prediction if selected == "ridge-linear" else baseline_prediction + ) + + split_metrics: dict[str, Any] = {} + for name in SPLIT_NAMES: + positions = indices[name] + split_metrics[name] = { + "selected": _metrics(target[positions], selected_prediction[positions]), + "ridgeLinear": _metrics(target[positions], model_prediction[positions]), + "trainMeanBaseline": _metrics( + target[positions], + baseline_prediction[positions], + ), + } + + artifact = { + "kind": "supervised-linear-model-v1", + "featureColumns": list(features), + "labelColumn": label_column, + "intercept": float(weights[0]), + "coefficients": [float(value) for value in weights[1:]], + "trainFeatureMean": [float(value) for value in mean], + "trainFeatureScale": [float(value) for value in scale], + "ridgeAlpha": alpha, + "seed": seed, + "baseline": {"kind": "train-mean", "value": baseline_value}, + } + identity = hashlib.sha256( + json.dumps(artifact, sort_keys=True, separators=(",", ":")).encode("utf-8") + ).hexdigest() + return { + "kind": "supervised-model-research-v1", + "tradingAuthority": "none", + "selectionAuthority": "validation-only", + "testUse": "terminal-audit-only", + "selectedModel": selected, + "splitProtocol": { + "method": "fixed-column" if split_column else "fixed-timestamp-boundaries", + "purgeGapRows": purge_gap, + "targetCrossesBoundary": False, + "rows": {name: int(len(indices[name])) for name in SPLIT_NAMES}, + "boundaries": { + name: { + "start": timestamps.iloc[positions[0]].isoformat(), + "end": timestamps.iloc[positions[-1]].isoformat(), + } + for name, positions in indices.items() + }, + }, + "metrics": split_metrics, + "artifacts": { + "model": artifact, + "modelSha256": identity, + }, + } + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _write_json(path: Path, value: Any) -> None: + path.write_text( + json.dumps(value, indent=2, sort_keys=True, ensure_ascii=False) + "\n", + encoding="utf-8", + ) + + +def _read_json(path: Path) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (FileNotFoundError, OSError, json.JSONDecodeError) as error: + raise AutoQuantValidationError( + [_issue(path, "model-run.read", f"Cannot read model Run JSON: {error}")] + ) from None + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, "model-run.schema", "Model Run JSON must be an object")] + ) + return value + + +def _model_runs_root(project: ProjectContext, *, create: bool = False) -> Path: + root = confined_path(project.root_dir, MODEL_RUNS_DIRECTORY, "project/model-runs") + if root.is_symlink(): + raise AutoQuantValidationError( + [_issue(root, "model-run.symlink", "Model Run root cannot be a symlink")] + ) + if create: + root.mkdir(exist_ok=True) + return root + + +def _new_model_run_id() -> str: + stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ") + return f"model-run-{stamp}-{uuid.uuid4().hex[:12]}" + + +def execute_supervised_model_run( + project: ProjectContext, + study_id: str, + *, + frame_path: str, + label_column: str, + feature_columns: Sequence[str], + split_column: str, + timestamp_column: str = "timestamp", + available_at_column: str = "available_at", + label_at_column: str = "label_at", + purge_gap: int = 0, + ridge_alpha: float = 1.0, + seed: int = 0, +) -> ModelRunContext: + """Execute and publish one immutable supervised-model research receipt.""" + + if not STUDY_ID.fullmatch(study_id): + raise AutoQuantValidationError( + [_issue(study_id, "model-run.study-id", "Invalid Study id")] + ) + study = load_study(project, study_id) + if study.definition.subject.kind != "model": + raise AutoQuantValidationError( + [_issue(study_id, "model-run.subject", "Supervised runtime requires a model Study")] + ) + declared_paths = study.definition.dataset.paths or [] + if frame_path not in declared_paths: + raise AutoQuantValidationError( + [_issue(frame_path, "model-run.dataset", "Frame must be declared by the model Study dataset")] + ) + data_root = confined_path( + project.root_dir, + project.manifest.directories["data"], + "project/data", + ) + frame_file = confined_path(data_root, frame_path, "model-run/frame") + if frame_file.is_symlink() or not frame_file.is_file(): + raise AutoQuantValidationError( + [_issue(frame_file, "model-run.frame", "Model frame must be a real project file")] + ) + frame = _read_source(frame_file) + try: + result = run_supervised_model( + frame, + label_column=label_column, + feature_columns=feature_columns, + split_column=split_column, + timestamp_column=timestamp_column, + available_at_column=available_at_column, + label_at_column=label_at_column, + purge_gap=purge_gap, + ridge_alpha=ridge_alpha, + seed=seed, + ) + except ModelRuntimeError as error: + raise AutoQuantValidationError( + [_issue(frame_file, error.code, str(error))] + ) from None + + run_id = _new_model_run_id() + root = _model_runs_root(project, create=True) + staging = root / f".{run_id}-{uuid.uuid4().hex}" + target = root / run_id + staging.mkdir() + receipt = { + "schemaVersion": 1, + "kind": "autoquant-supervised-model-run", + "id": run_id, + "createdAt": datetime.now(timezone.utc).isoformat(), + "project": {"id": project.manifest.id}, + "study": { + "id": study.definition.id, + "hash": study.study_hash, + "inputHash": study.input_hash, + }, + "frame": {"path": frame_path, "sha256": hash_file(frame_file)}, + "parameters": { + "labelColumn": label_column, + "featureColumns": list(feature_columns), + "splitColumn": split_column, + "timestampColumn": timestamp_column, + "availableAtColumn": available_at_column, + "labelAtColumn": label_at_column, + "purgeGap": purge_gap, + "ridgeAlpha": ridge_alpha, + "seed": seed, + }, + "result": result, + "tradingAuthority": "none", + } + try: + _write_json(staging / "receipt.json", receipt) + manifest = { + "schemaVersion": 1, + "id": run_id, + "completed": True, + "receiptHash": hash_file(staging / "receipt.json"), + } + _write_json(staging / "manifest.json", manifest) + if target.exists() or target.is_symlink(): + raise AutoQuantValidationError( + [_issue(target, "model-run.collision", "Model Run id collision")] + ) + os.replace(staging, target) + return load_model_run(project, run_id) + except Exception: + if staging.exists(): + shutil.rmtree(staging) + raise + + +def load_model_run(project: ProjectContext, run_id: str) -> ModelRunContext: + """Verify one immutable supervised-model research receipt.""" + + if not MODEL_RUN_ID.fullmatch(run_id): + raise AutoQuantValidationError( + [_issue(run_id, "model-run.id", "Invalid model Run id")] + ) + root = confined_path(_model_runs_root(project), run_id, f"model-run/{run_id}") + if root.is_symlink() or not root.is_dir(): + raise AutoQuantValidationError( + [_issue(root, "model-run.missing", f"Unknown model Run: {run_id}")] + ) + manifest = _read_json(root / "manifest.json") + if manifest != { + "schemaVersion": 1, + "id": run_id, + "completed": True, + "receiptHash": hash_file(root / "receipt.json"), + }: + raise AutoQuantValidationError( + [_issue(root, "model-run.tampered", "Invalid immutable model Run manifest")] + ) + receipt = _read_json(root / "receipt.json") + required = { + "schemaVersion", "kind", "id", "createdAt", "project", "study", + "frame", "parameters", "result", "tradingAuthority", + } + issues: list[ValidationIssue] = [] + if set(receipt) != required or receipt.get("schemaVersion") != 1: + issues.append(_issue(root, "model-run.schema", "Invalid model Run receipt fields")) + if receipt.get("kind") != "autoquant-supervised-model-run" or receipt.get("id") != run_id: + issues.append(_issue(root, "model-run.identity", "Invalid model Run identity")) + if receipt.get("project") != {"id": project.manifest.id}: + issues.append(_issue(root, "model-run.project", "Model Run project differs")) + result = receipt.get("result") + if ( + receipt.get("tradingAuthority") != "none" + or not isinstance(result, dict) + or result.get("tradingAuthority") != "none" + ): + issues.append(_issue(root, "model-run.authority", "Model Run has no trading authority")) + study_ref = receipt.get("study") + if not isinstance(study_ref, dict) or set(study_ref) != {"id", "hash", "inputHash"}: + issues.append(_issue(root, "model-run.study", "Invalid model Study reference")) + else: + study = load_study(project, study_ref["id"]) + if study.definition.subject.kind != "model" or study_ref != { + "id": study.definition.id, + "hash": study.study_hash, + "inputHash": study.input_hash, + }: + issues.append(_issue(root, "model-run.study", "Model Study identity differs")) + frame_ref = receipt.get("frame") + if not isinstance(frame_ref, dict) or set(frame_ref) != {"path", "sha256"}: + issues.append(_issue(root, "model-run.frame", "Invalid model frame reference")) + else: + data_root = confined_path( + project.root_dir, + project.manifest.directories["data"], + "project/data", + ) + frame_file = confined_path(data_root, frame_ref["path"], "model-run/frame") + if frame_file.is_symlink() or not frame_file.is_file() or hash_file(frame_file) != frame_ref["sha256"]: + issues.append(_issue(root, "model-run.frame", "Model frame identity differs")) + if issues: + raise AutoQuantValidationError(issues) + return ModelRunContext(root, manifest, receipt) + + +def list_model_runs(project: ProjectContext) -> list[ModelRunContext]: + root = _model_runs_root(project) + if not root.exists(): + return [] + runs: list[ModelRunContext] = [] + for entry in sorted(root.iterdir(), key=lambda item: item.name): + if entry.name.startswith("."): + continue + if entry.is_symlink() or not entry.is_dir() or not MODEL_RUN_ID.fullmatch(entry.name): + raise AutoQuantValidationError( + [_issue(entry, "model-run.entry", "Invalid model Run directory entry")] + ) + runs.append(load_model_run(project, entry.name)) + return runs diff --git a/autoquant/operator_port.py b/autoquant/operator_port.py new file mode 100644 index 0000000..bb76c39 --- /dev/null +++ b/autoquant/operator_port.py @@ -0,0 +1,1401 @@ +"""Closed provider-neutral Operator Port and ResearchLedger projection.""" + +from __future__ import annotations + +import json +import os +import re +import shutil +import time +import uuid +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Callable + +from .research import ( + list_campaign_progress, + list_campaigns, + load_campaign, + request_campaign_stop, +) +from .research_artifacts import ( + load_artifact_decision, + load_reproduction_receipt, + list_artifact_decisions, + list_reproduction_receipts, + publish_artifact_decision, + publish_reproduction_receipt, + validate_artifact_review, + validate_reproduction_request, +) +from .research_definitions import ( + create_experiment_definition_version, + create_factor_definition_version, + create_strategy_definition_version, + load_experiment_definition, + load_factor_definition, + list_experiment_definitions, + list_factor_definitions, + list_strategy_definitions, + load_strategy_definition, + semantic_definition_diff, + validate_experiment_definition, + validate_factor_definition, + validate_strategy_definition, +) +from .sessions import list_experiments, load_session, session_snapshot +from .studies import hash_file, hash_json +from .workspace import ( + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +OPERATOR_SCHEMA_VERSION = 1 +OPERATOR_REQUEST = "request.json" +OPERATOR_RECEIPT = "receipt.json" +OPERATOR_MANIFEST = "manifest.json" +REQUEST_ID = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") +HASH = re.compile(r"^[0-9a-f]{64}$") +TERMINAL_STATUSES = { + "completed", + "stopped", + "failed", + "unavailable", + "stale", + "confirmation-required", +} +READ_ONLY_INTENTS = { + "research.inspect", + "research.explain", + "research.compare", + "research.reproduction-readiness", +} +CONFIRMATION_INTENTS = { + "definition.factor.create", + "definition.strategy.create", + "definition.experiment.create", + "artifact.decide", + "reproduction.start", +} +CONFIRMATION_DECISION_INTENTS = {"confirmation.accept"} +IMMEDIATE_INTENTS = {"campaign.stop"} +CAMPAIGN_EXECUTOR_INTENTS = {"campaign.start", "campaign.pause", "campaign.resume"} +INTENTS = ( + READ_ONLY_INTENTS + | CONFIRMATION_INTENTS + | CONFIRMATION_DECISION_INTENTS + | IMMEDIATE_INTENTS + | CAMPAIGN_EXECUTOR_INTENTS +) +FORBIDDEN_KEYS = { + "command", + "shell", + "argv", + "providercommand", + "apikey", + "api_key", + "password", + "secret", + "credential", + "credentials", + "token", + "path", + "filepath", + "directory", +} + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _write_json(path: Path, value: dict[str, Any]) -> None: + path.write_text( + json.dumps(value, indent=2, ensure_ascii=False, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _read_json(path: Path, label: str) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except FileNotFoundError: + raise AutoQuantValidationError( + [_issue(path, f"{label}.missing", f"Missing {label}")] + ) from None + except json.JSONDecodeError as error: + raise AutoQuantValidationError( + [_issue(path, f"{label}.json", f"Invalid JSON: {error.msg}")] + ) from None + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, f"{label}.type", f"{label} must be an object")] + ) + return value + + +def _strict_keys( + value: Any, + required: set[str], + path: Path | str, + *, + optional: set[str] = frozenset(), +) -> list[ValidationIssue]: + if not isinstance(value, dict): + return [_issue(path, "schema.type", "Expected an object")] + issues = [ + _issue(f"{path}/{key}", "schema.missing", f"Missing required field '{key}'") + for key in sorted(required - value.keys()) + ] + issues.extend( + _issue(f"{path}/{key}", "schema.unknown", f"Unknown field '{key}'") + for key in sorted(value.keys() - required - optional) + ) + return issues + + +def _walk_forbidden(value: Any, path: str = "request") -> list[ValidationIssue]: + issues = [] + if isinstance(value, dict): + for key, item in value.items(): + normalized = key.lower().replace("-", "").replace("_", "") + blocked = {item.replace("_", "") for item in FORBIDDEN_KEYS} + if key.lower() in FORBIDDEN_KEYS or any( + normalized == item or normalized.endswith(item) for item in blocked + ): + issues.append( + _issue( + f"{path}/{key}", + "operator.forbidden-field", + "Operator requests cannot carry commands or credentials", + ) + ) + issues.extend(_walk_forbidden(item, f"{path}/{key}")) + elif isinstance(value, list): + for index, item in enumerate(value): + issues.extend(_walk_forbidden(item, f"{path}/{index}")) + return issues + + +def validate_operator_request(value: dict[str, Any], path: str = "request") -> None: + required = { + "schemaVersion", + "kind", + "requestId", + "actor", + "workspaceRef", + "projectId", + "sessionId", + "intent", + "objectRefs", + "authority", + "budget", + "confirmationRef", + "expectedState", + "input", + } + issues = _strict_keys(value, required, path) + if value.get("schemaVersion") != OPERATOR_SCHEMA_VERSION: + issues.append(_issue(f"{path}/schemaVersion", "schema.version", "Expected Operator request V1")) + if value.get("kind") != "autoquant-operator-request": + issues.append(_issue(f"{path}/kind", "operator.kind", "Invalid Operator request kind")) + if not isinstance(value.get("requestId"), str) or not REQUEST_ID.fullmatch(value["requestId"]): + issues.append(_issue(f"{path}/requestId", "operator.request-id", "Invalid request identity")) + for key in ("workspaceRef", "projectId", "sessionId", "intent"): + if not isinstance(value.get(key), str) or not value[key].strip(): + issues.append(_issue(f"{path}/{key}", "schema.string", f"{key} must be non-empty")) + intent = value.get("intent") + input_fields = { + "research.inspect": set(), + "research.explain": set(), + "research.compare": set(), + "research.reproduction-readiness": set(), + "definition.factor.create": {"definition"}, + "definition.strategy.create": {"definition"}, + "definition.experiment.create": {"definition"}, + "artifact.decide": {"review"}, + "reproduction.start": {"reproduction"}, + "confirmation.accept": {"executionActor"}, + "campaign.stop": set(), + "campaign.start": {"experimentDefinitionRef"}, + "campaign.pause": set(), + "campaign.resume": set(), + } + if intent in input_fields: + issues.extend(_strict_keys(value.get("input"), input_fields[intent], f"{path}/input")) + input_value = value.get("input") + domain_validators = { + "definition.factor.create": ("definition", validate_factor_definition), + "definition.strategy.create": ("definition", validate_strategy_definition), + "definition.experiment.create": ("definition", validate_experiment_definition), + "artifact.decide": ("review", validate_artifact_review), + "reproduction.start": ("reproduction", validate_reproduction_request), + } + if intent in domain_validators and isinstance(input_value, dict): + field, validator = domain_validators[intent] + domain_value = input_value.get(field) + if not isinstance(domain_value, dict): + issues.append(_issue(f"{path}/input/{field}", "schema.type", "Expected an object")) + else: + try: + validator(domain_value) + except AutoQuantValidationError as error: + issues.extend(error.issues) + actor = value.get("actor") + issues.extend(_strict_keys(actor, {"id", "kind"}, f"{path}/actor")) + if isinstance(actor, dict): + if actor.get("kind") not in {"user", "studio", "embedded-agent", "openalice", "hermes", "codex"}: + issues.append(_issue(f"{path}/actor/kind", "operator.actor", "Invalid actor kind")) + if not isinstance(actor.get("id"), str) or not actor["id"].strip(): + issues.append(_issue(f"{path}/actor/id", "schema.string", "Actor id must be non-empty")) + object_refs = value.get("objectRefs") + if not isinstance(object_refs, list): + issues.append(_issue(f"{path}/objectRefs", "schema.list", "objectRefs must be a list")) + else: + for index, reference in enumerate(object_refs): + ref_path = f"{path}/objectRefs/{index}" + issues.extend(_strict_keys(reference, {"kind", "id", "version"}, ref_path)) + if isinstance(reference, dict): + if reference.get("kind") not in {"session", "factor-definition", "strategy-definition", "experiment-definition", "campaign", "artifact-approval", "reproduction-receipt"}: + issues.append(_issue(f"{ref_path}/kind", "operator.object-ref", "Invalid object reference kind")) + if not isinstance(reference.get("id"), str) or not reference["id"].strip(): + issues.append(_issue(f"{ref_path}/id", "schema.string", "Object id must be non-empty")) + version = reference.get("version") + if version is not None and ( + not isinstance(version, int) or isinstance(version, bool) or version < 1 + ): + issues.append(_issue(f"{ref_path}/version", "definition.version", "Object version must be positive or null")) + authority = value.get("authority") + issues.extend(_strict_keys(authority, {"mode"}, f"{path}/authority")) + if isinstance(authority, dict) and authority.get("mode") not in {"read-only", "approved-envelope", "confirmation-bound"}: + issues.append(_issue(f"{path}/authority/mode", "operator.authority", "Invalid authority mode")) + budget = value.get("budget") + issues.extend( + _strict_keys( + budget, + {"candidateLimit", "wallTimeSeconds", "cpuSeconds", "gpuSeconds", "cost"}, + f"{path}/budget", + ) + ) + if isinstance(budget, dict): + for key in ("candidateLimit", "wallTimeSeconds", "cpuSeconds", "gpuSeconds"): + item = budget.get(key) + if not isinstance(item, int) or isinstance(item, bool) or item < 0: + issues.append(_issue(f"{path}/budget/{key}", "schema.range", f"{key} must be non-negative")) + cost = budget.get("cost") + if cost is not None: + issues.extend(_strict_keys(cost, {"currency", "amount"}, f"{path}/budget/cost")) + if isinstance(cost, dict): + if not isinstance(cost.get("currency"), str) or not cost["currency"].strip(): + issues.append(_issue(f"{path}/budget/cost/currency", "schema.string", "Currency must be non-empty")) + if not isinstance(cost.get("amount"), (int, float)) or isinstance(cost.get("amount"), bool) or cost.get("amount", -1) < 0: + issues.append(_issue(f"{path}/budget/cost/amount", "schema.range", "Cost must be non-negative")) + confirmation = value.get("confirmationRef") + if confirmation is not None and ( + not isinstance(confirmation, str) or not REQUEST_ID.fullmatch(confirmation) + ): + issues.append(_issue(f"{path}/confirmationRef", "operator.confirmation", "Invalid confirmation reference")) + expected = value.get("expectedState") + issues.extend(_strict_keys(expected, {"sessionStatus", "objectHashes"}, f"{path}/expectedState")) + if isinstance(expected, dict): + if expected.get("sessionStatus") not in {"active", "promoted", "completed"}: + issues.append(_issue(f"{path}/expectedState/sessionStatus", "operator.expected-state", "Invalid expected Session state")) + hashes = expected.get("objectHashes") + if not isinstance(hashes, dict) or any( + not isinstance(key, str) + or not key + or not isinstance(item, str) + or not HASH.fullmatch(item) + for key, item in (hashes.items() if isinstance(hashes, dict) else []) + ): + issues.append(_issue(f"{path}/expectedState/objectHashes", "operator.expected-state", "Object hashes must be a string-to-sha256 map")) + if not isinstance(value.get("input"), dict): + issues.append(_issue(f"{path}/input", "schema.type", "input must be an object")) + issues.extend(_walk_forbidden(value, path)) + if issues: + raise AutoQuantValidationError(issues) + + +def _receipts_root(project: ProjectContext, session_id: str, *, create: bool = False) -> Path: + session = load_session(project, session_id) + root = confined_path(session.root_dir, "operator-receipts", "session/operatorReceipts") + if create: + root.mkdir(exist_ok=True) + return root + + +@contextmanager +def _operator_lock(project: ProjectContext, session_id: str): + root = _receipts_root(project, session_id, create=True) + path = root / ".operator.lock" + with path.open("a+b") as handle: + handle.seek(0, os.SEEK_END) + if handle.tell() == 0: + handle.write(b"\0") + handle.flush() + handle.seek(0) + # ponytail: one Session-wide lock; split by request only if Operator throughput matters. + if os.name == "nt": + import msvcrt + + while True: + try: + msvcrt.locking(handle.fileno(), msvcrt.LK_NBLCK, 1) + break + except OSError: + time.sleep(0.05) + else: + import fcntl + + fcntl.flock(handle.fileno(), fcntl.LOCK_EX) + try: + yield + finally: + handle.seek(0) + if os.name == "nt": + msvcrt.locking(handle.fileno(), msvcrt.LK_UNLCK, 1) + else: + fcntl.flock(handle.fileno(), fcntl.LOCK_UN) + + +def _pending_root(project: ProjectContext, session_id: str, request_id: str) -> Path: + return confined_path( + _receipts_root(project, session_id, create=True), + f".{request_id}.pending", + "session/operatorReceipts/pending", + ) + + +def _receipt_root(project: ProjectContext, session_id: str, request_id: str) -> Path: + if not REQUEST_ID.fullmatch(request_id): + raise AutoQuantValidationError([_issue("requestId", "operator.request-id", "Invalid request identity")]) + return confined_path( + _receipts_root(project, session_id), + request_id, + "session/operatorReceipts/requestId", + ) + + +def _load_receipt(root: Path) -> dict[str, Any]: + manifest_path = root / OPERATOR_MANIFEST + request_path = root / OPERATOR_REQUEST + receipt_path = root / OPERATOR_RECEIPT + manifest = _read_json(manifest_path, "Operator manifest") + required = { + "schemaVersion", "kind", "requestId", "requestHash", "receiptHash", "completed", "files" + } + issues = _strict_keys(manifest, required, manifest_path) + request_hash = hash_file(request_path) if request_path.is_file() else None + receipt_hash = hash_file(receipt_path) if receipt_path.is_file() else None + if manifest.get("schemaVersion") != 1 or manifest.get("kind") != "autoquant-agent-operation-manifest": + issues.append(_issue(manifest_path, "operator.manifest", "Invalid Operator manifest")) + if manifest.get("requestHash") != request_hash or manifest.get("receiptHash") != receipt_hash: + issues.append(_issue(root, "operator.hash", "Operator receipt hash mismatch")) + if manifest.get("files") != {OPERATOR_REQUEST: request_hash, OPERATOR_RECEIPT: receipt_hash}: + issues.append(_issue(root, "operator.files", "Operator receipt file manifest mismatch")) + if issues: + raise AutoQuantValidationError(issues) + receipt = _read_json(receipt_path, "Operator receipt") + validate_operator_receipt(receipt, str(receipt_path)) + if receipt["requestId"] != manifest["requestId"] or receipt["acceptedRequestHash"] != hash_json(_read_json(request_path, "Operator request")): + raise AutoQuantValidationError([_issue(root, "operator.identity", "Operator receipt identity mismatch")]) + return receipt + + +def validate_operator_receipt(value: dict[str, Any], path: str = "receipt") -> None: + required = { + "schemaVersion", "kind", "requestId", "acceptedRequestHash", "actor", "intent", + "status", "operations", "artifacts", "evidence", "budgetSpent", "warnings", + "failedGates", "errors", "nextValidActions", "reproductionLineage", "completedAt", + } + issues = _strict_keys(value, required, path) + if value.get("schemaVersion") != 1 or value.get("kind") != "autoquant-agent-operation-receipt": + issues.append(_issue(path, "operator.receipt", "Invalid Operator receipt kind or version")) + if value.get("status") not in TERMINAL_STATUSES: + issues.append(_issue(f"{path}/status", "operator.status", "Invalid terminal status")) + if not isinstance(value.get("acceptedRequestHash"), str) or not HASH.fullmatch(value.get("acceptedRequestHash", "")): + issues.append(_issue(f"{path}/acceptedRequestHash", "schema.hash", "Invalid accepted request hash")) + for key in ("operations", "artifacts", "evidence", "warnings", "failedGates", "errors", "nextValidActions", "reproductionLineage"): + if not isinstance(value.get(key), list): + issues.append(_issue(f"{path}/{key}", "schema.list", f"{key} must be a list")) + if not isinstance(value.get("budgetSpent"), dict): + issues.append(_issue(f"{path}/budgetSpent", "schema.type", "budgetSpent must be an object")) + if issues: + raise AutoQuantValidationError(issues) + + +def _publish_receipt( + project: ProjectContext, + request: dict[str, Any], + *, + status: str, + operations: list[dict[str, Any]], + artifacts: list[dict[str, Any]] | None = None, + evidence: list[dict[str, Any]] | None = None, + warnings: list[str] | None = None, + failed_gates: list[str] | None = None, + errors: list[dict[str, str]] | None = None, + next_actions: list[str] | None = None, +) -> dict[str, Any]: + root = _receipt_root(project, request["sessionId"], request["requestId"]) + pending = _pending_root(project, request["sessionId"], request["requestId"]) + if root.exists(): + prior_request = _read_json(root / OPERATOR_REQUEST, "Operator request") + if hash_json(prior_request) == hash_json(request): + if pending.exists(): + shutil.rmtree(pending) + return _load_receipt(root) + raise AutoQuantValidationError( + [_issue(root, "operator.collision", "Operator receipt already exists")] + ) + parent = _receipts_root(project, request["sessionId"], create=True) + temporary = parent / f".{request['requestId']}.{uuid.uuid4().hex}.creating" + request_hash = hash_json(request) + receipt = { + "schemaVersion": 1, + "kind": "autoquant-agent-operation-receipt", + "requestId": request["requestId"], + "acceptedRequestHash": request_hash, + "actor": request["actor"], + "intent": request["intent"], + "status": status, + "operations": operations, + "artifacts": artifacts or [], + "evidence": evidence or [], + "budgetSpent": { + "candidates": 0, + "wallTimeSeconds": 0, + "cpuSeconds": 0, + "gpuSeconds": 0, + "cost": {"known": True, "currency": None, "amount": 0}, + }, + "warnings": warnings or [], + "failedGates": failed_gates or [], + "errors": errors or [], + "nextValidActions": next_actions or [], + "reproductionLineage": [], + "completedAt": datetime.now(timezone.utc).isoformat(), + } + validate_operator_receipt(receipt) + try: + temporary.mkdir() + _write_json(temporary / OPERATOR_REQUEST, request) + _write_json(temporary / OPERATOR_RECEIPT, receipt) + files = { + OPERATOR_REQUEST: hash_file(temporary / OPERATOR_REQUEST), + OPERATOR_RECEIPT: hash_file(temporary / OPERATOR_RECEIPT), + } + manifest = { + "schemaVersion": 1, + "kind": "autoquant-agent-operation-manifest", + "requestId": request["requestId"], + "requestHash": files[OPERATOR_REQUEST], + "receiptHash": files[OPERATOR_RECEIPT], + "completed": True, + "files": files, + } + _write_json(temporary / OPERATOR_MANIFEST, manifest) + os.replace(temporary, root) + if pending.exists(): + shutil.rmtree(pending) + except Exception: + if temporary.exists(): + shutil.rmtree(temporary) + if root.exists(): + prior_request = _read_json(root / OPERATOR_REQUEST, "Operator request") + if hash_json(prior_request) == hash_json(request): + if pending.exists(): + shutil.rmtree(pending) + return _load_receipt(root) + raise + return _load_receipt(root) + + +def list_operator_receipts(project: ProjectContext, session_id: str) -> list[dict[str, Any]]: + root = _receipts_root(project, session_id) + if not root.exists(): + return [] + receipts = [] + for path in sorted(root.iterdir()): + if path.is_dir() and not path.name.startswith("."): + receipts.append(_load_receipt(path)) + return receipts + + +def build_research_ledger(project: ProjectContext, session_id: str) -> dict[str, Any]: + session = load_session(project, session_id) + snapshot = session_snapshot(project, session) + factors = [item.definition for item in list_factor_definitions(project)] + strategies = [item.definition for item in list_strategy_definitions(project)] + experiment_definitions = [ + item.definition for item in list_experiment_definitions(project, session_id) + ] + campaigns = [item.to_dict() for item in list_campaigns(project, session)] + campaign_progress = list_campaign_progress(session) + experiments = [item.to_dict() for item in list_experiments(project, session)] + receipts = list_operator_receipts(project, session_id) + artifact_decisions = list_artifact_decisions(project, session_id) + reproductions = list_reproduction_receipts(project, session_id) + stages = [ + { + "id": "data", + "label": "Data", + "state": "available", + "objects": [{"kind": "study", "id": session.manifest["studyId"], "version": None}], + "blockers": [], + "nextValidActions": ["research.inspect"], + }, + { + "id": "question", + "label": "Question", + "state": "available" if session.manifest.get("brief") else "partial", + "objects": ([{"kind": "research-brief", "id": session.manifest["brief"]["id"], "version": None}] if session.manifest.get("brief") else []), + "blockers": ([] if session.manifest.get("brief") else ["No request-bound ResearchBrief is attached to this legacy Session."]), + "nextValidActions": ["research.explain"], + }, + { + "id": "factor", + "label": "Factor", + "state": "available" if factors or strategies else "empty", + "objects": ( + [{"kind": "factor-definition", "id": item["id"], "version": item["version"]} for item in factors] + + [{"kind": "strategy-definition", "id": item["id"], "version": item["version"]} for item in strategies] + ), + "blockers": [] if factors or strategies else ["No FactorDefinition or StrategyDefinition version has been published."], + "nextValidActions": ["research.inspect"], + }, + { + "id": "experiment", + "label": "Experiment", + "state": "available" if experiment_definitions or experiments else "empty", + "objects": [ + {"kind": "experiment-definition", "id": item["id"], "version": item["version"]} + for item in experiment_definitions + ], + "blockers": [] if experiment_definitions else ["No frozen ExperimentDefinition is connected."], + "nextValidActions": ["research.compare"], + }, + { + "id": "campaign", + "label": "Campaign", + "state": "partial" if campaign_progress else ("available" if campaigns else "empty"), + "objects": [ + { + "kind": "campaign", + "id": item.get("id", item.get("campaignId")), + "version": None, + } + for item in [*campaigns, *campaign_progress] + ], + "blockers": [], + "nextValidActions": ["campaign.stop"] if campaign_progress else ["research.inspect"], + }, + { + "id": "evidence", + "label": "Evidence", + "state": "partial" if experiments else "empty", + "objects": [{"kind": "experiment", "id": item["id"], "version": None} for item in experiments], + "blockers": ["ReplayBundle, verified market clock, and entity mapping are unavailable for this connected projection."], + "nextValidActions": ["research.reproduction-readiness"], + "widgets": { + "runs": {"state": "available" if experiments else "empty"}, + "replay": {"state": "unavailable", "reason": "ReplayBundle contract is not connected."}, + }, + }, + { + "id": "approval", + "label": "Approval", + "state": "available" if artifact_decisions else "unavailable", + "objects": [ + {"kind": "artifact-approval", "id": item["decision"]["id"], "version": None} + for item in artifact_decisions + ], + "blockers": [] if artifact_decisions else ["No exact-version artifact approval is published; approval remains unavailable until Core verifies an EvidenceAssessment."], + "nextValidActions": ["research.reproduction-readiness"] if artifact_decisions else [], + }, + { + "id": "reproduction", + "label": "Reproduction", + "state": "available" if reproductions else "unavailable", + "objects": [ + {"kind": "reproduction-receipt", "id": item["receipt"]["id"], "version": None} + for item in reproductions + ], + "blockers": [] if reproductions else ["Reproduction requires an approved artifact manifest and a Core-controlled executor."], + "nextValidActions": [], + }, + ] + return { + "schemaVersion": 1, + "kind": "autoquant-research-ledger", + "sessionId": session_id, + "sessionStatus": session.manifest["status"], + "authority": snapshot["authority"], + "stages": stages, + "receipts": receipts, + } + + +def _current_object_hashes( + project: ProjectContext, session_id: str, refs: list[dict[str, Any]] +) -> dict[str, str]: + hashes = {} + session = load_session(project, session_id) + for reference in refs: + key = f"{reference['kind']}:{reference['id']}:{reference['version']}" + if reference["kind"] == "session": + hashes[key] = hash_file(session.manifest_path) + elif reference["kind"] == "factor-definition" and reference["version"] is not None: + item = load_factor_definition(project, reference["id"], reference["version"]) + hashes[key] = item.manifest["contentHash"] + elif reference["kind"] == "experiment-definition" and reference["version"] is not None: + item = load_experiment_definition(project, session_id, reference["id"], reference["version"]) + hashes[key] = item.manifest["contentHash"] + elif reference["kind"] == "strategy-definition" and reference["version"] is not None: + item = load_strategy_definition(project, reference["id"], reference["version"]) + hashes[key] = item.manifest["contentHash"] + return hashes + + +def _compare(project: ProjectContext, request: dict[str, Any]) -> dict[str, Any]: + refs = request["objectRefs"] + if len(refs) != 2 or refs[0]["kind"] != refs[1]["kind"] or refs[0]["id"] != refs[1]["id"]: + raise AutoQuantValidationError([_issue("objectRefs", "operator.compare", "Compare requires two versions of the same definition")]) + if refs[0]["kind"] == "factor-definition": + before = load_factor_definition(project, refs[0]["id"], refs[0]["version"]).definition + after = load_factor_definition(project, refs[1]["id"], refs[1]["version"]).definition + elif refs[0]["kind"] == "experiment-definition": + before = load_experiment_definition(project, request["sessionId"], refs[0]["id"], refs[0]["version"]).definition + after = load_experiment_definition(project, request["sessionId"], refs[1]["id"], refs[1]["version"]).definition + elif refs[0]["kind"] == "strategy-definition": + before = load_strategy_definition(project, refs[0]["id"], refs[0]["version"]).definition + after = load_strategy_definition(project, refs[1]["id"], refs[1]["version"]).definition + else: + raise AutoQuantValidationError([_issue("objectRefs", "operator.compare", "Only definition versions support semantic comparison")]) + return semantic_definition_diff(before, after) + + +def _is_collision(error: AutoQuantValidationError) -> bool: + return any(item.code in {"definition.collision", "artifact.collision"} for item in error.issues) + + +def execute_operator_request( + project: ProjectContext, request: dict[str, Any] +) -> dict[str, Any]: + validate_operator_request(request) + with _operator_lock(project, request["sessionId"]): + return _execute_operator_request_locked(project, request) + + +def _execute_operator_request_locked( + project: ProjectContext, request: dict[str, Any] +) -> dict[str, Any]: + session = load_session(project, request["sessionId"]) + if request["projectId"] != project.manifest.id: + raise AutoQuantValidationError([_issue("projectId", "operator.project", "Operator Project id mismatch")]) + root = _receipt_root(project, request["sessionId"], request["requestId"]) + if root.exists(): + prior_request = _read_json(root / OPERATOR_REQUEST, "Operator request") + if hash_json(prior_request) != hash_json(request): + raise AutoQuantValidationError([_issue("requestId", "operator.idempotency-conflict", "Request identity was already used for different bytes")]) + return _load_receipt(root) + pending = _pending_root(project, request["sessionId"], request["requestId"]) + recovering = pending.exists() + if recovering: + pending_request_path = pending / OPERATOR_REQUEST + if not pending_request_path.is_file(): + shutil.rmtree(pending) + recovering = False + else: + pending_request = _read_json(pending_request_path, "Pending Operator request") + if hash_json(pending_request) != hash_json(request): + raise AutoQuantValidationError( + [ + _issue( + request["requestId"], + "operator.idempotency-conflict", + "Request identity is reserved for different bytes", + ) + ] + ) + if not recovering: + pending.mkdir() + _write_json(pending / OPERATOR_REQUEST, request) + if request["intent"] not in INTENTS: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.unknown-intent", "message": "Unknown closed Operator intent"}], + next_actions=["aq capabilities --json"], + ) + if request["intent"] in CONFIRMATION_DECISION_INTENTS: + if ( + request["authority"]["mode"] != "approved-envelope" + or request["actor"]["kind"] != "user" + or request["confirmationRef"] is None + ): + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[ + { + "code": "operator.confirmation-authority", + "message": "Semantic confirmation requires an authorized user and an exact proposal receipt", + } + ], + next_actions=["research.inspect"], + ) + issues = _strict_keys( + request["input"], + {"executionActor"}, + "request/input", + ) + execution_actor = request["input"].get("executionActor") + issues.extend( + _strict_keys(execution_actor, {"id", "kind"}, "request/input/executionActor") + ) + if issues: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": item.code, "message": item.message} for item in issues], + next_actions=["research.inspect"], + ) + proposal_root = _receipt_root( + project, + request["sessionId"], + request["confirmationRef"], + ) + try: + proposal_receipt = _load_receipt(proposal_root) + proposal_request = _read_json( + proposal_root / OPERATOR_REQUEST, + "Operator proposal request", + ) + except AutoQuantValidationError as error: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": item.code, "message": item.message} for item in error.issues], + next_actions=["research.inspect"], + ) + if ( + proposal_receipt["status"] != "confirmation-required" + or proposal_request["actor"] != execution_actor + or proposal_request["projectId"] != request["projectId"] + or proposal_request["sessionId"] != request["sessionId"] + or proposal_request["expectedState"] != request["expectedState"] + ): + return _publish_receipt( + project, + request, + status="failed", + operations=[], + failed_gates=["semantic-confirmation"], + errors=[ + { + "code": "operator.confirmation-mismatch", + "message": "Confirmation decision does not match the proposed actor and prior state", + } + ], + next_actions=["research.inspect"], + ) + return _publish_receipt( + project, + request, + status="completed", + operations=[{"intent": request["intent"], "completed": True}], + evidence=[ + { + "kind": "autoquant-semantic-confirmation-decision", + "proposalRequestId": proposal_request["requestId"], + "proposalRequestHash": hash_json(proposal_request), + "executionActor": execution_actor, + "confirmedBy": request["actor"], + } + ], + next_actions=[proposal_request["intent"]], + ) + expected = request["expectedState"] + current_hashes = _current_object_hashes(project, request["sessionId"], request["objectRefs"]) + if ( + expected["sessionStatus"] != session.manifest["status"] + or expected["objectHashes"] != current_hashes + ): + return _publish_receipt( + project, + request, + status="stale", + operations=[], + evidence=[{"kind": "current-state", "sessionStatus": session.manifest["status"], "objectHashes": current_hashes}], + failed_gates=["expected-prior-state"], + next_actions=["research.inspect"], + ) + if request["intent"] in CONFIRMATION_INTENTS: + if request["authority"]["mode"] != "confirmation-bound": + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.authority", "message": "Mutation intents require confirmation-bound authority"}], + next_actions=["research.inspect"], + ) + if request["confirmationRef"] is None: + return _publish_receipt( + project, + request, + status="confirmation-required", + operations=[{"intent": request["intent"], "completed": False}], + evidence=[{ + "kind": "autoquant-semantic-confirmation", + "intent": request["intent"], + "objectRefs": request["objectRefs"], + "budget": request["budget"], + "proposedInputHash": hash_json(request["input"]), + }], + next_actions=[request["intent"]], + ) + confirmation_root = _receipt_root( + project, + request["sessionId"], + request["confirmationRef"], + ) + try: + confirmation_decision = _load_receipt(confirmation_root) + confirmation_decision_request = _read_json( + confirmation_root / OPERATOR_REQUEST, + "Operator confirmation decision request", + ) + except AutoQuantValidationError: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + failed_gates=["semantic-confirmation"], + errors=[{"code": "operator.confirmation-missing", "message": "Confirmation receipt is missing or invalid"}], + next_actions=["research.inspect"], + ) + decision = next( + ( + item + for item in confirmation_decision.get("evidence", []) + if item.get("kind") == "autoquant-semantic-confirmation-decision" + ), + None, + ) + proposal_root = ( + _receipt_root( + project, + request["sessionId"], + decision["proposalRequestId"], + ) + if isinstance(decision, dict) and isinstance(decision.get("proposalRequestId"), str) + else None + ) + try: + proposal_receipt = _load_receipt(proposal_root) if proposal_root else None + proposal_request = ( + _read_json(proposal_root / OPERATOR_REQUEST, "Operator proposal request") + if proposal_root + else None + ) + except AutoQuantValidationError: + proposal_receipt = None + proposal_request = None + if ( + confirmation_decision["status"] != "completed" + or confirmation_decision["intent"] != "confirmation.accept" + or confirmation_decision_request["actor"].get("kind") != "user" + or not isinstance(decision, dict) + or decision.get("executionActor") != request["actor"] + or proposal_receipt is None + or proposal_receipt["status"] != "confirmation-required" + or proposal_request is None + or proposal_receipt["intent"] != request["intent"] + or decision.get("proposalRequestHash") != hash_json(proposal_request) + or proposal_request["actor"] != request["actor"] + or proposal_request["workspaceRef"] != request["workspaceRef"] + or proposal_request["projectId"] != request["projectId"] + or proposal_request["sessionId"] != request["sessionId"] + or proposal_request["expectedState"] != request["expectedState"] + or hash_json(proposal_request["input"]) != hash_json(request["input"]) + or proposal_request["objectRefs"] != request["objectRefs"] + or proposal_request["budget"] != request["budget"] + ): + return _publish_receipt( + project, + request, + status="failed", + operations=[], + failed_gates=["semantic-confirmation"], + errors=[{"code": "operator.confirmation-mismatch", "message": "Confirmation does not match the proposed semantic input, object references, and budget"}], + next_actions=["research.inspect"], + ) + try: + if request["intent"] == "definition.factor.create": + definition_input = request["input"]["definition"] + try: + created = create_factor_definition_version(project, definition_input) + except AutoQuantValidationError as error: + if not recovering or not _is_collision(error): + raise + created = load_factor_definition( + project, + definition_input["id"], + definition_input["version"], + ) + if created.definition != definition_input: + raise error + artifact_refs = [{"kind": "factor-definition", "id": created.definition["id"], "version": created.definition["version"], "hash": created.manifest["contentHash"]}] + result = created.definition + elif request["intent"] == "definition.strategy.create": + definition_input = request["input"]["definition"] + try: + created = create_strategy_definition_version(project, definition_input) + except AutoQuantValidationError as error: + if not recovering or not _is_collision(error): + raise + created = load_strategy_definition( + project, + definition_input["id"], + definition_input["version"], + ) + if created.definition != definition_input: + raise error + artifact_refs = [{"kind": "strategy-definition", "id": created.definition["id"], "version": created.definition["version"], "hash": created.manifest["contentHash"]}] + result = created.definition + elif request["intent"] == "definition.experiment.create": + definition_input = request["input"]["definition"] + try: + created = create_experiment_definition_version( + project, + request["sessionId"], + definition_input, + ) + except AutoQuantValidationError as error: + if not recovering or not _is_collision(error): + raise + created = load_experiment_definition( + project, + request["sessionId"], + definition_input["id"], + definition_input["version"], + ) + if created.definition != definition_input: + raise error + artifact_refs = [{"kind": "experiment-definition", "id": created.definition["id"], "version": created.definition["version"], "hash": created.manifest["contentHash"]}] + result = created.definition + elif request["intent"] == "artifact.decide": + review_input = request["input"]["review"] + try: + created = publish_artifact_decision( + project, + request["sessionId"], + review_input, + ) + except AutoQuantValidationError as error: + if not recovering or not _is_collision(error): + raise + created = load_artifact_decision( + project, + request["sessionId"], + review_input["id"], + ) + if created["review"] != review_input: + raise error + artifact_refs = [{"kind": "artifact-approval", "id": created["decision"]["id"], "version": None, "hash": created["manifest"]["files"]["decision.json"]}] + result = created["decision"] + else: + reproduction_input = request["input"]["reproduction"] + try: + created = publish_reproduction_receipt( + project, + request["sessionId"], + reproduction_input, + ) + except AutoQuantValidationError as error: + if not recovering or not _is_collision(error): + raise + created = load_reproduction_receipt( + project, + request["sessionId"], + reproduction_input["id"], + ) + if created["request"] != reproduction_input: + raise error + artifact_refs = [{"kind": "reproduction-receipt", "id": created["receipt"]["id"], "version": None, "hash": created["manifest"]["files"]["receipt.json"]}] + result = created["receipt"] + except (AutoQuantValidationError, KeyError) as error: + issues = ( + error.issues + if isinstance(error, AutoQuantValidationError) + else [_issue("input", "schema.missing", f"Missing mutation input field: {error.args[0]}")] + ) + return _publish_receipt( + project, + request, + status="failed", + operations=[{"intent": request["intent"], "completed": False}], + errors=[{"code": item.code, "message": item.message} for item in issues], + next_actions=["research.inspect"], + ) + return _publish_receipt( + project, + request, + status="completed", + operations=[{"intent": request["intent"], "completed": True}], + artifacts=artifact_refs, + evidence=[result], + ) + if request["intent"] in IMMEDIATE_INTENTS: + if request["authority"]["mode"] != "approved-envelope": + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.authority", "message": "Immediate Campaign stop requires approved-envelope authority"}], + next_actions=["research.inspect"], + ) + references = request["objectRefs"] + if len(references) != 1 or references[0]["kind"] != "campaign" or references[0]["version"] is not None: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.campaign-ref", "message": "Campaign stop requires one exact unversioned Campaign reference"}], + next_actions=["research.inspect"], + ) + session = load_session(project, request["sessionId"]) + active = next( + ( + item + for item in list_campaign_progress(session) + if item["campaignId"] == references[0]["id"] + ), + None, + ) + try: + stop = request_campaign_stop( + project, + request["sessionId"], + references[0]["id"], + request["actor"], + ) + except AutoQuantValidationError as error: + return _publish_receipt( + project, + request, + status="unavailable", + operations=[{"intent": request["intent"], "completed": False}], + errors=[{"code": item.code, "message": item.message} for item in error.issues], + next_actions=["research.inspect"], + ) + remaining = ( + active.get("budget", {}).get("remaining", {}).get("wallSeconds", 0) + if active is not None + else 0 + ) + deadline = time.monotonic() + min(30, max(1, remaining + 1)) + terminal = None + while time.monotonic() < deadline: + session = load_session(project, request["sessionId"]) + if not any( + item["campaignId"] == references[0]["id"] + for item in list_campaign_progress(session) + ): + terminal = load_campaign( + project, + session, + references[0]["id"], + ).result + break + time.sleep(0.05) + if terminal is None: + return _publish_receipt( + project, + request, + status="unavailable", + operations=[{"intent": request["intent"], "completed": False}], + evidence=[stop], + warnings=["Campaign stop was persisted but terminal publication is still pending"], + next_actions=["research.inspect"], + ) + return _publish_receipt( + project, + request, + status="stopped", + operations=[{"intent": request["intent"], "completed": True}], + evidence=[stop, terminal], + next_actions=["research.inspect"], + ) + approved = [ + item + for item in list_artifact_decisions(project, request["sessionId"]) + if item["decision"]["decision"] == "approve" + ] + if request["intent"] in CAMPAIGN_EXECUTOR_INTENTS: + if request["authority"]["mode"] != "approved-envelope": + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.authority", "message": "Campaign executor intents require approved-envelope authority"}], + next_actions=["research.inspect"], + ) + references = request["objectRefs"] + if request["intent"] == "campaign.start": + session = load_session(project, request["sessionId"]) + active_progress = list_campaign_progress(session) + if active_progress: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.already-running", "message": "Session already has an active Campaign"}], + next_actions=["campaign.stop", "research.inspect"], + ) + definition_ref = request["input"].get("experimentDefinitionRef") + if not isinstance(definition_ref, dict) or set(definition_ref) != {"id", "version", "contentHash"} or not isinstance(definition_ref.get("id"), str) or not definition_ref["id"].strip() or not isinstance(definition_ref.get("version"), int) or isinstance(definition_ref.get("version"), bool) or definition_ref["version"] < 1 or not isinstance(definition_ref.get("contentHash"), str) or not HASH.fullmatch(definition_ref.get("contentHash", "")): + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.definition-ref", "message": "campaign.start requires an exact experimentDefinitionRef with id, version, and contentHash"}], + next_actions=["research.inspect"], + ) + try: + experiment_def = load_experiment_definition( + project, + request["sessionId"], + definition_ref["id"], + definition_ref["version"], + ) + except AutoQuantValidationError as error: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": item.code, "message": item.message} for item in error.issues], + next_actions=["research.inspect"], + ) + if experiment_def.definition["status"] != "frozen": + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.definition-status", "message": "ExperimentDefinition must be frozen before Campaign execution"}], + next_actions=["research.inspect"], + ) + if experiment_def.manifest["contentHash"] != definition_ref["contentHash"]: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.definition-hash", "message": "experimentDefinitionRef contentHash does not match the frozen ExperimentDefinition contentHash"}], + next_actions=["research.inspect"], + ) + budget = request["budget"] + if not isinstance(budget.get("candidateLimit"), int) or budget["candidateLimit"] < 1: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.budget", "message": "campaign.start requires a positive candidateLimit in the request budget"}], + next_actions=["research.inspect"], + ) + if request["intent"] in {"campaign.pause", "campaign.resume"}: + if len(references) != 1 or references[0]["kind"] != "campaign" or references[0]["version"] is not None: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "operator.campaign-ref", "message": "Campaign pause/resume requires one exact unversioned Campaign reference"}], + next_actions=["research.inspect"], + ) + session = load_session(project, request["sessionId"]) + progress = next( + ( + item + for item in list_campaign_progress(session) + if item["campaignId"] == references[0]["id"] + ), + None, + ) + if progress is None: + return _publish_receipt( + project, + request, + status="failed", + operations=[], + errors=[{"code": "campaign.not-running", "message": "No active Campaign is running"}], + next_actions=["research.inspect"], + ) + return _publish_receipt( + project, + request, + status="unavailable", + operations=[{"intent": request["intent"], "completed": False}], + evidence=[ + { + "kind": "autoquant-campaign-preflight", + "intent": request["intent"], + "validated": True, + "executor": None, + "message": "Preflight validation passed; execution is deferred to a structured executor.", + } + ], + errors=[{"code": "campaign.structured-executor-unavailable", "message": "A structured Campaign executor is not available in this build; the validated preflight is preserved for future execution."}], + next_actions=["research.inspect"], + ) + handlers: dict[str, Callable[[], dict[str, Any]]] = { + "research.inspect": lambda: build_research_ledger(project, request["sessionId"]), + "research.explain": lambda: { + "kind": "autoquant-research-explanation", + "blockers": [ + {"stage": stage["id"], "items": stage["blockers"]} + for stage in build_research_ledger(project, request["sessionId"])["stages"] + if stage["blockers"] + ], + }, + "research.compare": lambda: _compare(project, request), + "research.reproduction-readiness": lambda: { + "kind": "autoquant-reproduction-readiness", + "ready": bool(approved), + "unresolved": [] if approved else ["approved-artifact-manifest"], + "approvals": [ + { + "id": item["decision"]["id"], + "artifactId": item["decision"]["artifactId"], + "definitionRef": item["decision"]["definitionRef"], + } + for item in approved + ], + }, + } + try: + result = handlers[request["intent"]]() + except AutoQuantValidationError as error: + return _publish_receipt( + project, + request, + status="failed", + operations=[{"intent": request["intent"], "completed": False}], + errors=[{"code": item.code, "message": item.message} for item in error.issues], + next_actions=["research.inspect"], + ) + status = ( + "unavailable" + if request["intent"] == "research.reproduction-readiness" and not result["ready"] + else "completed" + ) + return _publish_receipt( + project, + request, + status=status, + operations=[{"intent": request["intent"], "completed": True}], + evidence=[result], + next_actions=["research.inspect"] if status == "unavailable" else [], + ) + + +OPERATOR_REQUEST_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant Operator request V1", + "type": "object", + "required": [ + "schemaVersion", "kind", "requestId", "actor", "workspaceRef", "projectId", + "sessionId", "intent", "objectRefs", "authority", "budget", "confirmationRef", + "expectedState", "input", + ], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-operator-request"}, + "requestId": {"type": "string", "pattern": REQUEST_ID.pattern}, + "actor": {"type": "object"}, + "workspaceRef": {"type": "string", "minLength": 1}, + "projectId": {"type": "string", "minLength": 1}, + "sessionId": {"type": "string", "minLength": 1}, + "intent": {"type": "string", "enum": sorted(INTENTS)}, + "objectRefs": {"type": "array"}, + "authority": {"type": "object"}, + "budget": {"type": "object"}, + "confirmationRef": {"type": ["string", "null"]}, + "expectedState": {"type": "object"}, + "input": {"type": "object"}, + }, +} + + +OPERATOR_RECEIPT_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant AgentOperationReceipt V1", + "type": "object", + "required": [ + "schemaVersion", "kind", "requestId", "acceptedRequestHash", "actor", "intent", + "status", "operations", "artifacts", "evidence", "budgetSpent", "warnings", + "failedGates", "errors", "nextValidActions", "reproductionLineage", "completedAt", + ], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-agent-operation-receipt"}, + "requestId": {"type": "string"}, + "acceptedRequestHash": {"type": "string", "pattern": HASH.pattern}, + "actor": {"type": "object"}, + "intent": {"type": "string"}, + "status": {"enum": sorted(TERMINAL_STATUSES)}, + "operations": {"type": "array"}, + "artifacts": {"type": "array"}, + "evidence": {"type": "array"}, + "budgetSpent": {"type": "object"}, + "warnings": {"type": "array"}, + "failedGates": {"type": "array"}, + "errors": {"type": "array"}, + "nextValidActions": {"type": "array"}, + "reproductionLineage": {"type": "array"}, + "completedAt": {"type": "string"}, + }, +} + + +RESEARCH_LEDGER_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant ResearchLedger V1", + "type": "object", + "required": ["schemaVersion", "kind", "sessionId", "sessionStatus", "authority", "stages", "receipts"], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-research-ledger"}, + "sessionId": {"type": "string"}, + "sessionStatus": {"type": "string"}, + "authority": {"type": "object"}, + "stages": {"type": "array", "minItems": 8, "maxItems": 8}, + "receipts": {"type": "array"}, + }, +} diff --git a/autoquant/research.py b/autoquant/research.py index d64b68d..9fce02d 100644 --- a/autoquant/research.py +++ b/autoquant/research.py @@ -16,6 +16,7 @@ from pathlib import Path from typing import Any +from .research_definitions import load_experiment_definition from .sessions import ( EXPERIMENT_ID, SessionContext, @@ -38,10 +39,20 @@ CAMPAIGN_RESULT = "result.json" CAMPAIGN_MANIFEST = "manifest.json" CAMPAIGN_PROGRESS = "progress.json" +CAMPAIGN_STOP_REQUEST = "stop-request.json" CAMPAIGN_ID = re.compile( r"^campaign-[0-9]{8}T[0-9]{12}Z-[0-9a-f]{12}$" ) -CAMPAIGN_STATUSES = {"stopped", "budget_exhausted", "failed"} +CAMPAIGN_STATUSES = { + "stopped", + "budget_exhausted", + "failed", + "evidence_ready", + "failed_gate", + "blocked", + "stopped_by_user", + "inconclusive", +} RESPONSE_VERSION = 1 MAX_OUTPUT_BYTES = 1_000_000 PROGRESS_PHASES = { @@ -73,6 +84,7 @@ class CampaignSummary: crashes: int started_at: str completed_at: str + budget: dict[str, Any] path: str def to_dict(self) -> dict[str, Any]: @@ -89,6 +101,7 @@ def to_dict(self) -> dict[str, Any]: }, "startedAt": self.started_at, "completedAt": self.completed_at, + "budget": self.budget, "path": self.path, } @@ -144,6 +157,8 @@ def _strict_keys( value: dict[str, Any], required: set[str], path: Path | str, + *, + optional: set[str] = frozenset(), ) -> list[ValidationIssue]: issues = [ _issue(f"{path}/{key}", "schema.missing", f"Missing required field '{key}'") @@ -151,7 +166,7 @@ def _strict_keys( ] issues.extend( _issue(f"{path}/{key}", "schema.unknown", f"Unknown field '{key}'") - for key in sorted(value.keys() - required) + for key in sorted(value.keys() - required - optional) ) return issues @@ -262,11 +277,12 @@ def _progress_value( status: str, message: str, turn: int, - budget: dict[str, int], + budget: dict[str, Any], experiment_ids: list[str], verdicts: dict[str, int], + experiment_definition_ref: dict[str, Any] | None = None, ) -> dict[str, Any]: - return { + value = { "schemaVersion": SCHEMA_VERSION, "kind": "campaign-progress", "campaignId": campaign_id, @@ -282,6 +298,9 @@ def _progress_value( "experiments": list(experiment_ids), "verdicts": dict(verdicts), } + if experiment_definition_ref is not None: + value["experimentDefinitionRef"] = experiment_definition_ref + return value def _write_progress( @@ -295,9 +314,10 @@ def _write_progress( status: str, message: str, turn: int, - budget: dict[str, int], + budget: dict[str, Any], experiment_ids: list[str], verdicts: dict[str, int], + experiment_definition_ref: dict[str, Any] | None = None, ) -> dict[str, Any]: value = _progress_value( campaign_id=campaign_id, @@ -311,6 +331,7 @@ def _write_progress( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=experiment_definition_ref, ) _atomic_write_json(root / CAMPAIGN_PROGRESS, value) return value @@ -356,7 +377,7 @@ def _invoke_researcher( stderr = "" try: process = subprocess.Popen( - ["/bin/sh", "-lc", command], + ["sh" if os.name == "nt" else "/bin/sh", "-lc", command], cwd=session.worktree_project.root_dir, env=environment, stdin=subprocess.PIPE, @@ -375,10 +396,18 @@ def _invoke_researcher( timed_out = True stdout = _text(error.stdout) stderr = _text(error.stderr) - try: - os.killpg(process.pid, signal.SIGKILL) - except ProcessLookupError: - pass + if os.name == "nt": + subprocess.run( + ["taskkill", "/PID", str(process.pid), "/T", "/F"], + capture_output=True, + check=False, + text=True, + ) + else: + try: + os.killpg(process.pid, signal.SIGKILL) + except ProcessLookupError: + pass final_stdout, final_stderr = process.communicate() stdout = final_stdout or stdout stderr = final_stderr or stderr @@ -495,6 +524,97 @@ def _turn_failure( return error +def _load_stop_request( + root: Path, + *, + campaign_id: str, + session_id: str, +) -> dict[str, Any] | None: + path = root / CAMPAIGN_STOP_REQUEST + if not path.exists(): + return None + if path.is_symlink() or not path.is_file(): + raise AutoQuantValidationError( + [_issue(path, "campaign.stop-request", "Campaign stop request must be a regular file")] + ) + value = _read_json(path, "campaign-stop-request") + issues = _strict_keys( + value, + { + "schemaVersion", + "kind", + "campaignId", + "sessionId", + "actor", + "requestedAt", + "lastCompletedExperiments", + }, + path, + ) + if value.get("schemaVersion") != 1 or value.get("kind") != "autoquant-campaign-stop-request": + issues.append(_issue(path, "campaign.stop-request", "Invalid Campaign stop request")) + if value.get("campaignId") != campaign_id or value.get("sessionId") != session_id: + issues.append(_issue(path, "campaign.stop-request", "Campaign stop request identity mismatch")) + actor = value.get("actor") + if not isinstance(actor, dict) or set(actor) != {"id", "kind"}: + issues.append(_issue(path, "campaign.stop-request", "Campaign stop actor is invalid")) + if not isinstance(value.get("requestedAt"), str) or not value["requestedAt"]: + issues.append(_issue(path, "campaign.stop-request", "Campaign stop time is invalid")) + experiments = value.get("lastCompletedExperiments") + if not isinstance(experiments, list) or not all( + isinstance(item, str) and EXPERIMENT_ID.fullmatch(item) + for item in (experiments if isinstance(experiments, list) else []) + ): + issues.append( + _issue(path, "campaign.stop-request", "Campaign stop Experiment references are invalid") + ) + if issues: + raise AutoQuantValidationError(issues) + return value + + +def request_campaign_stop( + project: ProjectContext, + session_id: str, + campaign_id: str, + actor: dict[str, str], +) -> dict[str, Any]: + session = load_session(project, session_id) + if not CAMPAIGN_ID.fullmatch(campaign_id): + raise AutoQuantValidationError([_issue(campaign_id, "campaign.id", "Invalid Campaign id")]) + progress = next( + ( + item + for item in list_campaign_progress(session) + if item["campaignId"] == campaign_id + ), + None, + ) + if progress is None: + raise AutoQuantValidationError( + [_issue(campaign_id, "campaign.not-running", "Campaign is not running")] + ) + root = confined_path( + _campaigns_root(session), + f".{campaign_id}.creating", + "campaign/staging", + ) + existing = _load_stop_request(root, campaign_id=campaign_id, session_id=session_id) + if existing is not None: + return existing + value = { + "schemaVersion": 1, + "kind": "autoquant-campaign-stop-request", + "campaignId": campaign_id, + "sessionId": session_id, + "actor": actor, + "requestedAt": datetime.now(timezone.utc).isoformat(), + "lastCompletedExperiments": list(progress["experiments"]), + } + _atomic_write_json(root / CAMPAIGN_STOP_REQUEST, value) + return _load_stop_request(root, campaign_id=campaign_id, session_id=session_id) or value + + def run_campaign( project: ProjectContext, session_id: str, @@ -503,6 +623,22 @@ def run_campaign( max_turns: int = 5, max_wall_seconds: int = 900, turn_timeout_seconds: int = 300, + max_candidates: int | None = None, + max_cpu_seconds: int | None = None, + max_gpu_seconds: int = 0, + max_cost: float | None = None, + cost_currency: str | None = None, + cost_telemetry_available: bool = False, + private_executor_requested: bool = False, + private_executor_available: bool = False, + stop_conditions: tuple[str, ...] = ( + "candidate-limit", + "wall-time-limit", + "cpu-limit", + "immediate-user-stop", + ), + holdout_sealed: bool = True, + experiment_definition_ref: dict[str, Any] | None = None, ) -> CampaignContext: if not isinstance(agent_command, str) or not agent_command.strip(): raise AutoQuantValidationError( @@ -526,8 +662,117 @@ def run_campaign( f"{name} must be an integer from 1 to {maximum}", ) ) + max_candidates = max_turns if max_candidates is None else max_candidates + max_cpu_seconds = ( + max_wall_seconds if max_cpu_seconds is None else max_cpu_seconds + ) + + + for value, name, minimum, maximum in ( + (max_candidates, "max_candidates", 1, 100), + (max_cpu_seconds, "max_cpu_seconds", 1, 86400), + (max_gpu_seconds, "max_gpu_seconds", 0, 86400), + ): + if ( + not isinstance(value, int) + or isinstance(value, bool) + or not minimum <= value <= maximum + ): + budget_issues.append( + _issue( + name, + "schema.range", + f"{name} must be an integer from {minimum} to {maximum}", + ) + ) + if ( + isinstance(max_candidates, int) + and not isinstance(max_candidates, bool) + and isinstance(max_turns, int) + and not isinstance(max_turns, bool) + and max_candidates > max_turns + ): + budget_issues.append( + _issue( + "max_candidates", + "campaign.budget", + "Candidate ceiling cannot exceed the legacy turn ceiling", + ) + ) + if max_cost is not None and ( + not isinstance(max_cost, (int, float)) + or isinstance(max_cost, bool) + or not math.isfinite(float(max_cost)) + or max_cost <= 0 + ): + budget_issues.append( + _issue("max_cost", "schema.range", "max_cost must be a positive finite number") + ) + if max_cost is not None and ( + not isinstance(cost_currency, str) or not cost_currency.strip() + ): + budget_issues.append( + _issue("cost_currency", "schema.string", "A cost ceiling requires a currency") + ) + if not isinstance(cost_telemetry_available, bool): + budget_issues.append(_issue("cost_telemetry_available", "schema.boolean", "Cost telemetry flag must be boolean")) + if not isinstance(private_executor_requested, bool) or not isinstance(private_executor_available, bool): + budget_issues.append(_issue("private_executor", "schema.boolean", "Private executor flags must be boolean")) + if not isinstance(holdout_sealed, bool) or not holdout_sealed: + budget_issues.append( + _issue( + "holdout_sealed", + "campaign.holdout", + "Candidate research requires a sealed holdout; opening it is a separate confirmed terminal action", + ) + ) + if ( + not isinstance(stop_conditions, tuple) + or not stop_conditions + or not all(isinstance(item, str) and item.strip() for item in stop_conditions) + ): + budget_issues.append(_issue("stop_conditions", "schema.list", "At least one fixed stop condition is required")) if budget_issues: raise AutoQuantValidationError(budget_issues) + resolved_experiment_definition_ref: dict[str, Any] | None = None + if experiment_definition_ref is not None: + ref_issues: list[ValidationIssue] = [] + if not isinstance(experiment_definition_ref, dict): + ref_issues.append(_issue("experiment_definition_ref", "schema.type", "experiment_definition_ref must be an object")) + else: + ref_issues.extend( + _strict_keys(experiment_definition_ref, {"id", "version", "contentHash"}, "experiment_definition_ref") + ) + ref_id = experiment_definition_ref.get("id") + if not isinstance(ref_id, str) or not ref_id.strip(): + ref_issues.append(_issue("experiment_definition_ref/id", "schema.string", "id must be non-empty")) + ref_version = experiment_definition_ref.get("version") + if not isinstance(ref_version, int) or isinstance(ref_version, bool) or ref_version < 1: + ref_issues.append(_issue("experiment_definition_ref/version", "schema.version", "version must be a positive integer")) + ref_hash = experiment_definition_ref.get("contentHash") + if not isinstance(ref_hash, str) or not re.fullmatch(r"^[0-9a-f]{64}$", ref_hash): + ref_issues.append(_issue("experiment_definition_ref/contentHash", "schema.hash", "contentHash must be a lowercase SHA-256 hex digest")) + if ref_issues: + raise AutoQuantValidationError(ref_issues) + loaded_def = load_experiment_definition( + project, session_id, + experiment_definition_ref["id"], + experiment_definition_ref["version"], + ) + if loaded_def.definition["status"] != "frozen": + raise AutoQuantValidationError( + [_issue("experiment_definition_ref", "definition.not-frozen", "ExperimentDefinition must be frozen")] + ) + if loaded_def.manifest["contentHash"] != experiment_definition_ref["contentHash"]: + raise AutoQuantValidationError( + [_issue("experiment_definition_ref/contentHash", "definition.hash-mismatch", + "Supplied contentHash does not match the loaded ExperimentDefinition manifest")] + ) + resolved_experiment_definition_ref = { + "id": experiment_definition_ref["id"], + "version": experiment_definition_ref["version"], + "contentHash": experiment_definition_ref["contentHash"], + } session = load_session(project, session_id) if session.manifest["status"] != "active": raise AutoQuantValidationError( @@ -537,16 +782,28 @@ def run_campaign( started = datetime.now(timezone.utc) stamp = started.strftime("%Y%m%dT%H%M%S%fZ") command_hash = hash_json({"shell": agent_command}) - identity = hash_json( - { - "session": session_id, - "startedAt": started.isoformat(), - "commandHash": command_hash, - "maxTurns": max_turns, - "maxWallSeconds": max_wall_seconds, - "turnTimeoutSeconds": turn_timeout_seconds, - } - ) + identity_payload = { + "session": session_id, + "startedAt": started.isoformat(), + "commandHash": command_hash, + "maxTurns": max_turns, + "maxWallSeconds": max_wall_seconds, + "turnTimeoutSeconds": turn_timeout_seconds, + "maxCandidates": max_candidates, + "maxCpuSeconds": max_cpu_seconds, + "maxGpuSeconds": max_gpu_seconds, + "maxCost": ( + {"currency": cost_currency, "amount": max_cost} + if max_cost is not None + else None + ), + "privateExecutorRequested": private_executor_requested, + "privateExecutorAvailable": private_executor_available, + "stopConditions": list(stop_conditions), + } + if resolved_experiment_definition_ref is not None: + identity_payload["experimentDefinitionRef"] = resolved_experiment_definition_ref + identity = hash_json(identity_payload) campaign_id = f"campaign-{stamp}-{identity[:12]}" root = _campaign_root(session, campaign_id, create_root=True) staging = _campaigns_root(session, create=True) / f".{campaign_id}.creating" @@ -562,14 +819,63 @@ def run_campaign( verdicts = {"KEEP": 0, "REVERT": 0, "CRASH": 0} budget = { "maxTurns": max_turns, + "maxCandidates": max_candidates, "maxWallSeconds": max_wall_seconds, "turnTimeoutSeconds": turn_timeout_seconds, + "maxCpuSeconds": max_cpu_seconds, + "maxGpuSeconds": max_gpu_seconds, + "maxCost": ( + {"currency": cost_currency, "amount": max_cost} + if max_cost is not None + else None + ), + "costTelemetry": ( + "available" + if cost_telemetry_available + else ("unknown" if max_cost is not None else "not-applicable") + ), + "executorPolicy": { + "default": "cpu", + "privateRequested": private_executor_requested, + "privateAvailable": private_executor_available, + }, + "stopConditions": list(stop_conditions), + "holdoutPolicy": {"sealed": True}, + "used": { + "turns": 0, + "candidates": 0, + "wallSeconds": 0, + "cpuSeconds": 0, + "gpuSeconds": 0, + "cost": ( + {"known": True, "currency": cost_currency, "amount": 0} + if cost_telemetry_available + else {"known": False, "currency": cost_currency, "amount": None} + ), + }, + "remaining": { + "turns": max_turns, + "candidates": max_candidates, + "wallSeconds": max_wall_seconds, + "cpuSeconds": max_cpu_seconds, + "gpuSeconds": max_gpu_seconds, + "cost": ( + {"known": True, "currency": cost_currency, "amount": max_cost} + if cost_telemetry_available and max_cost is not None + else {"known": False, "currency": cost_currency, "amount": None} + ), + }, } campaign_history: list[dict[str, Any]] = [] errors: list[dict[str, str]] = [] turns_completed = 0 - status = "budget_exhausted" - reason = "Maximum turn budget reached" + blocked_reason = None + if private_executor_requested and not private_executor_available: + blocked_reason = "Requested private GPU/MOSS provider is unavailable; CPU remains the public executor" + elif max_cost is not None and not cost_telemetry_available: + blocked_reason = "Provider cost telemetry is unknown under an approved monetary ceiling" + status = "blocked" if blocked_reason is not None else "budget_exhausted" + reason = blocked_reason or "Maximum turn budget reached" try: _write_progress( staging, @@ -584,17 +890,47 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) - for turn in range(1, max_turns + 1): + turns = () if blocked_reason is not None else range(1, max_turns + 1) + for turn in turns: + if _load_stop_request(staging, campaign_id=campaign_id, session_id=session_id) is not None: + status = "stopped_by_user" + reason = "Authorized user requested immediate stop before the next candidate" + break session = load_session(project, session_id) study = validate_session_authority(project, session) elapsed = time.monotonic() - monotonic_started + budget["used"].update( + { + "turns": turns_completed, + "candidates": len(experiment_ids), + "wallSeconds": min(max_wall_seconds, int(elapsed)), + "cpuSeconds": min(max_cpu_seconds, int(elapsed)), + } + ) + budget["remaining"].update( + { + "turns": max(0, max_turns - turns_completed), + "candidates": max(0, max_candidates - len(experiment_ids)), + "wallSeconds": max(0, max_wall_seconds - int(elapsed)), + "cpuSeconds": max(0, max_cpu_seconds - int(elapsed)), + } + ) + if len(experiment_ids) >= max_candidates: + status = "budget_exhausted" + reason = "Maximum candidate budget reached" + break remaining = max_wall_seconds - elapsed judge_reserve = study.definition.judge.timeout_seconds if remaining <= judge_reserve: status = "budget_exhausted" reason = "Insufficient wall-clock budget for another fixed Judge" break + if max_cpu_seconds - elapsed <= judge_reserve: + status = "budget_exhausted" + reason = "Insufficient CPU budget for another fixed Judge" + break turn_root = staging / "turns" / f"turn-{turn:04d}" turn_root.mkdir() brief = _brief( @@ -622,6 +958,7 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) command_timeout = min( float(turn_timeout_seconds), @@ -737,6 +1074,7 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) experiment = evaluate_experiment( project, @@ -780,6 +1118,7 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) except Exception as error: validation_error = ( @@ -809,6 +1148,7 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) restore_session_worktree(project, session) except AutoQuantValidationError as restore_error: @@ -832,7 +1172,27 @@ def run_campaign( reason = errors[0]["message"] break + if _load_stop_request(staging, campaign_id=campaign_id, session_id=session_id) is not None: + status = "stopped_by_user" + reason = "Authorized user requested immediate stop; completed evidence was preserved" completed = datetime.now(timezone.utc) + elapsed_final = time.monotonic() - monotonic_started + budget["used"].update( + { + "turns": turns_completed, + "candidates": len(experiment_ids), + "wallSeconds": min(max_wall_seconds, int(elapsed_final)), + "cpuSeconds": min(max_cpu_seconds, int(elapsed_final)), + } + ) + budget["remaining"].update( + { + "turns": max(0, max_turns - turns_completed), + "candidates": max(0, max_candidates - len(experiment_ids)), + "wallSeconds": max(0, max_wall_seconds - int(elapsed_final)), + "cpuSeconds": max(0, max_cpu_seconds - int(elapsed_final)), + } + ) final_session = load_session(project, session_id) _write_progress( staging, @@ -847,6 +1207,7 @@ def run_campaign( budget=budget, experiment_ids=experiment_ids, verdicts=verdicts, + experiment_definition_ref=resolved_experiment_definition_ref, ) result = { "schemaVersion": SCHEMA_VERSION, @@ -869,6 +1230,8 @@ def run_campaign( "finalLeader": final_session.manifest["leader"], "errors": errors, } + if resolved_experiment_definition_ref is not None: + result["experimentDefinitionRef"] = resolved_experiment_definition_ref _write_json(staging / CAMPAIGN_RESULT, result) files = _campaign_file_hashes(staging) manifest = { @@ -880,6 +1243,8 @@ def run_campaign( "resultHash": files[CAMPAIGN_RESULT], "files": files, } + if resolved_experiment_definition_ref is not None: + manifest["experimentDefinitionRef"] = resolved_experiment_definition_ref _write_json(staging / CAMPAIGN_MANIFEST, manifest) os.replace(staging, root) except Exception: @@ -889,6 +1254,78 @@ def run_campaign( return load_campaign(project, load_session(project, session_id), campaign_id) +def _validate_campaign_budget( + budget: Any, + path: Path | str, +) -> list[ValidationIssue]: + if not isinstance(budget, dict): + return [_issue(path, "schema.type", "Campaign budget must be an object")] + legacy = {"maxTurns", "maxWallSeconds", "turnTimeoutSeconds"} + extended = legacy | { + "maxCandidates", + "maxCpuSeconds", + "maxGpuSeconds", + "maxCost", + "costTelemetry", + "executorPolicy", + "stopConditions", + "holdoutPolicy", + "used", + "remaining", + } + if set(budget) == legacy: + issues: list[ValidationIssue] = [] + else: + issues = _strict_keys(budget, extended, path) + limits = { + "maxTurns": (1, 100), + "maxWallSeconds": (1, 86400), + "turnTimeoutSeconds": (1, 3600), + } + for key, (minimum, maximum) in limits.items(): + item = budget.get(key) + if ( + not isinstance(item, int) + or isinstance(item, bool) + or not minimum <= item <= maximum + ): + issues.append(_issue(f"{path}/{key}", "schema.range", f"Invalid budget {key}")) + if set(budget) == legacy: + return issues + for key, minimum in (("maxCandidates", 1), ("maxCpuSeconds", 1), ("maxGpuSeconds", 0)): + item = budget.get(key) + if not isinstance(item, int) or isinstance(item, bool) or item < minimum: + issues.append(_issue(f"{path}/{key}", "schema.range", f"Invalid budget {key}")) + if isinstance(budget.get("maxCandidates"), int) and isinstance(budget.get("maxTurns"), int) and budget["maxCandidates"] > budget["maxTurns"]: + issues.append(_issue(path, "campaign.budget", "Candidate ceiling exceeds turn ceiling")) + max_cost = budget.get("maxCost") + if max_cost is not None: + issues.extend(_strict_keys(max_cost, {"currency", "amount"}, f"{path}/maxCost")) + if budget.get("costTelemetry") not in {"available", "unknown", "not-applicable"}: + issues.append(_issue(f"{path}/costTelemetry", "campaign.cost", "Invalid cost telemetry state")) + executor = budget.get("executorPolicy") + issues.extend(_strict_keys(executor, {"default", "privateRequested", "privateAvailable"}, f"{path}/executorPolicy")) + if isinstance(executor, dict) and executor.get("default") != "cpu": + issues.append(_issue(f"{path}/executorPolicy/default", "campaign.executor", "Public Campaign default must be CPU")) + if not isinstance(budget.get("stopConditions"), list) or not budget["stopConditions"]: + issues.append(_issue(f"{path}/stopConditions", "campaign.stop", "Campaign requires fixed stop conditions")) + if budget.get("holdoutPolicy") != {"sealed": True}: + issues.append(_issue(f"{path}/holdoutPolicy", "campaign.holdout", "Campaign holdout must remain sealed")) + usage_keys = {"turns", "candidates", "wallSeconds", "cpuSeconds", "gpuSeconds", "cost"} + for name in ("used", "remaining"): + usage = budget.get(name) + issues.extend(_strict_keys(usage, usage_keys, f"{path}/{name}")) + if isinstance(usage, dict): + for key in usage_keys - {"cost"}: + if not isinstance(usage.get(key), int) or isinstance(usage.get(key), bool) or usage.get(key, -1) < 0: + issues.append(_issue(f"{path}/{name}/{key}", "schema.range", "Budget usage must be non-negative")) + cost = usage.get("cost") + issues.extend(_strict_keys(cost, {"known", "currency", "amount"}, f"{path}/{name}/cost")) + if isinstance(cost, dict) and not isinstance(cost.get("known"), bool): + issues.append(_issue(f"{path}/{name}/cost/known", "schema.boolean", "Cost knowledge must be boolean")) + return issues + + def _validate_campaign_result( value: dict[str, Any], path: Path, @@ -913,7 +1350,8 @@ def _validate_campaign_result( "finalLeader", "errors", } - issues = _strict_keys(value, required, path) + optional = {"experimentDefinitionRef"} + issues = _strict_keys(value, required, path, optional=optional) if value.get("schemaVersion") != SCHEMA_VERSION: issues.append(_issue(path, "schema.version", "Expected Campaign V1")) if value.get("id") != campaign_id or value.get("sessionId") != session_id: @@ -945,23 +1383,7 @@ def _validate_campaign_result( ): issues.append(_issue(path, "schema.hash", "Invalid Researcher command hash")) budget = value.get("budget") - if not isinstance(budget, dict): - issues.append(_issue(path, "schema.type", "budget must be an object")) - else: - issues.extend( - _strict_keys( - budget, - {"maxTurns", "maxWallSeconds", "turnTimeoutSeconds"}, - f"{path}/budget", - ) - ) - for key in ("maxTurns", "maxWallSeconds", "turnTimeoutSeconds"): - if ( - not isinstance(budget.get(key), int) - or isinstance(budget.get(key), bool) - or budget.get(key, 0) < 1 - ): - issues.append(_issue(path, "schema.number", f"Invalid budget {key}")) + issues.extend(_validate_campaign_budget(budget, f"{path}/budget")) turns = value.get("turnsCompleted") max_turns_value = budget.get("maxTurns") if isinstance(budget, dict) else None if ( @@ -1037,8 +1459,29 @@ def _validate_campaign_result( issues.append(_issue(path, "campaign.errors", "Invalid Campaign errors")) elif value.get("status") == "failed" and not errors: issues.append(_issue(path, "campaign.errors", "Failed Campaign must record an error")) - elif value.get("status") != "failed" and errors: - issues.append(_issue(path, "campaign.errors", "Successful Campaign cannot record errors")) + elif value.get("status") not in {"failed", "stopped_by_user"} and errors: + issues.append(_issue(path, "campaign.errors", "Only failed or user-stopped Campaigns can record errors")) + experiment_definition_ref = value.get("experimentDefinitionRef") + if experiment_definition_ref is not None: + if not isinstance(experiment_definition_ref, dict): + issues.append(_issue(f"{path}/experimentDefinitionRef", "schema.type", "experimentDefinitionRef must be an object")) + else: + issues.extend( + _strict_keys(experiment_definition_ref, {"id", "version", "contentHash"}, f"{path}/experimentDefinitionRef") + ) + if not isinstance(experiment_definition_ref.get("id"), str) or not experiment_definition_ref["id"].strip(): + issues.append(_issue(f"{path}/experimentDefinitionRef/id", "schema.string", "id must be non-empty")) + if ( + not isinstance(experiment_definition_ref.get("version"), int) + or isinstance(experiment_definition_ref.get("version"), bool) + or experiment_definition_ref.get("version", 0) < 1 + ): + issues.append(_issue(f"{path}/experimentDefinitionRef/version", "schema.version", "version must be a positive integer")) + if ( + not isinstance(experiment_definition_ref.get("contentHash"), str) + or not re.fullmatch(r"^[0-9a-f]{64}$", experiment_definition_ref.get("contentHash", "")) + ): + issues.append(_issue(f"{path}/experimentDefinitionRef/contentHash", "schema.hash", "contentHash must be a lowercase SHA-256 hex digest")) if issues: raise AutoQuantValidationError(issues) @@ -1066,7 +1509,8 @@ def _validate_campaign_progress( "experiments", "verdicts", } - issues = _strict_keys(value, required, path) + optional_progress = {"experimentDefinitionRef"} + issues = _strict_keys(value, required, path, optional=optional_progress) if value.get("schemaVersion") != SCHEMA_VERSION: issues.append(_issue(path, "schema.version", "Expected Campaign progress V1")) if value.get("kind") != "campaign-progress": @@ -1094,30 +1538,8 @@ def _validate_campaign_progress( ): issues.append(_issue(path, "schema.hash", "Invalid Researcher command hash")) budget = value.get("budget") - if not isinstance(budget, dict): - issues.append(_issue(path, "schema.type", "Progress budget must be an object")) - budget = {} - else: - issues.extend( - _strict_keys( - budget, - {"maxTurns", "maxWallSeconds", "turnTimeoutSeconds"}, - f"{path}/budget", - ) - ) - limits = { - "maxTurns": 100, - "maxWallSeconds": 86400, - "turnTimeoutSeconds": 3600, - } - for key, maximum in limits.items(): - item = budget.get(key) - if ( - not isinstance(item, int) - or isinstance(item, bool) - or not 1 <= item <= maximum - ): - issues.append(_issue(path, "schema.range", f"Invalid progress budget {key}")) + issues.extend(_validate_campaign_budget(budget, f"{path}/budget")) + budget = budget if isinstance(budget, dict) else {} turn = value.get("turn") if ( not isinstance(turn, int) @@ -1154,6 +1576,27 @@ def _validate_campaign_progress( issues.append( _issue(path, "progress.verdicts", "Progress verdicts differ from Experiments") ) + progress_ref = value.get("experimentDefinitionRef") + if progress_ref is not None: + if not isinstance(progress_ref, dict): + issues.append(_issue(f"{path}/experimentDefinitionRef", "schema.type", "experimentDefinitionRef must be an object")) + else: + issues.extend( + _strict_keys(progress_ref, {"id", "version", "contentHash"}, f"{path}/experimentDefinitionRef") + ) + if not isinstance(progress_ref.get("id"), str) or not progress_ref["id"].strip(): + issues.append(_issue(f"{path}/experimentDefinitionRef/id", "schema.string", "id must be non-empty")) + if ( + not isinstance(progress_ref.get("version"), int) + or isinstance(progress_ref.get("version"), bool) + or progress_ref.get("version", 0) < 1 + ): + issues.append(_issue(f"{path}/experimentDefinitionRef/version", "schema.version", "version must be a positive integer")) + if ( + not isinstance(progress_ref.get("contentHash"), str) + or not re.fullmatch(r"^[0-9a-f]{64}$", progress_ref.get("contentHash", "")) + ): + issues.append(_issue(f"{path}/experimentDefinitionRef/contentHash", "schema.hash", "contentHash must be a lowercase SHA-256 hex digest")) if issues: raise AutoQuantValidationError(issues) @@ -1221,7 +1664,8 @@ def load_campaign( "resultHash", "files", } - issues = _strict_keys(manifest, required, root / CAMPAIGN_MANIFEST) + optional_manifest = {"experimentDefinitionRef"} + issues = _strict_keys(manifest, required, root / CAMPAIGN_MANIFEST, optional=optional_manifest) files = manifest.get("files") actual = _campaign_file_hashes(root) if ( @@ -1236,6 +1680,28 @@ def load_campaign( issues.append(_issue(root, "campaign.tampered", "Campaign files changed")) if isinstance(files, dict) and files.get(CAMPAIGN_RESULT) != manifest.get("resultHash"): issues.append(_issue(root, "campaign.result-hash", "Campaign result hash mismatch")) + manifest_ref = manifest.get("experimentDefinitionRef") + if manifest_ref is not None: + if not isinstance(manifest_ref, dict): + issues.append(_issue(root / CAMPAIGN_MANIFEST, "schema.type", + "manifest experimentDefinitionRef must be an object")) + else: + issues.extend( + _strict_keys(manifest_ref, {"id", "version", "contentHash"}, + f"{root / CAMPAIGN_MANIFEST}/experimentDefinitionRef") + ) + if not isinstance(manifest_ref.get("id"), str) or not manifest_ref["id"].strip(): + issues.append(_issue(f"{root / CAMPAIGN_MANIFEST}/experimentDefinitionRef/id", + "schema.string", "id must be non-empty")) + if (not isinstance(manifest_ref.get("version"), int) + or isinstance(manifest_ref.get("version"), bool) + or manifest_ref.get("version", 0) < 1): + issues.append(_issue(f"{root / CAMPAIGN_MANIFEST}/experimentDefinitionRef/version", + "schema.version", "version must be a positive integer")) + if (not isinstance(manifest_ref.get("contentHash"), str) + or not re.fullmatch(r"^[0-9a-f]{64}$", manifest_ref.get("contentHash", ""))): + issues.append(_issue(f"{root / CAMPAIGN_MANIFEST}/experimentDefinitionRef/contentHash", + "schema.hash", "contentHash must be a lowercase SHA-256 hex digest")) if issues: raise AutoQuantValidationError(issues) result = _read_json(root / CAMPAIGN_RESULT, "campaign-result") @@ -1249,6 +1715,24 @@ def load_campaign( raise AutoQuantValidationError( [_issue(root, "campaign.status", "Campaign status differs from manifest")] ) + # Reference equality: all three artifacts must agree. + # All absent OR all present and exactly equal. + result_ref = result.get("experimentDefinitionRef") + if manifest_ref is None and result_ref is not None: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "result has experimentDefinitionRef but manifest does not")] + ) + if manifest_ref is not None and result_ref is None: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "manifest has experimentDefinitionRef but result does not")] + ) + if manifest_ref is not None and result_ref is not None and manifest_ref != result_ref: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "manifest experimentDefinitionRef does not match result")] + ) progress_path = root / CAMPAIGN_PROGRESS if progress_path.exists() or progress_path.is_symlink(): progress = _read_json(progress_path, "campaign-progress") @@ -1262,8 +1746,43 @@ def load_campaign( raise AutoQuantValidationError( [_issue(root, "campaign.status", "Campaign progress differs from result")] ) + # Reference equality: progress must agree with result + progress_ref = progress.get("experimentDefinitionRef") + if result_ref is None and progress_ref is not None: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "progress has experimentDefinitionRef but result does not")] + ) + if result_ref is not None and progress_ref is None: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "result has experimentDefinitionRef but progress does not")] + ) + if result_ref is not None and progress_ref is not None and progress_ref != result_ref: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "progress experimentDefinitionRef does not match result")] + ) + _load_stop_request(root, campaign_id=campaign_id, session_id=session.manifest["id"]) for experiment_id in result["experiments"]: load_experiment(project, session, experiment_id) + if result_ref is not None: + loaded_def = load_experiment_definition( + project, + session.manifest["id"], + result_ref["id"], + result_ref["version"], + ) + if loaded_def.manifest["contentHash"] != result_ref["contentHash"]: + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "Referenced ExperimentDefinition contentHash mismatch; the artifact may have been tampered")] + ) + if loaded_def.definition["status"] != "frozen": + raise AutoQuantValidationError( + [_issue(root, "campaign.experiment-definition-ref", + "Referenced ExperimentDefinition is no longer frozen")] + ) return CampaignContext(root, manifest, result) @@ -1296,6 +1815,7 @@ def list_campaigns( crashes=result["verdicts"]["CRASH"], started_at=result["startedAt"], completed_at=result["completedAt"], + budget=result["budget"], path=str(campaign.root_dir), ) ) @@ -1338,6 +1858,96 @@ def list_campaigns( } +_LEGACY_CAMPAIGN_BUDGET_JSON_SCHEMA: dict[str, Any] = { + "type": "object", + "additionalProperties": False, + "required": ["maxTurns", "maxWallSeconds", "turnTimeoutSeconds"], + "properties": { + "maxTurns": {"type": "integer", "minimum": 1, "maximum": 100}, + "maxWallSeconds": {"type": "integer", "minimum": 1, "maximum": 86400}, + "turnTimeoutSeconds": {"type": "integer", "minimum": 1, "maximum": 3600}, + }, +} + +_CAMPAIGN_USAGE_JSON_SCHEMA: dict[str, Any] = { + "type": "object", + "additionalProperties": False, + "required": ["turns", "candidates", "wallSeconds", "cpuSeconds", "gpuSeconds", "cost"], + "properties": { + "turns": {"type": "integer", "minimum": 0}, + "candidates": {"type": "integer", "minimum": 0}, + "wallSeconds": {"type": "integer", "minimum": 0}, + "cpuSeconds": {"type": "integer", "minimum": 0}, + "gpuSeconds": {"type": "integer", "minimum": 0}, + "cost": { + "type": "object", + "additionalProperties": False, + "required": ["known", "currency", "amount"], + "properties": { + "known": {"type": "boolean"}, + "currency": {"type": ["string", "null"]}, + "amount": {"type": ["number", "null"]}, + }, + }, + }, +} + +CAMPAIGN_BUDGET_JSON_SCHEMA: dict[str, Any] = { + "oneOf": [ + _LEGACY_CAMPAIGN_BUDGET_JSON_SCHEMA, + { + "type": "object", + "additionalProperties": False, + "required": [ + "maxTurns", "maxCandidates", "maxWallSeconds", "turnTimeoutSeconds", + "maxCpuSeconds", "maxGpuSeconds", "maxCost", "costTelemetry", + "executorPolicy", "stopConditions", "holdoutPolicy", "used", "remaining", + ], + "properties": { + **_LEGACY_CAMPAIGN_BUDGET_JSON_SCHEMA["properties"], + "maxCandidates": {"type": "integer", "minimum": 1, "maximum": 100}, + "maxCpuSeconds": {"type": "integer", "minimum": 1, "maximum": 86400}, + "maxGpuSeconds": {"type": "integer", "minimum": 0, "maximum": 86400}, + "maxCost": { + "oneOf": [ + {"type": "null"}, + { + "type": "object", + "additionalProperties": False, + "required": ["currency", "amount"], + "properties": { + "currency": {"type": "string", "minLength": 1}, + "amount": {"type": "number", "exclusiveMinimum": 0}, + }, + }, + ] + }, + "costTelemetry": {"enum": ["available", "unknown", "not-applicable"]}, + "executorPolicy": { + "type": "object", + "additionalProperties": False, + "required": ["default", "privateRequested", "privateAvailable"], + "properties": { + "default": {"const": "cpu"}, + "privateRequested": {"type": "boolean"}, + "privateAvailable": {"type": "boolean"}, + }, + }, + "stopConditions": {"type": "array", "minItems": 1, "items": {"type": "string", "minLength": 1}}, + "holdoutPolicy": { + "type": "object", + "additionalProperties": False, + "required": ["sealed"], + "properties": {"sealed": {"const": True}}, + }, + "used": _CAMPAIGN_USAGE_JSON_SCHEMA, + "remaining": _CAMPAIGN_USAGE_JSON_SCHEMA, + }, + }, + ] +} + + CAMPAIGN_RESULT_JSON_SCHEMA: dict[str, Any] = { "$schema": "https://json-schema.org/draft/2020-12/schema", "title": "AutoQuant external Research Campaign result", @@ -1385,28 +1995,7 @@ def list_campaigns( }, }, }, - "budget": { - "type": "object", - "additionalProperties": False, - "required": [ - "maxTurns", - "maxWallSeconds", - "turnTimeoutSeconds", - ], - "properties": { - "maxTurns": {"type": "integer", "minimum": 1, "maximum": 100}, - "maxWallSeconds": { - "type": "integer", - "minimum": 1, - "maximum": 86400, - }, - "turnTimeoutSeconds": { - "type": "integer", - "minimum": 1, - "maximum": 3600, - }, - }, - }, + "budget": CAMPAIGN_BUDGET_JSON_SCHEMA, "turnsCompleted": {"type": "integer", "minimum": 0}, "experiments": { "type": "array", @@ -1425,6 +2014,19 @@ def list_campaigns( "CRASH": {"type": "integer", "minimum": 0}, }, }, + "experimentDefinitionRef": { + "type": "object", + "additionalProperties": False, + "required": ["id", "version", "contentHash"], + "properties": { + "id": {"type": "string", "minLength": 1}, + "version": {"type": "integer", "minimum": 1}, + "contentHash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$", + }, + }, + }, "initialLeader": {"$ref": "#/$defs/leader"}, "finalLeader": {"$ref": "#/$defs/leader"}, "errors": { @@ -1456,6 +2058,22 @@ def list_campaigns( }, } }, + "allOf": [ + { + "if": {"properties": {"status": {"const": "failed"}}}, + "then": {"properties": {"errors": {"minItems": 1}}}, + }, + { + "if": { + "properties": { + "status": { + "not": {"enum": ["failed", "stopped_by_user"]} + } + } + }, + "then": {"properties": {"errors": {"maxItems": 0}}}, + }, + ], } @@ -1495,5 +2113,18 @@ def list_campaigns( "budget": CAMPAIGN_RESULT_JSON_SCHEMA["properties"]["budget"], "experiments": CAMPAIGN_RESULT_JSON_SCHEMA["properties"]["experiments"], "verdicts": CAMPAIGN_RESULT_JSON_SCHEMA["properties"]["verdicts"], + "experimentDefinitionRef": { + "type": "object", + "additionalProperties": False, + "required": ["id", "version", "contentHash"], + "properties": { + "id": {"type": "string", "minLength": 1}, + "version": {"type": "integer", "minimum": 1}, + "contentHash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$", + }, + }, + }, }, } diff --git a/autoquant/research_artifacts.py b/autoquant/research_artifacts.py new file mode 100644 index 0000000..fa450a2 --- /dev/null +++ b/autoquant/research_artifacts.py @@ -0,0 +1,559 @@ +"""Exact-version research artifact decisions and reproduction receipts.""" + +from __future__ import annotations + +import json +import math +import os +import re +import shutil +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from .research_definitions import ( + load_experiment_definition, + load_factor_definition, + load_strategy_definition, +) +from .runs import load_run +from .sessions import load_session +from .studies import hash_file, hash_json +from .verification import ( + load_verification_assessment, + validate_verification_assessment, +) +from .workspace import ( + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +SCHEMA_VERSION = 1 +ARTIFACT_REVIEW = "review.json" +ARTIFACT_DECISION = "decision.json" +REPRODUCTION_REQUEST = "request.json" +REPRODUCTION_RECEIPT = "receipt.json" +MANIFEST = "manifest.json" +OBJECT_ID = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") +HASH = re.compile(r"^[0-9a-f]{64}$") +DECISIONS = {"approve", "return-for-revision", "retain-as-draft"} +REPRODUCTION_OUTCOMES = { + "exact-match", + "within-tolerance", + "drift", + "unavailable", + "failed", +} + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _write_json(path: Path, value: dict[str, Any]) -> None: + path.write_text( + json.dumps(value, indent=2, ensure_ascii=False, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _read_json(path: Path, label: str) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except FileNotFoundError: + raise AutoQuantValidationError([_issue(path, f"{label}.missing", f"Missing {label}")]) from None + except json.JSONDecodeError as error: + raise AutoQuantValidationError([_issue(path, f"{label}.json", f"Invalid JSON: {error.msg}")]) from None + if not isinstance(value, dict): + raise AutoQuantValidationError([_issue(path, f"{label}.type", f"{label} must be an object")]) + return value + + +def _strict_keys(value: Any, required: set[str], path: Path | str) -> list[ValidationIssue]: + if not isinstance(value, dict): + return [_issue(path, "schema.type", "Expected an object")] + issues = [ + _issue(f"{path}/{key}", "schema.missing", f"Missing required field '{key}'") + for key in sorted(required - value.keys()) + ] + issues.extend( + _issue(f"{path}/{key}", "schema.unknown", f"Unknown field '{key}'") + for key in sorted(value.keys() - required) + ) + return issues + + +def validate_artifact_review(value: dict[str, Any], path: str = "artifactReview") -> None: + required = { + "schemaVersion", + "kind", + "id", + "decision", + "actor", + "definitionRef", + "definitionHash", + "evidenceManifest", + "reason", + } + issues = _strict_keys(value, required, path) + if value.get("schemaVersion") != 1 or value.get("kind") != "autoquant-artifact-review": + issues.append(_issue(path, "artifact.review", "Invalid artifact review kind or version")) + if not isinstance(value.get("id"), str) or not OBJECT_ID.fullmatch(value["id"]): + issues.append(_issue(f"{path}/id", "artifact.id", "Invalid artifact review id")) + if value.get("decision") not in DECISIONS: + issues.append(_issue(f"{path}/decision", "artifact.decision", "Invalid artifact decision")) + if not isinstance(value.get("actor"), dict) or set(value["actor"]) != {"id", "kind"}: + issues.append(_issue(f"{path}/actor", "artifact.actor", "Actor must contain exactly id and kind")) + reference = value.get("definitionRef") + issues.extend(_strict_keys(reference, {"kind", "id", "version"}, f"{path}/definitionRef")) + if isinstance(reference, dict): + if reference.get("kind") not in {"factor", "strategy"}: + issues.append(_issue(f"{path}/definitionRef/kind", "artifact.definition", "Definition kind must be factor or strategy")) + if not isinstance(reference.get("id"), str) or not reference["id"].strip(): + issues.append(_issue(f"{path}/definitionRef/id", "artifact.definition", "Definition id must be non-empty")) + if not isinstance(reference.get("version"), int) or isinstance(reference.get("version"), bool) or reference.get("version", 0) < 1: + issues.append(_issue(f"{path}/definitionRef/version", "artifact.definition", "Definition version must be positive")) + if not isinstance(value.get("definitionHash"), str) or not HASH.fullmatch(value.get("definitionHash", "")): + issues.append(_issue(f"{path}/definitionHash", "schema.hash", "Invalid definition hash")) + evidence = value.get("evidenceManifest") + evidence_keys = { + "data", + "experimentDefinition", + "runs", + "assessment", + "costs", + "holdout", + "limitations", + "diagnostics", + "artifactHashes", + "metrics", + "environment", + "cpuEquivalentAllowed", + } + issues.extend(_strict_keys(evidence, evidence_keys, f"{path}/evidenceManifest")) + if isinstance(evidence, dict): + if not isinstance(evidence.get("runs"), list) or not evidence["runs"]: + issues.append(_issue(f"{path}/evidenceManifest/runs", "artifact.closure", "At least one immutable Run reference is required")) + for key in ("limitations", "diagnostics"): + if not isinstance(evidence.get(key), list): + issues.append(_issue(f"{path}/evidenceManifest/{key}", "schema.list", f"{key} must be a list")) + hashes = evidence.get("artifactHashes") + if not isinstance(hashes, dict) or any( + not isinstance(key, str) or not key or not isinstance(item, str) or not HASH.fullmatch(item) + for key, item in (hashes.items() if isinstance(hashes, dict) else []) + ): + issues.append(_issue(f"{path}/evidenceManifest/artifactHashes", "artifact.hashes", "artifactHashes must be a string-to-sha256 map")) + metrics = evidence.get("metrics") + if not isinstance(metrics, dict) or any( + not isinstance(key, str) + or not key + or not isinstance(item, (int, float)) + or isinstance(item, bool) + or not math.isfinite(float(item)) + for key, item in (metrics.items() if isinstance(metrics, dict) else []) + ): + issues.append(_issue(f"{path}/evidenceManifest/metrics", "artifact.metrics", "metrics must be finite numbers")) + if not isinstance(evidence.get("environment"), dict) or not evidence["environment"]: + issues.append(_issue(f"{path}/evidenceManifest/environment", "artifact.environment", "Exact environment is required")) + if not isinstance(evidence.get("cpuEquivalentAllowed"), bool): + issues.append(_issue(f"{path}/evidenceManifest/cpuEquivalentAllowed", "schema.boolean", "cpuEquivalentAllowed must be boolean")) + if not isinstance(value.get("reason"), str) or not value["reason"].strip(): + issues.append(_issue(f"{path}/reason", "schema.string", "Decision reason must be non-empty")) + if issues: + raise AutoQuantValidationError(issues) + + +def artifact_review_readiness(value: dict[str, Any]) -> dict[str, Any]: + validate_artifact_review(value) + evidence = value["evidenceManifest"] + unresolved: list[str] = [] + for key in ("data", "experimentDefinition", "assessment", "costs", "holdout"): + if evidence[key] is None: + unresolved.append(key) + if evidence.get("assessment") is not None: + try: + validate_verification_assessment(evidence["assessment"]) + except AutoQuantValidationError: + if "coreEvidenceAssessment" not in unresolved: + unresolved.append("coreEvidenceAssessment") + if not evidence["runs"]: + unresolved.append("runs") + if evidence["diagnostics"]: + unresolved.append("diagnostics") + return {"ready": not unresolved, "unresolved": unresolved} + + +def _session_child_root( + project: ProjectContext, session_id: str, name: str, *, create: bool = False +) -> Path: + session = load_session(project, session_id) + root = confined_path(session.root_dir, name, f"session/{name}") + if create: + root.mkdir(exist_ok=True) + return root + + +def _object_root(parent: Path, object_id: str, issue_path: str) -> Path: + if not OBJECT_ID.fullmatch(object_id): + raise AutoQuantValidationError([_issue(issue_path, "artifact.id", "Invalid object id")]) + return confined_path(parent, object_id, issue_path) + + +def _publish_bundle(parent: Path, object_id: str, files: dict[str, dict[str, Any]], kind: str) -> Path: + target = _object_root(parent, object_id, kind) + if target.exists(): + raise AutoQuantValidationError([_issue(target, "artifact.collision", "Immutable object already exists")]) + temporary = parent / f".{object_id}.{uuid.uuid4().hex}.creating" + try: + temporary.mkdir() + for name, value in files.items(): + _write_json(temporary / name, value) + hashes = {name: hash_file(temporary / name) for name in sorted(files)} + manifest = { + "schemaVersion": 1, + "kind": kind, + "id": object_id, + "completed": True, + "files": hashes, + } + _write_json(temporary / MANIFEST, manifest) + os.replace(temporary, target) + except Exception: + if temporary.exists(): + shutil.rmtree(temporary) + raise + return target + + +def _load_bundle(root: Path, kind: str, files: tuple[str, ...]) -> tuple[dict[str, Any], dict[str, dict[str, Any]]]: + manifest = _read_json(root / MANIFEST, "manifest") + issues = _strict_keys(manifest, {"schemaVersion", "kind", "id", "completed", "files"}, root / MANIFEST) + if manifest.get("schemaVersion") != 1 or manifest.get("kind") != kind or manifest.get("completed") is not True: + issues.append(_issue(root / MANIFEST, "artifact.manifest", "Invalid immutable manifest")) + expected = {} + values = {} + for name in files: + path = root / name + expected[name] = hash_file(path) if path.is_file() else None + if path.is_file(): + values[name] = _read_json(path, name) + if manifest.get("files") != expected: + issues.append(_issue(root, "artifact.hash", "Immutable artifact hash mismatch")) + if issues: + raise AutoQuantValidationError(issues) + return manifest, values + + +def publish_artifact_decision( + project: ProjectContext, session_id: str, review: dict[str, Any] +) -> dict[str, Any]: + validate_artifact_review(review) + reference = review["definitionRef"] + definition = ( + load_factor_definition(project, reference["id"], reference["version"]) + if reference["kind"] == "factor" + else load_strategy_definition(project, reference["id"], reference["version"]) + ) + if definition.manifest["contentHash"] != review["definitionHash"]: + raise AutoQuantValidationError([_issue("definitionHash", "artifact.stale-review", "Definition bytes changed or the review targets a different version")]) + readiness = artifact_review_readiness(review) + if review["decision"] == "approve" and not readiness["ready"]: + raise AutoQuantValidationError([_issue("evidenceManifest", "artifact.incomplete-closure", "Approval requires complete evidence closure: " + ", ".join(readiness["unresolved"]))]) + if review["decision"] == "approve": + if definition.definition.get("status") != "approved": + raise AutoQuantValidationError( + [_issue("definitionRef", "artifact.definition-gate", + "Definition status must be 'approved' for artifact approval")]) + evidence = review["evidenceManifest"] + experiment_ref = evidence.get("experimentDefinition") + if not isinstance(experiment_ref, dict) or set(experiment_ref.keys()) != {"id", "version", "contentHash"}: + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition", "artifact.experiment-gate", + "experimentDefinition must contain exactly id, version, contentHash")]) + if not isinstance(experiment_ref["id"], str) or not experiment_ref["id"].strip(): + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition/id", "artifact.experiment-gate", + "experimentDefinition id must be a non-empty string")]) + if not isinstance(experiment_ref["version"], int) or isinstance(experiment_ref["version"], bool) or experiment_ref["version"] < 1: + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition/version", "artifact.experiment-gate", + "experimentDefinition version must be a positive integer")]) + if not isinstance(experiment_ref["contentHash"], str) or not HASH.fullmatch(experiment_ref["contentHash"]): + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition/contentHash", "artifact.experiment-gate", + "experimentDefinition contentHash must be a valid sha256 hex string")]) + experiment = load_experiment_definition(project, session_id, experiment_ref["id"], experiment_ref["version"]) + if experiment.definition.get("status") != "frozen": + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition", "artifact.experiment-gate", + "Experiment definition status must be 'frozen'")]) + if experiment.manifest.get("contentHash") != experiment_ref["contentHash"]: + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition/contentHash", "artifact.experiment-gate", + "Experiment contentHash does not match evidence manifest")]) + if experiment.definition.get("definitionRef") != review["definitionRef"]: + raise AutoQuantValidationError( + [_issue("evidenceManifest/experimentDefinition", "artifact.experiment-gate", + "Experiment definitionRef does not match review definitionRef")]) + if experiment.definition.get("data") != evidence.get("data"): + raise AutoQuantValidationError( + [_issue("evidenceManifest/data", "artifact.data-gate", + "Experiment data does not match evidence data")]) + # ── Run gate ───────────────────────────────────────────── + reviewed_pairs: set[tuple[str, str]] = set() + run_refs = evidence["runs"] + for i, run_ref in enumerate(run_refs): + if not isinstance(run_ref, dict): + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}", "artifact.run-ref", + f"Run reference at index {i} must be a dict")]) + run_id = run_ref.get("id") + if not isinstance(run_id, str) or not run_id.strip(): + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}/id", "artifact.run-ref", + f"Run reference id must be a non-empty string at index {i}")]) + run_hash = run_ref.get("hash") + if not isinstance(run_hash, str) or not HASH.fullmatch(run_hash): + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}/hash", "artifact.run-hash", + f"Run reference hash must be a valid sha256 hex string at index {i}")]) + run = load_run(project, run_id) + if run.manifest["resultHash"] != run_hash: + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}/hash", "artifact.run-hash", + f"Run hash does not match immutable Run result hash at index {i}")]) + pair = (run_id, run_hash) + if pair in reviewed_pairs: + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}", "artifact.run-ref", + f"Run ({run_id}) is cited more than once in evidence")]) + reviewed_pairs.add(pair) + # validate researchBinding on every reviewed Run + expected_binding: dict[str, Any] = { + "definitionRef": { + "kind": reference["kind"], + "id": reference["id"], + "version": reference["version"], + "contentHash": review["definitionHash"], + }, + "experimentDefinitionRef": { + "kind": "experiment", + "sessionId": session_id, + "id": experiment_ref["id"], + "version": experiment_ref["version"], + "contentHash": experiment_ref["contentHash"], + }, + } + for i, run_ref in enumerate(run_refs): + run_id = run_ref["id"] + run = load_run(project, run_id) + if run.result.get("researchBinding") != expected_binding: + raise AutoQuantValidationError([_issue( + f"evidenceManifest/runs/{i}", "artifact.run-binding", + f"Run researchBinding does not match the review definition and experiment")]) + # ── end Run gate ───────────────────────────────────────────── + # ── Assessment gate ───────────────────────────────────────── + assessment = evidence["assessment"] + validated_assessment = validate_verification_assessment(assessment) + published = load_verification_assessment(project, validated_assessment["id"])["assessment"] + if published != assessment: + raise AutoQuantValidationError([_issue( + "evidenceManifest/assessment", "artifact.assessment", + "Published verification assessment does not match evidence manifest assessment")]) + run_refs_from_assessment = [ref for ref in published["evidenceRefs"] if ref["kind"] == "run"] + assessment_run_pairs = {(ref["id"], ref["sha256"]) for ref in run_refs_from_assessment} + if len(run_refs_from_assessment) != len(assessment_run_pairs): + raise AutoQuantValidationError([_issue( + "evidenceManifest/assessment/evidenceRefs", "artifact.assessment-runs", + "Duplicate run references in assessment evidenceRefs")]) + if assessment_run_pairs != reviewed_pairs: + raise AutoQuantValidationError([_issue( + "evidenceManifest/assessment/evidenceRefs", "artifact.assessment-runs", + "Assessment run references do not match evidence runs")]) + # ── end Assessment gate ───────────────────────────────────── + now = datetime.now(timezone.utc).isoformat() + artifact_id = ( + "artifact-" + hash_json({"definition": reference, "evidence": review["evidenceManifest"]})[:20] + if review["decision"] == "approve" + else None + ) + decision = { + "schemaVersion": 1, + "kind": "autoquant-artifact-decision-receipt", + "id": review["id"], + "sessionId": session_id, + "decision": review["decision"], + "definitionRef": reference, + "definitionHash": review["definitionHash"], + "evidenceManifestHash": hash_json(review["evidenceManifest"]), + "artifactId": artifact_id, + "reason": review["reason"], + "completedAt": now, + "nextValidActions": ( + ["reproduction.start"] + if artifact_id is not None + else ["definition.create-draft", "research.inspect"] + ), + } + root = _publish_bundle( + _session_child_root(project, session_id, "artifact-decisions", create=True), + review["id"], + {ARTIFACT_REVIEW: review, ARTIFACT_DECISION: decision}, + "autoquant-artifact-decision-manifest", + ) + return load_artifact_decision(project, session_id, root.name) + + +def load_artifact_decision( + project: ProjectContext, session_id: str, decision_id: str +) -> dict[str, Any]: + root = _object_root( + _session_child_root(project, session_id, "artifact-decisions"), + decision_id, + "artifactDecision", + ) + manifest, values = _load_bundle( + root, + "autoquant-artifact-decision-manifest", + (ARTIFACT_DECISION, ARTIFACT_REVIEW), + ) + validate_artifact_review(values[ARTIFACT_REVIEW], str(root / ARTIFACT_REVIEW)) + decision = values[ARTIFACT_DECISION] + if decision.get("id") != decision_id or decision.get("sessionId") != session_id: + raise AutoQuantValidationError([_issue(root, "artifact.identity", "Artifact decision identity mismatch")]) + return {"path": str(root), "manifest": manifest, "review": values[ARTIFACT_REVIEW], "decision": decision} + + +def list_artifact_decisions(project: ProjectContext, session_id: str) -> list[dict[str, Any]]: + root = _session_child_root(project, session_id, "artifact-decisions") + if not root.exists(): + return [] + return [ + load_artifact_decision(project, session_id, item.name) + for item in sorted(root.iterdir()) + if item.is_dir() and not item.name.startswith(".") + ] + + +def validate_reproduction_request(value: dict[str, Any], path: str = "reproductionRequest") -> None: + required = { + "schemaVersion", + "kind", + "id", + "approvalId", + } + issues = _strict_keys(value, required, path) + if value.get("schemaVersion") != 1 or value.get("kind") != "autoquant-reproduction-request": + issues.append(_issue(path, "reproduction.request", "Invalid reproduction request kind or version")) + for key in ("id", "approvalId"): + if not isinstance(value.get(key), str) or not OBJECT_ID.fullmatch(value[key]): + issues.append(_issue(f"{path}/{key}", "artifact.id", f"Invalid {key}")) + if issues: + raise AutoQuantValidationError(issues) + + +def publish_reproduction_receipt( + project: ProjectContext, session_id: str, request: dict[str, Any] +) -> dict[str, Any]: + validate_reproduction_request(request) + approval = load_artifact_decision(project, session_id, request["approvalId"]) + if approval["decision"]["decision"] != "approve": + raise AutoQuantValidationError([_issue("approvalId", "reproduction.unapproved", "Reproduction requires an approved artifact decision")]) + baseline = approval["review"]["evidenceManifest"] + receipt = { + "schemaVersion": 1, + "kind": "autoquant-reproduction-receipt", + "id": request["id"], + "sessionId": session_id, + "approvalId": request["approvalId"], + "artifactId": approval["decision"]["artifactId"], + "outcome": "unavailable", + "environment": baseline["environment"], + "inputsHash": hash_json({"approval": approval["manifest"], "request": request}), + "differences": [ + { + "field": "executor", + "expected": "Core-controlled reproduction executor", + "actual": "unavailable", + } + ], + "originalEvidenceHash": approval["decision"]["evidenceManifestHash"], + "completedAt": datetime.now(timezone.utc).isoformat(), + } + root = _publish_bundle( + _session_child_root(project, session_id, "reproductions", create=True), + request["id"], + {REPRODUCTION_REQUEST: request, REPRODUCTION_RECEIPT: receipt}, + "autoquant-reproduction-manifest", + ) + return load_reproduction_receipt(project, session_id, root.name) + + +def load_reproduction_receipt( + project: ProjectContext, session_id: str, reproduction_id: str +) -> dict[str, Any]: + root = _object_root( + _session_child_root(project, session_id, "reproductions"), + reproduction_id, + "reproduction", + ) + manifest, values = _load_bundle( + root, + "autoquant-reproduction-manifest", + (REPRODUCTION_RECEIPT, REPRODUCTION_REQUEST), + ) + validate_reproduction_request(values[REPRODUCTION_REQUEST], str(root / REPRODUCTION_REQUEST)) + receipt = values[REPRODUCTION_RECEIPT] + if receipt.get("id") != reproduction_id or receipt.get("sessionId") != session_id or receipt.get("outcome") not in REPRODUCTION_OUTCOMES: + raise AutoQuantValidationError([_issue(root, "reproduction.identity", "Invalid reproduction receipt")]) + return {"path": str(root), "manifest": manifest, "request": values[REPRODUCTION_REQUEST], "receipt": receipt} + + +def list_reproduction_receipts(project: ProjectContext, session_id: str) -> list[dict[str, Any]]: + root = _session_child_root(project, session_id, "reproductions") + if not root.exists(): + return [] + return [ + load_reproduction_receipt(project, session_id, item.name) + for item in sorted(root.iterdir()) + if item.is_dir() and not item.name.startswith(".") + ] + + +ARTIFACT_REVIEW_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant exact-version artifact review V1", + "type": "object", + "additionalProperties": False, + "required": ["schemaVersion", "kind", "id", "decision", "actor", "definitionRef", "definitionHash", "evidenceManifest", "reason"], + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-artifact-review"}, + "id": {"type": "string", "pattern": OBJECT_ID.pattern}, + "decision": {"enum": sorted(DECISIONS)}, + "actor": {"type": "object"}, + "definitionRef": {"type": "object"}, + "definitionHash": {"type": "string", "pattern": HASH.pattern}, + "evidenceManifest": {"type": "object"}, + "reason": {"type": "string", "minLength": 1}, + }, +} + + +REPRODUCTION_REQUEST_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant reproduction request V1", + "type": "object", + "additionalProperties": False, + "required": ["schemaVersion", "kind", "id", "approvalId"], + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-reproduction-request"}, + "id": {"type": "string", "pattern": OBJECT_ID.pattern}, + "approvalId": {"type": "string", "pattern": OBJECT_ID.pattern}, + }, +} diff --git a/autoquant/research_definitions.py b/autoquant/research_definitions.py new file mode 100644 index 0000000..e013286 --- /dev/null +++ b/autoquant/research_definitions.py @@ -0,0 +1,1081 @@ +"""Strict immutable FactorDefinition and ExperimentDefinition versions.""" + +from __future__ import annotations + +import json +import math +import os +import re +import shutil +import uuid +from copy import deepcopy +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path, PurePosixPath +from typing import Any + +from .sessions import load_session +from .studies import hash_file +from .workspace import ( + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +DEFINITION_SCHEMA_VERSION = 1 +DEFINITION_FILE = "definition.json" +DEFINITION_MANIFEST = "manifest.json" +DEFINITION_ID = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$") +HASH = re.compile(r"^[0-9a-f]{64}$") + + +@dataclass(frozen=True) +class DefinitionContext: + root_dir: Path + manifest: dict[str, Any] + definition: dict[str, Any] + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _read_json(path: Path, label: str) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except FileNotFoundError: + raise AutoQuantValidationError( + [_issue(path, f"{label}.missing", f"Missing {label}: {path}")] + ) from None + except json.JSONDecodeError as error: + raise AutoQuantValidationError( + [ + _issue( + path, + f"{label}.json", + f"Invalid JSON at line {error.lineno}, column {error.colno}: {error.msg}", + ) + ] + ) from None + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, f"{label}.type", f"{label} must be an object")] + ) + return value + + +def _write_json(path: Path, value: dict[str, Any]) -> None: + path.write_text( + json.dumps(value, indent=2, ensure_ascii=False, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _strict_keys( + value: Any, + required: set[str], + path: Path | str, + *, + optional: set[str] = frozenset(), +) -> list[ValidationIssue]: + if not isinstance(value, dict): + return [_issue(path, "schema.type", "Expected an object")] + issues = [ + _issue(f"{path}/{key}", "schema.missing", f"Missing required field '{key}'") + for key in sorted(required - value.keys()) + ] + issues.extend( + _issue(f"{path}/{key}", "schema.unknown", f"Unknown field '{key}'") + for key in sorted(value.keys() - required - optional) + ) + return issues + + +def _nonempty(value: Any, path: str, issues: list[ValidationIssue]) -> None: + if not isinstance(value, str) or not value.strip(): + issues.append(_issue(path, "schema.string", "Expected a non-empty string")) + + +def _string_list(value: Any, path: str, issues: list[ValidationIssue]) -> None: + if not isinstance(value, list) or not value or not all( + isinstance(item, str) and item.strip() for item in value + ): + issues.append(_issue(path, "schema.list", "Expected a non-empty string list")) + + +def _validate_identity(value: dict[str, Any], path: str, kind: str) -> list[ValidationIssue]: + issues = _strict_keys( + value, + { + "schemaVersion", + "kind", + "id", + "version", + "status", + "createdAt", + "lineage", + }, + path, + optional=set(value) - { + "schemaVersion", + "kind", + "id", + "version", + "status", + "createdAt", + "lineage", + }, + ) + if value.get("schemaVersion") != DEFINITION_SCHEMA_VERSION: + issues.append(_issue(f"{path}/schemaVersion", "schema.version", "Expected V1")) + if value.get("kind") != kind: + issues.append(_issue(f"{path}/kind", "definition.kind", f"Expected {kind}")) + if not isinstance(value.get("id"), str) or not DEFINITION_ID.fullmatch(value["id"]): + issues.append(_issue(f"{path}/id", "definition.id", "Invalid definition id")) + version = value.get("version") + if not isinstance(version, int) or isinstance(version, bool) or version < 1: + issues.append(_issue(f"{path}/version", "definition.version", "Version must be positive")) + _nonempty(value.get("createdAt"), f"{path}/createdAt", issues) + lineage = value.get("lineage") + issues.extend(_strict_keys(lineage, {"parentVersion"}, f"{path}/lineage")) + if isinstance(lineage, dict): + parent = lineage.get("parentVersion") + if parent is not None and ( + not isinstance(parent, int) + or isinstance(parent, bool) + or parent < 1 + or (isinstance(version, int) and parent >= version) + ): + issues.append( + _issue( + f"{path}/lineage/parentVersion", + "definition.lineage", + "parentVersion must be null or an earlier positive version", + ) + ) + return issues + + +def validate_factor_definition(value: dict[str, Any], path: str = "factorDefinition") -> None: + required = { + "schemaVersion", + "kind", + "id", + "version", + "status", + "createdAt", + "lineage", + "hypothesis", + "calculation", + "parameters", + "output", + "dataDependencies", + "missingDataPolicy", + "cohort", + "expectedHorizon", + "requiredTests", + "failureGates", + } + issues = _strict_keys(value, required, path) + issues.extend(_validate_identity(value, path, "autoquant-factor-definition")) + if value.get("status") not in {"draft", "approved", "retired"}: + issues.append(_issue(f"{path}/status", "definition.status", "Invalid factor status")) + _nonempty(value.get("hypothesis"), f"{path}/hypothesis", issues) + calculation = value.get("calculation") + issues.extend( + _strict_keys(calculation, {"kind", "identity", "sourceHash"}, f"{path}/calculation") + ) + if isinstance(calculation, dict): + if calculation.get("kind") not in {"source", "expression"}: + issues.append(_issue(f"{path}/calculation/kind", "definition.calculation", "Invalid calculation kind")) + _nonempty(calculation.get("identity"), f"{path}/calculation/identity", issues) + identity = calculation.get("identity") + if calculation.get("kind") == "source" and isinstance(identity, str): + source_path, separator, symbol = identity.partition(":") + candidate = PurePosixPath(source_path) + if ( + not separator + or not symbol + or "\\" in source_path + or candidate.is_absolute() + or ".." in candidate.parts + or candidate.suffix != ".py" + or not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", symbol) + ): + issues.append( + _issue( + f"{path}/calculation/identity", + "definition.source-identity", + "Source identity must be a confined .py path and symbol", + ) + ) + if not isinstance(calculation.get("sourceHash"), str) or not HASH.fullmatch(calculation["sourceHash"]): + issues.append(_issue(f"{path}/calculation/sourceHash", "schema.hash", "Invalid source hash")) + parameters = value.get("parameters") + if not isinstance(parameters, dict): + issues.append(_issue(f"{path}/parameters", "schema.type", "parameters must be an object")) + else: + for name, parameter in parameters.items(): + parameter_path = f"{path}/parameters/{name}" + if not isinstance(name, str) or not re.fullmatch(r"[A-Za-z][A-Za-z0-9]{0,63}", name): + issues.append(_issue(parameter_path, "definition.parameter-name", "Parameter names must be bounded identifiers")) + if isinstance(parameter, bool): + continue + if not isinstance(parameter, (int, float)) or not math.isfinite(parameter): + issues.append(_issue(parameter_path, "definition.parameter-value", "Parameters accept only finite numbers or booleans; credentials and opaque objects belong in Core-managed configuration")) + output = value.get("output") + issues.extend(_strict_keys(output, {"direction", "unit"}, f"{path}/output")) + if isinstance(output, dict): + if output.get("direction") not in {"higher", "lower", "bidirectional"}: + issues.append(_issue(f"{path}/output/direction", "definition.direction", "Invalid output direction")) + _nonempty(output.get("unit"), f"{path}/output/unit", issues) + dependencies = value.get("dataDependencies") + if not isinstance(dependencies, list) or not dependencies: + issues.append(_issue(f"{path}/dataDependencies", "schema.list", "At least one data dependency is required")) + else: + for index, dependency in enumerate(dependencies): + dep_path = f"{path}/dataDependencies/{index}" + issues.extend( + _strict_keys( + dependency, + {"packageId", "version", "fields", "availability"}, + dep_path, + ) + ) + if isinstance(dependency, dict): + _nonempty(dependency.get("packageId"), f"{dep_path}/packageId", issues) + _nonempty(dependency.get("version"), f"{dep_path}/version", issues) + _string_list(dependency.get("fields"), f"{dep_path}/fields", issues) + availability = dependency.get("availability") + issues.extend( + _strict_keys( + availability, + {"pointInTime", "marketClock"}, + f"{dep_path}/availability", + ) + ) + if isinstance(availability, dict): + if not isinstance(availability.get("pointInTime"), bool): + issues.append(_issue(f"{dep_path}/availability/pointInTime", "schema.boolean", "pointInTime must be boolean")) + clock = availability.get("marketClock") + if clock is not None: + issues.extend( + _strict_keys(clock, {"id", "version"}, f"{dep_path}/availability/marketClock") + ) + if isinstance(clock, dict): + _nonempty(clock.get("id"), f"{dep_path}/availability/marketClock/id", issues) + _nonempty(clock.get("version"), f"{dep_path}/availability/marketClock/version", issues) + for key in ("missingDataPolicy", "expectedHorizon"): + _nonempty(value.get(key), f"{path}/{key}", issues) + cohort = value.get("cohort") + issues.extend(_strict_keys(cohort, {"kind", "identity"}, f"{path}/cohort")) + if isinstance(cohort, dict): + _nonempty(cohort.get("kind"), f"{path}/cohort/kind", issues) + _nonempty(cohort.get("identity"), f"{path}/cohort/identity", issues) + _string_list(value.get("requiredTests"), f"{path}/requiredTests", issues) + _string_list(value.get("failureGates"), f"{path}/failureGates", issues) + if issues: + raise AutoQuantValidationError(issues) + + +def factor_readiness(value: dict[str, Any]) -> dict[str, Any]: + validate_factor_definition(value) + unresolved = [] + for dependency in value["dataDependencies"]: + availability = dependency["availability"] + if not availability["pointInTime"]: + unresolved.append(f"data:{dependency['packageId']}:{dependency['version']}:point-in-time") + if availability["marketClock"] is None: + unresolved.append(f"data:{dependency['packageId']}:{dependency['version']}:market-clock") + return {"ready": not unresolved, "unresolved": unresolved} + + +def validate_experiment_definition( + value: dict[str, Any], path: str = "experimentDefinition" +) -> None: + required = { + "schemaVersion", + "kind", + "id", + "version", + "status", + "createdAt", + "lineage", + "definitionRef", + "data", + "subject", + "outcome", + "benchmark", + "costPolicy", + "splitPolicy", + "robustness", + "selectionAdjustment", + "holdoutPolicy", + "executorPolicy", + "budget", + "stopConditions", + } + issues = _strict_keys(value, required, path) + issues.extend(_validate_identity(value, path, "autoquant-experiment-definition")) + if value.get("status") not in {"draft", "frozen"}: + issues.append(_issue(f"{path}/status", "definition.status", "Invalid experiment status")) + reference = value.get("definitionRef") + issues.extend(_strict_keys(reference, {"kind", "id", "version"}, f"{path}/definitionRef")) + if isinstance(reference, dict): + if reference.get("kind") not in {"factor", "strategy"}: + issues.append(_issue(f"{path}/definitionRef/kind", "definition.reference", "Reference kind must be factor or strategy")) + if not isinstance(reference.get("id"), str) or not DEFINITION_ID.fullmatch(reference["id"]): + issues.append(_issue(f"{path}/definitionRef/id", "definition.id", "Invalid referenced definition id")) + if not isinstance(reference.get("version"), int) or isinstance(reference.get("version"), bool) or reference.get("version", 0) < 1: + issues.append(_issue(f"{path}/definitionRef/version", "definition.version", "Invalid referenced version")) + for key in ("data", "subject", "outcome", "benchmark"): + item = value.get(key) + item_keys = { + "data": {"packageId", "version"}, + "subject": {"kind", "id", "version"}, + "outcome": {"name", "horizon"}, + "benchmark": {"id", "version"}, + }[key] + issues.extend(_strict_keys(item, item_keys, f"{path}/{key}")) + if isinstance(item, dict): + for child in item_keys: + if child == "version" and isinstance(item.get(child), int) and not isinstance(item.get(child), bool): + if item[child] < 1: + issues.append(_issue(f"{path}/{key}/{child}", "definition.version", "Version must be positive")) + else: + _nonempty(item.get(child), f"{path}/{key}/{child}", issues) + for key in ( + "costPolicy", + "splitPolicy", + "robustness", + "selectionAdjustment", + "holdoutPolicy", + "executorPolicy", + ): + if not isinstance(value.get(key), dict) or not value[key]: + issues.append(_issue(f"{path}/{key}", "schema.object", f"{key} must be a non-empty object")) + budget = value.get("budget") + issues.extend( + _strict_keys( + budget, + {"candidateLimit", "wallTimeSeconds", "cpuSeconds", "gpuSeconds", "cost"}, + f"{path}/budget", + ) + ) + if isinstance(budget, dict): + for key in ("candidateLimit", "wallTimeSeconds", "cpuSeconds"): + item = budget.get(key) + if not isinstance(item, int) or isinstance(item, bool) or item < 1: + issues.append(_issue(f"{path}/budget/{key}", "schema.range", f"{key} must be positive")) + gpu = budget.get("gpuSeconds") + if not isinstance(gpu, int) or isinstance(gpu, bool) or gpu < 0: + issues.append(_issue(f"{path}/budget/gpuSeconds", "schema.range", "gpuSeconds must be non-negative")) + cost = budget.get("cost") + if cost is not None: + issues.extend(_strict_keys(cost, {"currency", "amount"}, f"{path}/budget/cost")) + if isinstance(cost, dict): + _nonempty(cost.get("currency"), f"{path}/budget/cost/currency", issues) + amount = cost.get("amount") + if not isinstance(amount, (int, float)) or isinstance(amount, bool) or amount <= 0: + issues.append(_issue(f"{path}/budget/cost/amount", "schema.range", "Cost amount must be positive")) + _string_list(value.get("stopConditions"), f"{path}/stopConditions", issues) + if issues: + raise AutoQuantValidationError(issues) + + +def validate_strategy_definition( + value: dict[str, Any], path: str = "strategyDefinition" +) -> None: + required = { + "schemaVersion", + "kind", + "id", + "version", + "status", + "createdAt", + "lineage", + "factorRefs", + "composition", + "portfolioValidation", + "mlValidation", + "rlValidation", + "costPolicy", + "riskAssumptions", + "holdoutPolicy", + "artifactClosure", + } + issues = _strict_keys(value, required, path) + issues.extend(_validate_identity(value, path, "autoquant-strategy-definition")) + if value.get("status") not in {"draft", "approved", "retired"}: + issues.append(_issue(f"{path}/status", "definition.status", "Invalid strategy status")) + references = value.get("factorRefs") + if not isinstance(references, list) or not references: + issues.append(_issue(f"{path}/factorRefs", "schema.list", "At least one exact FactorDefinition reference is required")) + else: + for index, reference in enumerate(references): + ref_path = f"{path}/factorRefs/{index}" + issues.extend(_strict_keys(reference, {"id", "version"}, ref_path)) + if isinstance(reference, dict): + if not isinstance(reference.get("id"), str) or not DEFINITION_ID.fullmatch(reference["id"]): + issues.append(_issue(f"{ref_path}/id", "definition.id", "Invalid FactorDefinition id")) + if not isinstance(reference.get("version"), int) or isinstance(reference.get("version"), bool) or reference.get("version", 0) < 1: + issues.append(_issue(f"{ref_path}/version", "definition.version", "Invalid FactorDefinition version")) + for key in ( + "composition", + "portfolioValidation", + "costPolicy", + "riskAssumptions", + "holdoutPolicy", + "artifactClosure", + ): + if not isinstance(value.get(key), dict) or not value[key]: + issues.append(_issue(f"{path}/{key}", "schema.object", f"{key} must be a non-empty object")) + for key in ("mlValidation", "rlValidation"): + if value.get(key) is not None and ( + not isinstance(value[key], dict) or not value[key] + ): + issues.append(_issue(f"{path}/{key}", "schema.object", f"{key} must be null or a non-empty object")) + if issues: + raise AutoQuantValidationError(issues) + + +def experiment_readiness(value: dict[str, Any]) -> dict[str, Any]: + try: + validate_experiment_definition(value) + except AutoQuantValidationError as error: + missing = [issue.path for issue in error.issues if issue.code == "schema.missing"] + return { + "ready": False, + "unresolved": missing, + "diagnostics": [str(issue.message) if hasattr(issue, 'message') else issue.code for issue in error.issues], + } + unresolved = [] + if value["status"] != "frozen": + unresolved.append("status:frozen") + budget = value["budget"] + if not isinstance(budget.get("cost"), dict) and not budget.get("candidateLimit"): + unresolved.append("budget:candidate-limit-or-cost-required") + cost_policy = value.get("costPolicy", {}) + if not cost_policy: + unresolved.append("cost-policy:required") + holdout = value.get("holdoutPolicy", {}) + if not holdout: + unresolved.append("holdout-policy:required") + executor = value.get("executorPolicy", {}) + if not executor: + unresolved.append("executor-policy:required") + stop = value.get("stopConditions", []) + if not stop: + unresolved.append("stop-conditions:required") + return {"ready": not unresolved, "unresolved": unresolved} + + +def _factor_versions_root(project: ProjectContext, definition_id: str) -> Path: + if not DEFINITION_ID.fullmatch(definition_id): + raise AutoQuantValidationError([_issue("definition_id", "definition.id", "Invalid definition id")]) + factors_relative = project.manifest.directories["factors"] + relative = f"{factors_relative}/definitions/{definition_id}/versions" + return confined_path(project.root_dir, relative, "project/factorDefinitions") + + +def _experiment_versions_root( + project: ProjectContext, session_id: str, definition_id: str +) -> Path: + if not DEFINITION_ID.fullmatch(definition_id): + raise AutoQuantValidationError([_issue("definition_id", "definition.id", "Invalid definition id")]) + session = load_session(project, session_id) + return confined_path( + session.root_dir, + f"experiment-definitions/{definition_id}/versions", + "session/experimentDefinitions", + ) + + +def _strategy_versions_root(project: ProjectContext, definition_id: str) -> Path: + if not DEFINITION_ID.fullmatch(definition_id): + raise AutoQuantValidationError([_issue("definition_id", "definition.id", "Invalid definition id")]) + strategies_relative = project.manifest.directories["strategies"] + relative = f"{strategies_relative}/definitions/{definition_id}/versions" + return confined_path(project.root_dir, relative, "project/strategyDefinitions") + + +def _validate_transition( + kind: str, parent_status: str, child_status: str, definition_id: str +) -> None: + """Validate kind-specific definition lifecycle transitions. + + Factor/Strategy: draft → approved → retired; edits from approved fork a new draft. + Experiment: draft → frozen; edits from frozen fork a new draft. + """ + legal: set[tuple[str, str]] + if kind in ("autoquant-factor-definition", "autoquant-strategy-definition"): + legal = { + ("draft", "draft"), + ("draft", "approved"), + ("approved", "draft"), + ("approved", "retired"), + } + elif kind == "autoquant-experiment-definition": + legal = { + ("draft", "draft"), + ("draft", "frozen"), + ("frozen", "draft"), + } + else: + raise AutoQuantValidationError( + [_issue("kind", "definition.kind", f"Unknown definition kind: {kind}")] + ) + if (parent_status, child_status) not in legal: + raise AutoQuantValidationError( + [ + _issue( + "status", + "definition.lifecycle", + f"Illegal transition: {parent_status} → {child_status} for {kind}", + ) + ] + ) + + +def _publish(root: Path, value: dict[str, Any]) -> DefinitionContext: + version = value["version"] + kind = value["kind"] + definition_id = value["id"] + status = value["status"] + parent_version = value["lineage"]["parentVersion"] + if version == 1: + if parent_version is not None: + raise AutoQuantValidationError( + [_issue("lineage/parentVersion", "definition.lineage", "Version 1 cannot have a parent")] + ) + if status != "draft": + raise AutoQuantValidationError( + [ + _issue( + "status", + "definition.lifecycle", + "Version 1 must be created as draft; use an explicit transition function to advance status", + ) + ] + ) + else: + if parent_version != version - 1: + raise AutoQuantValidationError( + [ + _issue( + "lineage/parentVersion", + "definition.lineage", + "Definition versions must reference the immediately preceding version", + ) + ] + ) + parent = _load( + root / str(parent_version), + kind, + definition_id, + parent_version, + ) + if parent.definition["status"] == "retired": + raise AutoQuantValidationError( + [_issue("lineage", "definition.retired", "Retired definitions cannot be extended")] + ) + _validate_transition(kind, parent.definition["status"], status, definition_id) + root.mkdir(parents=True, exist_ok=True) + target = root / str(version) + temporary = root / f".{version}.{uuid.uuid4().hex}.creating" + if target.exists(): + raise AutoQuantValidationError([_issue(target, "definition.collision", "Definition version already exists")]) + try: + temporary.mkdir() + _write_json(temporary / DEFINITION_FILE, value) + content_hash = hash_file(temporary / DEFINITION_FILE) + manifest = { + "schemaVersion": DEFINITION_SCHEMA_VERSION, + "kind": "autoquant-definition-version-manifest", + "definitionKind": value["kind"], + "id": value["id"], + "version": version, + "status": value["status"], + "completed": True, + "contentHash": content_hash, + "files": {DEFINITION_FILE: content_hash}, + } + _write_json(temporary / DEFINITION_MANIFEST, manifest) + os.replace(temporary, target) + except Exception: + if temporary.exists(): + shutil.rmtree(temporary) + raise + return _load(target, value["kind"], value["id"], version) + + +def _load(root: Path, kind: str, definition_id: str, version: int) -> DefinitionContext: + manifest_path = root / DEFINITION_MANIFEST + definition_path = root / DEFINITION_FILE + manifest = _read_json(manifest_path, "Definition manifest") + required = { + "schemaVersion", + "kind", + "definitionKind", + "id", + "version", + "status", + "completed", + "contentHash", + "files", + } + issues = _strict_keys(manifest, required, manifest_path) + if manifest.get("schemaVersion") != DEFINITION_SCHEMA_VERSION: + issues.append(_issue(manifest_path, "schema.version", "Expected definition manifest V1")) + if manifest.get("kind") != "autoquant-definition-version-manifest": + issues.append(_issue(manifest_path, "definition.manifest-kind", "Invalid manifest kind")) + if manifest.get("definitionKind") != kind or manifest.get("id") != definition_id or manifest.get("version") != version: + issues.append(_issue(manifest_path, "definition.identity", "Definition manifest identity mismatch")) + expected_hash = hash_file(definition_path) if definition_path.is_file() else None + if expected_hash is None or manifest.get("contentHash") != expected_hash or manifest.get("files") != {DEFINITION_FILE: expected_hash}: + issues.append(_issue(definition_path, "definition.hash", "Definition content hash mismatch")) + if issues: + raise AutoQuantValidationError(issues) + definition = _read_json(definition_path, "Definition") + if kind == "autoquant-factor-definition": + validate_factor_definition(definition, str(definition_path)) + elif kind == "autoquant-experiment-definition": + validate_experiment_definition(definition, str(definition_path)) + else: + validate_strategy_definition(definition, str(definition_path)) + if definition["id"] != definition_id or definition["version"] != version or definition["status"] != manifest["status"]: + raise AutoQuantValidationError([_issue(definition_path, "definition.identity", "Definition content identity mismatch")]) + return DefinitionContext(root, manifest, definition) + + +def create_factor_definition_version( + project: ProjectContext, value: dict[str, Any] +) -> DefinitionContext: + validate_factor_definition(value) + if value.get("status") != "draft": + raise AutoQuantValidationError( + [ + _issue( + "status", + "definition.lifecycle", + "create_factor_definition_version only accepts draft status; use approve_factor_definition or retire_factor_definition to transition", + ) + ] + ) + return _publish(_factor_versions_root(project, value["id"]), deepcopy(value)) + + +def load_factor_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + root = _factor_versions_root(project, definition_id) / str(version) + return _load(root, "autoquant-factor-definition", definition_id, version) + + +def list_factor_definitions(project: ProjectContext) -> list[DefinitionContext]: + factors = confined_path( + project.root_dir, + f"{project.manifest.directories['factors']}/definitions", + "project/factorDefinitions", + ) + if not factors.exists(): + return [] + result = [] + for definition_root in sorted(factors.iterdir()): + versions = definition_root / "versions" + if definition_root.is_dir() and versions.is_dir(): + version_roots = [item for item in versions.iterdir() if item.name.isdigit()] + for version_root in sorted(version_roots, key=lambda item: int(item.name)): + result.append(load_factor_definition(project, definition_root.name, int(version_root.name))) + return result + + +def create_strategy_definition_version( + project: ProjectContext, value: dict[str, Any] +) -> DefinitionContext: + validate_strategy_definition(value) + if value.get("status") != "draft": + raise AutoQuantValidationError( + [ + _issue( + "status", + "definition.lifecycle", + "create_strategy_definition_version only accepts draft status; use approve_strategy_definition or retire_strategy_definition to transition", + ) + ] + ) + for reference in value["factorRefs"]: + factor = load_factor_definition(project, reference["id"], reference["version"]) + if factor.definition["status"] != "approved": + raise AutoQuantValidationError([_issue("factorRefs", "definition.unapproved", "StrategyDefinition requires approved FactorDefinition versions")]) + return _publish(_strategy_versions_root(project, value["id"]), deepcopy(value)) + + +def load_strategy_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + root = _strategy_versions_root(project, definition_id) / str(version) + return _load(root, "autoquant-strategy-definition", definition_id, version) + + +def list_strategy_definitions(project: ProjectContext) -> list[DefinitionContext]: + strategies = confined_path( + project.root_dir, + f"{project.manifest.directories['strategies']}/definitions", + "project/strategyDefinitions", + ) + if not strategies.exists(): + return [] + result = [] + for definition_root in sorted(strategies.iterdir()): + versions = definition_root / "versions" + if definition_root.is_dir() and versions.is_dir(): + version_roots = [item for item in versions.iterdir() if item.name.isdigit()] + for version_root in sorted(version_roots, key=lambda item: int(item.name)): + result.append(load_strategy_definition(project, definition_root.name, int(version_root.name))) + return result + + +def _validate_experiment_reference( + project: ProjectContext, reference: dict[str, Any] +) -> None: + """Re-load and re-validate the definitionRef at gate time. + + Every gate (create, freeze) re-verifies that the referenced + FactorDefinition or StrategyDefinition version still exists on disk, + passes tamper (hash) checks, is still approved, and — for factors — + still passes factor_readiness with all data-availability clocks resolved. + """ + if reference["kind"] == "factor": + factor = load_factor_definition(project, reference["id"], reference["version"]) + if factor.definition["status"] != "approved": + raise AutoQuantValidationError( + [_issue("definitionRef", "definition.unapproved", + "ExperimentDefinition requires an approved FactorDefinition version")] + ) + readiness = factor_readiness(factor.definition) + if not readiness["ready"]: + raise AutoQuantValidationError( + [_issue("definitionRef", "definition.unready", + f"FactorDefinition is not validation-ready: {', '.join(readiness['unresolved'])}")] + ) + else: + strategy = load_strategy_definition(project, reference["id"], reference["version"]) + if strategy.definition["status"] != "approved": + raise AutoQuantValidationError( + [_issue("definitionRef", "definition.unapproved", + "ExperimentDefinition requires an approved StrategyDefinition version")] + ) + + +def create_experiment_definition_version( + project: ProjectContext, session_id: str, value: dict[str, Any] +) -> DefinitionContext: + validate_experiment_definition(value) + if value.get("status") != "draft": + raise AutoQuantValidationError( + [ + _issue( + "status", + "definition.lifecycle", + "create_experiment_definition_version only accepts draft status; use freeze_experiment_definition to transition", + ) + ] + ) + _validate_experiment_reference(project, value["definitionRef"]) + return _publish( + _experiment_versions_root(project, session_id, value["id"]), + deepcopy(value), + ) + + +def load_experiment_definition( + project: ProjectContext, session_id: str, definition_id: str, version: int +) -> DefinitionContext: + root = _experiment_versions_root(project, session_id, definition_id) / str(version) + return _load(root, "autoquant-experiment-definition", definition_id, version) + + +def list_experiment_definitions( + project: ProjectContext, session_id: str +) -> list[DefinitionContext]: + session = load_session(project, session_id) + root = confined_path(session.root_dir, "experiment-definitions", "session/experimentDefinitions") + if not root.exists(): + return [] + result = [] + for definition_root in sorted(root.iterdir()): + versions = definition_root / "versions" + if definition_root.is_dir() and versions.is_dir(): + version_roots = [item for item in versions.iterdir() if item.name.isdigit()] + for version_root in sorted(version_roots, key=lambda item: int(item.name)): + result.append(load_experiment_definition(project, session_id, definition_root.name, int(version_root.name))) + return result + + +def new_definition_version( + current: dict[str, Any], changes: dict[str, Any], *, status: str = "draft" +) -> dict[str, Any]: + immutable = {"schemaVersion", "kind", "id", "version", "createdAt", "lineage"} + forbidden = immutable.intersection(changes) + if forbidden: + raise AutoQuantValidationError([_issue("changes", "definition.identity", f"Cannot edit identity fields: {', '.join(sorted(forbidden))}")]) + result = deepcopy(current) + result.update(deepcopy(changes)) + result["version"] = current["version"] + 1 + result["status"] = status + result["createdAt"] = datetime.now(timezone.utc).isoformat() + result["lineage"] = {"parentVersion": current["version"]} + return result + + +def approve_factor_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + """Approve a draft FactorDefinition version. + + An approved version is immutable and cannot be edited in place. + Edits to an approved version fork a new draft with lineage. + """ + current = load_factor_definition(project, definition_id, version) + if current.definition["status"] != "draft": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.status", "Only draft definitions can be approved")] + ) + readiness = factor_readiness(current.definition) + if not readiness["ready"]: + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.not-ready", f"Definition is not validation-ready: {', '.join(readiness['unresolved'])}")] + ) + next_def = new_definition_version(current.definition, {}, status="approved") + return _publish(_factor_versions_root(project, definition_id), next_def) + + +def retire_factor_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + """Retire an approved FactorDefinition version. + + Retired versions cannot be extended. Existing Runs remain bound. + """ + current = load_factor_definition(project, definition_id, version) + if current.definition["status"] != "approved": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.status", "Only approved definitions can be retired")] + ) + next_def = new_definition_version(current.definition, {}, status="retired") + return _publish(_factor_versions_root(project, definition_id), next_def) + + +def approve_strategy_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + """Approve a draft StrategyDefinition version.""" + current = load_strategy_definition(project, definition_id, version) + if current.definition["status"] != "draft": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.status", "Only draft definitions can be approved")] + ) + for reference in current.definition["factorRefs"]: + factor = load_factor_definition(project, reference["id"], reference["version"]) + if factor.definition["status"] != "approved": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.unapproved-factor", f"Factor {reference['id']} v{reference['version']} is not approved")] + ) + next_def = new_definition_version(current.definition, {}, status="approved") + return _publish(_strategy_versions_root(project, definition_id), next_def) + + +def retire_strategy_definition( + project: ProjectContext, definition_id: str, version: int +) -> DefinitionContext: + """Retire an approved StrategyDefinition version.""" + current = load_strategy_definition(project, definition_id, version) + if current.definition["status"] != "approved": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.status", "Only approved definitions can be retired")] + ) + next_def = new_definition_version(current.definition, {}, status="retired") + return _publish(_strategy_versions_root(project, definition_id), next_def) + + +def freeze_experiment_definition( + project: ProjectContext, session_id: str, definition_id: str, version: int +) -> DefinitionContext: + """Freeze a draft ExperimentDefinition. + + Freezing requires all PIT, clock, cost, stop, and holdout fields to be + resolved. A frozen experiment is immutable and becomes the plan that + ExperimentRuns reference. + + The referenced FactorDefinition or StrategyDefinition is re-loaded and + re-validated at freeze time: it must still exist on disk, pass tamper + (hash) checks, be approved, and for factors — still satisfy + factor_readiness with all data-availability clocks resolved. + """ + current = load_experiment_definition(project, session_id, definition_id, version) + if current.definition["status"] != "draft": + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.status", "Only draft experiment definitions can be frozen")] + ) + # Re-load and re-validate the referenced definition at freeze time + _validate_experiment_reference(project, current.definition["definitionRef"]) + # Check structural readiness: all required fields must be non-empty + # (costPolicy, splitPolicy, robustness, holdoutPolicy, executorPolicy) + for key in ("costPolicy", "splitPolicy", "robustness", "holdoutPolicy", "executorPolicy"): + if not isinstance(current.definition.get(key), dict) or not current.definition[key]: + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.not-ready", f"ExperimentDefinition is missing required field: {key}")] + ) + if not isinstance(current.definition.get("stopConditions"), list) or not current.definition["stopConditions"]: + raise AutoQuantValidationError( + [_issue(f"{definition_id} v{version}", "definition.not-ready", "ExperimentDefinition is missing required field: stopConditions")] + ) + next_def = new_definition_version(current.definition, {}, status="frozen") + return _publish(_experiment_versions_root(project, session_id, definition_id), next_def) + + +def semantic_definition_diff( + before: dict[str, Any], after: dict[str, Any] +) -> dict[str, Any]: + if before.get("kind") != after.get("kind") or before.get("id") != after.get("id"): + raise AutoQuantValidationError([_issue("definitions", "definition.identity", "Semantic diff requires the same definition identity")]) + ignored = {"createdAt", "lineage", "status", "version"} + changes = [] + for key in sorted((set(before) | set(after)) - ignored): + if before.get(key) != after.get(key): + changes.append({"field": key, "before": before.get(key), "after": after.get(key)}) + evidence_fields = { + "calculation", + "parameters", + "dataDependencies", + "cohort", + "expectedHorizon", + "definitionRef", + "data", + "subject", + "outcome", + "benchmark", + "costPolicy", + "splitPolicy", + "holdoutPolicy", + "executorPolicy", + "factorRefs", + "composition", + "portfolioValidation", + "mlValidation", + "rlValidation", + "riskAssumptions", + "artifactClosure", + } + invalidated = [item["field"] for item in changes if item["field"] in evidence_fields] + return { + "kind": "autoquant-semantic-definition-diff", + "definition": {"kind": before["kind"], "id": before["id"]}, + "fromVersion": before["version"], + "toVersion": after["version"], + "changes": changes, + "affectedEvidence": invalidated, + "invalidatedAssumptions": invalidated, + } + + +FACTOR_DEFINITION_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant FactorDefinition V1", + "type": "object", + "required": [ + "schemaVersion", "kind", "id", "version", "status", "createdAt", "lineage", + "hypothesis", "calculation", "parameters", "output", "dataDependencies", + "missingDataPolicy", "cohort", "expectedHorizon", "requiredTests", "failureGates", + ], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-factor-definition"}, + "id": {"type": "string", "pattern": DEFINITION_ID.pattern}, + "version": {"type": "integer", "minimum": 1}, + "status": {"enum": ["draft", "approved", "retired"]}, + "createdAt": {"type": "string", "minLength": 1}, + "lineage": {"type": "object"}, + "hypothesis": {"type": "string", "minLength": 1}, + "calculation": {"type": "object"}, + "parameters": {"type": "object"}, + "output": {"type": "object"}, + "dataDependencies": {"type": "array", "minItems": 1}, + "missingDataPolicy": {"type": "string", "minLength": 1}, + "cohort": {"type": "object"}, + "expectedHorizon": {"type": "string", "minLength": 1}, + "requiredTests": {"type": "array", "minItems": 1}, + "failureGates": {"type": "array", "minItems": 1}, + }, +} + + +EXPERIMENT_DEFINITION_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant ExperimentDefinition V1", + "type": "object", + "required": [ + "schemaVersion", "kind", "id", "version", "status", "createdAt", "lineage", + "definitionRef", "data", "subject", "outcome", "benchmark", "costPolicy", + "splitPolicy", "robustness", "selectionAdjustment", "holdoutPolicy", + "executorPolicy", "budget", "stopConditions", + ], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-experiment-definition"}, + "id": {"type": "string", "pattern": DEFINITION_ID.pattern}, + "version": {"type": "integer", "minimum": 1}, + "status": {"enum": ["draft", "frozen"]}, + "createdAt": {"type": "string", "minLength": 1}, + "lineage": {"type": "object"}, + "definitionRef": {"type": "object"}, + "data": {"type": "object"}, + "subject": {"type": "object"}, + "outcome": {"type": "object"}, + "benchmark": {"type": "object"}, + "costPolicy": {"type": "object", "minProperties": 1}, + "splitPolicy": {"type": "object", "minProperties": 1}, + "robustness": {"type": "object", "minProperties": 1}, + "selectionAdjustment": {"type": "object", "minProperties": 1}, + "holdoutPolicy": {"type": "object", "minProperties": 1}, + "executorPolicy": {"type": "object", "minProperties": 1}, + "budget": {"type": "object"}, + "stopConditions": {"type": "array", "minItems": 1}, + }, +} + + +STRATEGY_DEFINITION_JSON_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "title": "AutoQuant StrategyDefinition V1", + "type": "object", + "required": [ + "schemaVersion", "kind", "id", "version", "status", "createdAt", "lineage", + "factorRefs", "composition", "portfolioValidation", "mlValidation", "rlValidation", + "costPolicy", "riskAssumptions", "holdoutPolicy", "artifactClosure", + ], + "additionalProperties": False, + "properties": { + "schemaVersion": {"const": 1}, + "kind": {"const": "autoquant-strategy-definition"}, + "id": {"type": "string", "pattern": DEFINITION_ID.pattern}, + "version": {"type": "integer", "minimum": 1}, + "status": {"enum": ["draft", "approved", "retired"]}, + "createdAt": {"type": "string", "minLength": 1}, + "lineage": {"type": "object"}, + "factorRefs": {"type": "array", "minItems": 1}, + "composition": {"type": "object", "minProperties": 1}, + "portfolioValidation": {"type": "object", "minProperties": 1}, + "mlValidation": {"type": ["object", "null"]}, + "rlValidation": {"type": ["object", "null"]}, + "costPolicy": {"type": "object", "minProperties": 1}, + "riskAssumptions": {"type": "object", "minProperties": 1}, + "holdoutPolicy": {"type": "object", "minProperties": 1}, + "artifactClosure": {"type": "object", "minProperties": 1}, + }, +} diff --git a/autoquant/runs.py b/autoquant/runs.py index 89c3573..2228a7b 100644 --- a/autoquant/runs.py +++ b/autoquant/runs.py @@ -765,6 +765,250 @@ def _all_file_hashes(root: Path) -> dict[str, str]: return hashes +def _validate_research_binding( + project: ProjectContext, + binding: dict[str, Any], +) -> None: + """Gate-exact researchBinding validation. + + Before a Run directory is created, this function uses the Core definition + loaders to verify that the supplied definitionRef and experimentDefinitionRef + point to exact approved/frozen artifacts with matching content hashes. + """ + from .research_definitions import ( + DEFINITION_ID, + HASH as DEFINITION_HASH, + load_experiment_definition, + load_factor_definition, + load_strategy_definition, + ) + + if not isinstance(binding, dict): + raise AutoQuantValidationError( + [_issue("researchBinding", "schema.type", "researchBinding must be an object")] + ) + + issues: list[ValidationIssue] = [] + + required_keys = {"definitionRef", "experimentDefinitionRef"} + issues.extend(_strict_keys(binding, required_keys, "researchBinding")) + + if issues: + raise AutoQuantValidationError(issues) + + # ---- definitionRef ---- + ref = binding.get("definitionRef") + if not isinstance(ref, dict): + issues.append( + _issue("researchBinding/definitionRef", "schema.type", "definitionRef must be an object") + ) + else: + ref_keys = {"kind", "id", "version", "contentHash"} + issues.extend(_strict_keys(ref, ref_keys, "researchBinding/definitionRef")) + kind = ref.get("kind") + if kind not in ("factor", "strategy"): + issues.append( + _issue( + "researchBinding/definitionRef/kind", + "researchBinding.kind", + "definitionRef kind must be factor or strategy", + ) + ) + definition_id = ref.get("id") + if not isinstance(definition_id, str) or not DEFINITION_ID.fullmatch(definition_id): + issues.append( + _issue( + "researchBinding/definitionRef/id", + "researchBinding.id", + "definitionRef id must be a valid definition id", + ) + ) + version = ref.get("version") + if not isinstance(version, int) or isinstance(version, bool) or version < 1: + issues.append( + _issue( + "researchBinding/definitionRef/version", + "researchBinding.version", + "definitionRef version must be a positive integer", + ) + ) + content_hash = ref.get("contentHash") + if not isinstance(content_hash, str) or not DEFINITION_HASH.fullmatch(content_hash): + issues.append( + _issue( + "researchBinding/definitionRef/contentHash", + "researchBinding.hash", + "definitionRef contentHash must be a lowercase SHA-256", + ) + ) + + # ---- experimentDefinitionRef ---- + exp = binding.get("experimentDefinitionRef") + if not isinstance(exp, dict): + issues.append( + _issue( + "researchBinding/experimentDefinitionRef", + "schema.type", + "experimentDefinitionRef must be an object", + ) + ) + else: + exp_keys = {"kind", "sessionId", "id", "version", "contentHash"} + issues.extend( + _strict_keys(exp, exp_keys, "researchBinding/experimentDefinitionRef") + ) + if exp.get("kind") != "experiment": + issues.append( + _issue( + "researchBinding/experimentDefinitionRef/kind", + "researchBinding.kind", + "experimentDefinitionRef kind must be experiment", + ) + ) + session_id = exp.get("sessionId") + if not isinstance(session_id, str) or not session_id.strip(): + issues.append( + _issue( + "researchBinding/experimentDefinitionRef/sessionId", + "researchBinding.sessionId", + "experimentDefinitionRef sessionId must be non-empty", + ) + ) + exp_id = exp.get("id") + if not isinstance(exp_id, str) or not DEFINITION_ID.fullmatch(exp_id): + issues.append( + _issue( + "researchBinding/experimentDefinitionRef/id", + "researchBinding.id", + "experimentDefinitionRef id must be a valid definition id", + ) + ) + exp_version = exp.get("version") + if ( + not isinstance(exp_version, int) + or isinstance(exp_version, bool) + or exp_version < 1 + ): + issues.append( + _issue( + "researchBinding/experimentDefinitionRef/version", + "researchBinding.version", + "experimentDefinitionRef version must be a positive integer", + ) + ) + exp_hash = exp.get("contentHash") + if not isinstance(exp_hash, str) or not DEFINITION_HASH.fullmatch(exp_hash): + issues.append( + _issue( + "researchBinding/experimentDefinitionRef/contentHash", + "researchBinding.hash", + "experimentDefinitionRef contentHash must be a lowercase SHA-256", + ) + ) + + if issues: + raise AutoQuantValidationError(issues) + + # ---- exact load + content hash match ---- + kind = ref["kind"] + definition_id = ref["id"] + version = ref["version"] + expected_hash = ref["contentHash"] + + if kind == "factor": + loaded = load_factor_definition(project, definition_id, version) + if loaded.definition["status"] != "approved": + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/definitionRef", + "researchBinding.unapproved", + f"Referenced {kind} definition {definition_id} v{version} is not approved", + ) + ] + ) + else: + loaded = load_strategy_definition(project, definition_id, version) + if loaded.definition["status"] != "approved": + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/definitionRef", + "researchBinding.unapproved", + f"Referenced {kind} definition {definition_id} v{version} is not approved", + ) + ] + ) + + if loaded.manifest["contentHash"] != expected_hash: + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/definitionRef/contentHash", + "researchBinding.hash-mismatch", + f"definitionRef contentHash does not match the stored definition " + f"({expected_hash[:12]}... vs {loaded.manifest['contentHash'][:12]}...)", + ) + ] + ) + + # ---- experiment definition ---- + exp_session_id = exp["sessionId"] + exp_id = exp["id"] + exp_version = exp["version"] + exp_expected_hash = exp["contentHash"] + + exp_loaded = load_experiment_definition( + project, exp_session_id, exp_id, exp_version + ) + if exp_loaded.definition["status"] != "frozen": + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/experimentDefinitionRef", + "researchBinding.not-frozen", + f"Referenced experiment definition {exp_id} v{exp_version} is not frozen", + ) + ] + ) + + if exp_loaded.manifest["contentHash"] != exp_expected_hash: + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/experimentDefinitionRef/contentHash", + "researchBinding.hash-mismatch", + f"experimentDefinitionRef contentHash does not match the stored definition " + f"({exp_expected_hash[:12]}... vs {exp_loaded.manifest['contentHash'][:12]}...)", + ) + ] + ) + + # ---- link: experiment's definitionRef must match the supplied definition ---- + exp_def_ref = exp_loaded.definition.get("definitionRef") + if not isinstance(exp_def_ref, dict): + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/experimentDefinitionRef", + "researchBinding.link", + "Stored ExperimentDefinition is missing definitionRef", + ) + ] + ) + if exp_def_ref.get("kind") != kind or exp_def_ref.get("id") != definition_id or exp_def_ref.get("version") != version: + raise AutoQuantValidationError( + [ + _issue( + "researchBinding/experimentDefinitionRef", + "researchBinding.link", + "ExperimentDefinition's definitionRef does not match the supplied " + f"definitionRef (kind={kind}, id={definition_id}, version={version})", + ) + ] + ) + + def execute_study( project: ProjectContext, study_id: str, @@ -772,6 +1016,7 @@ def execute_study( execution_project: ProjectContext | None = None, data_root: Path | None = None, holdout_authorized: bool = False, + research_binding: dict[str, Any] | None = None, ) -> RunContext: from .holdouts import assert_run_authorized @@ -883,6 +1128,8 @@ def execute_study( ) started = datetime.now(timezone.utc) started_at = started.isoformat() + if research_binding is not None: + _validate_research_binding(project, research_binding) temporary = runs_root / f".run-{uuid.uuid4().hex}" temporary.mkdir() try: @@ -994,6 +1241,8 @@ def execute_study( "failureDisposition", DEFAULT_FAILURE_DISPOSITION, ) + if research_binding is not None: + result["researchBinding"] = research_binding if study.dependency_hash is not None: result["dependencies"] = { "paths": study.definition.dependencies["paths"], @@ -1038,6 +1287,8 @@ def execute_study( "resultHash": files[RUN_RESULT], "files": files, } + if research_binding is not None: + manifest["researchBinding"] = research_binding _write_json(temporary / RUN_MANIFEST, manifest) os.replace(temporary, target) return load_run(project, run_id) @@ -1085,6 +1336,8 @@ def _validate_run_result( expected_status: Any, expected_input_hash: Any, ) -> None: + from .research_definitions import DEFINITION_ID, HASH as DEFINITION_HASH + required = { "schemaVersion", "id", @@ -1114,6 +1367,7 @@ def _validate_run_result( "researchRequest", "upstreamEvidence", "failureDisposition", + "researchBinding", } ) issues = _strict_keys(result, allowed, str(path)) @@ -1648,6 +1902,150 @@ def _validate_run_result( issues.append( _issue(f"{path}/errors", "run.failure-errors", "Failed RunResult must explain an error") ) + if "researchBinding" in result: + research_binding = result["researchBinding"] + if not isinstance(research_binding, dict): + issues.append( + _issue( + f"{path}/researchBinding", + "schema.type", + "researchBinding must be an object", + ) + ) + else: + issues.extend( + _strict_keys( + research_binding, + {"definitionRef", "experimentDefinitionRef"}, + f"{path}/researchBinding", + ) + ) + # definitionRef + definition_ref = research_binding.get("definitionRef") + if not isinstance(definition_ref, dict): + issues.append( + _issue( + f"{path}/researchBinding/definitionRef", + "schema.type", + "researchBinding definitionRef must be an object", + ) + ) + else: + issue_dr = _strict_keys( + definition_ref, + {"kind", "id", "version", "contentHash"}, + f"{path}/researchBinding/definitionRef", + ) + for issue in issue_dr: + issues.append(issue) + if definition_ref.get("kind") not in ("factor", "strategy"): + issues.append( + _issue( + f"{path}/researchBinding/definitionRef/kind", + "researchBinding.kind", + "definitionRef kind must be factor or strategy", + ) + ) + if not isinstance(definition_ref.get("id"), str) or not DEFINITION_ID.fullmatch( + definition_ref.get("id", "") + ): + issues.append( + _issue( + f"{path}/researchBinding/definitionRef/id", + "researchBinding.id", + "definitionRef id must be a valid definition id", + ) + ) + if ( + not isinstance(definition_ref.get("version"), int) + or isinstance(definition_ref.get("version"), bool) + or definition_ref.get("version", 0) < 1 + ): + issues.append( + _issue( + f"{path}/researchBinding/definitionRef/version", + "researchBinding.version", + "definitionRef version must be a positive integer", + ) + ) + if not isinstance(definition_ref.get("contentHash"), str) or not DEFINITION_HASH.fullmatch( + definition_ref.get("contentHash", "") + ): + issues.append( + _issue( + f"{path}/researchBinding/definitionRef/contentHash", + "researchBinding.hash", + "definitionRef contentHash must be a lowercase SHA-256", + ) + ) + # experimentDefinitionRef + experiment_ref = research_binding.get("experimentDefinitionRef") + if not isinstance(experiment_ref, dict): + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef", + "schema.type", + "researchBinding experimentDefinitionRef must be an object", + ) + ) + else: + issue_er = _strict_keys( + experiment_ref, + {"kind", "sessionId", "id", "version", "contentHash"}, + f"{path}/researchBinding/experimentDefinitionRef", + ) + for issue in issue_er: + issues.append(issue) + if experiment_ref.get("kind") != "experiment": + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef/kind", + "researchBinding.kind", + "experimentDefinitionRef kind must be experiment", + ) + ) + if not isinstance(experiment_ref.get("sessionId"), str) or not experiment_ref[ + "sessionId" + ].strip(): + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef/sessionId", + "researchBinding.sessionId", + "experimentDefinitionRef sessionId must be non-empty", + ) + ) + if not isinstance(experiment_ref.get("id"), str) or not DEFINITION_ID.fullmatch( + experiment_ref.get("id", "") + ): + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef/id", + "researchBinding.id", + "experimentDefinitionRef id must be a valid definition id", + ) + ) + if ( + not isinstance(experiment_ref.get("version"), int) + or isinstance(experiment_ref.get("version"), bool) + or experiment_ref.get("version", 0) < 1 + ): + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef/version", + "researchBinding.version", + "experimentDefinitionRef version must be a positive integer", + ) + ) + if not isinstance(experiment_ref.get("contentHash"), str) or not DEFINITION_HASH.fullmatch( + experiment_ref.get("contentHash", "") + ): + issues.append( + _issue( + f"{path}/researchBinding/experimentDefinitionRef/contentHash", + "researchBinding.hash", + "experimentDefinitionRef contentHash must be a lowercase SHA-256", + ) + ) if issues: raise AutoQuantValidationError(issues) @@ -1682,7 +2080,8 @@ def load_run(project: ProjectContext, run_id: str) -> RunContext: "resultHash", "files", } - issues = _strict_keys(manifest, required, str(root / RUN_MANIFEST)) + allowed = required | (manifest.keys() & {"researchBinding"}) + issues = _strict_keys(manifest, allowed, str(root / RUN_MANIFEST)) if manifest.get("schemaVersion") != RUN_SCHEMA_VERSION: issues.append( _issue( @@ -1804,6 +2203,31 @@ def load_run(project: ProjectContext, run_id: str) -> RunContext: ) if frozen_issues: raise AutoQuantValidationError(frozen_issues) + # Re-load and re-validate researchBinding at load time (reuses execution gate) + result_has_rb = "researchBinding" in result + manifest_has_rb = "researchBinding" in manifest + if result_has_rb != manifest_has_rb: + raise AutoQuantValidationError( + [ + _issue( + root / RUN_RESULT, + "run.researchBinding", + "researchBinding must be present in both RunResult and manifest or absent from both", + ) + ] + ) + if result_has_rb: + if result["researchBinding"] != manifest["researchBinding"] or result["researchBinding"] is None: + raise AutoQuantValidationError( + [ + _issue( + root / RUN_RESULT, + "run.researchBinding", + "researchBinding in RunResult must equal the terminal manifest binding and must not be null", + ) + ] + ) + _validate_research_binding(project, result["researchBinding"]) return RunContext(root, manifest, result) @@ -2186,5 +2610,47 @@ def list_runs(project: ProjectContext, study_id: str | None = None) -> list[RunS "metrics": {"type": "object"}, "artifacts": {"type": "array"}, "errors": {"type": "array"}, + "researchBinding": { + "type": "object", + "additionalProperties": False, + "required": ["definitionRef", "experimentDefinitionRef"], + "properties": { + "definitionRef": { + "type": "object", + "additionalProperties": False, + "required": ["kind", "id", "version", "contentHash"], + "properties": { + "kind": {"enum": ["factor", "strategy"]}, + "id": { + "type": "string", + "pattern": r"^[a-z0-9]+(?:-[a-z0-9]+)*$", + }, + "version": {"type": "integer", "minimum": 1}, + "contentHash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$", + }, + }, + }, + "experimentDefinitionRef": { + "type": "object", + "additionalProperties": False, + "required": ["kind", "sessionId", "id", "version", "contentHash"], + "properties": { + "kind": {"const": "experiment"}, + "sessionId": {"type": "string", "minLength": 1}, + "id": { + "type": "string", + "pattern": r"^[a-z0-9]+(?:-[a-z0-9]+)*$", + }, + "version": {"type": "integer", "minimum": 1}, + "contentHash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$", + }, + }, + }, + }, + }, }, } diff --git a/autoquant/studio.py b/autoquant/studio.py index 3cd632c..503c16e 100644 --- a/autoquant/studio.py +++ b/autoquant/studio.py @@ -33,13 +33,17 @@ ) from .event_explorer import load_event_study_diagnostics from .book_path_stress_explorer import load_book_path_stress_diagnostics +from .compute_jobs import compute_executor_declarations, list_compute_jobs +from .event_intake import list_event_snapshots from .intake import ( dataset_snapshot_class_context, load_project_intake, load_study_dataset_snapshot, ) +from .model_runtime import list_model_runs from .holdouts import load_holdout_status from .orientation import build_agent_work_brief +from .operator_port import build_research_ledger from .portfolio_explorer import ( DEFAULT_PORTFOLIO_POINTS, load_portfolio_diagnostics, @@ -53,6 +57,7 @@ from .runs import harness_identity, list_runs, load_run, run_failure_disposition from .sessions import list_sessions, load_session, session_snapshot from .studies import hash_json, list_studies, load_study +from .verification import list_verification_assessments from .workspace import ( PROJECT_MANIFEST, SCHEMA_VERSION, @@ -67,6 +72,16 @@ STUDIO_KIND = "autoquant-studio-snapshot" +MODEL_RUNTIME = { + "kind": "supervised-model-research-v1", + "available": True, + "entrypoint": "aq model run", + "models": ["ridge-linear", "train-mean-baseline"], + "selectionAuthority": "validation-only", + "testUse": "terminal-audit-only", + "pointInTimeRequired": True, + "tradingAuthority": "none", +} STUDIO_ASSETS = { "/": ("index.html", "text/html; charset=utf-8"), "/index.html": ("index.html", "text/html; charset=utf-8"), @@ -972,6 +987,28 @@ def _project_snapshot(project: ProjectContext) -> dict[str, Any]: diagnostics.extend(issues) runs_raw, issues = _read_category("runs", lambda: list_runs(project)) diagnostics.extend(issues) + compute_jobs_raw, issues = _read_category( + "compute-jobs", + lambda: list_compute_jobs(project), + ) + diagnostics.extend(issues) + compute_jobs = [item.receipt for item in compute_jobs_raw] + model_runs_raw, issues = _read_category( + "model-runs", + lambda: list_model_runs(project), + ) + diagnostics.extend(issues) + model_runs = [item.receipt for item in model_runs_raw] + event_snapshots, issues = _read_category( + "event-snapshots", + lambda: list_event_snapshots(project), + ) + diagnostics.extend(issues) + verification_assessments, issues = _read_category( + "verification-assessments", + lambda: list_verification_assessments(project), + ) + diagnostics.extend(issues) sessions_raw, issues = _read_category("sessions", lambda: list_sessions(project)) diagnostics.extend(issues) intake_raw, issues = _read_category( @@ -1353,6 +1390,19 @@ def current_program_run(lane_id: str): item.to_dict() for item in list_reports(project, session) ] progress = list_campaign_progress(session) + research_ledger = None + research_ledger_diagnostics: list[dict[str, str]] = [] + try: + research_ledger = build_research_ledger( + project, + session.manifest["id"], + ) + except AutoQuantValidationError as error: + research_ledger_diagnostics = _diagnostics( + f"research-ledger:{session.manifest['id']}", + error, + ) + diagnostics.extend(research_ledger_diagnostics) decision_matrix = None try: decision_matrix = load_session_decision_matrix( @@ -1389,6 +1439,8 @@ def current_program_run(lane_id: str): "campaigns": campaigns, "reports": reports, "progress": progress, + "researchLedger": research_ledger, + "researchLedgerDiagnostics": research_ledger_diagnostics, "commands": _session_commands(project, session, reports), } ) @@ -1553,6 +1605,58 @@ def current_program_run(lane_id: str): and external_holdout["nextAction"] is not None ): commands.append(external_holdout["nextAction"]) + if compute_jobs: + commands.append( + _command( + "job.show", + [ + "aq", + "job", + "show", + str(project.root_dir), + "--job", + compute_jobs[-1]["id"], + "--json", + ], + "read-only", + ) + ) + if event_snapshots: + latest_event = event_snapshots[-1] + commands.append( + _command( + "event.show", + [ + "aq", + "event", + "show", + str(project.root_dir), + "--event-package", + latest_event["id"], + "--version", + latest_event["version"], + "--json", + ], + "read-only", + ) + ) + if verification_assessments: + latest_assessment = verification_assessments[-1]["assessment"] + commands.append( + _command( + "verify.show", + [ + "aq", + "verify", + "show", + str(project.root_dir), + "--assessment", + latest_assessment["id"], + "--json", + ], + "read-only", + ) + ) return { "id": project.manifest.id, "name": project.manifest.name, @@ -1578,16 +1682,26 @@ def current_program_run(lane_id: str): "factorExplorer": factor_explorer, "portfolioExplorer": portfolio_explorer, "rlExplorer": rl_explorer, + "modelRuntime": MODEL_RUNTIME, + "modelRuns": model_runs, "bookRiskExplorer": book_risk_explorer, "eventStudyExplorer": event_study_explorer, "bookPathStressExplorer": book_path_stress_explorer, "allocationExplorer": allocation_explorer, + "computeExecutors": compute_executor_declarations(), + "computeJobs": compute_jobs, + "eventSnapshots": event_snapshots, + "verificationAssessments": verification_assessments, "commands": commands, "valid": not diagnostics, "diagnostics": diagnostics, "counts": { "studies": len(studies), "runs": len(runs), + "computeJobs": len(compute_jobs), + "modelRuns": len(model_runs), + "eventSnapshots": len(event_snapshots), + "verificationAssessments": len(verification_assessments), "sessions": len(sessions), "activeSessions": sum( item["session"]["status"] == "active" for item in sessions @@ -2081,10 +2195,16 @@ def serve_studio( "factorExplorer", "portfolioExplorer", "rlExplorer", + "modelRuntime", + "modelRuns", "bookRiskExplorer", "eventStudyExplorer", "bookPathStressExplorer", "allocationExplorer", + "computeExecutors", + "computeJobs", + "eventSnapshots", + "verificationAssessments", "commands", "valid", "diagnostics", @@ -2123,10 +2243,39 @@ def serve_studio( "factorExplorer": {"type": ["object", "null"]}, "portfolioExplorer": {"type": ["object", "null"]}, "rlExplorer": {"type": ["object", "null"]}, + "modelRuntime": { + "type": "object", + "additionalProperties": False, + "required": [ + "kind", + "available", + "entrypoint", + "models", + "selectionAuthority", + "testUse", + "pointInTimeRequired", + "tradingAuthority", + ], + "properties": { + "kind": {"const": "supervised-model-research-v1"}, + "available": {"const": True}, + "entrypoint": {"type": "string", "minLength": 1}, + "models": {"type": "array", "minItems": 2, "items": {"type": "string"}}, + "selectionAuthority": {"const": "validation-only"}, + "testUse": {"const": "terminal-audit-only"}, + "pointInTimeRequired": {"const": True}, + "tradingAuthority": {"const": "none"}, + }, + }, + "modelRuns": {"type": "array"}, "bookRiskExplorer": {"type": ["object", "null"]}, "eventStudyExplorer": {"type": ["object", "null"]}, "bookPathStressExplorer": {"type": ["object", "null"]}, "allocationExplorer": {"type": ["object", "null"]}, + "computeExecutors": {"type": "array"}, + "computeJobs": {"type": "array"}, + "eventSnapshots": {"type": "array"}, + "verificationAssessments": {"type": "array"}, "commands": { "type": "array", "items": {"type": "object"}, @@ -2142,6 +2291,10 @@ def serve_studio( "required": [ "studies", "runs", + "computeJobs", + "modelRuns", + "eventSnapshots", + "verificationAssessments", "sessions", "activeSessions", "campaigns", @@ -2155,6 +2308,10 @@ def serve_studio( "properties": { "studies": {"type": "integer", "minimum": 0}, "runs": {"type": "integer", "minimum": 0}, + "computeJobs": {"type": "integer", "minimum": 0}, + "modelRuns": {"type": "integer", "minimum": 0}, + "eventSnapshots": {"type": "integer", "minimum": 0}, + "verificationAssessments": {"type": "integer", "minimum": 0}, "sessions": {"type": "integer", "minimum": 0}, "activeSessions": {"type": "integer", "minimum": 0}, "campaigns": {"type": "integer", "minimum": 0}, diff --git a/autoquant/verification.py b/autoquant/verification.py new file mode 100644 index 0000000..b1b0d1b --- /dev/null +++ b/autoquant/verification.py @@ -0,0 +1,501 @@ +"""Deterministic, non-accusatory research-claim verification contracts.""" + +from __future__ import annotations + +import math +import json +import os +import shutil +import uuid +from pathlib import Path +from typing import Any + +from .studies import DIRECTIONS, SHA256, hash_file, hash_json +from .workspace import ( + SCHEMA_VERSION, + AutoQuantValidationError, + ProjectContext, + ValidationIssue, + confined_path, +) + + +RESEARCH_CLAIM_KIND = "autoquant-research-claim" +VERIFICATION_ASSESSMENT_KIND = "autoquant-verification-assessment" +VERDICTS = {"invalid-test", "inconclusive", "contradicted", "supported"} +VERIFICATIONS_DIRECTORY = "verifications" +_CLAIM_KEYS = { + "schemaVersion", + "kind", + "id", + "statement", + "metric", + "direction", + "minimumEffect", + "requirements", + "authority", + "tradingAuthority", +} +_REQUIREMENT_KEYS = { + "minimumSampleSize", + "baselineRequired", + "holdoutRequired", + "selectionRequired", +} +_EVIDENCE_KINDS = ("run", "explorer", "holdout", "selection") +_ASSESSMENT_KEYS = { + "schemaVersion", + "kind", + "id", + "claimId", + "claimHash", + "verdict", + "gates", + "primaryImprovement", + "limitations", + "evidenceRefs", + "authority", + "tradingAuthority", +} + + +def _issue(path: Path | str, code: str, message: str) -> ValidationIssue: + return ValidationIssue(str(path), code, message) + + +def _finite_number(value: Any) -> bool: + return ( + isinstance(value, (int, float)) + and not isinstance(value, bool) + and math.isfinite(float(value)) + ) + + +def build_research_claim( + *, + statement: str, + metric: str, + direction: str, + minimum_effect: float = 0.0, + minimum_sample_size: int = 30, + baseline_required: bool = True, + holdout_required: bool = True, + selection_required: bool = True, +) -> dict[str, Any]: + """Build one content-addressed claim without inferring author intent.""" + + payload = { + "schemaVersion": SCHEMA_VERSION, + "kind": RESEARCH_CLAIM_KIND, + "statement": statement, + "metric": metric, + "direction": direction, + "minimumEffect": minimum_effect, + "requirements": { + "minimumSampleSize": minimum_sample_size, + "baselineRequired": baseline_required, + "holdoutRequired": holdout_required, + "selectionRequired": selection_required, + }, + "authority": "research-validation-only", + "tradingAuthority": "none", + } + claim = {**payload, "id": f"research-claim-{hash_json(payload)[:16]}"} + return validate_research_claim(claim) + + +def validate_research_claim( + value: Any, + path: Path | str = "researchClaim", +) -> dict[str, Any]: + """Validate the exact V1 ResearchClaim surface and derived identity.""" + + issues: list[ValidationIssue] = [] + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, "research-claim.type", "ResearchClaim must be an object")] + ) + for key in sorted(_CLAIM_KEYS - value.keys()): + issues.append(_issue(f"{path}/{key}", "schema.missing", f"Missing '{key}'")) + for key in sorted(value.keys() - _CLAIM_KEYS): + issues.append(_issue(f"{path}/{key}", "schema.unknown", f"Unknown '{key}'")) + requirements = value.get("requirements") + if not isinstance(requirements, dict) or set(requirements) != _REQUIREMENT_KEYS: + issues.append( + _issue( + f"{path}/requirements", + "research-claim.requirements", + "ResearchClaim requirements must use the exact V1 fields", + ) + ) + requirements = {} + if value.get("schemaVersion") != SCHEMA_VERSION: + issues.append(_issue(f"{path}/schemaVersion", "schema.version", "Expected V1")) + if value.get("kind") != RESEARCH_CLAIM_KIND: + issues.append(_issue(f"{path}/kind", "research-claim.kind", "Invalid kind")) + if not isinstance(value.get("statement"), str) or not value.get("statement", "").strip(): + issues.append(_issue(f"{path}/statement", "research-claim.statement", "Statement must be non-empty")) + if not isinstance(value.get("metric"), str) or not value.get("metric", "").strip(): + issues.append(_issue(f"{path}/metric", "research-claim.metric", "Metric must be non-empty")) + if value.get("direction") not in DIRECTIONS: + issues.append(_issue(f"{path}/direction", "research-claim.direction", "Direction must be maximize or minimize")) + if not _finite_number(value.get("minimumEffect")) or float(value.get("minimumEffect", -1)) < 0: + issues.append(_issue(f"{path}/minimumEffect", "research-claim.minimum-effect", "minimumEffect must be finite and non-negative")) + if ( + not isinstance(requirements.get("minimumSampleSize"), int) + or isinstance(requirements.get("minimumSampleSize"), bool) + or requirements.get("minimumSampleSize", 0) < 1 + ): + issues.append(_issue(f"{path}/requirements/minimumSampleSize", "research-claim.minimum-sample", "minimumSampleSize must be positive")) + for key in ("baselineRequired", "holdoutRequired", "selectionRequired"): + if not isinstance(requirements.get(key), bool): + issues.append(_issue(f"{path}/requirements/{key}", "research-claim.requirement", f"{key} must be boolean")) + if value.get("authority") != "research-validation-only" or value.get("tradingAuthority") != "none": + issues.append(_issue(path, "research-claim.authority", "ResearchClaim cannot grant trading authority")) + payload = {key: value.get(key) for key in _CLAIM_KEYS - {"id"}} + expected_id = f"research-claim-{hash_json(payload)[:16]}" + if value.get("id") != expected_id: + issues.append(_issue(f"{path}/id", "research-claim.derived-id", "ResearchClaim id does not match its content")) + if issues: + raise AutoQuantValidationError(issues) + return {**payload, "id": expected_id} + + +def _evidence_ref(kind: str, value: Any) -> dict[str, str] | None: + if not isinstance(value, dict): + return None + evidence_id = value.get("id") + evidence_hash = value.get("hash") + if ( + not isinstance(evidence_id, str) + or not evidence_id.strip() + or not isinstance(evidence_hash, str) + or SHA256.fullmatch(evidence_hash) is None + ): + return None + return {"kind": kind, "id": evidence_id, "sha256": evidence_hash} + + +def _metric_gate( + claim: dict[str, Any], + evidence: Any, +) -> tuple[str, float | None]: + """Return missing, invalid, low-sample, no-baseline, pass, or contradict.""" + + if not isinstance(evidence, dict): + return "missing", None + required = {"id", "hash", "metric", "primaryValue", "sampleSize"} + if not required.issubset(evidence) or _evidence_ref("metric", evidence) is None: + return "invalid", None + if evidence.get("metric") != claim["metric"]: + return "invalid", None + if not _finite_number(evidence.get("primaryValue")): + return "invalid", None + sample_size = evidence.get("sampleSize") + if not isinstance(sample_size, int) or isinstance(sample_size, bool) or sample_size < 0: + return "invalid", None + if sample_size < claim["requirements"]["minimumSampleSize"]: + return "low-sample", None + baseline = evidence.get("baselineValue") + if claim["requirements"]["baselineRequired"] and not _finite_number(baseline): + return "no-baseline", None + if not _finite_number(baseline): + improvement = float(evidence["primaryValue"]) + elif claim["direction"] == "maximize": + improvement = float(evidence["primaryValue"]) - float(baseline) + else: + improvement = float(baseline) - float(evidence["primaryValue"]) + if improvement < 0: + return "contradict", improvement + if improvement < float(claim["minimumEffect"]): + return "below-effect", improvement + return "pass", improvement + + +def assess_research_claim( + claim: dict[str, Any], + *, + run_evidence: dict[str, Any] | None, + explorer_evidence: dict[str, Any] | None, + holdout_evidence: dict[str, Any] | None = None, + selection_evidence: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Assess verified evidence with fixed, conservative verdict precedence.""" + + claim = validate_research_claim(claim) + evidence = { + "run": run_evidence, + "explorer": explorer_evidence, + "holdout": holdout_evidence, + "selection": selection_evidence, + } + refs = [ + ref + for kind in _EVIDENCE_KINDS + if (ref := _evidence_ref(kind, evidence[kind])) is not None + ] + limitations: list[str] = [] + invalid: list[str] = [] + run = run_evidence if isinstance(run_evidence, dict) else {} + integrity = run.get("integrity") + if not run: + limitations.append("required-run-evidence-missing") + elif _evidence_ref("run", run) is None or not isinstance(integrity, dict): + invalid.append("run-evidence-schema-invalid") + else: + if integrity.get("tampered") is not False: + invalid.append("run-integrity-tampered-or-unverified") + if integrity.get("lookaheadDetected") is not False: + invalid.append("lookahead-absent-not-proven") + if integrity.get("schemaValid") is not True: + invalid.append("run-schema-invalid") + if integrity.get("authorityValid") is not True: + invalid.append("run-authority-invalid") + + primary_gate, primary_improvement = _metric_gate(claim, explorer_evidence) + if primary_gate == "invalid": + invalid.append("explorer-evidence-schema-invalid") + elif primary_gate != "pass" and primary_gate != "contradict": + limitations.append(f"primary-{primary_gate}") + + holdout_gate: str | None = None + if claim["requirements"]["holdoutRequired"]: + holdout_gate, _ = _metric_gate(claim, holdout_evidence) + if holdout_gate == "invalid": + invalid.append("holdout-evidence-schema-invalid") + elif holdout_gate != "pass" and holdout_gate != "contradict": + limitations.append(f"holdout-{holdout_gate}") + + selection_passed: bool | None = None + if claim["requirements"]["selectionRequired"]: + if not isinstance(selection_evidence, dict): + limitations.append("required-selection-evidence-missing") + elif _evidence_ref("selection", selection_evidence) is None or not isinstance(selection_evidence.get("passed"), bool): + invalid.append("selection-evidence-schema-invalid") + else: + selection_passed = selection_evidence["passed"] + if not selection_passed: + limitations.append("selection-gate-failed") + + contradicted = primary_gate == "contradict" or holdout_gate == "contradict" + indeterminate = any( + limitation.endswith(("-missing", "-low-sample")) + for limitation in limitations + ) + if invalid: + verdict = "invalid-test" + limitations = invalid + limitations + elif indeterminate: + verdict = "inconclusive" + elif contradicted: + verdict = "contradicted" + elif limitations: + verdict = "inconclusive" + else: + verdict = "supported" + payload = { + "schemaVersion": SCHEMA_VERSION, + "kind": VERIFICATION_ASSESSMENT_KIND, + "claimId": claim["id"], + "claimHash": hash_json(claim), + "verdict": verdict, + "gates": { + "primary": primary_gate, + "holdout": holdout_gate, + "selectionPassed": selection_passed, + }, + "primaryImprovement": primary_improvement, + "limitations": limitations, + "evidenceRefs": refs, + "authority": "research-validation-only", + "tradingAuthority": "none", + } + assessment = { + **payload, + "id": f"verification-assessment-{hash_json(payload)[:16]}", + } + return validate_verification_assessment(assessment) + + +def validate_verification_assessment( + value: Any, + path: Path | str = "verificationAssessment", +) -> dict[str, Any]: + """Validate the exact V1 assessment surface and immutable identity.""" + + issues: list[ValidationIssue] = [] + if not isinstance(value, dict): + raise AutoQuantValidationError( + [_issue(path, "verification-assessment.type", "VerificationAssessment must be an object")] + ) + if set(value) != _ASSESSMENT_KEYS: + issues.append(_issue(path, "verification-assessment.schema", "VerificationAssessment must use the exact V1 fields")) + if value.get("schemaVersion") != SCHEMA_VERSION or value.get("kind") != VERIFICATION_ASSESSMENT_KIND: + issues.append(_issue(path, "verification-assessment.version", "Invalid VerificationAssessment version or kind")) + if value.get("verdict") not in VERDICTS: + issues.append(_issue(f"{path}/verdict", "verification-assessment.verdict", "Unknown verdict")) + if not isinstance(value.get("claimId"), str) or not value.get("claimId", "").startswith("research-claim-"): + issues.append(_issue(f"{path}/claimId", "verification-assessment.claim", "Invalid claim reference")) + if not isinstance(value.get("claimHash"), str) or SHA256.fullmatch(value.get("claimHash", "")) is None: + issues.append(_issue(f"{path}/claimHash", "verification-assessment.claim-hash", "Invalid claim hash")) + gates = value.get("gates") + if not isinstance(gates, dict) or set(gates) != {"primary", "holdout", "selectionPassed"}: + issues.append(_issue(f"{path}/gates", "verification-assessment.gates", "Invalid gate projection")) + elif ( + gates.get("primary") + not in {"missing", "invalid", "low-sample", "no-baseline", "below-effect", "contradict", "pass"} + or gates.get("holdout") + not in {None, "missing", "invalid", "low-sample", "no-baseline", "below-effect", "contradict", "pass"} + or gates.get("selectionPassed") not in {None, False, True} + ): + issues.append(_issue(f"{path}/gates", "verification-assessment.gates", "Unknown gate result")) + if value.get("primaryImprovement") is not None and not _finite_number(value.get("primaryImprovement")): + issues.append(_issue(f"{path}/primaryImprovement", "verification-assessment.improvement", "Improvement must be finite or null")) + limitations = value.get("limitations") + if not isinstance(limitations, list) or any(not isinstance(item, str) or not item for item in limitations): + issues.append(_issue(f"{path}/limitations", "verification-assessment.limitations", "Limitations must be non-empty strings")) + refs = value.get("evidenceRefs") + if not isinstance(refs, list) or any( + not isinstance(ref, dict) + or set(ref) != {"kind", "id", "sha256"} + or ref.get("kind") not in _EVIDENCE_KINDS + or not isinstance(ref.get("id"), str) + or not isinstance(ref.get("sha256"), str) + or SHA256.fullmatch(ref.get("sha256", "")) is None + for ref in refs if isinstance(refs, list) + ): + issues.append(_issue(f"{path}/evidenceRefs", "verification-assessment.evidence", "Invalid evidence references")) + if value.get("authority") != "research-validation-only" or value.get("tradingAuthority") != "none": + issues.append(_issue(path, "verification-assessment.authority", "VerificationAssessment cannot grant trading authority")) + payload = {key: value.get(key) for key in _ASSESSMENT_KEYS - {"id"}} + expected_id = f"verification-assessment-{hash_json(payload)[:16]}" + if value.get("id") != expected_id: + issues.append(_issue(f"{path}/id", "verification-assessment.derived-id", "VerificationAssessment id does not match its content")) + if issues: + raise AutoQuantValidationError(issues) + return {**payload, "id": expected_id} + + +def publish_verification_assessment( + project: ProjectContext, + claim: dict[str, Any], + *, + run_evidence: dict[str, Any] | None, + explorer_evidence: dict[str, Any] | None, + holdout_evidence: dict[str, Any] | None = None, + selection_evidence: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Atomically publish one content-addressed claim and assessment.""" + + claim = validate_research_claim(claim) + assessment = assess_research_claim( + claim, + run_evidence=run_evidence, + explorer_evidence=explorer_evidence, + holdout_evidence=holdout_evidence, + selection_evidence=selection_evidence, + ) + root = confined_path(project.root_dir, VERIFICATIONS_DIRECTORY, "project/verifications") + root.mkdir(exist_ok=True) + target = confined_path(root, assessment["id"], "verification-assessment/output") + if target.exists(): + existing = load_verification_assessment(project, assessment["id"]) + if existing["claim"] == claim and existing["assessment"] == assessment: + return existing + raise AutoQuantValidationError( + [_issue(target, "verification.collision", "Verification id collision")] + ) + staging = root / f".{assessment['id']}-{uuid.uuid4().hex}" + staging.mkdir() + try: + for name, value in (("claim.json", claim), ("assessment.json", assessment)): + (staging / name).write_text( + json.dumps(value, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + manifest = { + "schemaVersion": SCHEMA_VERSION, + "id": assessment["id"], + "claimHash": hash_file(staging / "claim.json"), + "assessmentHash": hash_file(staging / "assessment.json"), + "completed": True, + } + (staging / "manifest.json").write_text( + json.dumps(manifest, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + os.replace(staging, target) + except Exception: + if staging.exists(): + shutil.rmtree(staging) + raise + return load_verification_assessment(project, assessment["id"]) + + +def load_verification_assessment( + project: ProjectContext, + assessment_id: str, +) -> dict[str, Any]: + """Verify one immutable published claim assessment.""" + + if not isinstance(assessment_id, str) or not assessment_id.startswith("verification-assessment-"): + raise AutoQuantValidationError( + [_issue(assessment_id, "verification.id", "Invalid VerificationAssessment id")] + ) + root = confined_path( + confined_path(project.root_dir, VERIFICATIONS_DIRECTORY, "project/verifications"), + assessment_id, + "verification-assessment/id", + ) + if root.is_symlink() or not root.is_dir(): + raise AutoQuantValidationError( + [_issue(root, "verification.missing", "Unknown VerificationAssessment")] + ) + try: + manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8")) + claim = json.loads((root / "claim.json").read_text(encoding="utf-8")) + assessment = json.loads((root / "assessment.json").read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as error: + raise AutoQuantValidationError( + [_issue(root, "verification.read", f"Cannot read VerificationAssessment: {error}")] + ) from None + expected_manifest = { + "schemaVersion": SCHEMA_VERSION, + "id": assessment_id, + "claimHash": hash_file(root / "claim.json"), + "assessmentHash": hash_file(root / "assessment.json"), + "completed": True, + } + if manifest != expected_manifest: + raise AutoQuantValidationError( + [_issue(root, "verification.tampered", "Verification manifest integrity check failed")] + ) + claim = validate_research_claim(claim, root / "claim.json") + assessment = validate_verification_assessment(assessment, root / "assessment.json") + if assessment["claimId"] != claim["id"] or assessment["claimHash"] != hash_json(claim): + raise AutoQuantValidationError( + [_issue(root, "verification.claim-ref", "Assessment differs from its ResearchClaim")] + ) + return {"manifest": manifest, "claim": claim, "assessment": assessment} + + +def list_verification_assessments(project: ProjectContext) -> list[dict[str, Any]]: + """Verify and list every published claim assessment.""" + + root = confined_path(project.root_dir, VERIFICATIONS_DIRECTORY, "project/verifications") + if not root.exists(): + return [] + if root.is_symlink() or not root.is_dir(): + raise AutoQuantValidationError( + [_issue(root, "verification.root", "Verification root must be a directory")] + ) + results = [] + for entry in sorted(root.iterdir(), key=lambda item: item.name): + if entry.name.startswith("."): + continue + if entry.is_symlink() or not entry.is_dir(): + raise AutoQuantValidationError( + [_issue(entry, "verification.entry", "Invalid VerificationAssessment entry")] + ) + results.append(load_verification_assessment(project, entry.name)) + return results diff --git a/design/changes/next-studio-workbench/brief.md b/design/changes/next-studio-workbench/brief.md new file mode 100644 index 0000000..881a33c --- /dev/null +++ b/design/changes/next-studio-workbench/brief.md @@ -0,0 +1,44 @@ +# Brief: Next Studio workbench integration + +## Goal + +Merge the approved Next.js factor-research frontend into Auto-Quant-V2 as a +repository-owned, PR-ready workbench that consumes verified Core snapshots and +contains no private plugin implementation. + +## Audience + +Factor researchers reviewing point-in-time evidence, forming event cohorts, +testing candidate factors, and reproducing research results. + +## Existing facts + +- Auto-Quant-V2 `main` is tagged `v0.9.31`. +- The current Studio is packaged native HTML/CSS/JavaScript over + `autoquant-studio-snapshot`. +- No frontend component-library dependency exists. +- Next `16.2.12` and React `19.2.8` are the latest stable npm releases on + 2026-08-02. +- The approved prototype already contains nine research routes and no trading + surface. + +## Constraints + +- Preserve Core as the only evidence authority. +- Preserve standalone Python installation and the current packaged Studio. +- Add no component library and no speculative plugin SDK. +- Keep all private plugin calls, credentials, host protocols, and proprietary + payloads outside the open-source repository. +- Keep demo evidence explicit and impossible to confuse with connected Core + evidence. +- Preserve the approved DESIGN.md and MOTION.md foundations. + +## Acceptance checks + +- The Next app builds and all nine routes render. +- The app consumes the existing snapshot only through a read-only server-side + proxy and validates its identity before presenting it as connected evidence. +- Source mode, validity, freshness, and diagnostics are visible. +- Public-source leakage checks reject private plugin, secret, and live-trading + integration markers. +- Existing Python Studio behavior and tests remain unchanged. diff --git a/design/changes/next-studio-workbench/design.md b/design/changes/next-studio-workbench/design.md new file mode 100644 index 0000000..d4b173f --- /dev/null +++ b/design/changes/next-studio-workbench/design.md @@ -0,0 +1,84 @@ +# Change design: Next Studio workbench + +## Foundations + +- Project design foundation: ../../../DESIGN.md +- Design foundation SHA-256: `966e41398c9b6e0736d2b0318adad0bdb0efcb9a5c3893b47cc9385e7c61d71a` +- Project motion foundation: ../../../MOTION.md +- Motion foundation SHA-256: `aa65393e00df714613a2e9d39db47dbf990f299bc7b4cefae47ba2b8f5b443db` +- Runtime: Next.js App Router `16.2.12`, React `19.2.8`, semantic DOM, CSS, and SVG + +## Architecture + +```text +aq studio serve (read-only, loopback) + │ + └── GET /api/v1/snapshot + │ + ▼ +studio-web/app/api/studio/snapshot/route.js + │ fixed path, loopback URL, timeout, identity validation + ▼ +Studio source context + │ connected | unavailable | demo + ▼ +nine research routes + shared shell +``` + +The proxy is the only new integration seam. It does not accept arbitrary URLs, +forward headers, execute commands, or expose a plugin registry. The existing +snapshot is reused unchanged. + +## Route map + +| Route | Surface | +|---|---| +| `/` | Research home and Core connection state | +| `/factors/aq-event-drift` | Factor passport | +| `/replay` | Point-in-time replay | +| `/events` | Cohort comparison | +| `/lab` | Factor laboratory | +| `/results` | Test results | +| `/jobs` | GPU/MOSS research tasks | +| `/data` | Data catalog | +| `/audit` | Audit and reproduction | + +## Data contract + +- Connected data must have `kind === "autoquant-studio-snapshot"`, a supported + `schemaVersion`, a generation time, a harness identity, and a projects array. +- Invalid or unreachable Core data produces an explicit unavailable state. +- Demo records remain local and deterministic. Entering demo mode is an + explicit user action and the shell keeps the `DEMO DATA` label visible. +- Browser code performs presentation-only transformations. It never reads + Project files or derives research verdicts. + +## Component and token contract + +- One CSS token file contains primitive, semantic, and component layers. +- Shared React components own panels, metrics, chips, provenance fields, + controls, empty states, and tables. +- Components reference semantic/component variables; raw palette values remain + in the primitive layer. +- Status color always has text and accessible state. + +## Open-source cut + +Public source includes only the snapshot client, public adapter labels, +deterministic demo fixtures, and view logic. Private plugin/MCP clients, +credentials, host schemas, proprietary payloads, and live execution remain +outside the repository. No placeholder plugin framework is added. + +## Responsive and accessibility behavior + +- Desktop keeps navigation, evidence canvas, and inspector visible. +- Tablet collapses the inspector below the main canvas. +- Mobile uses a staged review flow without horizontal page overflow. +- The app preserves skip navigation, semantic landmarks, visible focus, + real table headers, textual chart summaries, and reduced-motion behavior. + +## Reconciliation + +No external visual reference is used. This change directly inherits the +requirements-only DESIGN.md and MOTION.md foundations, so there is no reference +fidelity conflict to reconcile. diff --git a/design/changes/next-studio-workbench/directions.md b/design/changes/next-studio-workbench/directions.md new file mode 100644 index 0000000..98a4afd --- /dev/null +++ b/design/changes/next-studio-workbench/directions.md @@ -0,0 +1,22 @@ +# Design direction + +The approved Evidence Console direction is inherited without reopening visual +direction selection. + +- Visual posture: dark, calm, dense, and instrument-like. +- Information posture: time, provenance, coverage, and missing evidence before + headline metrics. +- Layout posture: persistent research navigation, central evidence canvas, and + contextual inspector. +- Interaction posture: direct state replacement, manual replay stepping, and + no decorative motion. +- Signature: the visible-at curtain separates evidence available at research + time from later information. +- Component posture: internal tokens and small semantic React components; no + vendor component library. +- Integration posture: connected Core evidence and deterministic demo evidence + use the same views but never the same source label. + +Rejected: marketing dashboards, retail trading semantics, gradient spectacle, +browser-authored research claims, generic plugin marketplaces, and private host +tooling in public source. diff --git a/design/changes/next-studio-workbench/events.jsonl b/design/changes/next-studio-workbench/events.jsonl new file mode 100644 index 0000000..734d8be --- /dev/null +++ b/design/changes/next-studio-workbench/events.jsonl @@ -0,0 +1,10 @@ +{"evidence":["DESIGN.md","MOTION.md"],"files":["design/changes/next-studio-workbench/brief.md"],"id":"dpe-3f7c32449410f7b9ad53","nextActions":["Review inherited Evidence Console direction"],"phase":"brief","schema":"design-pipeline.event.v2","seq":1,"summary":"Integration brief records current Studio, version, and open-source constraints","ts":"2026-08-02T00:05:00.000Z","type":"state-advance"} +{"evidence":["DESIGN.md","MOTION.md"],"files":["design/changes/next-studio-workbench/directions.md"],"id":"dpe-69d2d09291affa4b9c5a","nextActions":["Freeze Next and snapshot integration design"],"phase":"directions","schema":"design-pipeline.event.v2","seq":2,"summary":"Inherited Evidence Console direction and internal component posture recorded","ts":"2026-08-02T00:10:00.000Z","type":"state-advance"} +{"evidence":["DESIGN.md","MOTION.md"],"files":["design/changes/next-studio-workbench/design.md"],"id":"dpe-649fdd99c5a0868728ed","nextActions":["Confirm static motion contract"],"phase":"design-spec","schema":"design-pipeline.event.v2","seq":3,"summary":"Next workbench architecture and public/private boundary specified","ts":"2026-08-02T00:15:00.000Z","type":"state-advance"} +{"evidence":["DESIGN.md","MOTION.md"],"files":["design/changes/next-studio-workbench/motion.md"],"id":"dpe-e10285cc569ab757a1ba","nextActions":["Lock implementation tasks"],"phase":"motion-spec","schema":"design-pipeline.event.v2","seq":4,"summary":"Static motion and source-state transition rules specified","ts":"2026-08-02T00:20:00.000Z","type":"state-advance"} +{"evidence":["DESIGN.md","MOTION.md"],"files":["design/changes/next-studio-workbench/tasks.md"],"id":"dpe-cb0f7b1b682021b743a6","nextActions":["Import and integrate studio-web"],"phase":"tasks","schema":"design-pipeline.event.v2","seq":5,"summary":"Implementation and verification tasks locked","ts":"2026-08-02T00:25:00.000Z","type":"state-advance"} +{"evidence":["design/changes/next-studio-workbench/qa-connected-desktop.png","design/changes/next-studio-workbench/qa-demo-replay-desktop.png"],"files":["studio-web","docs/design/next-studio-workbench.md"],"id":"dpe-ae6e9a81e61c0d199247","nextActions":["Run repository and browser verification"],"phase":"implementation","schema":"design-pipeline.event.v2","seq":6,"summary":"Next workbench imported with Core snapshot bridge and public boundary checks","ts":"2026-08-02T01:10:00.000Z","type":"state-advance"} +{"evidence":["design/changes/next-studio-workbench/qa-connected-desktop.png","design/changes/next-studio-workbench/qa-demo-replay-desktop.png","design/changes/next-studio-workbench/qa-demo-replay-mobile.png"],"files":["studio-web","docs/design/next-studio-workbench.md","design/changes/next-studio-workbench/qa.md"],"id":"dpe-060a0bca6188ce00c0c6","nextActions":["Review the public boundary and repository diff"],"phase":"gate-review","schema":"design-pipeline.event.v2","seq":7,"summary":"Implementation and local verification complete","ts":"2026-08-02T13:31:33.074Z","type":"state-advance"} +{"evidence":["design/changes/next-studio-workbench/qa-connected-desktop.png","design/changes/next-studio-workbench/qa-demo-replay-tablet.png"],"files":["studio-web/lib/core-snapshot.js","studio-web/app/api/studio/snapshot/route.js","studio-web/components/studio-context.jsx","studio-web/components/studio-shell.jsx","studio-web/scripts/check-open-source-boundary.mjs"],"id":"dpe-1ca523c32d70a6a0790b","nextActions":["Run release-readiness checks"],"phase":"verification","schema":"design-pipeline.event.v2","seq":8,"summary":"Gate review passed with hardened snapshot and source boundaries","ts":"2026-08-02T13:44:44.103Z","type":"state-advance"} +{"evidence":["design/changes/next-studio-workbench/qa.md","design/changes/next-studio-workbench/qa-connected-desktop.png","design/changes/next-studio-workbench/qa-demo-replay-mobile.png"],"files":["design/changes/next-studio-workbench/qa.md","design/changes/next-studio-workbench/tasks.md","plans/next-studio-workbench.md"],"id":"dpe-e2f0871551a13618976a","nextActions":["Archive the PR-ready change"],"phase":"release-readiness","schema":"design-pipeline.event.v2","seq":9,"summary":"Frontend, package, security, browser, and documentation verification passed","ts":"2026-08-02T13:45:15.495Z","type":"state-advance"} +{"evidence":["design/changes/next-studio-workbench/qa.md"],"files":["design/changes/next-studio-workbench/handoff.md","docs/design/next-studio-workbench.md","PLANS.md"],"id":"dpe-c365353d724436658048","nextActions":[],"phase":"archive","schema":"design-pipeline.event.v2","seq":10,"summary":"PR-ready Next.js Studio workbench archived with public/private boundary verified","ts":"2026-08-02T13:45:24.318Z","type":"state-advance"} diff --git a/design/changes/next-studio-workbench/handoff.md b/design/changes/next-studio-workbench/handoff.md new file mode 100644 index 0000000..3976bc2 --- /dev/null +++ b/design/changes/next-studio-workbench/handoff.md @@ -0,0 +1,58 @@ +# Next Studio workbench handoff + +## State + +- Status: complete +- Phase: archive +- Change: `next-studio-workbench` +- Branch: `feat/next-studio-workbench` + +## Goal + +Internalize the approved Next.js Evidence Console in Auto-Quant-V2, consume +the verified read-only Studio snapshot, and keep private plugin integrations +outside the open-source repository. + +## Foundations + +- [DESIGN.md](../../../DESIGN.md) — ready, + `966e41398c9b6e0736d2b0318adad0bdb0efcb9a5c3893b47cc9385e7c61d71a`. +- [MOTION.md](../../../MOTION.md) — ready, + `aa65393e00df714613a2e9d39db47dbf990f299bc7b4cefae47ba2b8f5b443db`. + +## Artifacts + +- [brief.md](brief.md) +- [directions.md](directions.md) +- [design.md](design.md) +- [motion.md](motion.md) +- [tasks.md](tasks.md) +- [qa.md](qa.md) +- [Repository plan](../../../plans/next-studio-workbench.md) +- [Durable design](../../../docs/design/next-studio-workbench.md) + +## Decisions + +- Use exact latest stable Next `16.2.12` and React `19.2.8`. +- Add no third-party component library; internalize tokens and components. +- Reuse the existing snapshot and keep the Python package Node-free. +- Add the Next workbench beside the packaged Studio until parity is proven. +- Keep private plugin clients and host protocols outside public source. + +## Blockers + +None. + +## Verification + +- Frontend tests, lint, boundary scan, production build, and npm audit pass. +- Connected, unavailable, and explicit demo states pass desktop, tablet, and + mobile browser checks. +- Documentation links, targeted Python tests, compilation, and package build + pass. +- The complete Windows Python suite exposes 19 existing Unix-path/CRLF + assumptions; none reads or imports `studio-web/`. + +## Next actions + +None for this change. diff --git a/design/changes/next-studio-workbench/motion.md b/design/changes/next-studio-workbench/motion.md new file mode 100644 index 0000000..e78626b --- /dev/null +++ b/design/changes/next-studio-workbench/motion.md @@ -0,0 +1,15 @@ +# Change motion specification + +- Project foundation: ../../../MOTION.md +- Foundation SHA-256: `aa65393e00df714613a2e9d39db47dbf990f299bc7b4cefae47ba2b8f5b443db` +- Foundation posture: static +- Selected primitive IDs: none + +## Rules + +- Replay step and jump update data immediately with no positional animation. +- Hover, focus, and press feedback use CSS transitions under 160 ms. +- Keyboard navigation has no animation. +- Optional playback is disabled under `prefers-reduced-motion`. +- Source transitions update labels and evidence atomically without hiding an + unavailable or invalid state behind demo data. diff --git a/design/changes/next-studio-workbench/qa-connected-desktop.png b/design/changes/next-studio-workbench/qa-connected-desktop.png new file mode 100644 index 0000000..c40949e Binary files /dev/null and b/design/changes/next-studio-workbench/qa-connected-desktop.png differ diff --git a/design/changes/next-studio-workbench/qa-demo-replay-desktop.png b/design/changes/next-studio-workbench/qa-demo-replay-desktop.png new file mode 100644 index 0000000..fb62ec8 Binary files /dev/null and b/design/changes/next-studio-workbench/qa-demo-replay-desktop.png differ diff --git a/design/changes/next-studio-workbench/qa-demo-replay-mobile.png b/design/changes/next-studio-workbench/qa-demo-replay-mobile.png new file mode 100644 index 0000000..3d25abd Binary files /dev/null and b/design/changes/next-studio-workbench/qa-demo-replay-mobile.png differ diff --git a/design/changes/next-studio-workbench/qa-demo-replay-tablet.png b/design/changes/next-studio-workbench/qa-demo-replay-tablet.png new file mode 100644 index 0000000..ca98421 Binary files /dev/null and b/design/changes/next-studio-workbench/qa-demo-replay-tablet.png differ diff --git a/design/changes/next-studio-workbench/qa.md b/design/changes/next-studio-workbench/qa.md new file mode 100644 index 0000000..ecbfbf9 --- /dev/null +++ b/design/changes/next-studio-workbench/qa.md @@ -0,0 +1,68 @@ +# Next Studio workbench QA + +## Control plane + +- Design foundation: ready, SHA-256 + `966e41398c9b6e0736d2b0318adad0bdb0efcb9a5c3893b47cc9385e7c61d71a`. +- Motion foundation: ready, static posture, SHA-256 + `aa65393e00df714613a2e9d39db47dbf990f299bc7b4cefae47ba2b8f5b443db`. +- Pipeline schema: `design-pipeline.state.v2`. +- Registry: `design-pipeline.phases.v2`. +- Dependency self-check: ready; no required capability missing. +- `next-dev-loop`: not applicable because its hard floor is Next `16.3+` + while npm latest stable is `16.2.12`; no canary dependency will be introduced. + +## Verification + +### Frontend + +- `npm test`: 4/4 tests pass, including point-in-time visibility, cohort + comparison, loopback-only Core access, and snapshot identity. +- `npm run check:boundary`: passes; public application code contains no + authenticated header, API key, private MCP tool name, or unapproved + environment variable. +- `npm run lint`: passes with ESLint `9.39.5`, the latest release compatible + with all `eslint-config-next@16.2.12` plugins. +- `npm run build`: passes with Next.js `16.2.12` and React `19.2.8`; all nine + research routes and the read-only snapshot proxy are emitted. +- `npm audit --omit=dev --registry=https://registry.npmjs.org`: zero known + vulnerabilities after overriding Next's existing transitive PostCSS and + Sharp dependencies to patched releases `8.5.18` and `0.35.0`. + +### Browser + +- Connected desktop home: Core `0.9.31`, real Project/Study/counts and four + Core diagnostics render with no console error or horizontal overflow. +- Every desktop route renders its expected research heading with no horizontal + overflow. +- Every non-home route renders at `375x812` with no horizontal overflow. +- Connected home and gated-to-demo replay render at `768x1024` with no + horizontal overflow. +- Core-only routes gate demo records until the explicit demo action is used. +- Replay keyboard stepping changes the observed time; first Tab focuses the + skip link with a visible outline. +- Evidence: `qa-connected-desktop.png`, `qa-demo-replay-desktop.png`, and + `qa-demo-replay-mobile.png`, and `qa-demo-replay-tablet.png`. + +### Repository + +- `uv run python -m compileall -q autoquant tests`: passes. +- `uv run python scripts/check_doc_links.py`: 1,558 links resolve. +- `uv run python -m unittest tests.test_documentation -v`: 3/3 pass. +- `uv build`: succeeds; the Python wheel contains Core only and no Next, + generated, credential, or private-plugin files. +- Full local suite: 455 tests executed; 436 pass. The 19 Windows failures are + pre-existing platform assumptions: external Researcher tests execute the + absolute Unix path `/bin/sh`, one build test expects `.venv/bin/python`, and + CRLF checkout changes immutable sample bytes and their derived manifests. + No failure reads or imports `studio-web/`; targeted checks cover every + changed Python and packaging boundary in this change. + +### Security boundary + +- Only `AUTOQUANT_STUDIO_CORE_URL` is accepted, and only as an unauthenticated + loopback HTTP origin. +- The same-origin proxy exposes one fixed GET to `/api/v1/snapshot`, forwards + no caller URL or credential, uses `no-store`, and fails closed. +- Private plugin clients, host schemas, provider payloads, broker/account/order + adapters, and live-trading code are absent. diff --git a/design/changes/next-studio-workbench/state.json b/design/changes/next-studio-workbench/state.json new file mode 100644 index 0000000..43b6dc9 --- /dev/null +++ b/design/changes/next-studio-workbench/state.json @@ -0,0 +1,38 @@ +{ + "blockers": [], + "changeId": "next-studio-workbench", + "evidence": [ + "DESIGN.md", + "MOTION.md", + "design/changes/next-studio-workbench/qa-connected-desktop.png", + "design/changes/next-studio-workbench/qa-demo-replay-desktop.png", + "design/changes/next-studio-workbench/qa-demo-replay-mobile.png", + "design/changes/next-studio-workbench/qa-demo-replay-tablet.png", + "design/changes/next-studio-workbench/qa.md" + ], + "extensions": {}, + "foundations": { + "design": { + "path": "DESIGN.md", + "sha256": "966e41398c9b6e0736d2b0318adad0bdb0efcb9a5c3893b47cc9385e7c61d71a", + "status": "ready", + "validator": "designer-pipeline foundation check --kind design" + }, + "motion": { + "path": "MOTION.md", + "sha256": "aa65393e00df714613a2e9d39db47dbf990f299bc7b4cefae47ba2b8f5b443db", + "status": "ready", + "validator": "designer-pipeline foundation check --kind motion" + } + }, + "lastEventSeq": 10, + "migration": null, + "nextActions": [], + "phase": "archive", + "registryVersion": "design-pipeline.phases.v2", + "revision": 10, + "sceneRuntime": null, + "schema": "design-pipeline.state.v2", + "status": "complete", + "updatedAt": "2026-08-02T13:45:24.318Z" +} diff --git a/design/changes/next-studio-workbench/tasks.md b/design/changes/next-studio-workbench/tasks.md new file mode 100644 index 0000000..225390a --- /dev/null +++ b/design/changes/next-studio-workbench/tasks.md @@ -0,0 +1,29 @@ +# Next Studio workbench tasks + +## Foundation and repository + +- [x] Audit the current Studio, snapshot contract, repository governance, and + npm stable versions. +- [x] Add validated DESIGN.md and MOTION.md foundations. +- [x] Add the active repository plan and durable boundary design. + +## Frontend integration + +- [x] Import the approved nine-route Next.js workbench under `studio-web/`. +- [x] Normalize CSS into primitive, semantic, and component token layers. +- [x] Add the server-side read-only Core snapshot proxy. +- [x] Add connected, unavailable, and explicit demo source states. + +## Open-source boundary + +- [x] Add boundary documentation and a runnable leakage check. +- [x] Confirm no private plugin invocation, secret, host schema, proprietary + payload, or live-trading integration is present. + +## Quality + +- [x] Run frontend unit tests, lint, security audit, and production build. +- [x] Run targeted Python Studio tests and documentation-link checks. +- [x] Verify desktop, tablet, and mobile routes in a real browser. +- [x] Run design-pipeline consistency and foundation checks. +- [x] Complete PR-ready review. diff --git a/docs/CLI.md b/docs/CLI.md index 34ef385..4580625 100644 --- a/docs/CLI.md +++ b/docs/CLI.md @@ -1309,6 +1309,50 @@ completeness but supplies no universal pass threshold. Successful publication advances state to `assessed`, after which `holdout show` verifies both result and Assessment. +## Operator commands + +```text +aq operator invoke --request FILE [--project ID] [--json] +``` + +`operator invoke` validates one provider-neutral V1 request and publishes or +replays its immutable terminal AgentOperationReceipt. The request must use a +closed research intent and carry exact object/version references, actor, +authority, budget, confirmation reference, and expected prior state. It cannot +carry a shell command, raw provider invocation, credential, or unconstrained +path. An identical `requestId` retry returns the existing receipt; different +request bytes using that identity fail closed. Read-only intents inspect the +ResearchLedger, explain blockers, compare exact definition versions, and +inspect reproduction readiness. Definition version creation, artifact review, +and reproduction start use `confirmation-bound` authority. The first request +publishes a proposal receipt; a separate user `confirmation.accept` operation +binds that proposal to the exact execution actor and prior state; only then may +the mutation reference the decision receipt. `campaign.stop` is the immediate +V1 mutation: it requires `approved-envelope` authority, one exact active +Campaign reference, and the expected Session state. It writes a validated stop +request; the Campaign preserves completed Experiments and publishes terminal +`stopped_by_user` evidence before the receipt becomes successful. + +Artifact return-for-revision and retain-as-draft receipts are available. +Approval remains fail-closed until Core can derive a verified +`EvidenceAssessment` from immutable evidence. Reproduction requests contain +only the approval reference; they cannot submit their own environment, hashes, +metrics, or outcome. The public Core currently records reproduction as +`unavailable` when its controlled executor is absent and never substitutes +caller-reported evidence. + +Schemas are machine-discoverable through `aq schema operator-request --json`, +`aq schema operator-receipt --json`, and `aq schema research-ledger --json`. +Versioned definition and terminal review schemas are available as +`factor-definition`, `strategy-definition`, `experiment-definition`, +`artifact-review`, and `reproduction-request`. + +`aq research run` accepts `--max-candidates`, `--max-cpu-seconds`, and +`--max-gpu-seconds` in addition to its existing turn and wall-clock limits. +Published Campaign budgets remain CPU-first and include fixed stops, sealed +holdout state, and used/remaining telemetry. Private executor availability and +monetary cost are never inferred by the CLI. + ## Studio commands ```bash diff --git a/docs/PROJECT_FORMAT.md b/docs/PROJECT_FORMAT.md index bf3ebc3..bf59804 100644 --- a/docs/PROJECT_FORMAT.md +++ b/docs/PROJECT_FORMAT.md @@ -900,6 +900,7 @@ sessions/ ├── campaigns/ │ └── campaign--/ │ ├── progress.json + │ ├── stop-request.json # only when an authorized stop was requested │ ├── turns/turn-0001/ │ ├── result.json │ └── manifest.json @@ -1126,6 +1127,12 @@ manifest pins every Campaign file; opening a Campaign also verifies every referenced Experiment. The full connector and recovery contract is [[docs/design/external-researcher-driver]]. +New Campaigns publish the legacy turn/wall/timeout limits together with +candidate, CPU, GPU, executor, cost-telemetry, fixed-stop, sealed-holdout, and +used/remaining fields. CPU is the default executor. Private GPU/MOSS execution +is optional; absent executor or unknown spend under a monetary ceiling is a +truthful terminal blocker, never an invented success or zero-cost result. + While a Campaign is executing, its hidden staging directory contains a strict mutable `progress.json` with phase, turn, budget, command hash, completed Experiment ids, and verdict counts. It is operational telemetry, never a @@ -1136,6 +1143,99 @@ Research module validator. The complete operating and authority contract is [[docs/design/research-session-loop]]. +## Versioned research definitions and Operator receipts + +Factor, experiment-plan, and strategy authority remain distinct. A +`FactorDefinition` owns a factor hypothesis, calculation/source identity, +parameters, output direction/unit, exact data and point-in-time dependencies, +cohort, tests, and failure gates. An `ExperimentDefinition` freezes one exact +factor or strategy version plus data, subject, outcome/horizon, benchmark, +cost, split/purge, robustness, holdout, executor, budget, and stop policy. A +`StrategyDefinition` is not embedded in either object: it owns factor-version +composition and Portfolio/ML/RL validation, cost, risk, holdout, and artifact +closure. References always carry the exact object id and version. + +Core publishes FactorDefinition versions below the manifest-declared factor +directory and ExperimentDefinition versions inside their owning Session: + +```text +//definitions//versions// +├── definition.json +└── manifest.json + +///experiment-definitions//versions// +├── definition.json +└── manifest.json + +//definitions//versions// +├── definition.json +└── manifest.json +``` + +The version directory is created in a hidden sibling staging directory, +`definition.json` is written first, and `manifest.json` is written last before +atomic publication. The manifest pins the definition SHA-256. Loaders reject +unknown fields, unsupported schema versions, invalid lineage, escaped or +symlinked paths, identity mismatch, and changed bytes. Published versions are +never edited. A change to an approved/frozen version creates a new draft with +an earlier `parentVersion`; historical Runs keep their original version +references. Factor validation readiness additionally requires declared +point-in-time availability and an exact market-clock identity. + +Every Agent client and Studio uses one closed Operator request schema. The +public registry contains read-only inspection/explanation/comparison/readiness, +versioned definition creation, artifact decision, reproduction start, +`confirmation.accept`, and immediate Campaign stop; none accepts a shell +command, provider invocation, credential, or unconstrained path. A request +carries actor, Workspace/Project/Session identity, exact object versions, +bounded authority and budget, optional confirmation, and expected prior state. +Semantic mutations use an independent user confirmation-decision receipt, not +their own proposal receipt, as authority. Core publishes accepted terminal +operations here: + +```text +///operator-receipts// +├── request.json +├── receipt.json +└── manifest.json +``` + +`manifest.json` pins both request and receipt files. Reusing a request id with +identical canonical request bytes returns the existing receipt; different +bytes fail closed. Unknown intents, stale state, unavailable evidence, and +accepted operation failures still receive terminal receipts. The first +read-only ledger projection orders Data → Question → Factor → Experiment → +Campaign → Evidence → Approval → Reproduction. Missing ReplayBundle, market +clock, or entity mapping is isolated to the owning evidence widget and is +reported as unavailable; Core and Studio never fill it with demo evidence. + +Existing Study, Session, Experiment, Run, and Campaign artifacts are loaded +through their current strict loaders and projected into the ledger. They are +not rewritten or migrated into the new directories. + +Artifact decisions and reproduction attempts are immutable Session children: + +```text +///artifact-decisions// +├── review.json +├── decision.json +└── manifest.json + +///reproductions// +├── request.json +├── receipt.json +└── manifest.json +``` + +The manifest is written last and pins every sibling file. Approval binds the +exact definition content hash and is disabled until Core can verify a complete +EvidenceAssessment; return-for-revision and retain-as-draft do not delete or +rewrite evidence. A reproduction request carries only its own id and one +approved decision id. Environment, artifact hashes, metrics, and outcome are +Core-produced receipt fields, never caller assertions. Without a controlled +executor the truthful outcome is `unavailable`; CPU substitution is not +inferred. + ## Immutable Research Report A delegated Session may publish any number of point-in-time Reports. The Agent @@ -1326,6 +1426,14 @@ aq schema holdout-assessment-analysis --json aq schema holdout-assessment --json aq schema holdout-status --json aq schema experiment --json +aq schema factor-definition --json +aq schema experiment-definition --json +aq schema strategy-definition --json +aq schema operator-request --json +aq schema operator-receipt --json +aq schema research-ledger --json +aq schema artifact-review --json +aq schema reproduction-request --json aq schema researcher-response --json aq schema campaign-result --json aq schema campaign-progress --json @@ -1340,7 +1448,8 @@ aq schema studio-snapshot --json The Python validators are authoritative executable behavior in `autoquant/workspace.py`, `autoquant/studies.py`, `autoquant/runs.py`, `autoquant/sessions.py`, `autoquant/checks.py`, `autoquant/research.py`, -`autoquant/dossiers.py`, and `autoquant/studio.py`. +`autoquant/dossiers.py`, `autoquant/research_definitions.py`, +`autoquant/operator_port.py`, and `autoquant/studio.py`. Delegated request/Brief parsing is in `autoquant/briefs.py`; immutable Report publication and verification are in `autoquant/reports.py`; Project Dossier composition and verification are in `autoquant/dossiers.py`. diff --git a/docs/design/agent-cli-contract.md b/docs/design/agent-cli-contract.md index f1a972d..f42afe1 100644 --- a/docs/design/agent-cli-contract.md +++ b/docs/design/agent-cli-contract.md @@ -3,7 +3,10 @@ Status: implemented for Workspace, Project, AI-first Project orientation, request-driven OHLCV intake, Study, Run, Session, Experiment, bounded Research Campaign, delegated request, Research Report, and Session comparison -operations. +operations, plus the closed Operator request/receipt projection and immediate +Campaign stop. Exact artifact approval and reproduction execution remain +unavailable until Core supplies a verified EvidenceAssessment and controlled +reproduction executor. Related: [[docs/CLI]], [[docs/PROJECT_FORMAT]], [[docs/design/workspace-project-boundaries]], and @@ -69,6 +72,11 @@ Study/Run evaluation rules. 13. `version` and `capabilities` project the same current seven-field Harness object, including build provenance. Machine discovery never requires parsing `--version` or terminal prose. +14. `operator.invoke` accepts only the published strict request schema. Actor + identity never creates a client-specific authority path, and request input + cannot contain commands, credentials, or unconstrained filesystem paths. +15. Accepted Operator outcomes publish manifest-pinned terminal receipts. + Identical retries replay the receipt; a conflicting retry fails closed. ## Operation effects @@ -96,6 +104,23 @@ only when their confirmation, progress, and evidence contracts are defined. `studio.serve` uses `long-running-server`, does not support terminal JSON, and exposes only fixed read-only HTTP routes. +`operator.invoke` uses `creates-artifact` because every accepted intent +publishes an immutable audit receipt. Its closed V1 registry contains +`research.inspect`, `research.explain`, `research.compare`, +`research.reproduction-readiness`, definition version creation, artifact +decision, reproduction start, the independent user `confirmation.accept` +decision, and the immediate mutation `campaign.stop`. A semantic mutation first +publishes `confirmation-required`; `confirmation.accept` binds the exact +proposal hash, execution actor, object versions, budget, input, and expected +state; the mutation then references that decision receipt. Stop requires +`approved-envelope` authority and one exact active Campaign reference; Core +persists the request, preserves completed Experiments, and publishes terminal +Campaign evidence before the operation succeeds. CLI JSON, embedded Agent +clients, and the Studio adapter consume the same request/receipt schemas; no +chat surface owns additional mutation or shell authority. Unknown intent, +stale state, unavailable dependency, and accepted failure are receipt statuses +rather than browser-derived conclusions. + `session.compare` is read-only. It returns one bounded Core-authored comparison object after verifying the Session, Experiments, and Runs; the CLI and Studio must not independently select metrics, infer preference direction, or include @@ -285,5 +310,6 @@ uv run python -m unittest tests.test_cli -v - No progress-event envelope exists. - No output section selection exists. -- No confirmation receipt exists for future Project mutations. -- Studio has no confirmed mutation routes. +- Confirmation-bound mutation intents remain outside the initial read-only + Operator slice. +- Studio has no confirmed mutation routes yet. diff --git a/docs/design/external-researcher-driver.md b/docs/design/external-researcher-driver.md index 4da028f..8186088 100644 --- a/docs/design/external-researcher-driver.md +++ b/docs/design/external-researcher-driver.md @@ -97,6 +97,7 @@ campaigns/ │ ├── response.json │ └── result.json ├── progress.json + ├── stop-request.json # only after an authorized immediate stop ├── result.json └── manifest.json ``` @@ -115,7 +116,11 @@ its hash in the Campaign manifest. Campaign terminal statuses are: - `stopped`: the Researcher returned a valid STOP; +- `stopped_by_user`: an authorized Operator stop was observed between bounded + turns; already completed Experiments remain evidence; - `budget_exhausted`: every allowed proposal turn completed; +- `blocked`: required private-executor or cost telemetry evidence was unavailable; +- `unavailable`: an optional executor was requested but could not be selected truthfully; - `failed`: command exit/timeout, malformed response, illegal or unchanged source. @@ -140,11 +145,22 @@ V1 requires: - `max_turns`: positive integer, capped at 100; - `max_wall_seconds`: positive integer, capped at 86400; - `turn_timeout_seconds`: positive integer, capped at 3600. +- `max_candidates`: positive integer, capped at 100 and no greater than + `max_turns`; +- `max_cpu_seconds`: positive integer, capped at 86400; +- `max_gpu_seconds`: non-negative integer, capped at 86400. Each command timeout is the smaller of the per-turn limit and remaining Campaign wall time. Judge time consumes the aggregate wall budget. Reaching `max_turns` after valid turns is `budget_exhausted`, not failure. +The published budget also records CPU-first executor policy, fixed stop +conditions, a sealed holdout policy, and used/remaining candidate, turn, +wall-time, CPU, GPU, and cost telemetry. A monetary ceiling is not treated as +zero spend when telemetry is unavailable: the Campaign terminates `blocked`. +An unavailable private GPU/MOSS executor is never silently replaced unless an +explicit equivalence policy permits it. + ## Invariants 1. A Campaign operates on one existing active Session. @@ -160,6 +176,7 @@ Campaign wall time. Judge time consumes the aggregate wall budget. Reaching - The shell command is not host-sandboxed. - There is no streaming progress envelope while a command is running. -- Token/cost budgets are not standardized. +- Token budgets are not standardized; monetary ceilings require observable + cost telemetry and otherwise fail closed. - Campaigns are linear and single-process. - Progress polling does not prove the Campaign process remains alive. diff --git a/docs/design/next-studio-workbench.md b/docs/design/next-studio-workbench.md new file mode 100644 index 0000000..18960f7 --- /dev/null +++ b/docs/design/next-studio-workbench.md @@ -0,0 +1,108 @@ +# Next Studio workbench + +Status: implemented and verified for pull request. + +Related: [[docs/design/studio-observation-surface]], [[docs/ARCHITECTURE]], and +[[plans/next-studio-workbench]]. + +## Scope + +This document owns the maintainable Next.js presentation that sits above the +existing verified Studio snapshot. It does not change Core evidence authority, +the snapshot schema, `aq studio serve`, or the Python package boundary. + +The workbench is a factor-research product. It covers factor passports, +point-in-time replay, event cohorts, factor experiments, test evidence, +research jobs, data authority, and reproduction. It has no account, broker, +order, exchange-execution, or live-trading surface. + +## Runtime boundary + +```text +AutoQuant Core loaders +→ versioned autoquant-studio-snapshot +→ loopback-only aq studio serve +→ server-side same-origin Next proxy +→ repository-owned React views +``` + +The Next application does not read Project files, run Core commands, recompute +metrics, or infer verdicts. It may select, filter, compare, and render values +already present in the snapshot. The proxy accepts one configured loopback +Core origin and exposes only the fixed snapshot response. + +The existing packaged Studio remains the default `aq studio serve` +presentation until the Next surface reaches behavioral parity. This avoids a +Node.js runtime dependency in the Python wheel and keeps the migration +reversible. + +## Source modes + +The workbench has two honest source states: + +- `connected`: a verified `autoquant-studio-snapshot` was loaded from Core; +- `demo`: deterministic repository fixtures are being shown for product + exploration. + +The source state is visible in persistent chrome. A failed, invalid, stale, or +unavailable Core response cannot silently fall back to verified-looking demo +data. The error remains visible and the user may deliberately enter demo mode. + +## Internal design system + +The workbench adds no third-party component library. Its design system is +repository-owned and follows three token layers: + +1. primitives define raw color, spacing, type, radius, and duration values; +2. semantic tokens assign research meaning such as evidence, warning, valid, + unavailable, canvas, and surface; +3. component tokens define the shared shell, panels, controls, tables, chips, + inspector, and chart surfaces. + +Components consume component or semantic tokens rather than raw colors. This +makes the Evidence Console identity portable without coupling the repository +to a vendor library. + +## Open-source and private extension boundary + +Open-source code may contain: + +- the versioned Core snapshot contract; +- public adapter labels and normalized provenance fields; +- deterministic demo records; +- a loopback Core URL configuration; +- UI state and pure research comparisons. + +Open-source code must not contain: + +- plugin or MCP invocation implementations; +- private host tool names, schemas, routing tables, or authentication flows; +- tokens, cookies, credentials, authenticated headers, or private endpoints; +- raw proprietary provider payloads or licensed content; +- broker, account, order, or live-execution adapters. + +Private hosts integrate outside this repository. They may materialize verified +Core evidence or supply the same normalized snapshot through the public +loopback boundary. The workbench does not need a generic plugin SDK or a +host-specific code path. + +## Invariants + +1. Core remains the only authority for verified research evidence. +2. Connected and demo evidence are always visibly distinct. +3. The frontend performs no Project write, command execution, verdict, or + trading action. +4. Private plugin implementations remain outside the repository. +5. The Python package stays operable without Node.js. +6. Next.js and React versions are pinned exactly for reproducible builds. +7. Accessibility and reduced-motion behavior remain part of the component + contract. +8. Vulnerable transitive build dependencies are overridden only to their + published patched versions and remain covered by the npm audit gate. + +## Migration gate + +Replacing the packaged Studio requires a separate plan after route and +behavioral parity, wheel/package strategy, offline asset policy, and the full +Python regression suite are proven. This change intentionally leaves that gate +closed. diff --git a/docs/design/studio-observation-surface.md b/docs/design/studio-observation-surface.md index 503e8df..f996804 100644 --- a/docs/design/studio-observation-surface.md +++ b/docs/design/studio-observation-surface.md @@ -102,6 +102,11 @@ Studio reads it through a strict Research module loader. It never uses progress to infer a verdict or completed Campaign. On terminal publication the final progress file becomes manifest-pinned Campaign evidence. +An authorized `campaign.stop` request is a strict, confined +`stop-request.json`, not chat text or a shell signal. The Campaign observes it +between bounded turns, preserves completed Experiment references, and includes +the request in its terminal manifest before the Operator receipt succeeds. + A stale hidden staging directory may represent an interrupted process. V1 shows its last update rather than claiming liveness. @@ -138,6 +143,15 @@ through the long evidence surface. It supports keyboard focus, narrow screens, reduced motion, empty Projects, invalid evidence diagnostics, manual refresh, and bounded automatic refresh. +The connected Next Studio also provides two program-level management views. +Factor Research projects the fixed Factor Study, data and source identities, +cohorts, qualification evidence, immutable Reports, and mutable +Session/Campaign progress. Strategy Research projects the declared +Factor-to-Portfolio dependencies, Portfolio and optional governed-RL lanes, +supervised-model runtime, external holdout, Dossier, artifacts, progression +gates, and exact Core-recommended action. These views organize the existing +snapshot; they do not create a parallel evaluator or infer missing state. + Before a delegated Session exists, the first viewport prioritizes mandate, requested assets versus research universe, dataset authority, immutable baseline evidence, and the exact next headless action. Generic object counts diff --git a/openspec/changes/add-agent-research-console/.openspec.yaml b/openspec/changes/add-agent-research-console/.openspec.yaml new file mode 100644 index 0000000..11c4b58 --- /dev/null +++ b/openspec/changes/add-agent-research-console/.openspec.yaml @@ -0,0 +1,4 @@ +schema: spec-driven +created: 2026-08-03 +goal: Deliver an implementation-ready, research-only Agent workflow from data + and hypothesis through evidence, exact-version approval, and reproduction. diff --git a/openspec/changes/add-agent-research-console/README.md b/openspec/changes/add-agent-research-console/README.md new file mode 100644 index 0000000..30b1321 --- /dev/null +++ b/openspec/changes/add-agent-research-console/README.md @@ -0,0 +1,3 @@ +# add-agent-research-console + +Specify the AutoQuant ResearchLedger console, unified Operator Port, editable FactorDefinition/Experiment, bounded ResearchCampaign, evidence approval, and reproduction workflow. diff --git a/openspec/changes/add-agent-research-console/design.md b/openspec/changes/add-agent-research-console/design.md new file mode 100644 index 0000000..61a07f8 --- /dev/null +++ b/openspec/changes/add-agent-research-console/design.md @@ -0,0 +1,171 @@ +## Context + +AutoQuant already has strong research evidence primitives: fixed Studies, versioned Sessions, immutable Experiments and Runs, bounded external Researcher Campaigns, ComputeJob receipts, Reports, Reviews, Dossiers, frozen holdouts, and a versioned Studio snapshot. The connected Next.js Studio projects much of this evidence and now has distinct Factor Research and Strategy Research management surfaces. + +The missing product layer is an operable research session. The current UI is organized around modules and read models; it lacks a unified request/receipt boundary, editable definition contracts, Campaign cost/compute authority, a persistent conversation-to-evidence ledger, and direct artifact approval/reproduction. + +The visual and interaction source of truth remains the existing Designer Pipeline change: + +- Change: `autoquant-replay-research-workbench` +- Console refinement: `agent-research-console.md` +- DESIGN SHA-256: `a16ac2cd0e06fa0e06ecda6f3a368c61ab05bbecca487b209bd44f5d63cdeec2` +- MOTION SHA-256: `3e061d31c8c29b1e09d8e715c2d96ddca4ef741e661bcaea41195ca32e5a38a0` + +This OpenSpec change owns implementation requirements and acceptance. It does not fork the visual foundation. + +## Goals / Non-Goals + +**Goals:** + +- Provide one resumable ResearchLedger from data and question through definition, experiment, bounded Campaign, evidence, approval, and reproduction. +- Give the Studio UI and every Agent client one Operator Port with the same authority, request, terminal receipt, and recovery semantics. +- Add editable FactorDefinition and ExperimentDefinition versions without mutating existing Run evidence. +- Extend Campaign authority to candidate, wall-time, compute, and cost budgets with explicit stop conditions and truthful terminal outcomes. +- Keep Factor and Strategy management distinct while preserving navigation and evidence links between them. +- Reuse existing Core loaders, Session/Experiment/Run authority, snapshot projection, Mantine 9.5.1, and lightweight-charts 5.2.0. + +**Non-Goals:** + +- Replacing Core validation, Judges, selection integrity, holdout authority, or immutable evidence with browser logic. +- Making the embedded chat a privileged executor. +- Adding a second component system, docking framework, financial chart runtime, or animation runtime. +- Requiring private GPU/MOSS providers in the public repository. +- Filling connected Replay gaps with demo evidence. +- Reimplementing Portfolio, supervised-ML, governed-RL, Report, Review, or Dossier evaluation semantics. + +## Decisions + +### 1. ResearchSession is a ledger projection, not a new evaluator + +The session console SHALL assemble references to existing and new Core-owned objects in causal order: + +`DataPackage → ResearchQuestion → FactorDefinition/StrategyDefinition → ExperimentDefinition → Campaign/ExperimentRun → EvidenceAssessment → ArtifactApproval → ReproductionReceipt`. + +The ledger records stage state, object version, author, timestamp, operation receipt, blockers, and next valid actions. It never calculates a research verdict. Existing Core loaders remain authoritative for evidence integrity. + +Alternative rejected: a chat transcript as the session record. Conversation prose is not sufficient authority, cannot guarantee idempotency, and cannot reconstruct exact evidence. + +### 2. One Operator Port wraps existing capabilities + +Add a Core-owned operator service used by CLI/JSON and the local Studio mutation route. The Next.js route is a same-origin transport adapter, not the authority owner. + +The request envelope contains: + +- schema version and `requestId`; +- Workspace, Project, session, and actor references; +- research intent; +- exact object/version references; +- requested authority and confirmation reference; +- candidate, time, compute, and cost budget; +- expected prior state for optimistic concurrency. + +The terminal receipt contains: + +- accepted request hash and idempotency result; +- actual operations and final status; +- created/read artifact and evidence references; +- budget spent; +- warnings, failed gates, and sanitized errors; +- next valid actions and reproduction lineage. + +Duplicate `requestId` plus identical request bytes returns the prior receipt. Duplicate identity with different bytes fails closed. Every accepted mutation produces a terminal receipt, including stop, failure, and unavailable outcomes. + +Alternative rejected: separate chat, UI, and OpenAlice adapters. Multiple authority paths would drift and would make receipts incomparable. + +### 3. Definition plans are mutable only through new versions + +FactorDefinition is a program-level research object containing hypothesis, calculation/source identity, parameters, direction/unit, exact data dependencies, availability/PIT semantics, universe/cohort, tests, failure gates, and version lineage. + +ExperimentDefinition freezes one executable test plan: definition versions, data snapshot, ResearchSubject, outcome/horizon, benchmark, costs, split/purge, robustness, selection adjustment, holdout policy, executor policy, budget, and stop conditions. + +StrategyDefinition remains separate and references exact approved factor versions while owning composition, rules, Portfolio/ML/RL validation, holdout, cost/risk assumptions, and artifact closure. + +Saving an approved definition creates a new draft version. ExperimentRuns retain the exact definition and plan version they evaluated. The implementation may place these records inside the existing Project artifact topology, but it must follow the repository's manifest-last, strict-loader, path-confinement, and immutable-publication conventions. + +Alternative rejected: editing Study JSON or reassigning historical Runs. That would invalidate existing evidence identity. + +### 4. Campaign extends existing Session/Experiment authority + +The existing external Researcher driver remains the candidate-edit and evaluation engine. This change adds an approved Campaign charter and product projection rather than replacing it. + +The charter freezes: + +- question, subject, outcome, horizon, benchmark, data, and holdout policy; +- candidate count/turn ceiling; +- aggregate wall time; +- CPU/GPU resource ceilings; +- monetary/token cost ceiling when measurable; +- fixed stop conditions and permitted candidate-generation families. + +Existing `max_turns`, `max_wall_seconds`, per-turn timeout, worktree restoration, immutable Experiment history, and fixed Judge rules remain valid. CPU is the default screening path. GPU/MOSS is selected only when an installed provider declaration and an approved resource/cost envelope exist. + +Campaign progress is mutable telemetry and never evidence. Terminal Campaign artifacts are manifest-pinned and preserve completed Experiments even when the Campaign later fails. + +Alternative rejected: frontend-only budgets. A visual progress bar without Core enforcement is not an authority boundary. + +### 5. Confirmation is semantic and narrow + +The Agent may inspect, explain, compare, draft, and execute within an already approved Experiment/Campaign envelope. The Operator Port pauses and requires a semantic confirmation receipt for: + +- importing or freezing a new data version; +- saving/finalizing a new definition version; +- changing subject, outcome, horizon, benchmark, costs, holdout, executor scope, or any budget ceiling; +- opening a frozen holdout; +- using a paid/private provider outside a standing approval; +- approving/rejecting an artifact or starting reproduction. + +Stop is immediate and does not require confirmation. It preserves completed evidence and publishes a terminal receipt. + +### 6. Evidence, approval, and reproduction remain separate states + +EvidenceAssessment uses the existing deterministic vocabulary: `supported`, `contradicted`, `inconclusive`, or `invalid-test`. Approval does not change that verdict; it records a human decision over one exact version and evidence closure. + +Reproduction always creates a new receipt and compares the reproduced evidence with the approved manifest. Outcomes are exact match, within declared tolerance, drift, unavailable dependency, or failure. Reproduction never overwrites the original artifact or Run. + +### 7. Studio uses a ResearchLedger workbench + +At 1440 px and above: + +- keep the existing 178 px navigation rail; +- use a 340–380 px conversation column; +- keep a flexible central authoring/evidence canvas with 560 px minimum; +- use a 320–380 px review/confirmation inspector; +- add a collapsible research-task tray. + +At 1024–1439 px, the inspector becomes a Mantine Drawer. Below 1024 px, the product supports reading evidence, approval history, stop, and reproduction status; complex definition editing and multi-cohort comparison remain desktop-first. + +Generic components use installed Mantine primitives through the AutoQuant adapter. New domain components are `ResearchLedger`, `AgentProposal`, `OperationReceiptCard`, `SemanticDiff`, `FactorDefinitionEditor`, `ExperimentDefinitionEditor`, `CampaignBudgetBar`, `CandidateRunTable`, `EvidenceReview`, `ConfirmationInspector`, and `ReproductionReceipt`. + +### 8. Connected and demo evidence remain explicit + +The console reads existing verified Studio snapshot categories and future Operator receipts. Missing ReplayBundle, market clock, entity mapping, definition, or provider contracts affect only the owning stage/widget. The route and ledger remain visible with a named unavailable state. + +Demo evidence remains isolated behind explicit demo entry and is never used to satisfy connected acceptance. + +## Risks / Trade-offs + +- **Operator Port becomes an overly broad command bus** → Use a closed intent registry, strict schemas, exact object references, path confinement, and Core capability adapters; no arbitrary shell or provider command. +- **New definition objects duplicate Study authority** → Treat definitions as versioned inputs and ExperimentDefinition as the frozen plan; Study/Judge and immutable Run evidence remain the evaluation authority. +- **Campaign budgets drift from existing Researcher limits** → Extend the existing Campaign contract and map legacy turn/wall budgets into the new charter rather than running two schedulers. +- **Conversation becomes the visual center** → Lock the central canvas minimum width and keep receipts/objects addressable independently of chat. +- **Dense UI harms accessibility** → Preserve visual-order tabbing, skip links, focus return, text state, table alternatives, and reduced-motion behavior; never move focus on polling updates. +- **Private GPU/MOSS paths contaminate the public build** → Expose provider-neutral availability and receipts only; keep credentials and invocation packages outside the public repository. +- **Documentation is mistaken for completion** → OpenSpec tasks remain unchecked until executable tests and browser scenarios pass; route compilation and fixture/demo output do not satisfy workflow acceptance. + +## Migration Plan + +1. Introduce strict Operator request/receipt schemas and a read-only receipt projection over existing Core capabilities. +2. Add the `/research` shell and ResearchLedger using existing snapshot evidence before enabling mutations. +3. Add FactorDefinition and ExperimentDefinition version storage/loaders plus semantic diff and confirmation. +4. Extend existing Campaign authority with compute/cost budgets and product-visible stop reasons. +5. Add evidence review, exact-version approval, and reproduction receipts. +6. Move existing Factor/Strategy/Results/Jobs surfaces into session-aware navigation while preserving their routes and deep links. +7. Add optional GPU/MOSS providers only after public CPU completion and provider-boundary tests pass. + +Rollback is additive: disable the new Operator mutation route and `/research` navigation entry while retaining existing connected evidence routes and immutable artifacts. Published receipts and versions are never deleted during rollback. + +## Open Questions + +- Which existing Project subdirectory is the best durable home for definition versions and Operator receipts while preserving current Project-format conventions? Resolve before implementation and update `docs/PROJECT_FORMAT.md` in the same source change. +- Should the first Operator Port transport be CLI-only plus the local Next.js bridge, or should `aq studio serve` gain a narrowly authenticated mutation endpoint? The first slice SHOULD keep the current same-origin local bridge and Core-owned validation unless a separate security design is approved. +- Which cost unit is authoritative for providers that cannot report currency or token spend? Unknown spend MUST remain unknown and MUST NOT be interpreted as zero. diff --git a/openspec/changes/add-agent-research-console/proposal.md b/openspec/changes/add-agent-research-console/proposal.md new file mode 100644 index 0000000..1d2f629 --- /dev/null +++ b/openspec/changes/add-agent-research-console/proposal.md @@ -0,0 +1,45 @@ +## Why + +The connected Studio can display real Studies, Runs, Campaign progress, diagnostics, Factor Research, and Strategy Research, but it does not yet provide one operable Agent research session. Conversation, editable definitions, execution, evidence review, approval, and reproduction are fragmented, while the current browser boundary remains mostly read-only. + +This change turns the approved Designer Pipeline direction into an executable product contract without replacing existing Core evidence models or adding a privileged chat path. + +## What Changes + +- Add a session-first ResearchLedger console covering Data → Question → Factor → Experiment → Campaign → Evidence → Approval → Reproduction. +- Add one structured Operator Port used by the embedded Agent, OpenAlice, Hermes, Codex, and the Studio UI, with idempotent requests, bounded authority, confirmation gates, and immutable operation receipts. +- Add versioned editable FactorDefinition and ExperimentDefinition workflows while preserving immutable ExperimentRun evidence and existing Study/Session semantics. +- Extend the existing linear Research Campaign into a product-visible bounded campaign with candidate, time, compute, and cost budgets, explicit stop conditions, truthful negative/inconclusive outcomes, and CPU-first routing. +- Add evidence review, exact-version artifact approval, and independent reproduction with explicit match, drift, unavailable, and failure outcomes. +- Reorganize the Studio entry around research sessions, data, factor assets, strategy assets, evidence, tasks, and audit while preserving existing deep links and connected evidence projections. +- Keep Mantine 9.5.1 as the generic component substrate and `lightweight-charts` 5.2.0 as the sole K-line runtime. No docking system, second UI library, second financial chart runtime, or chat-specific executor is added. + +Non-goals: + +- Replacing immutable Core evidence, Session/Experiment authority, fixed Judges, or existing provenance checks with browser logic. +- Making demo Replay data appear connected when ReplayBundle, market-clock, or entity mapping contracts are unavailable. +- Requiring GPU/MOSS or a private provider for the public workflow to function. +- Rebuilding the existing Factor, Portfolio, ML, governed-RL, Report, Review, or Dossier evaluators in the frontend. + +## Capabilities + +### New Capabilities + +- `research-session-console`: One resumable ResearchLedger workbench with synchronized conversation, stage canvas, review inspector, task tray, responsive review, keyboard access, and truthful product states. +- `operator-port`: One provider-neutral research request/receipt boundary shared by every Agent client and the Studio UI, including authority, confirmation, idempotency, budget, artifact, evidence, and recovery semantics. +- `versioned-research-definitions`: Editable FactorDefinition and ExperimentDefinition versions with semantic diffs, confirmation, frozen inputs, immutable Run linkage, and separate definition/evidence lifecycles. +- `bounded-research-campaigns`: Autonomous but bounded candidate research with declared candidate/time/compute/cost ceilings, fixed stop conditions, CPU-first screening, optional private executors, and terminal negative/inconclusive states. +- `research-artifact-approval`: Evidence closure, exact-version approval/rejection, immutable factor/strategy research artifacts, and independent reproduction receipts with drift comparison. + +### Modified Capabilities + +None. This repository has no existing OpenSpec main specs; the change formalizes behavior currently distributed across repository design documents and the upstream Designer Pipeline change. + +## Impact + +- Core/API: new Operator Port envelope/receipt projection over existing loaders and future versioned definition contracts; extension of Campaign budgets without weakening existing Session, Experiment, Run, Judge, restoration, or manifest authority. +- Studio: new `/research` session surfaces, navigation reorganization, editable research forms, campaign/evidence/approval components, and localized human-language projections. +- Data and evidence: existing DataPackage, ResearchSubject, Study, Run, ReplayBundle, Report, Review, Dossier, and diagnostic identities remain authoritative. +- Providers: CPU remains the public default. GPU/MOSS remain optional provider adapters with explicit availability, resource, cost, and receipt states. +- Dependencies: no new runtime dependency is proposed. +- Upstream design authority: the canonical Designer Pipeline change remains `autoquant-replay-research-workbench`, with DESIGN hash `a16ac2cd0e06fa0e06ecda6f3a368c61ab05bbecca487b209bd44f5d63cdeec2` and MOTION hash `3e061d31c8c29b1e09d8e715c2d96ddca4ef741e661bcaea41195ca32e5a38a0`. diff --git a/openspec/changes/add-agent-research-console/specs/bounded-research-campaigns/spec.md b/openspec/changes/add-agent-research-console/specs/bounded-research-campaigns/spec.md new file mode 100644 index 0000000..2911193 --- /dev/null +++ b/openspec/changes/add-agent-research-console/specs/bounded-research-campaigns/spec.md @@ -0,0 +1,61 @@ +## ADDED Requirements + +### Requirement: Approved Campaign charter +Every autonomous ResearchCampaign SHALL bind one active Session and one approved ExperimentDefinition and SHALL freeze the research question, subject, data, outcome, horizon, benchmark, holdout policy, candidate policy, executor policy, budgets, and stop conditions. + +#### Scenario: Start a valid Campaign +- **GIVEN** an active Session and approved ExperimentDefinition with complete budgets and stop rules +- **WHEN** the user approves Campaign start +- **THEN** Core publishes the charter identity before the first candidate begins and every turn references that charter + +#### Scenario: Research boundary changes mid-Campaign +- **GIVEN** a Campaign is running +- **WHEN** an Agent proposes a different outcome, horizon, benchmark, data version, or holdout policy +- **THEN** the Campaign pauses and requires a new semantic confirmation rather than silently changing selection authority + +### Requirement: Enforced multi-dimensional budgets +Core SHALL enforce positive candidate/turn and wall-time ceilings plus declared CPU, GPU, and cost ceilings when those resources are measurable. Unknown provider spend SHALL remain unknown and SHALL NOT be interpreted as zero. + +#### Scenario: Candidate budget exhausted +- **GIVEN** the Campaign has completed its maximum permitted candidates +- **WHEN** no prior stop condition ended the Campaign +- **THEN** the Campaign terminates as budget-exhausted and no new candidate is generated + +#### Scenario: Cost telemetry unavailable +- **GIVEN** a provider cannot report authoritative cost +- **WHEN** the Agent requests use under a monetary ceiling +- **THEN** the provider is unavailable for that authorized path unless a separately approved non-monetary resource envelope exists + +### Requirement: CPU-first executor routing +The public workflow SHALL use CPU for cheap validation and rejection before optional private GPU/MOSS work unless the approved ExperimentDefinition records why CPU screening is inapplicable. GPU/MOSS execution SHALL require an installed provider declaration and an approved resource/cost envelope. + +#### Scenario: Private provider absent +- **GIVEN** no GPU/MOSS provider is installed +- **WHEN** a Campaign reaches a candidate that requests that executor +- **THEN** the candidate reports provider-unavailable and the public CPU workflow remains usable + +#### Scenario: CPU screen rejects candidate +- **GIVEN** a candidate fails a fixed cheap leakage, coverage, or validity gate +- **WHEN** executor routing is evaluated +- **THEN** no private compute starts and the candidate row records the exact failed gate + +### Requirement: Truthful terminal outcomes +Campaigns SHALL terminate with evidence-ready, budget-exhausted, failed-gate, blocked, stopped-by-user, inconclusive, or failed semantics. They SHALL preserve negative and invalid Experiments and SHALL NOT continue searching until a favorable result appears. + +#### Scenario: Valid but inconclusive Campaign +- **GIVEN** all planned candidates complete without adequate support or contradiction +- **WHEN** the declared stop condition is reached +- **THEN** the Campaign terminates as inconclusive with completed receipts and does not request automatic budget expansion + +#### Scenario: Protocol failure after valid Experiments +- **GIVEN** earlier Experiments completed and a later external Researcher response is malformed +- **WHEN** Core restores the leader worktree and terminates the Campaign +- **THEN** the Campaign is failed, earlier Experiments remain valid, and the failure is not rewritten as missing evidence + +### Requirement: Holdout protection +The Campaign SHALL NOT expose or use frozen holdout outcomes for candidate generation, selection, budget extension, or definition editing. Opening a holdout SHALL be a separately confirmed, auditable, terminal validation action. + +#### Scenario: Agent requests holdout during selection +- **GIVEN** candidate selection is still active +- **WHEN** the Agent requests frozen holdout evidence +- **THEN** the Operator Port rejects the request and preserves the holdout seal diff --git a/openspec/changes/add-agent-research-console/specs/operator-port/spec.md b/openspec/changes/add-agent-research-console/specs/operator-port/spec.md new file mode 100644 index 0000000..b2b1e57 --- /dev/null +++ b/openspec/changes/add-agent-research-console/specs/operator-port/spec.md @@ -0,0 +1,61 @@ +## ADDED Requirements + +### Requirement: One shared Operator Port +The system SHALL expose one closed, provider-neutral Operator Port for the Studio UI, embedded Agent, OpenAlice, Hermes, and Codex. No client SHALL receive a chat-specific mutation path, UI-scraping authority, or arbitrary shell capability. + +#### Scenario: Equivalent clients invoke the same operation +- **GIVEN** two clients submit the same authorized research request with distinct actor identities +- **WHEN** Core accepts each request +- **THEN** both requests use the same schema, capability adapter, validation, receipt format, and evidence authority + +### Requirement: Strict request envelope +Every Operator request SHALL include a schema version, idempotent request identity, actor, Workspace/Project/session references, closed research intent, exact object/version references, requested authority, budget, confirmation reference when required, and expected prior state. + +#### Scenario: Unknown intent is rejected +- **GIVEN** a request names an intent outside the closed Operator capability registry +- **WHEN** the port validates the request +- **THEN** the request fails before any Project mutation and returns a sanitized terminal receipt + +#### Scenario: Stale prior state is rejected +- **GIVEN** the referenced definition or session state changed after the client prepared its request +- **WHEN** the client submits the stale expected state +- **THEN** the operation fails closed with current object references and no partial mutation + +### Requirement: Idempotent operation receipts +An accepted Operator operation SHALL produce an immutable terminal receipt describing the accepted request hash, actual operations, final status, artifacts, evidence, budget spent, warnings, gates, sanitized errors, next valid actions, and reproduction lineage. + +#### Scenario: Identical retry +- **GIVEN** a terminal receipt exists for a request identity and request hash +- **WHEN** the identical request is retried +- **THEN** the prior receipt is returned without repeating the operation or consuming additional budget + +#### Scenario: Conflicting retry +- **GIVEN** a terminal receipt exists for a request identity +- **WHEN** different request bytes reuse that identity +- **THEN** the port rejects the conflict and does not mutate research state + +#### Scenario: Failure still produces a receipt +- **GIVEN** an accepted operation fails because an input, provider, or execution condition is unavailable +- **WHEN** the operation reaches a terminal state +- **THEN** the port publishes a failed or unavailable receipt with preserved completed evidence and a named recovery action + +### Requirement: Confirmation-bound authority +The Operator Port SHALL allow inspection, explanation, comparison, drafting, and execution within an already approved Experiment/Campaign envelope without repeated confirmation. It SHALL require semantic confirmation for new/frozen data or definition versions, changed research boundaries, budget expansion, frozen holdout opening, private paid provider use outside standing authority, artifact decisions, and reproduction start. + +#### Scenario: Work continues inside approved budget +- **GIVEN** an ExperimentDefinition and Campaign charter are approved +- **WHEN** the Agent screens another permitted candidate without changing inputs or ceilings +- **THEN** the operation proceeds and publishes a receipt without a new confirmation prompt + +#### Scenario: Budget expansion pauses +- **GIVEN** a Campaign has reached its approved candidate ceiling +- **WHEN** the Agent requests additional candidates +- **THEN** the port pauses the operation and returns an awaiting-confirmation receipt containing only the proposed budget diff + +### Requirement: Immediate stop +An authorized user stop SHALL take precedence over new automated research, require no additional confirmation, preserve completed evidence, and publish a terminal stopped receipt. + +#### Scenario: Stop a running Campaign +- **GIVEN** a Campaign is running and has completed one or more Experiments +- **WHEN** the user issues stop +- **THEN** no new candidate begins, completed Experiments remain valid, mutable progress closes, and the stopped receipt identifies the last completed state diff --git a/openspec/changes/add-agent-research-console/specs/research-artifact-approval/spec.md b/openspec/changes/add-agent-research-console/specs/research-artifact-approval/spec.md new file mode 100644 index 0000000..dc25927 --- /dev/null +++ b/openspec/changes/add-agent-research-console/specs/research-artifact-approval/spec.md @@ -0,0 +1,61 @@ +## ADDED Requirements + +### Requirement: Evidence closure before approval +The system SHALL present the exact factor or strategy version, data, ExperimentDefinition, Runs, deterministic assessment, costs, holdout state, limitations, and unresolved diagnostics before artifact approval. + +#### Scenario: Incomplete evidence closure +- **GIVEN** a StrategyDefinition references a required holdout that has not been assessed +- **WHEN** the user opens artifact review +- **THEN** the review identifies the missing holdout assessment and disables approval without hiding other valid evidence + +#### Scenario: Negative evidence remains visible +- **GIVEN** a Campaign contains contradicted, invalid, or inconclusive candidates +- **WHEN** an artifact review is opened for the selected version +- **THEN** those terminal outcomes remain in the evidence closure and are not removed by selecting a preferred candidate + +### Requirement: Exact-version artifact decision +Approval or rejection SHALL bind one exact FactorDefinition or StrategyDefinition version and its evidence manifest. Approval SHALL freeze a new immutable research artifact and SHALL NOT mutate the definition verdict or any Run. + +#### Scenario: Approve an exact factor version +- **GIVEN** FactorDefinition version 4 has a complete evidence closure +- **WHEN** the user approves version 4 +- **THEN** the system publishes an immutable artifact referencing version 4 and its exact evidence while leaving other drafts unchanged + +#### Scenario: Definition changes during review +- **GIVEN** an approval review was prepared for StrategyDefinition version 2 +- **WHEN** version 3 is created before confirmation +- **THEN** the stale review cannot approve version 3 or silently transfer its decision; the user must review the new semantic diff + +### Requirement: Approval choices preserve research state +The review SHALL support approve, reject/return for revision, and retain as research draft. Rejection or revision SHALL preserve all completed evidence and SHALL NOT delete the reviewed version. + +#### Scenario: Return artifact for revision +- **GIVEN** evidence is valid but the user rejects the proposed artifact +- **WHEN** the user selects return for revision +- **THEN** the reviewed version and evidence remain immutable and a new draft may be created with the rejection receipt linked + +### Requirement: Independent reproduction receipt +Reproduction SHALL run from an approved artifact manifest and exact available environment, create a new immutable receipt, preserve the original artifact, and classify the result as exact match, within declared tolerance, drift, unavailable dependency, or failure. + +#### Scenario: Exact reproduction +- **GIVEN** all referenced inputs and the approved CPU environment are available +- **WHEN** reproduction completes with matching artifacts and metrics +- **THEN** the receipt records exact match and links both original and reproduced evidence + +#### Scenario: Reproduction drift +- **GIVEN** reproduction completes but one metric or artifact hash differs beyond declared tolerance +- **WHEN** the comparison is published +- **THEN** the receipt records drift, names the differing evidence, and leaves the original approval unchanged + +#### Scenario: Private executor unavailable +- **GIVEN** the approved artifact references a private executor that is not installed +- **WHEN** reproduction is requested +- **THEN** the system publishes an unavailable receipt and does not substitute CPU unless the artifact explicitly permits an equivalent CPU environment + +### Requirement: Audit continuity +Every approval, rejection, reproduction, stop, failure, and unavailable outcome SHALL be reachable from the ResearchLedger and Audit through stable object and receipt references. + +#### Scenario: Audit a reproduced artifact +- **GIVEN** an approved artifact has multiple reproduction attempts +- **WHEN** the user opens Audit +- **THEN** each attempt appears as a separate immutable receipt with its environment, inputs, result, drift, and lineage diff --git a/openspec/changes/add-agent-research-console/specs/research-session-console/spec.md b/openspec/changes/add-agent-research-console/specs/research-session-console/spec.md new file mode 100644 index 0000000..ae0cdaf --- /dev/null +++ b/openspec/changes/add-agent-research-console/specs/research-session-console/spec.md @@ -0,0 +1,56 @@ +## ADDED Requirements + +### Requirement: Resumable ResearchLedger +The system SHALL provide one resumable research session ledger with the ordered stages Data, Question, Factor, Experiment, Campaign, Evidence, Approval, and Reproduction. Each stage SHALL expose its state, exact object/version references, receipts, blockers, and next valid actions without calculating research conclusions in the browser. + +#### Scenario: Resume an existing research session +- **GIVEN** a session has a frozen data version, approved ExperimentDefinition, terminal Campaign, and pending artifact review +- **WHEN** the user or an Agent client reopens the session +- **THEN** the ledger restores the same stage states and object references and opens the pending review without replaying prior operations + +#### Scenario: Partial session remains navigable +- **GIVEN** a session has valid Runs but no connected ReplayBundle +- **WHEN** the Evidence stage is opened +- **THEN** the route, ledger, valid Run evidence, and diagnostics remain visible while Replay shows a named unavailable state + +### Requirement: Research-first workbench layout +The Studio SHALL present research sessions through a conversation region, a dominant authoring/evidence canvas, a review/confirmation inspector, and a collapsible research-task tray. The interface SHALL preserve existing deep links to Factor, Strategy, Replay, Results, Jobs, and Audit evidence. + +#### Scenario: Desktop research layout +- **GIVEN** a viewport of at least 1440 CSS pixels +- **WHEN** a research session is opened +- **THEN** conversation, central canvas, and review inspector are simultaneously available and the central canvas retains at least 560 CSS pixels + +#### Scenario: Medium-width confirmation +- **GIVEN** a viewport between 1024 and 1439 CSS pixels +- **WHEN** a confirmation is required +- **THEN** the inspector opens in a focus-managed Drawer and focus returns to its initiating control on close + +#### Scenario: Small-screen review +- **GIVEN** a viewport below 1024 CSS pixels +- **WHEN** the session is opened +- **THEN** the user can read the question, evidence, conclusion, approval history, stop status, and reproduction receipt while complex authoring is explicitly deferred to desktop + +### Requirement: Structured research conversation +The conversation surface SHALL distinguish user questions, Agent proposals, user decisions, operation receipts, and evidence conclusions. Every Agent proposal SHALL identify affected objects/versions, evidence inputs, budget impact, semantic changes, and required confirmation. + +#### Scenario: Agent proposes a definition change +- **GIVEN** an approved FactorDefinition exists +- **WHEN** the Agent proposes a changed calculation or dependency +- **THEN** the conversation links to a semantic diff for a new draft version and does not imply that the approved version or its Runs changed + +### Requirement: Accessible and stable interaction +The console SHALL provide visual-order keyboard navigation, a skip link to the active canvas, visible focus, text alternatives for charts, focus return for overlays, polite status announcements, and reduced-motion behavior. Polling or task updates SHALL NOT steal focus or reorder a focused row. + +#### Scenario: Campaign update during keyboard review +- **GIVEN** a keyboard user is inspecting an earlier candidate row +- **WHEN** Campaign progress updates +- **THEN** the row remains focused, status is announced politely, and the table does not automatically reorder + +### Requirement: Human-language primary states +Primary UI copy SHALL describe the research meaning of a state. Study, Run, ComputeJob, ReplayBundle, schema, and hash identities SHALL remain available in Technical Details and Audit. + +#### Scenario: Missing contract copy +- **GIVEN** a required ReplayBundle contract is unavailable +- **WHEN** the user opens evidence review +- **THEN** primary copy names the missing research evidence and consequence while Technical Details exposes the exact internal contract state diff --git a/openspec/changes/add-agent-research-console/specs/versioned-research-definitions/spec.md b/openspec/changes/add-agent-research-console/specs/versioned-research-definitions/spec.md new file mode 100644 index 0000000..be65798 --- /dev/null +++ b/openspec/changes/add-agent-research-console/specs/versioned-research-definitions/spec.md @@ -0,0 +1,56 @@ +## ADDED Requirements + +### Requirement: Separate research definition objects +The system SHALL model FactorDefinition, ExperimentDefinition, and StrategyDefinition as separate versioned objects. FactorDefinition SHALL own factor hypothesis/calculation/dependencies/cohort/tests; ExperimentDefinition SHALL own a frozen validation plan; StrategyDefinition SHALL own approved factor composition, rules, Portfolio/ML/RL validation, holdout, costs, risk assumptions, and artifact closure. + +#### Scenario: Strategy references a factor +- **GIVEN** an approved FactorDefinition version exists +- **WHEN** a StrategyDefinition uses that factor +- **THEN** it references the exact factor version and does not copy or assume ownership of the factor definition lifecycle + +#### Scenario: Factor edit does not edit a strategy +- **GIVEN** a StrategyDefinition references FactorDefinition version 3 +- **WHEN** a researcher creates FactorDefinition version 4 +- **THEN** the strategy remains bound to version 3 until a separately reviewed StrategyDefinition version changes the reference + +### Requirement: Immutable version lineage +Saving changes to an approved definition SHALL create a new draft version. Published versions, semantic diffs, approvals, and evidence references SHALL be immutable and strictly loadable. + +#### Scenario: Edit an approved FactorDefinition +- **GIVEN** FactorDefinition version 2 is approved and has immutable Runs +- **WHEN** the user saves a changed calculation +- **THEN** the system creates a new draft version, preserves version 2 unchanged, and leaves all existing Runs bound to version 2 + +#### Scenario: Tampered definition version +- **GIVEN** published definition bytes no longer match their manifest +- **WHEN** Core loads the version +- **THEN** the version is invalid evidence, dependent run/approval actions are disabled, and no browser fallback reconstructs the missing content + +### Requirement: Complete FactorDefinition semantics +A validation-ready FactorDefinition SHALL declare hypothesis, executable calculation or source identity, parameters, output direction/unit, exact data versions and fields, availability/PIT semantics, missing-data policy, universe/cohort, expected horizon, required tests, and failure conditions. + +#### Scenario: Missing market clock blocks validation readiness +- **GIVEN** a factor depends on time-sensitive inputs with no verified availability or market-clock semantics +- **WHEN** the user requests validation readiness +- **THEN** the definition remains draft with named unresolved dependencies and cannot produce a credibility-bearing ExperimentDefinition + +### Requirement: Frozen ExperimentDefinition +An executable ExperimentDefinition SHALL freeze exact FactorDefinition or StrategyDefinition versions, DataPackage and ResearchSubject identity, outcome/horizon, benchmark, costs, split/purge, robustness, selection adjustment, holdout policy, executor policy, budgets, and stop conditions. + +#### Scenario: Run uses exact plan version +- **GIVEN** ExperimentDefinition version 5 is frozen +- **WHEN** an ExperimentRun starts +- **THEN** the Run receipt records version 5 and all frozen input identities, and later plan edits cannot change that Run + +#### Scenario: Invalid plan cannot run +- **GIVEN** an ExperimentDefinition lacks a cost policy or required stop condition +- **WHEN** execution is requested +- **THEN** Core rejects the request before compute begins and returns the exact missing plan fields + +### Requirement: Semantic diff and confirmation +Definition confirmation SHALL present semantic field changes, affected evidence, newly invalid assumptions, and the new version identity instead of a raw JSON diff alone. + +#### Scenario: Data dependency changes +- **GIVEN** a draft changes the input DataPackage version +- **WHEN** confirmation is requested +- **THEN** the review identifies the old/new data identities, evidence that cannot transfer, and the Experiments that require a new Run diff --git a/openspec/changes/add-agent-research-console/tasks.md b/openspec/changes/add-agent-research-console/tasks.md new file mode 100644 index 0000000..9bd59e4 --- /dev/null +++ b/openspec/changes/add-agent-research-console/tasks.md @@ -0,0 +1,93 @@ +## 1. Contract and documentation foundation + +- [x] 1.1 Resolve and document the Project-format locations, schemas, manifest rules, and loaders for FactorDefinition, ExperimentDefinition, Operator receipts, artifact approvals, and reproduction receipts in `docs/PROJECT_FORMAT.md` and the relevant `docs/design/` contracts. +- [x] 1.2 Add strict schema fixtures for the new objects, including valid, unknown-field, stale-version, path-escape, tampered, and unsupported-version cases. +- [x] 1.3 Extend `autoquant/capabilities.py` and CLI documentation with closed Operator intents and machine-readable request/receipt schemas; do not expose arbitrary commands. +- [x] 1.4 Add compatibility rules that project existing Study/Session/Experiment/Run/Campaign evidence without rewriting or migrating immutable artifacts. + +## 2. Unified Operator Port + +- [x] 2.1 Implement the Core-owned Operator request validator, closed capability registry, expected-state checks, and idempotency store over existing Core operations. +- [x] 2.2 Implement immutable terminal AgentOperationReceipt publication for completed, stopped, failed, unavailable, stale, and confirmation-required outcomes. +- [x] 2.3 Route an initial read-only set of inspect, explain, compare, and reproduce-readiness operations through the Operator Port and prove CLI/JSON parity. +- [x] 2.4 Add same-origin Next.js transport through one `/api/studio/operator` adapter that delegates validation and mutation authority to Core. +- [x] 2.5 Add regression tests proving identical retries do not repeat work, conflicting retries fail closed, stale state cannot mutate, and every accepted failure receives a receipt. +- [x] 2.6 Add boundary tests proving the port rejects shell commands, raw provider invocations, credential payloads, unknown intents, unconstrained paths, and chat-specific authority. + +## 3. Versioned research definitions + +- [x] 3.1 Implement strict FactorDefinition version creation/loading with hypothesis, calculation/source identity, parameters, direction/unit, exact data/PIT dependencies, cohort, tests, failure gates, lifecycle, and lineage. +- [x] 3.2 Implement strict ExperimentDefinition draft, confirmation, and freeze flow over exact definition versions, data/subject, outcome/horizon, benchmark, costs, split/purge, robustness, holdout, executor, budgets, and stop conditions. +- [x] 3.3 Preserve StrategyDefinition as a separate object that references exact factor versions and owns composition, Portfolio/ML/RL validation, costs, risk, holdout, and artifact closure. +- [x] 3.4 Implement Core semantic diffs for changed definition fields, affected evidence, invalidated assumptions, and new version identity. +- [x] 3.5 Bind every new ExperimentRun and Operator receipt to the exact FactorDefinition/StrategyDefinition and ExperimentDefinition versions used. +- [x] 3.6 Add tests proving approved versions are immutable, edits fork drafts, historical Runs do not move, tampering fails closed, and unresolved PIT/clock/cost/stop fields block execution readiness. + +## 4. Bounded ResearchCampaign + +- [x] 4.1 Extend the existing Researcher Campaign charter with approved candidate, wall-time, CPU, GPU, and cost ceilings plus fixed stop conditions while preserving legacy turn/wall compatibility. +- [x] 4.2 Enforce all budgets in Core, preserve unknown provider spend as unknown, and publish used/remaining budget in progress and terminal receipts. +- [x] 4.3 Add CPU-first screening and fixed-gate rejection before optional GPU/MOSS dispatch; require provider availability and an approved resource/cost envelope for private executors. +- [ ] 4.4 Add evidence-ready, budget-exhausted, failed-gate, blocked, stopped-by-user, inconclusive, and failed terminal projections without weakening existing immutable Campaign statuses. +- [ ] 4.5 Enforce frozen holdout isolation during candidate generation and selection and require a separate audited confirmation before opening it. +- [ ] 4.6 Add tests for every budget dimension, immediate stop, malformed Researcher response after valid Experiments, worktree restoration, private-provider absence, negative/inconclusive campaigns, and holdout-access rejection. + +## 5. Read-only ResearchLedger shell + +- [x] 5.1 Add `/research` and `/research/[sessionId]` routes that assemble existing verified snapshot evidence into Data → Question → Factor → Experiment → Campaign → Evidence → Approval → Reproduction without browser-side verdicts. +- [x] 5.2 Build `ResearchLedger`, structured conversation entries, `OperationReceiptCard`, dominant stage canvas, review inspector, and collapsible task tray using the existing AutoQuant/Mantine adapter. +- [x] 5.3 Reorganize primary navigation into Work, Assets, Evidence, and Operations while preserving all existing route URLs and deep links. +- [x] 5.4 Implement truthful loading, empty, partial, unavailable, invalid, stale, error, and demo-isolated states for each ledger stage. +- [x] 5.5 Add frontend projection tests proving missing ReplayBundle, market clock, definition, approval, or provider evidence affects only its owning widget and never fabricates connected completion. + +## 6. Editable console and confirmation flow + +- [x] 6.1 Build `FactorDefinitionEditor` and `ExperimentDefinitionEditor` from installed Mantine form primitives with Core validation, draft/version identity, and Technical Details disclosure. +- [x] 6.2 Build `SemanticDiff` and `ConfirmationInspector` with exactly one primary confirmation action plus save-draft, return-for-revision, and stop actions where valid. +- [ ] 6.3 Connect data freeze, new definition version, changed boundary, budget expansion, private provider, holdout opening, artifact decision, and reproduction start to Operator confirmation receipts. +- [ ] 6.4 Prove inspect, explain, compare, draft, and approved-envelope execution do not generate redundant confirmation prompts. +- [ ] 6.5 Add frontend and Core integration tests for stale confirmation, version races, retry idempotency, validation errors, and successful new-version creation. + +## 7. Campaign and evidence workspaces + +- [x] 7.1 Build `CampaignBudgetBar` for candidate/time/compute/cost usage and `CandidateRunTable` for candidate version, stage, executor, spend, failed gate, best evidence, stop reason, and next valid action. +- [ ] 7.2 Connect bounded start, pause, resume, and immediate stop through the Operator Port while preserving mutable-progress versus terminal-evidence labeling. +- [x] 7.3 Build `EvidenceReview` with Outcome, Replay, Cohorts, Robustness, Costs, and Provenance views over verified Core projections only. +- [ ] 7.4 Keep `lightweight-charts` as the sole K-line runtime and add synchronized semantic tables/inspectors for every meaningful chart mark. +- [ ] 7.5 Add tests proving budget exhaustion does not auto-expand, table focus survives polling, negative/invalid candidates remain visible, and connected evidence never falls through to demo data. + +## 8. Artifact approval and reproduction + +- [x] 8.1 Implement exact-version artifact review over definition, data, Experiment, Run, assessment, cost, holdout, limitation, and diagnostic closure. +- [x] 8.2 Implement approve, reject/return, and retain-as-draft receipts without changing definition verdicts or deleting reviewed evidence. +- [ ] 8.3 Implement reproduction from an approved manifest with separate exact-match, within-tolerance, drift, unavailable, and failed receipts. +- [ ] 8.4 Project approval and every reproduction attempt into the ResearchLedger, Studio snapshot, Audit, CLI/JSON, and stable deep links. +- [ ] 8.5 Add tests for stale review, incomplete closure, negative evidence retention, private executor absence, CPU equivalence policy, metric/hash drift, and immutable original artifacts. + +## 9. Responsive, accessibility, and motion verification + +- [x] 9.1 Implement the 1440 px three-region layout and 1024–1439 px Drawer behavior with the central canvas minimum width defined by the Designer Pipeline specification. +- [x] 9.2 Implement review-only behavior below 1024 px without hiding evidence, decisions, stop status, or reproduction receipts. +- [ ] 9.3 Add skip-link, visual-order tabbing, visible focus, focus return, polite live regions, keyboard shortcuts, table alternatives, and no-focus-steal polling behavior. +- [x] 9.4 Apply only CSS focus/press feedback and bounded Drawer/Modal transitions; suppress nonessential motion under `prefers-reduced-motion`. +- [x] 9.5 Run manual browser acceptance at 375×812, 768×1024, 1024×768, 1440×900, and 1920×1080 with zero horizontal overflow at supported layouts. + +## 10. Completion evidence + +- [x] 10.1 Run `openspec validate add-agent-research-console --strict --json` and resolve every issue before implementation approval and again before archive. +- [x] 10.2 Run targeted Core suites for Operator Port, definitions, Campaigns, approvals, reproduction, Studio snapshot, and CLI capabilities. +- [ ] 10.3 Run `uv run python scripts/check_doc_links.py` and `uv run python -m unittest discover -s tests -v`. +- [x] 10.4 Run frontend tests, ESLint, `npm run check:boundary`, and the Next.js production build. +- [ ] 10.5 Execute browser scenarios for factor discovery, strategy verification, immediate stop, budget exhaustion, missing ReplayBundle, stale confirmation, exact reproduction, and reproduction drift with clean console logs. +- [ ] 10.6 Verify that OpenAlice, Hermes, Codex, the embedded Agent, and Studio projections use the same Operator schema and receipt fixtures. +- [x] 10.7 Record actual verification evidence in this checklist and repository status/design documents; do not mark the OpenSpec change complete from routes, documentation, fixtures, or available Core inventory alone. + +## Verification evidence (2026-08-04) + +- Core targeted suites: `uv run python -m unittest tests.test_operator_port tests.test_research_definitions tests.test_research_artifacts tests.test_research tests.test_runs tests.test_run_binding_preflight tests.test_run_binding_persistence tests.test_run_binding_drift tests.test_studio tests.test_cli -v` — 157 tests passed. A preceding system-Python invocation was rejected at import time because it lacked the repository dependency `exchange_calendars`; no test body ran in that invocation. +- Frontend: `npm test` — 86 tests passed; `npm run lint`, `npm run check:boundary`, and `npm run build` passed. The production build generated `/research` and `/research/[sessionId]` successfully. +- OpenSpec: `openspec validate add-agent-research-console --strict --json` passed before this checklist update; the final strict validation is rerun after every checklist edit. +- Documentation links: `python scripts/check_doc_links.py` — 1,570 links resolved. +- Browser: verified responsive/review-only behavior and no horizontal overflow at 375×812, 768×1024, the 1024 breakpoint from both adjacent device-pixel sizes, 1440×900, and 1920×1080; Drawer focus return, skip-link focus, polite live region, receipt visibility, and a clean error console were also checked. A real `campaign.stop` request disabled both Stop entries in flight; after Core returned a terminal `stopped` receipt with matching `autoquant-campaign-stop-request` evidence, the Inspector action disappeared, the tray action stayed disabled, and a second DOM click did not issue another request. When the external researcher outlived Core's wait window, Core returned `unavailable` with terminal publication pending; this remains a 7.2 blocker and is not treated as stopped by the frontend. +- Independent review status: **REQUEST CHANGES** overall; 6.2 is accepted. Tasks 4.4–4.6, 6.3–6.5, 7.2, 7.4–7.5, 8.3–8.5, 9.3, and the remaining completion scenarios stay unchecked until their implementation and evidence exist. Structured definition, artifact-review, and reproduction-request editors now exist, but exact reproduction outcomes still require a Core-controlled executor and Campaign/accessibility acceptance remains incomplete. +- No commit, push, PR, CI, deployment, OpenSpec archive, or external account/provider mutation was performed. diff --git a/openspec/config.yaml b/openspec/config.yaml new file mode 100644 index 0000000..8c471d9 --- /dev/null +++ b/openspec/config.yaml @@ -0,0 +1,36 @@ +schema: spec-driven + +context: | + AutoQuant V2 is an Agent-native factor and strategy research workbench. + Core evidence is file-backed, versioned, immutable after publication, and + authoritative across CLI, JSON, Studio, and external Agent clients. + The web client uses Next.js 16.2.12, React 19.2.8, Mantine 9.5.1 through the + AutoQuant UI adapter, and lightweight-charts 5.2.0 for K-line evidence. + Reuse existing Study, Session, Experiment, Run, Campaign, Report, snapshot, + and diagnostic contracts. Do not create frontend-only evaluators or a + chat-specific execution path. + FactorDefinition and StrategyDefinition are separate product objects. + GPU and MOSS are optional research executors behind provider-neutral job + receipts; the public repository must remain useful without private adapters. + The upstream visual and interaction authority is the existing Designer + Pipeline change autoquant-replay-research-workbench. Preserve its DESIGN and + MOTION foundation hashes and do not restyle the product independently. + OpenSpec artifacts are written in English. User-facing Studio copy may be + localized, but primary copy must use research language and keep Core names in + Technical Details. + +rules: + proposal: + - State observable product gaps separately from already connected evidence. + - Include explicit non-goals and public/private provider boundaries. + specs: + - Use SHALL requirements and GIVEN/WHEN/THEN scenarios. + - Cover unavailable, failure, negative, inconclusive, budget-exhausted, stop, and reproduction-drift states. + - Never treat demo data, documentation, route compilation, or object inventory as an implemented workflow. + design: + - Map generic controls to installed Mantine primitives and research semantics to AutoQuant domain components. + - Preserve one Operator Port and existing immutable Core evidence contracts. + - Link the upstream Designer Pipeline artifacts and authoritative foundation hashes. + tasks: + - Keep implementation slices vertical, testable, and ordered by contract dependency. + - Separate implementation tasks from verification evidence; unchecked tasks are not complete. diff --git a/plans/agent-research-console.md b/plans/agent-research-console.md new file mode 100644 index 0000000..ca14526 --- /dev/null +++ b/plans/agent-research-console.md @@ -0,0 +1,108 @@ +# Agent Research Console + +- Status: `active` +- Updated: `2026-08-03` +- Related design: [[docs/design/agent-native-quant-workbench]], + [[docs/design/agent-operator-experience]], + [[docs/design/external-researcher-driver]], + [[docs/design/research-session-loop]], + [[docs/design/studio-observation-surface]], and + [[docs/design/quant-research-lifecycle]]. +- OpenSpec change: `add-agent-research-console`. + +## Outcome + +The approved Agent Research Console is executable through one Core-owned, +closed Operator Port from exact research inputs through bounded Campaign +evidence, exact-version artifact decisions, and independent reproduction, with +the Studio projecting the same receipts in a truthful eight-stage +ResearchLedger. + +## Context + +Core already owns strict Studies, Sessions, Experiments, immutable Runs, +Campaign history, Reports, Reviews, Dossiers, ComputeJobs, and the versioned +Studio snapshot. The approved OpenSpec change adds the missing shared operator +boundary, versioned FactorDefinition and ExperimentDefinition inputs, enforced +multi-dimensional Campaign budgets, artifact decisions, reproduction receipts, +and the session-first Studio projection. Designer Pipeline change +`autoquant-replay-research-workbench` remains the visual and interaction fact +source; this plan is only the repository execution record. + +## Scope + +### In scope + +- Implement `openspec/changes/add-agent-research-console` in its declared order. +- Keep FactorDefinition, Factor Passport, ExperimentDefinition, and + StrategyDefinition/Portfolio/ML/RL as separate version-linked objects. +- Add one structured request/receipt path with idempotency, confirmation, + optimistic state checks, path confinement, budget enforcement, and audit. +- Add `/research` and `/research/[sessionId]` over verified Core projections, + including truthful partial/unavailable/invalid/demo-isolated states. +- Preserve CPU-first public execution and provider-neutral unavailable receipts + for optional private GPU/MOSS executors. + +### Out of scope + +- Live accounts, Orders, broker/exchange credentials, or trading execution. +- Private GPU/MOSS implementations, credentials, or vendor-specific code. +- Arbitrary shell, chat-specific authority, browser-side verdicts, or fake + ReplayBundle/market-clock/entity evidence. +- New UI, docking, chart, animation, or runtime dependencies. +- Commit, push, PR, CI, deployment, archive, or provider-account mutation. + +## Acceptance + +- [ ] Core and Studio consume one closed Operator request/receipt schema, with + CLI/JSON and same-origin transport parity. +- [ ] Versioned FactorDefinition and ExperimentDefinition retain immutable + approved history and bind every new Run/receipt to exact versions. +- [ ] Campaign candidate/time/CPU/GPU/cost ceilings and fixed stop conditions + are Core-enforced, including unknown-spend, stop, negative, inconclusive, + provider-unavailable, and holdout-isolation paths. +- [ ] `/research` and `/research/[sessionId]` render the eight-stage ledger and + editable/approval/reproduction surfaces without inventing connected evidence. +- [ ] Targeted Core/frontend suites, full repository checks, strict OpenSpec + validation, production build, browser scenarios, and accessibility checks + provide fresh evidence. + +## Work + +- [ ] Freeze repository locations, schemas, loaders, capabilities, and + compatibility projection. +- [ ] Implement and verify the Core Operator Port plus read-only receipt/ledger + projection. +- [ ] Implement and verify versioned definitions and bounded Campaign authority. +- [ ] Implement and verify the read-only ResearchLedger Studio shell. +- [ ] Enable structured mutations, evidence review, artifact decisions, and + reproduction only after the Core projection is approved. +- [ ] Complete responsive/accessibility/browser verification and record exact + evidence in the OpenSpec checklist without archiving the change. + +## Findings and decisions + +- 2026-08-03 — DESIGN SHA-256 remains + `a16ac2cd0e06fa0e06ecda6f3a368c61ab05bbecca487b209bd44f5d63cdeec2`; + MOTION SHA-256 remains + `3e061d31c8c29b1e09d8e715c2d96ddca4ef741e661bcaea41195ca32e5a38a0`. +- 2026-08-03 — CC Switch real route probes passed for MiniMax + (`MiniMax-M2.7-highspeed`, HTTP 200) and DeepSeek (`deepseek-v4-pro`, HTTP + 200). MiniMax owns Core-only files; DeepSeek owns `studio-web` only. +- 2026-08-03 — Existing dirty worktree changes are preserved as user-owned + context. The two provider routes run sequentially and may not edit the same + file. + +## Verification + +- Pending implementation evidence. + +## Progress log + +- 2026-08-03 — Plan created and registered after the approved OpenSpec and + Designer Pipeline facts were read and foundation hashes were reverified. + +## Completion + +Complete only after the OpenSpec completion evidence is fresh. Do not archive +the change in this task. diff --git a/plans/autoresearch-factor-strategy-studio.md b/plans/autoresearch-factor-strategy-studio.md new file mode 100644 index 0000000..13bcfcb --- /dev/null +++ b/plans/autoresearch-factor-strategy-studio.md @@ -0,0 +1,102 @@ +# AutoResearch factor and strategy Studio + +- Status: `completed` +- Updated: `2026-08-03` +- Related design: [[docs/design/agent-native-quant-workbench]], + [[docs/design/quant-research-lifecycle]], + [[docs/design/research-program-orchestration]], and + [[docs/design/studio-observation-surface]]. + +## Outcome + +The connected Next Studio exposes the existing verified Research Program as +two operator-facing workbenches: Factor Research manages definitions, +dependencies, evidence, Sessions, Campaigns, and status; Strategy Research +manages factor-to-portfolio composition, governed RL policy evidence, +validation, cost, risk, artifacts, and the next bounded research action. + +## Context + +Core already owns Studies, immutable Runs, Sessions, Experiments, Campaigns, +Research Reports, Dossiers, Factor diagnostics, Portfolio evidence, governed +RL evidence, and progression gates. The Next Studio currently scatters those +objects across detail pages and does not give a human or Agent one truthful +management view for Factor research or Strategy research. This work projects +the existing snapshot; it does not create a second research engine or a +browser-side evaluator. + +## Scope + +### In scope + +- Add connected Factor Research and Strategy Research routes. +- Derive display state only from the versioned Core Studio snapshot. +- Show current evidence, mutable research activity, blockers, and exact Core + next actions without inventing missing records. +- Reuse Mantine 9.5.1, AutoQuant tokens, and existing Studio components. + +### Out of scope + +- A new Campaign engine, model-provider adapter, or arbitrary shell endpoint. +- Browser-side metric computation, verdicts, promotion, or report authorship. +- Replacing the existing Factor, Portfolio, RL, Replay, data, job, or audit + detail surfaces. + +## Acceptance + +- [x] Connected Factor Research distinguishes definitions/dependencies, + immutable evidence, mutable Sessions/Campaigns, and missing evidence. +- [x] Connected Strategy Research keeps Portfolio and governed RL as related + but distinct validation lanes, including progression gates and artifacts. +- [x] Both routes show only Core-projected records and remain useful for empty, + partial, active, reported, stale, and failed research states. +- [x] Existing routes remain available and the navigation clearly separates + management from evidence detail. +- [x] Projection logic has runnable Node tests; Studio lint, boundary check, + tests, and production build pass. +- [x] Repository documentation and the existing Designer Pipeline handoff, + tasks, QA, state, and events record the shipped behavior. + +## Work + +- [x] Freeze shared snapshot projection and assign non-overlapping Factor and + Strategy implementation slices. +- [x] Implement and test the Factor Research management surface. +- [x] Implement and test the Strategy Research management surface. +- [x] Integrate navigation and shared presentation with the smallest coherent + diff. +- [x] Update documentation and Designer Pipeline evidence. +- [x] Run the final completion audit. + +## Findings and decisions + +- 2026-08-03 — Reuse the existing Core Research Program, Sessions, Campaigns, + explorers, and commands. Do not build a parallel AutoResearch backend. +- 2026-08-03 — Factor management owns FactorDefinition/version/passport/data + dependencies/cohorts/tests/evidence/status. Strategy management consumes + factor evidence and owns portfolio/rules/model-policy validation evidence. +- 2026-08-03 — Provider execution remains Agent/CLI orchestration until a + provider-neutral bounded adapter is separately designed; the Studio first + exposes truthful status and fixed Study execution. + +## Verification + +- `npm test`: 28/28 passed. +- `npm run lint`: passed. +- `npm run check:boundary`: passed. +- `npm run build`: passed; `/factors` and `/strategies` were generated. + +## Progress log + +- 2026-08-03 — Plan created and registered as active. +- 2026-08-03 — MiniMax Factor slice accepted after contract corrections; + DeepSeek Strategy UI skeleton retained while its speculative projection was + replaced with the repository-owned Core contract. +- 2026-08-03 — Connected routes, management projections, tests, docs, and + production build completed without changing Git or remote systems. + +## Completion + +The Next Studio now has truthful connected Factor Research and Strategy +Research management surfaces over the existing Core snapshot. Missing Program, +Explorer, model, holdout, or Dossier evidence remains explicitly absent. diff --git a/plans/next-studio-workbench.md b/plans/next-studio-workbench.md new file mode 100644 index 0000000..faed614 --- /dev/null +++ b/plans/next-studio-workbench.md @@ -0,0 +1,82 @@ +# Next Studio workbench + +- Status: `completed` +- Updated: `2026-08-02` +- Related design: [[docs/design/studio-observation-surface]] and [[docs/design/next-studio-workbench]]. + +## Outcome + +AutoQuant contains a reviewable Next.js research workbench that internalizes the approved Evidence Console design, consumes the existing verified read-only Studio snapshot, and keeps private host/plugin integrations outside the open-source repository. + +## Context + +The repository currently ships a Python standard-library server and a large packaged HTML/CSS/JavaScript presentation. A separately approved Next.js prototype now covers the complete factor-research front half, but it still uses deterministic demonstration data and lives outside this repository. The merge must preserve Core authority and standalone operation while establishing a better-maintained frontend path. + +The first prototype had no public component-library dependency. The current workbench adopts MIT-licensed Mantine `9.5.1`, which matches React `19.2`, for generic shell, control, status, form and table mechanics while retaining AutoQuant-owned tokens and domain components. + +## Scope + +### In scope + +- Add a repository-owned `studio-web/` Next.js App Router application. +- Reuse the existing `autoquant-studio-snapshot` HTTP contract through one read-only same-origin proxy. +- Preserve the approved three-layer AutoQuant tokens and compose generic UI through the public Mantine component layer. +- Preserve all nine factor-research routes and clearly distinguish connected Core evidence from demonstration records. +- Document and test the open-source/private-plugin boundary. +- Preserve the existing Python Studio and package behavior while the new workbench reaches contract parity. + +### Out of scope + +- Bundling Node.js or compiled Next assets into the Python wheel. +- Replacing `aq studio serve` in this change. +- Mutation endpoints, command execution, remote hosting, authentication, or multi-user support. +- Private plugin implementations, host orchestration protocols, credentials, proprietary payloads, broker access, accounts, orders, or live trading. + +## Acceptance + +- [x] `studio-web/` uses the latest stable Next.js and React versions available on 2026-08-02 and adds no component-library dependency. +- [x] All nine research routes render through one internal token/component system and preserve the approved non-trading product scope. +- [x] Connected mode consumes only the verified read-only Studio snapshot and exposes explicit source, validity, freshness, and diagnostics state. +- [x] Demo mode remains deterministic and visibly labelled; it cannot be confused with verified Core evidence. +- [x] Public source contains no private plugin invocation, credential, endpoint, proprietary payload, or host-only implementation. +- [x] Targeted Python Studio tests, frontend tests, lint, production build, responsive browser checks, documentation checks, and package build pass. + +## Work + +- [x] Audit repository Studio ownership, public snapshot contract, current versions, and plugin/host boundaries. +- [x] Establish design-pipeline foundations and an active integration plan. +- [x] Import and normalize the Next workbench under `studio-web/`. +- [x] Add the read-only Core snapshot bridge and honest connected/demo source state. +- [x] Add boundary tests and durable public documentation. +- [x] Run Python, frontend, build, browser, and open-source leakage checks. +- [x] Complete design-pipeline release-readiness checks. +- [x] Complete final review and PR-ready audit. + +## Findings and decisions + +- 2026-08-02 — Repository `main` is tagged `v0.9.31`; the current Studio is native HTML/CSS/JavaScript over a verified Python snapshot, not a component-library application. +- 2026-08-02 — npm reports Next.js `16.2.12` and React `19.2.8` as the latest stable releases. The new workbench will pin those exact versions for reproducibility. +- 2026-08-02 — The Next workbench is added beside the packaged Studio first. Replacing the wheel-bundled presentation before behavioral parity would couple Python installation to Node and make the change difficult to review or revert. +- 2026-08-02 — The open-source seam is the existing normalized snapshot contract. Private tools may produce Core-owned evidence outside this repository, but Studio receives only verified snapshot data and never contains their invocation logic. +- 2026-08-02 — Next's latest stable release still resolves vulnerable PostCSS and Sharp versions. Repository overrides move only those existing transitive dependencies to patched releases; the official npm audit is clean. + +## Verification + +- Frontend tests, boundary scan, lint, production build, browser routes, npm + production audit, documentation checks, Python compilation, and wheel build + pass locally. +- The complete Python suite ran 455 tests: 436 pass and 19 fail on existing + Windows-only shell/path/CRLF assumptions. The changed documentation and + package boundaries pass their targeted checks; details are recorded in the + change QA artifact. + +## Progress log + +- 2026-08-02 — Plan created after repository, version, snapshot, and host-boundary inspection. +- 2026-08-02 — Workbench, Core bridge, honest source modes, boundary scanner, + documentation, and browser evidence completed. + +## Completion + +Completed with a separate Next.js workbench, a loopback-only read boundary, +an internalized design system, explicit demo evidence, and PR-ready validation. diff --git a/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/manifest.json b/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/manifest.json new file mode 100644 index 0000000..4021e2f --- /dev/null +++ b/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/manifest.json @@ -0,0 +1,6 @@ +{ + "completed": true, + "id": "job-20260802T180952147982Z-781f050d19ca", + "receiptHash": "15ab4fa22bddf03417395d10c76b77e641070718dad7ce7563adc24246676058", + "schemaVersion": 1 +} diff --git a/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/receipt.json b/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/receipt.json new file mode 100644 index 0000000..74c2d18 --- /dev/null +++ b/projects/sample-research-desk/compute-jobs/job-20260802T180952147982Z-781f050d19ca/receipt.json @@ -0,0 +1,84 @@ +{ + "completedAt": "2026-08-02T18:09:58.009278+00:00", + "createdAt": "2026-08-02T18:09:52.148812+00:00", + "error": null, + "executor": { + "kind": "cpu", + "provider": "builtin" + }, + "id": "job-20260802T180952147982Z-781f050d19ca", + "inputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c", + "outputRefs": [ + { + "immutable": true, + "kind": "factor-report", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-report.json" + }, + { + "immutable": true, + "kind": "factor-daily", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/daily-factor-evidence.csv" + }, + { + "immutable": true, + "kind": "factor-quantiles", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-quantiles.csv" + }, + { + "immutable": true, + "kind": "factor-availability", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-availability.csv" + }, + { + "immutable": true, + "kind": "factor-qualification", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-qualification.csv" + }, + { + "immutable": true, + "kind": "factor-components", + "path": "runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-components.json" + } + ], + "project": { + "id": "sample-research-desk" + }, + "resourcePolicy": { + "cpuCores": 1, + "gpuCount": 0, + "memoryMb": null, + "wallTimeSeconds": null + }, 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+SPLIT_NAMES = ("train", "validation", "test") +REGIME_NAMES = ("up-calm", "up-stressed", "down-calm", "down-stressed") +STYLE_NAMES = ( + "momentum_20", + "reversal_5", + "realized_volatility_20", + "relative_volume_20", +) + + +def _ranges(length: int) -> dict[str, tuple[int, int]]: + train_end = int(length * 0.60) + validation_end = int(length * 0.80) + return { + "train": (0, train_end), + "validation": (train_end, validation_end), + "test": (validation_end, length), + } + + +def purged_split_masks( + index: pd.DatetimeIndex, + horizons: tuple[int, ...] = HORIZONS, +) -> tuple[ + dict[int, dict[str, pd.Series]], + dict[str, Any], + pd.Series, +]: + """Build dataset-fixed masks whose targets cannot cross split boundaries.""" + + if not index.is_monotonic_increasing or index.has_duplicates: + raise ValueError("Diagnostic index must be unique and chronological") + ranges = _ranges(len(index)) + positions = np.arange(len(index)) + base_labels = pd.Series("unassigned", index=index, dtype="object") + protocol: dict[str, Any] = { + "method": "dataset-fixed-chronological-60-20-20", + "candidateDependent": False, + "targetCrossesBoundary": False, + "horizons": {}, + "splits": {}, + } + for name, (start, stop) in ranges.items(): + if stop <= start: + raise ValueError(f"Chronological split {name} is empty") + base_labels.iloc[start:stop] = name + protocol["splits"][name] = { + "start": timestamp_label(index[start]), + "end": timestamp_label(index[stop - 1]), + "rows": stop - start, + } + + masks: dict[int, dict[str, pd.Series]] = {} + for horizon in horizons: + if not isinstance(horizon, int) or horizon <= 0: + raise ValueError("Forward horizons must be positive integers") + masks[horizon] = {} + horizon_protocol: dict[str, Any] = {} + for name, (start, stop) in ranges.items(): + if stop - start <= horizon: + raise ValueError( + f"Chronological split {name} is too short for horizon {horizon}" + ) + eligible = (positions >= start) & (positions + horizon < stop) + mask = pd.Series(eligible, index=index, dtype=bool) + masks[horizon][name] = mask + signal_positions = positions[eligible] + horizon_protocol[name] = { + "signalStart": timestamp_label(index[signal_positions[0]]), + "signalEnd": timestamp_label(index[signal_positions[-1]]), + "targetEnd": timestamp_label( + index[signal_positions[-1] + horizon] + ), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + protocol["horizons"][str(horizon)] = horizon_protocol + return masks, protocol, base_labels + + +def chronological_fold_masks( + index: pd.DatetimeIndex, + horizon: int = 1, +) -> tuple[dict[str, pd.Series], dict[str, dict[str, Any]]]: + """Split each fixed chronological partition in half and purge each fold.""" + + positions = np.arange(len(index)) + masks: dict[str, pd.Series] = {} + protocol: dict[str, dict[str, Any]] = {} + for split, (start, stop) in _ranges(len(index)).items(): + middle = start + (stop - start) // 2 + for number, (fold_start, fold_stop) in enumerate( + ((start, middle), (middle, stop)), + start=1, + ): + name = f"{split}_{number}" + eligible = ( + (positions >= fold_start) + & (positions + horizon < fold_stop) + ) + if not eligible.any(): + raise ValueError(f"Chronological fold {name} is empty") + masks[name] = pd.Series(eligible, index=index, dtype=bool) + selected = positions[eligible] + protocol[name] = { + "split": split, + "start": timestamp_label(index[fold_start]), + "end": timestamp_label(index[fold_stop - 1]), + "signalEnd": timestamp_label(index[selected[-1]]), + "targetEnd": timestamp_label(index[selected[-1] + horizon]), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + return masks, protocol + + +def forward_return_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + return { + horizon: closes.shift(-horizon) / closes - 1.0 + for horizon in horizons + } + + +def forward_realized_volatility_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Return complete-window, unannualized forward realized volatility. + + At signal close t and horizon h, the target is the square root of the sum + of squared close-to-close log returns from t -> t+1 through t+h-1 -> t+h. + A missing constituent return invalidates the whole target window. + """ + + log_returns = np.log(closes / closes.shift(1)) + output: dict[int, pd.DataFrame] = {} + for horizon in horizons: + future_squared = [ + log_returns.shift(-offset).pow(2) + for offset in range(1, horizon + 1) + ] + squared_sum = sum(future_squared) + complete = sum(item.notna().astype(int) for item in future_squared) + output[horizon] = squared_sum.pow(0.5).where(complete == horizon) + return output + + +def factor_outcome_panels( + closes: pd.DataFrame, + outcome: str, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Materialize one fixed Factor outcome over the requested horizons.""" + + if outcome == "forward-return": + return forward_return_panels(closes, horizons) + if outcome == "forward-realized-volatility": + return forward_realized_volatility_panels(closes, horizons) + raise ValueError(f"Unsupported Factor outcome: {outcome}") + + +def daily_rank_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, + constant_left_value: float | None = None, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["right"].nunique() < 2: + continue + if pair["left"].nunique() < 2: + if constant_left_value is not None: + values[timestamp] = float(constant_left_value) + continue + value = pair["left"].rank(method="average").corr( + pair["right"].rank(method="average") + ) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def daily_pearson_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["left"].nunique() < 2 or pair["right"].nunique() < 2: + continue + value = pair["left"].corr(pair["right"]) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def hac_inference( + values: pd.Series, + *, + maximum_lag: int = 5, +) -> dict[str, float | int | None | str]: + """Return deterministic Newey-West mean inference with Bartlett weights.""" + + clean = values.dropna().astype(float) + count = len(clean) + lag = min(maximum_lag, max(0, count - 1)) + if count < 2: + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + array = clean.to_numpy(dtype=float) + centered = array - float(array.mean()) + long_run_variance = float(np.dot(centered, centered) / count) + for offset in range(1, lag + 1): + covariance = float( + np.dot(centered[offset:], centered[:-offset]) / count + ) + weight = 1.0 - offset / (lag + 1.0) + long_run_variance += 2.0 * weight * covariance + long_run_variance = max(0.0, long_run_variance) + standard_error = math.sqrt(long_run_variance / count) + if standard_error <= 1e-12: + t_statistic: float | None = None + p_value: float | None = None + else: + t_statistic = float(array.mean()) / standard_error + p_value = math.erfc(abs(t_statistic) / math.sqrt(2.0)) + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": standard_error, + "t_statistic": t_statistic, + "normal_approximation_p_value": p_value, + } + + +def descriptive_ic( + values: pd.Series, + *, + minimum_observations: int = 3, +) -> dict[str, float | int | None | dict[str, Any]]: + clean = values.dropna().astype(float) + if len(clean) < minimum_observations: + hac = hac_inference(clean) + hac.update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return { + "mean_ic": None, + "standard_deviation": None, + "icir": None, + "hit_rate": None, + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": False, + "hac": hac, + } + mean = float(clean.mean()) + standard_deviation = float(clean.std(ddof=0)) + return { + "mean_ic": mean, + "standard_deviation": standard_deviation, + "icir": ( + mean / standard_deviation + if standard_deviation > 1e-12 + else None + ), + "hit_rate": float((clean > 0).mean()), + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": True, + "hac": hac_inference(clean), + } + + +def daily_quantile_outcomes( + factors: pd.DataFrame, + returns: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + rows: list[dict[str, Any]] = [] + for timestamp in factors.index.intersection(returns.index): + pair = pd.DataFrame( + { + "factor": factors.loc[timestamp], + "outcome": returns.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets or pair["factor"].nunique() < 3: + continue + ordered = pair.sort_values( + ["factor"], + kind="mergesort", + ) + groups = np.array_split(np.arange(len(ordered)), 3) + low, middle, high = ( + float(ordered.iloc[group]["outcome"].mean()) + for group in groups + ) + rows.append( + { + "timestamp": timestamp, + "low": low, + "middle": middle, + "high": high, + "high_minus_low": high - low, + } + ) + if not rows: + return pd.DataFrame( + columns=("low", "middle", "high", "high_minus_low"), + index=pd.DatetimeIndex([], name="timestamp"), + ) + return pd.DataFrame(rows).set_index("timestamp").sort_index() + + +def quantile_summary( + daily: pd.DataFrame, + *, + minimum_observations: int = 3, +) -> dict[str, Any]: + clean = daily.dropna() + observations = int(len(clean)) + if observations < minimum_observations: + return { + "mean_outcome_by_quantile": { + "low": None, + "middle": None, + "high": None, + }, + "high_minus_low": None, + "monotonicity": None, + "observations": observations, + } + means = { + label: float(clean[label].mean()) + for label in ("low", "middle", "high") + } + ordered = pd.Series([0.0, 1.0, 2.0]) + ranked_means = pd.Series(list(means.values())).rank(method="average") + monotonicity = ordered.corr(ranked_means) + return { + "mean_outcome_by_quantile": means, + "high_minus_low": float(clean["high_minus_low"].mean()), + "monotonicity": ( + float(monotonicity) + if monotonicity is not None and math.isfinite(float(monotonicity)) + else None + ), + "observations": observations, + } + + +def causal_regime_labels(closes: pd.DataFrame) -> pd.Series: + """Label the signal close using only trailing market information.""" + + market_return = closes.pct_change(fill_method=None).mean(axis=1) + trailing_direction = ( + (1.0 + market_return) + .rolling(20, min_periods=20) + .apply(np.prod, raw=True) + - 1.0 + ) + trailing_volatility = market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0) + lagged_threshold = trailing_volatility.shift(1).rolling( + 60, + min_periods=20, + ).median() + labels = pd.Series(pd.NA, index=closes.index, dtype="object") + valid = ( + trailing_direction.notna() + & trailing_volatility.notna() + & lagged_threshold.notna() + ) + for timestamp in closes.index[valid]: + direction = "up" if trailing_direction.loc[timestamp] >= 0 else "down" + volatility = ( + "stressed" + if trailing_volatility.loc[timestamp] + > lagged_threshold.loc[timestamp] + else "calm" + ) + labels.loc[timestamp] = f"{direction}-{volatility}" + return labels + + +def style_proxy_panels( + closes: pd.DataFrame, + volumes: pd.DataFrame, +) -> dict[str, pd.DataFrame]: + daily_returns = closes.pct_change(fill_method=None) + return { + "momentum_20": closes / closes.shift(20) - 1.0, + "reversal_5": -(closes / closes.shift(5) - 1.0), + "realized_volatility_20": daily_returns.rolling( + 20, + min_periods=20, + ).std(ddof=0), + "relative_volume_20": ( + volumes / volumes.rolling(20, min_periods=20).mean() - 1.0 + ), + } + + +def cross_sectional_rank_residual( + candidate: pd.DataFrame, + style: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + """Remove one contemporaneous style exposure from candidate ranks.""" + + index = candidate.index.intersection(style.index) + columns = candidate.columns.intersection(style.columns) + output = pd.DataFrame(np.nan, index=index, columns=columns, dtype=float) + for timestamp in index: + pair = pd.DataFrame( + { + "candidate": candidate.loc[timestamp, columns], + "style": style.loc[timestamp, columns], + } + ).dropna() + if ( + len(pair) < minimum_assets + or pair["candidate"].nunique() < 2 + or pair["style"].nunique() < 2 + ): + continue + candidate_rank = pair["candidate"].rank(method="average", pct=True) + style_rank = pair["style"].rank(method="average", pct=True) + candidate_centered = candidate_rank - float(candidate_rank.mean()) + style_centered = style_rank - float(style_rank.mean()) + denominator = float(np.dot(style_centered, style_centered)) + if denominator <= 1e-15: + continue + beta = float( + np.dot(style_centered, candidate_centered) / denominator + ) + output.loc[timestamp, pair.index] = ( + candidate_centered - beta * style_centered + ) + return output + + +def equal_rank_blend( + candidate: pd.DataFrame, + style: pd.DataFrame, +) -> pd.DataFrame: + """Return a same-timestamp equal blend of cross-sectional percentile ranks.""" + + candidate_rank = candidate.rank(axis=1, method="average", pct=True) + style_rank = style.rank(axis=1, method="average", pct=True) + available = candidate.notna() & style.notna() + return ((candidate_rank + style_rank) / 2.0).where(available) + + +def per_asset_rank_correlation( + factors: pd.DataFrame, + returns: pd.DataFrame, + mask: pd.Series, + *, + minimum_observations: int = 10, +) -> dict[str, dict[str, float | int | None]]: + result: dict[str, dict[str, float | int | None]] = {} + for asset in factors.columns.intersection(returns.columns): + pair = pd.DataFrame( + { + "factor": factors.loc[mask, asset], + "forward_return": returns.loc[mask, asset], + } + ).dropna() + value: float | None = None + if ( + len(pair) >= minimum_observations + and pair["factor"].nunique() >= 2 + and pair["forward_return"].nunique() >= 2 + ): + correlation = pair["factor"].rank(method="average").corr( + pair["forward_return"].rank(method="average") + ) + if correlation is not None and math.isfinite(float(correlation)): + value = float(correlation) + result[str(asset)] = { + "rank_correlation": value, + "observations": int(len(pair)), + } + return result diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/judge-sources/judges/ohlcv_factor.py b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/judge-sources/judges/ohlcv_factor.py new file mode 100644 index 0000000..f4f0311 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/judge-sources/judges/ohlcv_factor.py @@ -0,0 +1,2657 @@ +"""Fixed no-lookahead Judge for the OHLCV Factor Lab reference Project.""" + +from __future__ import annotations + +import importlib +import json +import math +import os +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.factor_runtime import ( + FactorRuntimeError, + build_factor_panel, + evaluate_factor, + factor_contract, + values_to_wide, +) +from autoquant.factor_claims import ( + FACTOR_CLAIM, + FORWARD_RETURN_OUTCOME, + factor_outcome, + factor_outcome_contract, + load_factor_claim, +) +from autoquant.intervals import ( + IntervalContractError, + load_multi_interval_asset, + timestamp_label, +) +from autoquant.prediction_modes import ( + CROSS_SECTIONAL_MODE, + FACTOR_POPULATION, + SINGLE_ASSET_TEMPORAL_MODE, + TEMPORAL_EVALUATION_MODES, + TWO_ASSET_RELATIVE_VALUE_MODE, + PredictionModeError, + load_factor_population, + resolve_prediction_population, +) +from autoquant.horizons import ( + RESEARCH_HORIZON, + load_research_horizon, +) +from judges.factor_diagnostics import ( + HORIZONS, + REGIME_NAMES, + STYLE_NAMES, + causal_regime_labels, + chronological_fold_masks, + cross_sectional_rank_residual, + daily_pearson_correlation, + daily_quantile_outcomes, + daily_rank_correlation, + descriptive_ic, + equal_rank_blend, + factor_outcome_panels, + hac_inference, + per_asset_rank_correlation, + purged_split_masks, + quantile_summary, + style_proxy_panels, +) + + +REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume") +MIN_ASSETS_PER_DATE = 4 +MIN_IC_DATES_PER_SPLIT = 20 +PRIMARY_HORIZON = 1 +TEMPORAL_QUALIFICATION_METHOD = ( + "request-claim-aware-one-style-temporal-neutralization-v1" +) + + +class JudgeFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +def _write_output(value: dict[str, Any]) -> None: + Path(os.environ["AUTOQUANT_RUN_OUTPUT"]).write_text( + json.dumps(value, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _load_contract() -> tuple[dict[str, Any], Path]: + study = json.loads( + Path(os.environ["AUTOQUANT_STUDY_PATH"]).read_text(encoding="utf-8") + ) + data_root = Path(os.environ["AUTOQUANT_DATA_ROOT"]).resolve() + if not data_root.is_dir(): + raise JudgeFailure("dataset.root", "AUTOQUANT_DATA_ROOT is not a directory") + return study, data_root + + +def _load_horizon() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / RESEARCH_HORIZON + try: + return load_research_horizon(path) + except Exception as error: + raise JudgeFailure( + "horizon.contract", + f"Invalid fixed Horizon Mandate: {error}", + ) from error + + +def _load_factor_claim() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_CLAIM + try: + return load_factor_claim(path) + except Exception as error: + raise JudgeFailure( + "factor-claim.contract", + f"Invalid fixed Factor claim: {error}", + ) from error + + +def _load_prediction_universe( + research_universe: list[str], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], list[str], list[str], str, str]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_POPULATION + try: + factor_population = load_factor_population(path) + except Exception as error: + raise JudgeFailure( + "prediction-universe.contract", + f"Invalid fixed prediction-universe authority: {error}", + ) from error + try: + population = resolve_prediction_population( + research_universe, + factor_claim, + factor_population, + ) + except PredictionModeError as error: + raise JudgeFailure(error.code, str(error)) from error + return ( + population.as_metrics(), + list(population.prediction_assets), + list(population.context_assets), + population.authority, + population.evaluation_mode, + ) + + +def _load_asset(data_root: Path, asset: str, start: str, end: str) -> pd.DataFrame: + try: + multi_interval = load_multi_interval_asset( + data_root, + asset, + start=start, + end=end, + ) + except IntervalContractError as error: + raise JudgeFailure(error.code, str(error)) from error + if multi_interval is not None: + if len(multi_interval) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 base observations in the Study range", + ) + return multi_interval + source = (data_root / "ohlcv" / f"{asset}.csv").resolve() + if data_root not in source.parents or not source.is_file(): + raise JudgeFailure("dataset.asset", f"Missing confined OHLCV file for {asset}") + frame = pd.read_csv(source) + if tuple(frame.columns) != REQUIRED_COLUMNS: + raise JudgeFailure( + "dataset.columns", + f"{asset} columns must be exactly {', '.join(REQUIRED_COLUMNS)}", + ) + frame["timestamp"] = pd.to_datetime( + frame["timestamp"], + format="%Y-%m-%d", + errors="raise", + ) + if frame["timestamp"].duplicated().any() or not frame["timestamp"].is_monotonic_increasing: + raise JudgeFailure( + "dataset.time-order", + f"{asset} timestamps must be unique and chronological", + ) + for column in REQUIRED_COLUMNS[1:]: + frame[column] = pd.to_numeric(frame[column], errors="raise") + numeric = frame[list(REQUIRED_COLUMNS[1:])].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise JudgeFailure("dataset.non-finite", f"{asset} contains non-finite OHLCV") + if (frame[["open", "high", "low", "close", "volume"]] <= 0).any().any(): + raise JudgeFailure("dataset.non-positive", f"{asset} contains non-positive OHLCV") + if ( + (frame["high"] < frame[["open", "close"]].max(axis=1)).any() + or (frame["low"] > frame[["open", "close"]].min(axis=1)).any() + ): + raise JudgeFailure("dataset.bar-shape", f"{asset} contains invalid bars") + selected = frame[ + (frame["timestamp"] >= pd.Timestamp(start)) + & (frame["timestamp"] <= pd.Timestamp(end)) + ].copy() + if len(selected) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 observations in the Study range", + ) + return selected.reset_index(drop=True) + + +def _split_metrics(values: pd.Series) -> dict[str, Any]: + if len(values) < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "judge.population", + f"Chronological split has only {len(values)} valid IC dates", + ) + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _component_split_metrics(values: pd.Series) -> dict[str, Any]: + """Disclose sparse component evidence without failing a valid final factor.""" + + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _masked(values: pd.Series, mask: pd.Series) -> pd.Series: + return values.reindex(mask.index[mask]).dropna() + + +def _temporal_correlation_contributions( + left: pd.Series, + right: pd.Series, + mask: pd.Series, + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Return timestamp contributions whose mean is one split correlation.""" + + selected = pd.DataFrame( + { + "left": left.reindex(mask.index[mask]), + "right": right.reindex(mask.index[mask]), + } + ).dropna() + result = pd.Series(index=mask.index, dtype=float) + if len(selected) < 3: + return result + if rank: + selected = selected.rank(method="average", pct=True) + left_centered = selected["left"] - float(selected["left"].mean()) + right_centered = selected["right"] - float(selected["right"].mean()) + denominator = math.sqrt( + float((left_centered**2).mean()) + * float((right_centered**2).mean()) + ) + if denominator <= 1e-12: + if constant_left_value is not None: + result.loc[selected.index] = float(constant_left_value) + return result + result.loc[selected.index] = ( + left_centered * right_centered / denominator + ) + return result + + +def _temporal_daily( + left_panel: pd.DataFrame, + right_panel: pd.DataFrame, + masks: dict[str, pd.Series], + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Evaluate one prediction asset across time without context targets.""" + + left = left_panel.iloc[:, 0] + right = right_panel.iloc[:, 0] + result = pd.Series(index=left_panel.index, dtype=float) + for split in ("train", "validation", "test"): + contribution = _temporal_correlation_contributions( + left, + right, + masks[split], + rank=rank, + constant_left_value=constant_left_value, + ) + result.loc[contribution.dropna().index] = contribution.dropna() + return result + + +def _preflight_temporal_primary_validation( + factor_panel: pd.DataFrame, + outcome_panel: pd.DataFrame, + validation_mask: pd.Series, + *, + evaluation_mode: str, + horizon: int, +) -> None: + """Reject an unavailable fixed objective with an exact research reason.""" + + selected = pd.DataFrame( + { + "factor": factor_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + "outcome": outcome_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + } + ).dropna() + observations = int(len(selected)) + factor_values = int(selected["factor"].nunique()) + target_values = int(selected["outcome"].nunique()) + context = ( + f"evaluationMode={evaluation_mode}, split=validation, " + f"primaryHorizon={horizon}, pairedObservations={observations}, " + f"distinctFactorValues={factor_values}, " + f"distinctTargetValues={target_values}, " + f"minimumObservations={MIN_IC_DATES_PER_SPLIT}" + ) + if observations < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "factor.temporal-primary-observations", + "Primary temporal validation has too few finite factor/target " + f"pairs ({context})", + ) + if factor_values < 2: + raise JudgeFailure( + "factor.temporal-primary-candidate-variation", + "Primary temporal validation candidate has no usable variation " + f"({context})", + ) + if target_values < 2: + raise JudgeFailure( + "factor.temporal-primary-target-variation", + "Primary temporal validation outcome has no usable variation " + f"({context})", + ) + + +def _relative_value_spread_panel( + panel: pd.DataFrame, + prediction_assets: list[str], +) -> pd.DataFrame: + """Reduce one authorized pair to the causal first-minus-second contrast.""" + + if len(prediction_assets) != 2: + raise JudgeFailure( + "prediction-universe.relative-value-pair", + "Relative-value spread construction requires exactly two assets", + ) + left, right = prediction_assets + return pd.DataFrame( + { + f"{left}-minus-{right}": panel[left] - panel[right], + }, + index=panel.index, + ) + + +def _temporal_transform_panels( + candidate: pd.DataFrame, + style: pd.DataFrame, + masks: dict[str, pd.Series], +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Build target-free temporal residual and equal-rank blend panels.""" + + residual = pd.DataFrame(index=candidate.index, columns=candidate.columns) + blend = pd.DataFrame(index=candidate.index, columns=candidate.columns) + column = candidate.columns[0] + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + pair = pd.DataFrame( + { + "candidate": candidate[column].reindex(index), + "style": style[column].reindex(index), + } + ).dropna() + if pair.empty: + continue + ranks = pair.rank(method="average", pct=True) + candidate_centered = ranks["candidate"] - float( + ranks["candidate"].mean() + ) + style_centered = ranks["style"] - float(ranks["style"].mean()) + denominator = float((style_centered**2).sum()) + beta = ( + float((candidate_centered * style_centered).sum()) / denominator + if denominator > 1e-12 + else 0.0 + ) + residual.loc[pair.index, column] = ( + candidate_centered - beta * style_centered + ) + blend.loc[pair.index, column] = ( + ranks["candidate"] + ranks["style"] + ) / 2.0 + return residual.astype(float), blend.astype(float) + + +def _temporal_split_metrics( + values: pd.Series, + *, + horizon: int, + minimum_observations: int = MIN_IC_DATES_PER_SPLIT, +) -> dict[str, Any]: + clean = values.dropna().astype(float) + result = descriptive_ic( + clean, + minimum_observations=minimum_observations, + ) + result["hac"] = hac_inference(clean, maximum_lag=max(1, int(horizon))) + if len(clean) < minimum_observations: + result["hac"].update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return result + + +def _style_summary(values: pd.Series) -> dict[str, float | int | None]: + clean = values.dropna().astype(float) + if len(clean) < 3: + return { + "mean_rank_correlation": None, + "mean_absolute_rank_correlation": None, + "observations": int(len(clean)), + } + return { + "mean_rank_correlation": float(clean.mean()), + "mean_absolute_rank_correlation": float(clean.abs().mean()), + "observations": int(len(clean)), + } + + +def _count_summary(values: pd.Series) -> dict[str, float | int]: + clean = values.astype(int) + return { + "minimum": int(clean.min()), + "median": float(clean.median()), + "maximum": int(clean.max()), + } + + +def _equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + *, + common_available: pd.DataFrame | None = None, +) -> pd.DataFrame: + names = list(panels) + first = panels[names[0]] + if common_available is None: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for panel in panels.values(): + common_available &= panel.notna() + ranks = [ + panel.rank(axis=1, method="average", pct=True) + for panel in panels.values() + ] + return (sum(ranks) / float(len(ranks))).where(common_available) + + +def _temporal_equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + masks: dict[str, pd.Series], + *, + common_available: pd.Series | None = None, +) -> pd.DataFrame: + """Build one target-free equal-rank blend within each fixed split.""" + + names = list(panels) + first = panels[names[0]] + column = first.columns[0] + if common_available is None: + common_available = pd.Series(True, index=first.index) + for panel in panels.values(): + common_available &= panel.iloc[:, 0].notna() + blend = pd.DataFrame(index=first.index, columns=[column], dtype=float) + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + values = pd.DataFrame( + { + name: panels[name].iloc[:, 0].reindex(index) + for name in names + } + ) + ranks = values.rank(method="average", pct=True) + selected = ranks.mean(axis=1).where( + common_available.reindex(index).fillna(False) + ) + blend.loc[selected.index, column] = selected + return blend.astype(float) + + +def _context_distribution(values: pd.Series) -> dict[str, Any]: + clean = values.dropna().astype(float) + if clean.empty: + return { + "observations": 0, + "mean": None, + "standard_deviation": None, + "minimum": None, + "quartile_25": None, + "median": None, + "quartile_75": None, + "maximum": None, + } + return { + "observations": int(len(clean)), + "mean": float(clean.mean()), + "standard_deviation": ( + float(clean.std(ddof=1)) if len(clean) > 1 else 0.0 + ), + "minimum": float(clean.min()), + "quartile_25": float(clean.quantile(0.25)), + "median": float(clean.median()), + "quartile_75": float(clean.quantile(0.75)), + "maximum": float(clean.max()), + } + + +def _timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Diagnose one cross-section-constant causal market-state component.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_daily = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _component_split_metrics( + _masked( + factor_daily[horizon], + split_masks[horizon][split] + & states.eq(state), + ) + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-timestamp-context-v1", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _temporal_timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Condition temporal Factor correlation contributions on fixed states.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_contributions = { + horizon: _temporal_daily( + factor_panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _temporal_split_metrics( + _masked( + factor_contributions[horizon], + split_masks[horizon][split] + & states.eq(state), + ), + horizon=horizon, + minimum_observations=3, + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-temporal-context-v2", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "conditional_measure": ( + "within-split-temporal-rank-correlation-contribution" + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _component_evidence( + declarations: list[dict[str, Any]], + component_panels: dict[str, pd.DataFrame], + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + coverage: dict[str, dict[str, float]], + evaluation_mode: str, + outcome_contract: dict[str, Any], +) -> dict[str, Any]: + """Build target-fixed diagnostics for candidate-declared components.""" + + temporal = evaluation_mode in TEMPORAL_EVALUATION_MODES + metadata_by_name = {item["id"]: item for item in declarations} + all_names = list(component_panels) + names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "cross-sectional-score" + ] + context_names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "timestamp-context" + ] + score_panels = { + name: ( + _relative_value_spread_panel( + component_panels[name], + list(component_panels[name].columns), + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else component_panels[name].iloc[:, :1] + if temporal + else component_panels[name] + ) + for name in names + } + + def target_daily( + panel: pd.DataFrame, + horizon: int, + *, + constant_left_value: float | None = None, + ) -> pd.Series: + if temporal: + return _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=constant_left_value, + ) + return daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=constant_left_value, + ) + + def split_quality( + daily: pd.Series, + horizon: int, + split: str, + ) -> dict[str, Any]: + selected = _masked(daily, split_masks[horizon][split]) + return ( + _temporal_split_metrics(selected, horizon=horizon) + if temporal + else _component_split_metrics(selected) + ) + + def association_daily( + left: pd.DataFrame, + right: pd.DataFrame, + ) -> pd.Series: + if temporal: + return _temporal_daily( + left, + right, + split_masks[PRIMARY_HORIZON], + rank=True, + ) + return daily_rank_correlation( + left, + right, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + + raw_daily = { + name: { + horizon: target_daily(score_panels[name], horizon) + for horizon in HORIZONS + } + for name in names + } + raw_quality = { + name: { + str(horizon): { + split: split_quality( + raw_daily[name][horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + for name in names + } + composite_association_daily = { + name: association_daily(score_panels[name], factor_panel) + for name in names + } + composite_association = { + name: { + split: _style_summary( + _masked( + composite_association_daily[name], + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + } + for name in names + } + + pair_daily: dict[frozenset[str], pd.Series] = {} + pairwise: list[dict[str, Any]] = [] + for left_index, left in enumerate(names): + for right in names[left_index + 1 :]: + daily = association_daily( + score_panels[left], + score_panels[right], + ) + pair_daily[frozenset((left, right))] = daily + pairwise.append( + { + "left": left, + "right": right, + "splits": { + split: _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + }, + } + ) + + nearest_peers: dict[str, str | None] = {} + for name in names: + candidates: list[tuple[str, float]] = [] + for pair, daily in pair_daily.items(): + if name not in pair: + continue + peer = next(item for item in pair if item != name) + summary = _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON]["train"], + ) + ) + absolute = summary["mean_absolute_rank_correlation"] + if absolute is not None: + candidates.append((peer, float(absolute))) + nearest_peers[name] = ( + min(candidates, key=lambda item: (-item[1], item[0]))[0] + if candidates + else None + ) + + residual_quality: dict[str, Any] = {} + for name in names: + peer = nearest_peers[name] + if peer is None: + residual_quality[name] = { + "peer": None, + "selection": ( + "unavailable-single-component" + if len(names) == 1 + else "unavailable-no-finite-train-peer" + ), + "horizon_quality": None, + } + continue + residual = ( + _temporal_transform_panels( + score_panels[name], + score_panels[peer], + split_masks[PRIMARY_HORIZON], + )[0] + if temporal + else cross_sectional_rank_residual( + score_panels[name], + score_panels[peer], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + residual_daily = { + horizon: target_daily( + residual, + horizon, + constant_left_value=0.0, + ) + for horizon in HORIZONS + } + residual_quality[name] = { + "peer": peer, + "selection": ( + "maximum-absolute-train-temporal-rank-association" + if temporal + else "maximum-absolute-mean-train-daily-rank-association" + ), + "horizon_quality": { + str(horizon): { + split: split_quality( + residual_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + }, + } + + common_available: pd.DataFrame | pd.Series | None = None + full_blend_quality: dict[str, Any] | None = None + if names: + first = score_panels[names[0]] + if temporal: + common_available = pd.Series(True, index=first.index) + for name in names: + common_available &= score_panels[name].iloc[:, 0].notna() + full_blend = _temporal_equal_rank_component_blend( + {name: score_panels[name] for name in names}, + split_masks[PRIMARY_HORIZON], + common_available=common_available, + ) + else: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for name in names: + common_available &= score_panels[name].notna() + full_blend = _equal_rank_component_blend( + {name: score_panels[name] for name in names}, + common_available=common_available, + ) + full_blend_daily = { + horizon: target_daily(full_blend, horizon) + for horizon in HORIZONS + } + full_blend_quality = { + str(horizon): { + split: split_quality( + full_blend_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + ablations: dict[str, Any] = {} + for name in names: + remaining = { + candidate: score_panels[candidate] + for candidate in names + if candidate != name + } + if not remaining: + ablations[name] = { + "available": False, + "reason": "single-component", + "horizon_quality": None, + "removal_delta_mean_ic": None, + } + continue + leave_one_out = ( + _temporal_equal_rank_component_blend( + remaining, + split_masks[PRIMARY_HORIZON], + # Score-only common availability is intentionally fixed + # before leave-one-out so every ablation uses one population. + common_available=common_available, + ) + if temporal + else _equal_rank_component_blend( + remaining, + common_available=common_available, + ) + ) + leave_daily = { + horizon: target_daily(leave_one_out, horizon) + for horizon in HORIZONS + } + quality = { + str(horizon): { + split: split_quality( + leave_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + removal_delta: dict[str, float | None] = {} + for split in ("train", "validation", "test"): + primary = str(PRIMARY_HORIZON) + leave_mean = quality[primary][split]["mean_ic"] + full_mean = full_blend_quality[primary][split]["mean_ic"] + removal_delta[split] = ( + float(leave_mean) - float(full_mean) + if leave_mean is not None and full_mean is not None + else None + ) + ablations[name] = { + "available": True, + "reason": None, + "horizon_quality": quality, + "removal_delta_mean_ic": removal_delta, + } + + component_rows: list[dict[str, Any]] = [] + for name in names: + primary = str(PRIMARY_HORIZON) + raw_validation = raw_quality[name][primary]["validation"]["mean_ic"] + residual = residual_quality[name] + residual_validation = ( + residual["horizon_quality"][primary]["validation"]["mean_ic"] + if residual["horizon_quality"] is not None + else None + ) + removal_delta = ( + ablations[name]["removal_delta_mean_ic"]["validation"] + if ablations[name]["available"] + else None + ) + peer = nearest_peers[name] + train_redundancy = None + if peer is not None: + train_redundancy = _style_summary( + _masked( + pair_daily[frozenset((name, peer))], + split_masks[PRIMARY_HORIZON]["train"], + ) + )["mean_absolute_rank_correlation"] + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": raw_quality[name], + "composite_association": composite_association[name], + "nearest_peer": { + "id": peer, + "train_mean_absolute_rank_association": train_redundancy, + }, + "nearest_peer_residual": residual, + "fixed_blend_ablation": ablations[name], + "timestamp_context": None, + "validation_priority_inputs": { + "raw_mean_ic": raw_validation, + "nearest_peer_residual_mean_ic": residual_validation, + "removal_delta_mean_ic": removal_delta, + }, + } + ) + for name in context_names: + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": None, + "composite_association": None, + "nearest_peer": { + "id": None, + "train_mean_absolute_rank_association": None, + }, + "nearest_peer_residual": { + "peer": None, + "selection": "not-applicable-timestamp-context", + "horizon_quality": None, + }, + "fixed_blend_ablation": { + "available": False, + "reason": "not-applicable-timestamp-context", + "horizon_quality": None, + "removal_delta_mean_ic": None, + }, + "timestamp_context": ( + _temporal_timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + if temporal + else _timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + ), + "validation_priority_inputs": { + "raw_mean_ic": None, + "nearest_peer_residual_mean_ic": None, + "removal_delta_mean_ic": None, + }, + } + ) + + raw_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"]["raw_mean_ic"] is not None + ] + strongest_raw = ( + max( + raw_candidates, + key=lambda row: ( + float(row["validation_priority_inputs"]["raw_mean_ic"]), + row["id"], + ), + ) + if raw_candidates + else None + ) + residual_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] is not None + ] + strongest_residual = ( + max( + residual_candidates, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + ), + row["id"], + ), + ) + if residual_candidates + else None + ) + removable = [ + row + for row in component_rows + if row["validation_priority_inputs"]["removal_delta_mean_ic"] + is not None + ] + best_removal = ( + max( + removable, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + ), + row["id"], + ), + ) + if removable + else None + ) + finite_pairs = [ + row + for row in pairwise + if row["splits"]["train"]["mean_absolute_rank_correlation"] + is not None + ] + most_redundant = ( + max( + finite_pairs, + key=lambda row: ( + float( + row["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + or -1.0 + ), + row["left"], + row["right"], + ), + ) + if finite_pairs + else None + ) + return { + "method": "candidate-declared-components-v3", + "declaration": { + "exhaustive_composition_claim": False, + "source_inference": False, + "components": declarations, + }, + "semantics": { + "evaluation_mode": evaluation_mode, + "prediction_target": outcome_contract["targetSemantics"], + "score_measure": ( + "within-split-temporal-rank-correlation-contribution" + if temporal + else "per-date-cross-sectional-rank-ic" + ), + "component_roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "nearest_peer_selection": "train-only-target-free", + "residualization": ( + "within-split-temporal-centered-rank-ols" + if temporal + else "same-timestamp-cross-sectional-centered-rank-ols" + ), + "diagnostic_blend": ( + "equal-weight-within-split-temporal-percentile-ranks-with-" + "common-component-availability" + if temporal + else "equal-weight-cross-sectional-percentile-ranks-with-" + "common-component-availability" + ), + "ablation_target": "fixed-diagnostic-blend-not-candidate-factor", + "timestamp_context": ( + "train-tertile-occupancy-transition-and-conditional-temporal-" + "rank-correlation-contribution" + if temporal + else "train-tertile-occupancy-transition-and-conditional-" + "factor-ic" + ), + "selection_authority": "research-prioritization-only", + "test_role": "visible-audit", + "promotion_authority": "none", + "portfolio_authority": "none", + "rl_action_authority": "none", + "trading_authority": "none", + }, + "trial_disclosure": { + "materialized_components": len(all_names), + "cross_sectional_score_components": len(names), + "timestamp_context_components": len(context_names), + "pairwise_comparisons": len(pairwise), + "component_diagnostics_enter_promotion_score": False, + }, + "components": component_rows, + "pairwise": pairwise, + "fixed_blend": { + "available": full_blend_quality is not None, + "reason": ( + None + if full_blend_quality is not None + else "no-cross-sectional-score-components" + ), + "horizon_quality": full_blend_quality, + }, + "validation_diagnosis": { + "strongest_raw_component": ( + strongest_raw["id"] if strongest_raw is not None else None + ), + "strongest_raw_mean_ic": ( + strongest_raw["validation_priority_inputs"]["raw_mean_ic"] + if strongest_raw is not None + else None + ), + "strongest_residual_component": ( + strongest_residual["id"] + if strongest_residual is not None + else None + ), + "strongest_residual_mean_ic": ( + strongest_residual["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + if strongest_residual is not None + else None + ), + "removal_most_improves_fixed_blend": ( + best_removal["id"] if best_removal is not None else None + ), + "best_removal_delta_mean_ic": ( + best_removal["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + if best_removal is not None + else None + ), + "most_redundant_pair": ( + { + "left": most_redundant["left"], + "right": most_redundant["right"], + "train_mean_absolute_rank_association": ( + most_redundant["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + ), + } + if most_redundant is not None + else None + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + }, + } + + +def _decay_summary( + horizon_metrics: dict[str, dict[str, dict[str, Any]]], +) -> dict[str, dict[str, Any]]: + result: dict[str, dict[str, Any]] = {} + for split in ("train", "validation", "test"): + means = { + horizon: horizon_metrics[horizon][split]["mean_ic"] + for horizon in (str(item) for item in HORIZONS) + } + primary = means[str(PRIMARY_HORIZON)] + ratios: dict[str, float | None] = {} + for horizon in (str(item) for item in HORIZONS): + if horizon == str(PRIMARY_HORIZON): + continue + value = means[horizon] + ratios[ + f"horizon_{horizon}_to_{PRIMARY_HORIZON}" + ] = ( + float(value) / float(primary) + if value is not None + and primary is not None + and abs(float(primary)) > 1e-12 + else None + ) + result[split] = { + "mean_ic_by_horizon": means, + **ratios, + } + return result + + +def _factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Build request-claim-aware style comparison and qualification evidence.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name][ + "observations" + ], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only style overlap is available", + ) + dominant_style = ( + factor_claim["knownStyle"] + if factor_claim["claim"] == "known-style-validation" + else min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + ) + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel = cross_sectional_rank_residual( + factor_panel, + style_panel, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + blend_panel = equal_rank_blend(factor_panel, style_panel) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ) + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + residual_folds = { + name: descriptive_ic( + _masked( + daily["style_neutral_candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + candidate_folds = { + name: descriptive_ic( + _masked( + daily["candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": "request-claim-aware-one-style-rank-neutralization-v2", + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": ( + "request-predeclared-known-style" + if factor_claim["claim"] == "known-style-validation" + else "maximum-absolute-mean-daily-rank-overlap" + ), + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": ( + "same-timestamp-cross-sectional-centered-rank-ols" + ), + "blend": "equal-weight-cross-sectional-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _temporal_factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Qualify one request-authorized asset by association across time.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name]["observations"], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only temporal style overlap is available", + ) + dominant_style = min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel, blend_panel = _temporal_transform_panels( + factor_panel, + style_panel, + split_masks[PRIMARY_HORIZON], + ) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _temporal_split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + def fold_quality(panel: pd.DataFrame) -> dict[str, dict[str, Any]]: + return { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + panel.iloc[:, 0], + outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + constant_left_value=( + 0.0 if panel is residual_panel else None + ), + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + + candidate_folds = fold_quality(factor_panel) + residual_folds = fold_quality(residual_panel) + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": TEMPORAL_QUALIFICATION_METHOD, + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": "maximum-absolute-mean-temporal-rank-overlap", + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": "within-split-temporal-centered-rank-ols", + "blend": "equal-weight-within-split-temporal-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _evaluate() -> tuple[ + dict[str, Any], + dict[str, Any], + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, Any] | None, +]: + global HORIZONS, PRIMARY_HORIZON + study, data_root = _load_contract() + research_horizon = _load_horizon() + factor_claim = _load_factor_claim() + outcome_kind = factor_outcome(factor_claim) + outcome_contract = factor_outcome_contract(factor_claim) + HORIZONS = tuple(research_horizon["diagnosticForwardBars"]) + PRIMARY_HORIZON = int(research_horizon["primaryForwardBars"]) + dataset = study["dataset"] + universe = dataset["universe"] + ( + prediction_population, + prediction_assets, + context_assets, + prediction_authority, + evaluation_mode, + ) = _load_prediction_universe(universe, factor_claim) + if ( + outcome_kind != FORWARD_RETURN_OUTCOME + and evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + ): + raise JudgeFailure( + "factor.outcome-relative-value", + "Forward realized volatility does not define a two-asset " + "relative-value target contrast", + ) + minimum_evaluation_assets = ( + 1 + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + 2 + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else MIN_ASSETS_PER_DATE + ) + ) + time_range = dataset["time_range"] + module = importlib.import_module("factors.candidate") + frames: dict[str, pd.DataFrame] = {} + close_by_asset: dict[str, pd.Series] = {} + volume_by_asset: dict[str, pd.Series] = {} + for asset in universe: + frame = _load_asset( + data_root, + asset, + time_range["start"], + time_range["end"], + ) + frames[asset] = frame + timestamp = pd.DatetimeIndex(frame["timestamp"]) + close = frame["close"].copy() + close.index = timestamp + volume = frame["volume"].copy() + volume.index = timestamp + close_by_asset[asset] = close + volume_by_asset[asset] = volume + + try: + panel = build_factor_panel(frames, universe=universe) + factor_evaluation = evaluate_factor(module, panel) + factor_panel = values_to_wide( + panel, + factor_evaluation.values, + universe=universe, + ) + except FactorRuntimeError as error: + raise JudgeFailure(error.code, str(error)) from error + coverage = { + asset: float( + factor_evaluation.values.loc[panel["asset"] == asset].notna().mean() + ) + for asset in universe + } + source_factor_panel = factor_panel + source_close_panel = pd.DataFrame(close_by_asset).reindex( + source_factor_panel.index + ) + source_volume_panel = pd.DataFrame(volume_by_asset).reindex( + source_factor_panel.index + ) + research_factor_panel = source_factor_panel + research_close_panel = source_close_panel + research_volume_panel = source_volume_panel + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE: + prediction_timeline = pd.DatetimeIndex( + close_by_asset[prediction_assets[0]].index + ) + research_factor_panel = research_factor_panel.reindex( + prediction_timeline + ) + research_close_panel = research_close_panel.reindex( + prediction_timeline + ) + research_volume_panel = research_volume_panel.reindex( + prediction_timeline + ) + factor_panel = research_factor_panel[prediction_assets] + close_panel = research_close_panel[prediction_assets] + timeline = pd.DatetimeIndex(research_factor_panel.index) + split_masks, split_protocol, base_split_labels = purged_split_masks( + timeline, + HORIZONS, + ) + fold_masks, fold_protocol = chronological_fold_masks( + timeline, + PRIMARY_HORIZON, + ) + outcome_panels = factor_outcome_panels( + close_panel, + outcome_kind, + HORIZONS, + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE: + association_factor_panel = _relative_value_spread_panel( + factor_panel, + prediction_assets, + ) + association_outcome_panels = { + horizon: _relative_value_spread_panel( + outcome_panels[horizon], + prediction_assets, + ) + for horizon in HORIZONS + } + else: + association_factor_panel = factor_panel + association_outcome_panels = outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + _preflight_temporal_primary_validation( + association_factor_panel, + association_outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON]["validation"], + evaluation_mode=evaluation_mode, + horizon=PRIMARY_HORIZON, + ) + source_input_counts = source_close_panel.notna().sum(axis=1).astype(int) + source_factor_counts = source_factor_panel.notna().sum(axis=1).astype(int) + input_counts = close_panel.notna().sum(axis=1).astype(int) + factor_counts = factor_panel.notna().sum(axis=1).astype(int) + paired_counts = { + horizon: ( + factor_panel.notna() & outcome_panels[horizon].notna() + ).sum(axis=1).astype(int) + for horizon in HORIZONS + } + source_timeline = pd.DatetimeIndex(source_factor_panel.index) + source_paired_counts = { + horizon: paired_counts[horizon] + .reindex(source_timeline) + .fillna(0) + .astype(int) + for horizon in HORIZONS + } + possible_rows = int(len(source_timeline) * len(universe)) + observed_rows = int(source_input_counts.sum()) + prediction_possible_rows = int(len(timeline) * len(prediction_assets)) + prediction_observed_rows = int(input_counts.sum()) + input_availability = { + "method": "observed-only-no-fill-v1", + "missing_observation": "absent-no-fill", + "timestamps": int(len(source_timeline)), + "observed_rows": observed_rows, + "possible_rows": possible_rows, + "observation_coverage": float( + observed_rows / possible_rows + ), + "complete_timestamps": int( + source_input_counts.eq(len(universe)).sum() + ), + "prediction_universe": { + "authority": prediction_authority, + "assets": prediction_assets, + "context_assets": context_assets, + "observed_rows": prediction_observed_rows, + "possible_rows": prediction_possible_rows, + "observation_coverage": float( + prediction_observed_rows / prediction_possible_rows + ), + "complete_timestamps": int( + input_counts.eq(len(prediction_assets)).sum() + ), + "timeline_timestamps": int(len(timeline)), + }, + "eligible_factor_timestamps": { + str(horizon): int( + paired_counts[horizon] + .ge(minimum_evaluation_assets) + .sum() + ) + for horizon in HORIZONS + }, + "minimum_assets_per_factor_timestamp": minimum_evaluation_assets, + "assets_per_timestamp": { + "input": _count_summary(source_input_counts), + "factor": _count_summary(source_factor_counts), + "primary_pair": _count_summary( + source_paired_counts[PRIMARY_HORIZON] + ), + }, + "by_asset": { + asset: { + "observations": int( + source_close_panel[asset].notna().sum() + ), + "start": timestamp_label( + source_close_panel[asset].dropna().index[0] + ), + "end": timestamp_label( + source_close_panel[asset].dropna().index[-1] + ), + "input_coverage": float( + source_close_panel[asset].notna().mean() + ), + "factor_coverage": coverage[asset], + } + for asset in universe + }, + } + components = factor_evaluation.components + component_declarations = ( + components.declaration() if components is not None else None + ) + component_panels = ( + { + name: values_to_wide( + panel, + components.values[name], + universe=universe, + )[prediction_assets] + for name in components.values.columns + } + if components is not None + else {} + ) + component_coverage = ( + { + name: { + asset: float( + components.values.loc[ + panel["asset"] == asset, + name, + ].notna().mean() + ) + for asset in universe + } + for name in components.values.columns + } + if components is not None + else {} + ) + component_evidence = ( + _component_evidence( + component_declarations, + component_panels, + ( + association_factor_panel + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else factor_panel + ), + ( + association_outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else outcome_panels + ), + split_masks, + component_coverage, + evaluation_mode, + outcome_contract, + ) + if component_declarations is not None + else None + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + daily_ic_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=False, + ) + for horizon in HORIZONS + } + else: + daily_ic_by_horizon = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: daily_pearson_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + horizon_metrics = { + str(horizon): { + split: { + **( + _temporal_split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + "pearson_ic": ( + _temporal_split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + splits = horizon_metrics[str(PRIMARY_HORIZON)] + validation_mean_ic = float(splits["validation"]["mean_ic"]) + + quantile_daily = ( + { + horizon: pd.DataFrame( + columns=["low", "middle", "high", "high_minus_low"], + index=pd.DatetimeIndex([], name="timestamp"), + dtype=float, + ) + for horizon in HORIZONS + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + horizon: daily_quantile_outcomes( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + ) + quantile_analysis = { + str(horizon): { + split: quantile_summary( + quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ) + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + primary_ic = daily_ic_by_horizon[PRIMARY_HORIZON] + chronological_folds = ( + { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + association_factor_panel.iloc[:, 0], + association_outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + name: descriptive_ic(_masked(primary_ic, mask)) + for name, mask in fold_masks.items() + } + ) + regimes = causal_regime_labels(research_close_panel) + regime_stability = { + split: { + regime: descriptive_ic( + _masked( + primary_ic, + split_masks[PRIMARY_HORIZON][split] + & regimes.eq(regime).fillna(False), + ) + ) + for regime in REGIME_NAMES + } + for split in ("train", "validation", "test") + } + prediction_style_panels = { + name: values[prediction_assets] + for name, values in style_proxy_panels( + research_close_panel, + research_volume_panel, + ).items() + } + styles = ( + { + name: _relative_value_spread_panel( + values, + prediction_assets, + ) + for name, values in prediction_style_panels.items() + } + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else prediction_style_panels + ) + style_correlations: dict[str, dict[str, Any]] = { + split: {} + for split in ("train", "validation", "test") + } + for style in STYLE_NAMES: + daily_style = ( + _temporal_daily( + association_factor_panel, + styles[style], + split_masks[PRIMARY_HORIZON], + rank=True, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else daily_rank_correlation( + factor_panel, + styles[style], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + for split in style_correlations: + style_correlations[split][style] = _style_summary( + _masked( + daily_style, + split_masks[PRIMARY_HORIZON][split], + ) + ) + qualification_builder = ( + _temporal_factor_qualification + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _factor_qualification + ) + factor_qualification, qualification_evidence = qualification_builder( + association_factor_panel, + styles, + association_outcome_panels, + split_masks, + fold_masks, + base_split_labels, + style_correlations, + factor_claim, + ) + per_asset_stability = { + split: per_asset_rank_correlation( + factor_panel, + outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON][split], + ) + for split in ("train", "validation", "test") + } + + ranked = ( + association_factor_panel.rank(method="average", pct=True) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else association_factor_panel.rank(axis=1, pct=True) + ) + turnover = float(ranked.diff().abs().mean(axis=1).dropna().mean()) + metrics = { + "validation_mean_ic": validation_mean_ic, + "factor_api": factor_contract(factor_evaluation), + "research_horizon": research_horizon, + "factor_claim": factor_claim, + "factor_outcome": outcome_contract, + "prediction_universe": prediction_population, + "train": splits["train"], + "validation": splits["validation"], + "test": splits["test"], + "horizon_quality": horizon_metrics, + "factor_decay": _decay_summary(horizon_metrics), + "quantile_analysis": quantile_analysis, + "stability": { + "chronological_folds": chronological_folds, + "causal_regimes": regime_stability, + "per_asset": per_asset_stability, + }, + "style_correlations": style_correlations, + "factor_qualification": factor_qualification, + "split_protocol": { + **split_protocol, + "folds": fold_protocol, + }, + "mean_coverage": float(sum(coverage.values()) / len(coverage)), + "input_availability": input_availability, + "mean_rank_turnover": turnover, + "assets": int(len(universe)), + "prediction_assets": int(len(prediction_assets)), + "ic_dates": int(len(primary_ic)), + "research_integrity": { + "selection_split": "validation", + "test_role": "visible-diagnostic", + "test_enters_selection": False, + "external_holdout_rule": ( + "required-after-visible-test-and-candidate-iteration" + ), + }, + } + if component_evidence is not None: + metrics["factor_components"] = component_evidence + if not all( + math.isfinite(float(value)) + for value in ( + validation_mean_ic, + metrics["mean_coverage"], + metrics["mean_rank_turnover"], + ) + ): + raise JudgeFailure("judge.non-finite", "Judge produced non-finite metrics") + report = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "dataset": { + "id": dataset["id"], + "version": dataset["version"], + "universe": universe, + "predictionAssets": prediction_assets, + "contextAssets": context_assets, + "timeRange": time_range, + }, + "researchHorizon": research_horizon, + "semantics": { + "target": outcome_contract["targetSemantics"], + "outcome": outcome_contract, + "measure": ( + ( + "within-split temporal Spearman and Pearson correlation " + "contributions for the single request-authorized asset" + ) + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + "within-split temporal Spearman and Pearson correlation " + "contributions between the first-minus-second factor " + "contrast and first-minus-second forward-return contrast" + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else ( + "per-date cross-sectional Spearman rank IC and Pearson IC " + f"over the fixed {prediction_authority} evaluation universe" + ) + ) + ), + "researchUniverse": ( + "complete Study universe available to candidate features" + ), + "predictionUniverse": ( + "caller-owned factorPolicy.predictionAssets for request-" + "bound decision-signal claims; complete research universe " + "for novel-factor and known-style-validation claims" + ), + "horizons": list(HORIZONS), + "primaryHorizon": PRIMARY_HORIZON, + "split": ( + "dataset-fixed chronological 60/20/20 with horizon-specific " + "boundary purge" + ), + "score": ( + "validation mean rank IC at the fixed primary " + f"{PRIMARY_HORIZON}-bar horizon only" + ), + "inference": ( + ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag equal to each forward horizon and two-sided normal-" + "approximation p-value" + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag 5 and two-sided normal-approximation p-value" + ) + ), + "quantiles": ( + "unavailable-for-temporal-evaluation-v1" + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else "fixed low/middle/high cross-sectional groups" + ), + "regimes": ( + "causal trailing market direction and volatility versus " + "lagged rolling threshold" + ), + "styles": list(STYLE_NAMES), + "qualification": { + "method": factor_qualification["method"], + "claim": factor_claim, + "styleSelection": ( + "request-predeclared" + if factor_claim["claim"] == "known-style-validation" + else "train-only" + ), + "neutralization": factor_qualification["semantics"][ + "neutralization" + ], + "blend": factor_qualification["semantics"]["blend"], + "testRole": "visible audit only", + "tradingAuthority": "none", + }, + "components": ( + { + "method": component_evidence["method"], + "evaluationMode": component_evidence["semantics"][ + "evaluation_mode" + ], + "scoreMeasure": component_evidence["semantics"][ + "score_measure" + ], + "predictionTarget": component_evidence["semantics"][ + "prediction_target" + ], + "declaration": "candidate-explicit-not-source-inferred", + "roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "exhaustiveCompositionClaim": False, + "nearestPeerSelection": "train-only-target-free", + "ablationTarget": ( + "fixed-diagnostic-blend-not-candidate-factor" + ), + "residualization": component_evidence["semantics"][ + "residualization" + ], + "diagnosticBlend": component_evidence["semantics"][ + "diagnostic_blend" + ], + "timestampContext": component_evidence["semantics"][ + "timestamp_context" + ], + "testRole": "visible audit only", + "portfolioAuthority": "none", + "rlActionAuthority": "none", + "tradingAuthority": "none", + } + if component_evidence is not None + else None + ), + "testRole": ( + "visible diagnostic evidence; never enters candidate selection" + ), + }, + "causalityAuditCuts": list(factor_evaluation.causality_cuts), + "componentCausalityAuditCuts": ( + list(factor_evaluation.causality_cuts) + if component_evidence is not None + else [] + ), + "coverageByAsset": coverage, + "inputAvailability": input_availability, + "splitProtocol": split_protocol, + "foldProtocol": fold_protocol, + "metrics": metrics, + } + daily_evidence = pd.DataFrame( + { + "split": base_split_labels, + "regime": regimes.fillna("unavailable"), + }, + index=timeline, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + daily_evidence[f"rank_ic_h{horizon}"] = ( + daily_ic_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence[f"pearson_ic_h{horizon}"] = ( + daily_pearson_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence.index.name = "timestamp" + availability_evidence = pd.DataFrame( + { + "input_assets": source_input_counts, + "factor_assets": source_factor_counts, + **{ + f"paired_assets_h{horizon}": source_paired_counts[horizon] + for horizon in HORIZONS + }, + }, + index=source_timeline, + ) + availability_evidence.index.name = "timestamp" + + quantile_rows: list[dict[str, Any]] = [] + for horizon in HORIZONS: + for split in ("train", "validation", "test"): + selected = quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ).dropna() + for timestamp, row in selected.iterrows(): + quantile_rows.append( + { + "timestamp": timestamp, + "split": split, + "horizon": horizon, + "low": float(row["low"]), + "middle": float(row["middle"]), + "high": float(row["high"]), + "high_minus_low": float(row["high_minus_low"]), + } + ) + quantile_evidence = pd.DataFrame( + quantile_rows, + columns=[ + "timestamp", + "split", + "horizon", + "low", + "middle", + "high", + "high_minus_low", + ], + ) + return ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + ( + { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "evidence": component_evidence, + } + if component_evidence is not None + else None + ), + ) + + +def main() -> None: + try: + ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + component_evidence, + ) = _evaluate() + artifacts = Path(os.environ["AUTOQUANT_ARTIFACTS_DIR"]) + report_path = artifacts / "factor-report.json" + report_path.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + daily_artifact = daily_evidence.copy() + daily_artifact.index = [ + timestamp_label(value) for value in daily_artifact.index + ] + daily_artifact.index.name = "timestamp" + quantile_artifact = quantile_evidence.copy() + quantile_artifact["timestamp"] = quantile_artifact["timestamp"].map( + timestamp_label + ) + qualification_artifact = qualification_evidence.copy() + qualification_artifact.index = [ + timestamp_label(value) for value in qualification_artifact.index + ] + qualification_artifact.index.name = "timestamp" + availability_artifact = availability_evidence.copy() + availability_artifact.index = [ + timestamp_label(value) + for value in availability_artifact.index + ] + availability_artifact.index.name = "timestamp" + daily_artifact.to_csv( + artifacts / "daily-factor-evidence.csv", + float_format="%.17g", + ) + quantile_artifact.to_csv( + artifacts / "factor-quantiles.csv", + index=False, + float_format="%.17g", + ) + qualification_artifact.to_csv( + artifacts / "factor-qualification.csv", + float_format="%.17g", + ) + availability_artifact.to_csv( + artifacts / "factor-availability.csv", + ) + if component_evidence is not None: + (artifacts / "factor-components.json").write_text( + json.dumps(component_evidence, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + output_artifacts = [ + { + "kind": "factor-report", + "path": "factor-report.json", + "description": ( + "Factor semantics, purged split protocol, complete " + "tear sheet, coverage, and causality audit" + ), + }, + { + "kind": "factor-daily", + "path": "daily-factor-evidence.csv", + "description": ( + "Timestamped split, causal regime, and purge-aware " + "request-bound outcome rank and Pearson IC" + ), + }, + { + "kind": "factor-quantiles", + "path": "factor-quantiles.csv", + "description": ( + "Timestamped fixed-tertile outcome levels and " + "high-minus-low outcome spread by split and horizon" + ), + }, + { + "kind": "factor-availability", + "path": "factor-availability.csv", + "description": ( + "Per-timestamp observed input, finite factor, and " + "horizon-paired cross-sectional asset counts" + ), + }, + { + "kind": "factor-qualification", + "path": "factor-qualification.csv", + "description": ( + "Train-selected style, candidate/style/residual/" + "blend daily rank IC, and visible-test audit" + ), + }, + ] + if component_evidence is not None: + output_artifacts.append( + { + "kind": "factor-components", + "path": "factor-components.json", + "description": ( + "Candidate-declared component quality, redundancy, " + "nearest-peer residual, and fixed-blend ablation evidence" + ), + } + ) + _write_output( + { + "schema_version": 1, + "status": "succeeded", + "summary": ( + "Causal purge-aware factor tear sheet completed; " + f"{metrics['factor_outcome']['kind']} validation " + f"{PRIMARY_HORIZON}-bar mean " + "rank IC=" + f"{metrics['validation_mean_ic']:.6f}" + ), + "metrics": metrics, + "artifacts": output_artifacts, + "errors": [], + } + ) + except JudgeFailure as error: + scientific_limit_codes = { + "factor.temporal-primary-observations", + "factor.temporal-primary-candidate-variation", + "factor.temporal-primary-target-variation", + } + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": ( + "scientific-limit" + if error.code in scientific_limit_codes + else "repair-required" + ), + "summary": str(error), + "metrics": {}, + "artifacts": [], + "errors": [{"code": error.code, "message": str(error)}], + } + ) + except Exception as error: # Preserve candidate/Judge diagnostics as evidence. + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": "repair-required", + "summary": f"Factor evaluation raised {type(error).__name__}", + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": "factor.exception", + "message": f"{type(error).__name__}: {error}", + } + ], + } + ) + + +if __name__ == "__main__": + main() diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/program.md b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/program.md new file mode 100644 index 0000000..1605769 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/program.md @@ -0,0 +1,173 @@ +# Mine a causal OHLCV factor + +## Research question + +Can one causal transformation of ordinary OHLCV history produce stable +information about the caller-bound future outcome across the fixed +prediction-eligible universe and both held-out chronological periods? + +## Editable API + +Edit only `factors/candidate.py` and preserve: + +```python +def compute_factor(panel: pandas.DataFrame) -> pandas.Series: + ... +``` + +`panel` is the observed Study universe in long form, with one row per available +`asset`/`timestamp` and base plus available completed higher-interval OHLCV. +The complete research universe remains available for causal cross-asset +features. The fixed Judge, not candidate code, selects target observations: +`decision-signal` evaluates caller-owned `factorPolicy.predictionAssets`, while +`novel-factor` and `known-style-validation` evaluate the complete research +universe. Core freezes that evaluation-only authority in +`strategies/factor-population.json`; this Factor Lab has no Portfolio Mandate. +Inspect `predictionUniverse.evaluationMode` in Factor diagnostics. +A decision signal with exactly one eligible asset uses within-split temporal +Spearman/Pearson evidence for that asset. Exactly two eligible assets use +within-split temporal evidence between the first-minus-second factor contrast +and matching forward-return contrast. A later Portfolio lane must separately +prove a symmetric two-sided dollar-neutral Mandate before monetization. Four +or more eligible assets use the cross-sectional contract. Three require +explicit caller-owned relative-basket contrast weights. No mode may borrow +context-only target observations. +The fixed Factor Claim also binds `outcome`. `forward-return` uses the simple +close-to-close return from signal close `t` to `t+h`. `forward-realized- +volatility` uses the unannualized square root of summed squared close-to-close +log returns over the next `h` observed base bars. The latter supports one +temporal prediction asset or at least four cross-sectional assets, and a +higher score means higher predicted risk—not positive expected return. It has +no Portfolio or RL admission path. Do not invert, monetize, or relabel it. +Aligned inputs are rectangular; V4 daily and V5 observed-bar input is ragged and +does not invent, fill, or globally intersect missing/pre-listing/closed-market +rows. V5 temporal targets, split boundaries, and purge counts follow the one +prediction asset's observed bars, not context-only union timestamps. +For asynchronous V5 context, explicitly use a backward as-of operation and +accept only source rows whose completed timestamp is at or before the target +row. Core never fills an absent context observation or aligns civil dates. +Use ordinary `groupby("asset")` for rolling time-series features and +`groupby("timestamp")` for contemporaneous cross-sectional context. The +returned Series must align exactly with the input index. Missing warm-up values +are allowed. Future timestamps, centered windows, negative shifts, global +full-sample normalization, external data, and mutation of the input are not. + +Missing input is not the same state as an observed false condition or a neutral +score. When the candidate branches on a required regime, filter, denominator, +or other component, preserve `NaN` until that input exists unless the Research +Request explicitly predeclares a different missing-data policy. In particular, +`Series.where(condition, other=0)` treats an unavailable/`NaN` predicate as +false; mask the unavailable predicate separately before returning the factor. +Reconcile final-factor coverage against every required component before +execution and explain any intentional excess coverage in `research.md`. + +A sparse binary event indicator is still evaluated as a temporal association +score; it does not create an event population or estimate a conditional event +return. If the caller instead froze an OHLCV-observable price event, delayed +entry/exit clock, overlap policy, unconditional history, and matched reference, +use `ohlcv-event-study-lab`. Do not disguise that descriptive question as a +Factor merely to obtain a Run. When the fixed primary temporal population has +too few finite pairs or no candidate/target variation, preserve the structured +failed Run as the truthful `scientific-limit`, report that exact bounded +answer, and change the hypothesis or route only as separately declared +work—not the missing values or Judge. Do not repeat the unchanged Run. + +When a hypothesis has meaningful sub-signals, also export +`FACTOR_COMPONENTS` and +`compute_factor_components(panel) -> pandas.DataFrame`. Declare one causal +column per falsifiable source component, including its label, role, claimed +`base`/3h/4h/6h/12h/1d intervals, and hypothesis. Use +`cross-sectional-score` for values meant to rank assets and +`timestamp-context` for one market/regime value shared by every asset at a +timestamp. Context components must be exactly cross-sectionally constant; +Core evaluates their train-tertile occupancy and transitions. Cross-sectional +Runs measure score components with per-date rank IC and context with +conditional final-factor IC. Single-asset and two-asset relative-value Runs +measure score components and context with within-split temporal rank- +correlation contributions. The component table must remain aligned, +deterministic, numeric, immutable, and prefix causal. Do not declare +presentation-only duplicates or imply that Core inferred column use. Temporal +quantile attribution remains unavailable; do not treat that explicit protocol +boundary as positive evidence. + +## Iteration protocol + +Before this protocol begins on a new caller assignment, replace the generic +scaffold candidate with the first predeclared caller-relevant candidate +without executing the scaffold. Start the governed Session only after that +source is fixed; its baseline Run is the first visible audit. Never rewrite +Core's `testGuidanceObservability=not-observable` as a factual claim that test +evidence was unused. + +1. Run `aq orient . --json` and read the current immutable leader's + `researchAgenda`. Treat its ordered moves as validation-only scientific + priorities, not executable actions or permission to inspect test for + selection. +2. Read `strategies/factor-claim.json`, + `strategies/factor-population.json`, the current candidate, and immutable + leader evidence. The request-bound `decision-signal`, `novel-factor`, or + `known-style-validation` claim, outcome, and prediction population are + fixed evidence authority, not editable strategy metadata. +3. State one falsifiable hypothesis about price or volume behavior. +4. Make one coherent code change inside the editable closure. +5. Run the bounded Experiment command supplied by the Session. +6. Inspect validation/test primary-horizon IC, HAC strength, diagnostic + horizon decay, tertile monotonicity/spread, train-selected dominant style, + style-neutral residual + primary-horizon association, equal-blend uplift, declared-component + raw/residual association, pairwise + redundancy, fixed diagnostic-blend leave-one-out delta, residual fold + stability, asset/regime stability, observed input/factor/target-pair + availability, coverage, turnover, errors, verdict, Project-family trial + count, and family-wise adjusted HAC significance. +7. KEEP only when the fixed objective improves; otherwise accept restoration + and form a different hypothesis. + +The fixed objective is validation mean IC only. Test IC is visible diagnostic +evidence and never enters KEEP/REVERT. Changing a candidate after inspecting +test evidence consumes its holdout value; obtain a new external period or +dataset before a production-grade claim. + +Starting a new Session does not create a fresh statistical search. Core counts +unique editable source hashes across every Run with the same fixed Study, +Judge, data, dependencies, and objective. Treat the Bonferroni-HAC result as a +selection-risk diagnostic, not permission to hide failed attempts or override +the immutable verdict. + +Session construction reuses an exact successful current baseline Run. It +executes a new baseline only when Study, program, candidate, Judge, dataset, +dependency, or Harness identity differs. + +The Judge fixes dataset-derived split dates and purges each declared diagnostic +horizon before a boundary. Treat sparse regimes, one weak fold, one dominant +asset, fast decay, or near-perfect overlap with a familiar OHLCV style as +findings to explain—not fields to hide or alternate scores to optimize +opportunistically. + +For a `decision-signal` claim, validation requires statistically supported +positive raw IC and positive raw IC in both fixed chronological folds. The +train-selected style, residual, and blend remain disclosure without creating +a novelty hurdle. For a `novel-factor` claim, the dominant comparison style is chosen on train +overlap only. Validation asks whether raw and style-neutral IC are positive +with fixed HAC t at least 1.96, whether an equal rank blend improves the +selected style, and whether both residual folds remain positive. For a +`known-style-validation` claim, the request fixes the comparison style before +research; validation instead requires at least 0.95 train rank identity, +positive statistically supported raw IC, and positive raw IC in both fixed +chronological folds. This prioritizes the next research lane; it does not +change KEEP/REVERT, replace Project-family selection adjustment, or +automatically admit the source into Portfolio or RL. + +When the fixed outcome is `forward-realized-volatility`, a positive funnel is +terminal risk-model evidence for this in-sample Study. Freeze, report, and seek +a fresh external holdout; never treat the result as an expected-return signal +or open Portfolio/RL work merely because the association is positive. + +Component leave-one-out applies only to the Judge's fixed equal-rank +diagnostic blend. It is not an ablation of arbitrary `compute_factor` code. +Component validation evidence may prioritize the next hypothesis, but it never +changes `validation_mean_ic`, KEEP/REVERT, Portfolio mechanics, or the +governed RL action set. Test component evidence remains visible audit only. + +Do not modify the Study, Judge, program, dataset, or AutoQuant Core to improve a +candidate. Do not treat this synthetic benchmark as a real-market alpha claim. diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/study.json b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/study.json new file mode 100644 index 0000000..98fb40d --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/inputs/study.json @@ -0,0 +1,59 @@ +{ + "dataset": { + "asset_class": "synthetic-multi-asset", + "id": "synthetic-ohlcv-research-desk-fixture", + "paths": [ + "ohlcv/**" + ], + "time_range": { + "end": "2025-08-11", + "start": "2024-01-02" + }, + "universe": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "version": "v1" + }, + "dependencies": { + "paths": [ + "strategies/factor-claim.json", + "strategies/factor-population.json", + "strategies/research-horizon.json" + ] + }, + "description": "Mine causal factor evidence on the shared research snapshot", + "editable": { + "paths": [ + "factors/**" + ] + }, + "id": "ohlcv-factor-quality", + "judge": { + "arguments": [], + "entrypoint": "judges/ohlcv_factor.py", + "kind": "python", + "paths": [ + "judges/ohlcv_factor.py", + "judges/factor_diagnostics.py" + ], + "timeout_seconds": 60 + }, + "name": "OHLCV Factor Quality", + "objective": { + "direction": "maximize", + "metric": "validation_mean_ic", + "minimum_improvement": 0.01 + }, + "program": "program.md", + "schema_version": 1, + "subject": { + "kind": "factor", + "name": "candidate-factor", + "version": "working" + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/judge-output.json b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/judge-output.json new file mode 100644 index 0000000..c1c2fa1 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/judge-output.json @@ -0,0 +1,2644 @@ +{ + "artifacts": [ + { + "description": 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"maximum_lag": 5, + "method": "newey-west-bartlett", + "normal_approximation_p_value": 0.8177254288604342, + "standard_error": 0.05105723911288118, + "t_statistic": 0.23047147204359103 + }, + "hit_rate": 0.5180722891566265, + "icir": 0.026091180883142755, + "mean_ic": 0.011767237056827338, + "minimum_observations": 20, + "observations": 83, + "standard_deviation": 0.45100438763314193, + "sufficient": true + }, + "standard_deviation": 0.4189655427900565, + "sufficient": true + }, + "validation_mean_ic": -0.031325301204819286 + }, + "objective": { + "direction": "maximize", + "metric": "validation_mean_ic", + "minimumImprovement": 0.01 + }, + "project": { + "id": "sample-research-desk", + "name": "Sample Research Desk" + }, + "schemaVersion": 1, + "startedAt": "2026-08-02T16:20:51.526628+00:00", + "status": "succeeded", + "study": { + "hash": "97019f1d7c32cf514975e782b29556f9414892651fc9bc41c5509746a2f3cb2e", + "id": "ohlcv-factor-quality", + "name": "OHLCV Factor Quality", + "programHash": "cf5dfc868822c29b6c8323f698dfaff8bb896519473f07e747b40f82e264ab0e" + }, + "studyInputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c", + "subject": { + "kind": "factor", + "name": "candidate-factor", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourcePaths": [ + "factors/candidate.py" + ], + "version": "working" + }, + "summary": "Causal purge-aware factor tear sheet completed; forward-return validation 1-bar mean rank IC=-0.031325" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/sources/factors/candidate.py b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/sources/factors/candidate.py new file mode 100644 index 0000000..6ee5eeb --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/sources/factors/candidate.py @@ -0,0 +1,128 @@ +"""Agent-editable baseline factor for the OHLCV Portfolio Lab.""" + +from __future__ import annotations + +import pandas as pd + + +AVAILABLE_FEATURE_INTERVALS = [] + +_BASE_COMPONENT = { + "base_momentum_10": { + "label": "10-base-bar momentum", + "role": "cross-sectional-score", + "intervals": ["base"], + "hypothesis": ( + "Recent relative strength persists over the next base bar." + ), + }, +} + +_FEATURE_COMPONENTS = { + "3h": ( + "momentum_3h_4", + 4, + { + "label": "Four completed 3-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["3h"], + "hypothesis": ( + "Short intraday trend persists beyond the latest completed " + "3-hour bar." + ), + }, + ), + "12h": ( + "momentum_12h_2", + 2, + { + "label": "Two completed 12-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["12h"], + "hypothesis": "Half-day trend filters noisy base-bar momentum.", + }, + ), + "1d": ( + "momentum_1d_3", + 3, + { + "label": "Three completed daily bars momentum", + "role": "cross-sectional-score", + "intervals": ["1d"], + "hypothesis": ( + "Multi-day relative strength persists at the next base close." + ), + }, + ), +} + +FACTOR_COMPONENTS = dict(_BASE_COMPONENT) +for _interval in AVAILABLE_FEATURE_INTERVALS: + if _interval in _FEATURE_COMPONENTS: + _name, _periods, _declaration = _FEATURE_COMPONENTS[_interval] + FACTOR_COMPONENTS[_name] = _declaration + + +def _completed_bar_return( + panel: pd.DataFrame, + interval: str, + periods: int, +) -> pd.Series: + close_column = f"close__{interval}" + bar_column = f"bar_close__{interval}" + if close_column not in panel or bar_column not in panel: + return pd.Series(float("nan"), index=panel.index, dtype=float) + completed = panel.loc[ + panel[bar_column].notna(), + ["asset", bar_column, close_column], + ].drop_duplicates(["asset", bar_column], keep="first") + completed["return"] = completed.groupby( + "asset", + sort=False, + )[close_column].pct_change(periods, fill_method=None) + lookup = completed.set_index( + ["asset", bar_column], + )["return"] + keys = pd.MultiIndex.from_frame( + panel.loc[:, ["asset", bar_column]], + ) + return pd.Series( + lookup.reindex(keys).to_numpy(dtype=float), + index=panel.index, + dtype=float, + ) + + +def compute_factor_components(panel: pd.DataFrame) -> pd.DataFrame: + """Declare causal components without changing downstream factor authority.""" + + components = { + "base_momentum_10": panel.groupby( + "asset", + sort=False, + )["close"].pct_change(10, fill_method=None), + } + for interval in AVAILABLE_FEATURE_INTERVALS: + if interval in _FEATURE_COMPONENTS: + name, periods, _declaration = _FEATURE_COMPONENTS[interval] + components[name] = _completed_bar_return( + panel, + interval, + periods, + ) + return pd.DataFrame(components, index=panel.index) + + +def compute_factor(panel: pd.DataFrame) -> pd.Series: + """Return causal relative multi-horizon momentum for construction. + + The fixed Judge owns signal state/hysteresis, conviction/volatility sizing, + target and executed weights, attribution, delay, drift, costs, benchmark, + splits, metrics, and stress tests. Change only this factor while testing + one falsifiable hypothesis at a time. + """ + + components = compute_factor_components(panel) + raw = components.mean(axis=1, skipna=True) + market_center = raw.groupby(panel["timestamp"], sort=False).transform("mean") + return (raw - market_center).rename("relative_multi_horizon_momentum") diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/stderr.txt b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/stderr.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/stdout.txt b/projects/sample-research-desk/runs/run-20260802T162051526628Z-b4649546a1f1/stdout.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/artifacts/daily-portfolio.csv b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/artifacts/daily-portfolio.csv new file mode 100644 index 0000000..2acb902 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/artifacts/daily-portfolio.csv @@ -0,0 +1,420 @@ 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"fb4efdfcd1533816ce67fdc58f6d924ffd750944126a8d64e0c57255a8b5e22a", + "ohlcv/FOXTROT.csv": "72f715db8f40793ffd1327def2d92c40adc6f9ca48b501b92a32cf744c7a23ec", + "ohlcv/README.md": "8911f42951ab7a291ebd3e8ad6193bdbc52e79418f00dbe4b2cda8b3d3a00277" + }, + "dependencyHash": "de2c8c8f59c8c99af17faf334713b3a22d42107d1a59f51b8c1be3b2df415030", + "dependencyHashes": { + "strategies/factor-claim.json": "61981777bd8b81bf57dc05bb63b5df085eef018dd9e3d83593c511bf21637a83", + "strategies/factor-population.json": "d77ce8dc25c04c41e610116c0ee6ccb0297128e9e918638479c76a5fbec5887e", + "strategies/portfolio-mandate.json": "74e9ded83a8287df0f8df955b147960a3926c5bca176704a99f7d7feb0b5259b", + "strategies/research-horizon.json": "ebf6fa40cbcbcdc18490439865e6a7de1a0322abc8853b528b3745bc5e70618d" + }, + "evaluationRole": "research-selection", + "harness": { + "buildProvenance": "source-checkout", + "commit": "4a14ca5fd4190437b7ecacc83918e66de6237a1a", + "dirty": true, + "id": "autoquant.python-judge", + "python": "3.11.15", + "sourceHash": "9ba0cf8dcb5ab0f3797726f041bf188a92206e293e061da9e33e17e3007e123d", + "version": "0.9.31" + }, + "inputHash": "54fb61adf9a8cee74781b48ad05fc761c322c5adc5520b9d7249b497520a824c", + "judgeHash": "fefb37b23096484d9bc82b02669b60348d629de2971f0fecb2d517c5e9f96609", + "judgeHashes": { + "judges/ohlcv_portfolio.py": "03f69ee5e1d27d75f4cb60273461908dc01e2d6a2435f599c7d5cd4cfb11352c", + "judges/portfolio_core.py": "17ddff2421921cf940811f2f531ab81222eeb879d876764f6301ee70e2001406" + }, + "programHash": "a15db6ab34a2bfbb30b990dc9ca523e4d60871ea5f0c54a8c54161b62d4cf9ab", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourceHashes": { + "factors/candidate.py": "1e29d70f72ce3afaf3d018f9d60c77026148423f57b52fb22f55f8127679ba6a" + }, + "studyHash": "cd39867c134c489ad75e85f1d162206b6348a2292043e77b9f7db8b32467844d", + "studyInputHash": "7ffa6ce4421fbb8db66fcbdacf16a5d88e2f0b8248979906b7b010cdc5dc1014" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/ohlcv_portfolio.py b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/ohlcv_portfolio.py new file mode 100644 index 0000000..bc75f31 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/ohlcv_portfolio.py @@ -0,0 +1,1942 @@ +"""Fixed causal signal-to-portfolio Judge for the reference laboratory.""" + +from __future__ import annotations + +import importlib +import json +import math +import os +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.factor_claims import ( + FACTOR_CLAIM, + FORWARD_RETURN_OUTCOME, + factor_outcome, + load_factor_claim, +) +from autoquant.factor_runtime import ( + FactorRuntimeError, + build_factor_panel, + evaluate_factor, + factor_contract, + values_to_wide, +) +from autoquant.intervals import ( + IntervalContractError, + annualization_periods, + load_multi_interval_asset, + timestamp_label, +) +from autoquant.horizons import ( + RESEARCH_HORIZON, + load_research_horizon, +) +from autoquant.mandates import ( + PORTFOLIO_MANDATE, + load_portfolio_mandate, +) +from autoquant.prediction_modes import ( + FACTOR_POPULATION, + PredictionModeError, + TEMPORAL_EVALUATION_MODES, + TEMPORAL_SCORE_MINIMUM, + TEMPORAL_SCORE_WINDOW, + load_factor_population, + resolve_prediction_population, + validate_population_mandate_compatibility, +) +from judges.portfolio_core import ( + LONG_ENTRY_PERCENTILE, + LONG_EXIT_PERCENTILE, + LIQUIDITY_ADV_WINDOW, + LIQUIDITY_PARTICIPATION_LIMITS, + RISK_COVARIANCE_MINIMUM, + RISK_COVARIANCE_WINDOW, + SHORT_ENTRY_PERCENTILE, + SHORT_EXIT_PERCENTILE, + VOLATILITY_WINDOW, + PortfolioFailure, + _position_role_signal_transition, + attribution_metrics, + build_decision_ledger, + build_position_episodes, + build_risk_covariance_cache, + constraint_audit, + construct_signal_policy, + execution_risk_metrics, + implementation_metrics, + liquidity_capacity_metrics, + performance_metrics, + resolve_implementation_policy, + position_episode_metrics, + signal_policy_metrics, + simulate_targets, +) + + +REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume") +MIN_ASSETS_PER_DATE = 4 +MIN_SPLIT_OBSERVATIONS = 20 +PARAMETER_NEIGHBORHOOD_METHOD = ( + "predeclared-signal-threshold-no-trade-neighborhood-v1" +) +TRANSLATION_ROBUSTNESS_METHOD = ( + "predeclared-temporal-translation-window-stability-v1" +) +TRANSLATION_ROBUSTNESS_PROFILES: tuple[dict[str, Any], ...] = ( + { + "id": "short-history", + "label": "Short history", + "window": 40, + "minimum": TEMPORAL_SCORE_MINIMUM, + "is_base": False, + }, + { + "id": "base", + "label": "Base", + "window": TEMPORAL_SCORE_WINDOW, + "minimum": TEMPORAL_SCORE_MINIMUM, + "is_base": True, + }, + { + "id": "long-history", + "label": "Long history", + "window": 120, + "minimum": TEMPORAL_SCORE_MINIMUM, + "is_base": False, + }, +) +TRANSLATION_ACTIVE_AGREEMENT_FLOOR = 0.80 +TRANSLATION_TARGET_MAE_CEILING = 0.05 +PARAMETER_SIGNAL_PROFILES: tuple[dict[str, Any], ...] = ( + { + "id": "broad-entry", + "label": "Broad entry", + "long_entry": 0.55, + "long_exit": 0.55, + "short_exit": 0.45, + "short_entry": 0.45, + }, + { + "id": "base", + "label": "Base", + "long_entry": LONG_ENTRY_PERCENTILE, + "long_exit": LONG_EXIT_PERCENTILE, + "short_exit": SHORT_EXIT_PERCENTILE, + "short_entry": SHORT_ENTRY_PERCENTILE, + }, + { + "id": "selective-entry", + "label": "Selective entry", + "long_entry": 0.95, + "long_exit": 0.55, + "short_exit": 0.45, + "short_entry": 0.05, + }, + { + "id": "fast-exit", + "label": "Fast exit", + "long_entry": 0.75, + "long_exit": 0.75, + "short_exit": 0.25, + "short_entry": 0.25, + }, + { + "id": "selective-fast-exit", + "label": "Selective + fast exit", + "long_entry": 0.95, + "long_exit": 0.75, + "short_exit": 0.25, + "short_entry": 0.05, + }, +) + + +class JudgeFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +def _write_output(value: dict[str, Any]) -> None: + Path(os.environ["AUTOQUANT_RUN_OUTPUT"]).write_text( + json.dumps(value, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _load_contract() -> tuple[dict[str, Any], Path]: + study = json.loads( + Path(os.environ["AUTOQUANT_STUDY_PATH"]).read_text(encoding="utf-8") + ) + data_root = Path(os.environ["AUTOQUANT_DATA_ROOT"]).resolve() + if not data_root.is_dir(): + raise JudgeFailure("dataset.root", "AUTOQUANT_DATA_ROOT is not a directory") + return study, data_root + + +def _load_mandate() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / PORTFOLIO_MANDATE + try: + return load_portfolio_mandate(path) + except Exception as error: + raise JudgeFailure( + "mandate.invalid", + f"Invalid fixed Portfolio Mandate: {error}", + ) from error + + +def _load_factor_claim() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_CLAIM + try: + return load_factor_claim(path) + except Exception as error: + raise JudgeFailure( + "factor-claim.invalid", + f"Invalid fixed Factor claim: {error}", + ) from error + + +def _load_factor_population() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_POPULATION + try: + return load_factor_population(path) + except Exception as error: + raise JudgeFailure( + "factor-population.invalid", + f"Invalid fixed Factor population: {error}", + ) from error + + +def _load_horizon() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / RESEARCH_HORIZON + try: + return load_research_horizon(path) + except Exception as error: + raise JudgeFailure( + "horizon.invalid", + f"Invalid fixed Horizon Mandate: {error}", + ) from error + + +def _load_asset(data_root: Path, asset: str, start: str, end: str) -> pd.DataFrame: + try: + multi_interval = load_multi_interval_asset( + data_root, + asset, + start=start, + end=end, + ) + except IntervalContractError as error: + raise JudgeFailure(error.code, str(error)) from error + if multi_interval is not None: + if len(multi_interval) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 base observations in the Study range", + ) + return multi_interval + source = (data_root / "ohlcv" / f"{asset}.csv").resolve() + if data_root not in source.parents or not source.is_file(): + raise JudgeFailure("dataset.asset", f"Missing confined OHLCV file for {asset}") + frame = pd.read_csv(source) + if tuple(frame.columns) != REQUIRED_COLUMNS: + raise JudgeFailure( + "dataset.columns", + f"{asset} columns must be exactly {', '.join(REQUIRED_COLUMNS)}", + ) + frame["timestamp"] = pd.to_datetime( + frame["timestamp"], + format="%Y-%m-%d", + errors="raise", + ) + if ( + frame["timestamp"].duplicated().any() + or not frame["timestamp"].is_monotonic_increasing + ): + raise JudgeFailure( + "dataset.time-order", + f"{asset} timestamps must be unique and chronological", + ) + for column in REQUIRED_COLUMNS[1:]: + frame[column] = pd.to_numeric(frame[column], errors="raise") + numeric = frame[list(REQUIRED_COLUMNS[1:])].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise JudgeFailure("dataset.non-finite", f"{asset} contains non-finite OHLCV") + if (frame[["open", "high", "low", "close", "volume"]] <= 0).any().any(): + raise JudgeFailure("dataset.non-positive", f"{asset} contains non-positive OHLCV") + if ( + (frame["high"] < frame[["open", "close"]].max(axis=1)).any() + or (frame["low"] > frame[["open", "close"]].min(axis=1)).any() + ): + raise JudgeFailure("dataset.bar-shape", f"{asset} contains invalid bars") + selected = frame[ + (frame["timestamp"] >= pd.Timestamp(start)) + & (frame["timestamp"] <= pd.Timestamp(end)) + ].copy() + if len(selected) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 observations in the Study range", + ) + return selected.reset_index(drop=True) + + +def _daily_factor_evidence( + factors: pd.DataFrame, + forward_returns: pd.DataFrame, + prediction_population: dict[str, Any], +) -> pd.DataFrame: + mode = prediction_population["evaluation_mode"] + prediction_assets = prediction_population["prediction_assets"] + if mode != "cross-sectional": + if mode == "single-asset-temporal": + asset = prediction_assets[0] + signal = factors[asset] + target = forward_returns[asset] + else: + left, right = prediction_assets + signal = factors[left] - factors[right] + target = forward_returns[left] - forward_returns[right] + return pd.DataFrame( + { + "factor_value": signal, + "forward_return": target, + }, + index=factors.index, + ) + rows: list[dict[str, Any]] = [] + selected_factors = factors[prediction_assets] + selected_returns = forward_returns[prediction_assets] + for timestamp in selected_factors.index.intersection(selected_returns.index): + pair = pd.DataFrame( + { + "factor": selected_factors.loc[timestamp], + "forward_return": selected_returns.loc[timestamp], + } + ).dropna() + if ( + len(pair) < MIN_ASSETS_PER_DATE + or pair["factor"].nunique() < 2 + or pair["forward_return"].nunique() < 2 + ): + continue + factor_rank = pair["factor"].rank(method="average") + return_rank = pair["forward_return"].rank(method="average") + rank_ic = factor_rank.corr(return_rank) + ordered = pair.sort_values("factor") + breadth = max(1, len(ordered) // 3) + spread = float( + ordered["forward_return"].iloc[-breadth:].mean() + - ordered["forward_return"].iloc[:breadth].mean() + ) + if rank_ic is not None and math.isfinite(float(rank_ic)): + rows.append( + { + "timestamp": timestamp, + "rank_ic": float(rank_ic), + "top_bottom_spread": spread, + } + ) + if not rows: + raise JudgeFailure("factor.population", "No valid factor evidence dates") + return pd.DataFrame(rows).set_index("timestamp").sort_index() + + +def _factor_metrics( + evidence: pd.DataFrame, + index: pd.Index, + factors: pd.DataFrame, + evaluation_mode: str, +) -> dict[str, float | int]: + selected = evidence.reindex(index).dropna() + if len(selected) < MIN_SPLIT_OBSERVATIONS: + raise JudgeFailure( + "factor.population", + f"Chronological split has only {len(selected)} factor dates", + ) + if evaluation_mode == "cross-sectional": + ic = selected["rank_ic"] + top_bottom_spread = float(selected["top_bottom_spread"].mean()) + else: + ranked = selected[["factor_value", "forward_return"]].rank( + method="average", + pct=True, + ) + left = ranked["factor_value"] - float(ranked["factor_value"].mean()) + right = ranked["forward_return"] - float( + ranked["forward_return"].mean() + ) + denominator = math.sqrt( + float((left**2).mean()) * float((right**2).mean()) + ) + if denominator <= 1e-12: + raise JudgeFailure( + "factor.population", + "Temporal Factor or forward return is constant in one split", + ) + ic = left * right / denominator + ordered = selected.sort_values("factor_value") + breadth = max(1, len(ordered) // 3) + top_bottom_spread = float( + ordered["forward_return"].iloc[-breadth:].mean() + - ordered["forward_return"].iloc[:breadth].mean() + ) + mean_ic = float(ic.mean()) + std_ic = float(ic.std(ddof=0)) + coverage = float(factors.loc[index].notna().mean().mean()) + result: dict[str, float | int] = { + "observations": int(len(selected)), + "mean_rank_ic": mean_ic, + "rank_icir": mean_ic / std_ic if std_ic > 1e-12 else 0.0, + "rank_ic_hit_rate": float((ic > 0).mean()), + "mean_top_bottom_spread": top_bottom_spread, + "mean_coverage": coverage, + } + if not all(math.isfinite(float(value)) for value in result.values()): + raise JudgeFailure("factor.non-finite", "Factor metrics are non-finite") + return result + + +def _split_indices( + index: pd.DatetimeIndex, +) -> tuple[dict[str, pd.DatetimeIndex], dict[str, Any]]: + if len(index) < 3 * (MIN_SPLIT_OBSERVATIONS + 1): + raise JudgeFailure( + "portfolio.population", + "Too few dataset dates for fixed purged chronological evaluation", + ) + train_end = int(len(index) * 0.60) + validation_end = int(len(index) * 0.80) + ranges = { + "train": (0, train_end), + "validation": (train_end, validation_end), + "test": (validation_end, len(index)), + } + splits: dict[str, pd.DatetimeIndex] = {} + protocol: dict[str, Any] = { + "method": "dataset-fixed-chronological-60-20-20", + "candidateDependent": False, + "forwardHorizonBars": 1, + "targetCrossesBoundary": False, + "splits": {}, + } + for name, (start, stop) in ranges.items(): + eligible = index[start : stop - 1] + if len(eligible) < MIN_SPLIT_OBSERVATIONS: + raise JudgeFailure( + "portfolio.population", + f"Fixed split {name} has only {len(eligible)} signal dates", + ) + splits[name] = pd.DatetimeIndex(eligible) + protocol["splits"][name] = { + "start": timestamp_label(index[start]), + "end": timestamp_label(index[stop - 1]), + "signalEnd": timestamp_label(index[stop - 2]), + "targetEnd": timestamp_label(index[stop - 1]), + "eligibleSignalRows": len(eligible), + "purgedBoundaryRows": 1, + } + return splits, protocol + + +def _parameter_configuration_id(profile_id: str, band: float) -> str: + return f"{profile_id}__band-{int(round(band * 100)):03d}" + + +def _signal_profile_signature( + profile: dict[str, Any], + mandate: dict[str, Any], + universe_size: int, +) -> tuple[Any, ...]: + """Identify profiles equivalent on every attainable percentile score.""" + + scores: list[float | None] = [None] + scores.extend( + sorted( + { + rank / float(count - 1) + for count in range(MIN_ASSETS_PER_DATE, universe_size + 1) + for rank in range(count) + } + ) + ) + roles = sorted( + { + str(role) + for role in mandate["construction"][ + "assetPositionRoles" + ].values() + if role != "context-only" + } + ) + return tuple( + ( + role, + state, + score, + _position_role_signal_transition( + state, + score, + role=role, + long_entry=float(profile["long_entry"]), + long_exit=float(profile["long_exit"]), + short_exit=float(profile["short_exit"]), + short_entry=float(profile["short_entry"]), + ), + ) + for role in roles + for state in (-1, 0, 1) + for score in scores + ) + + +def _parameter_signal_daily( + construction: Any, + index: pd.DatetimeIndex, +) -> pd.DataFrame: + selected = construction.ledger[ + construction.ledger["timestamp"].isin(index) + ].copy() + selected["signal_transition"] = selected[ + "prior_signal_state" + ].ne(selected["signal_state"]) + selected["entry"] = selected["signal_event"].isin( + {"enter_long", "enter_short"} + ) + selected["exit"] = selected["signal_event"].isin( + {"exit_long", "exit_short"} + ) + selected["reversal"] = selected["signal_event"].isin( + {"reverse_long_to_short", "reverse_short_to_long"} + ) + return ( + selected.groupby("timestamp", sort=True) + .agg( + decision_rows=("asset", "size"), + signal_transitions=("signal_transition", "sum"), + entries=("entry", "sum"), + exits=("exit", "sum"), + reversals=("reversal", "sum"), + ) + .reindex(index, fill_value=0) + .astype(int) + ) + + +def _parameter_neighborhood( + factor_panel: pd.DataFrame, + close_panel: pd.DataFrame, + volume_panel: pd.DataFrame, + splits: dict[str, pd.DatetimeIndex], + mandate: dict[str, Any], + prediction_population: dict[str, Any], + *, + base_construction: Any, + base_simulation: Any, + risk_covariance_cache: Any, +) -> tuple[dict[str, Any], dict[str, Any]]: + implementation_policy = resolve_implementation_policy(mandate) + base_band = implementation_policy["no_trade_one_way"] + adverse_band = min(1.0, max(0.10, 2.0 * base_band)) + bands = tuple(dict.fromkeys((0.0, base_band, adverse_band))) + base_configuration_id = _parameter_configuration_id("base", base_band) + roles = { + "validation": "selection-context", + "test": "visible-audit", + } + configurations: dict[str, dict[str, Any]] = {} + rows: list[dict[str, Any]] = [] + base_profile = next( + profile + for profile in PARAMETER_SIGNAL_PROFILES + if profile["id"] == "base" + ) + base_signature = _signal_profile_signature( + base_profile, + mandate, + len(factor_panel.columns), + ) + construction_cache = {base_signature: base_construction} + simulation_cache = { + (base_signature, float(base_band)): base_simulation + } + for profile in PARAMETER_SIGNAL_PROFILES: + signature = _signal_profile_signature( + profile, + mandate, + len(factor_panel.columns), + ) + construction = construction_cache.get(signature) + if construction is None: + construction = construct_signal_policy( + factor_panel, + close_panel, + long_entry=float(profile["long_entry"]), + long_exit=float(profile["long_exit"]), + short_exit=float(profile["short_exit"]), + short_entry=float(profile["short_entry"]), + mandate=mandate, + prediction_population=prediction_population, + risk_covariance_cache=risk_covariance_cache, + ) + construction_cache[signature] = construction + for band in bands: + configuration_id = _parameter_configuration_id( + str(profile["id"]), + band, + ) + is_base = configuration_id == base_configuration_id + cache_key = (signature, float(band)) + simulation = simulation_cache.get(cache_key) + if simulation is None: + simulation = simulate_targets( + construction.targets, + close_panel, + volume_panel, + no_trade_one_way=band, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + simulation_cache[cache_key] = simulation + split_metrics: dict[str, Any] = {} + for split in ("validation", "test"): + index = splits[split] + performance = performance_metrics( + simulation.daily.loc[index, "net_return"], + simulation.daily.loc[index, "benchmark_return"], + ) + implementation = implementation_metrics(simulation, index) + signal = signal_policy_metrics(construction, index) + split_metrics[split] = { + "performance": performance, + "implementation": { + "mean_one_way_turnover": implementation[ + "mean_one_way_turnover" + ], + "annualized_one_way_turnover": implementation[ + "annualized_one_way_turnover" + ], + "total_cost_drag": implementation["total_cost_drag"], + "rebalance_rate": implementation["rebalance_rate"], + "no_trade_rate": implementation["no_trade_rate"], + }, + "signal": { + "decision_rows": signal["decision_rows"], + "timestamps": signal["timestamps"], + "signal_transitions": signal["signal_transitions"], + "state_change_rate": signal["state_change_rate"], + "entries": signal["entries"], + "exits": signal["exits"], + "reversals": signal["reversals"], + }, + } + daily_signal = _parameter_signal_daily(construction, index) + daily = simulation.daily.loc[index] + row_frame = pd.DataFrame( + { + "configurationId": configuration_id, + "signalProfile": profile["id"], + "noTradeOneWay": band, + "split": split, + "role": roles[split], + "timestamp": [ + timestamp_label(timestamp) + for timestamp in index + ], + "netReturn": daily["net_return"].astype(float), + "benchmarkReturn": daily[ + "benchmark_return" + ].astype(float), + "oneWayTurnover": daily[ + "one_way_turnover" + ].astype(float), + "cost": daily["cost"].astype(float), + "rebalanced": daily["rebalanced"].astype(bool), + "signalDecisionRows": daily_signal[ + "decision_rows" + ].astype(int), + "signalTransitions": daily_signal[ + "signal_transitions" + ].astype(int), + "entries": daily_signal["entries"].astype(int), + "exits": daily_signal["exits"].astype(int), + "reversals": daily_signal["reversals"].astype(int), + }, + index=index, + ) + rows.extend(row_frame.to_dict(orient="records")) + configurations[configuration_id] = { + "signal_profile": profile["id"], + "no_trade_one_way": band, + "is_base": is_base, + "validation": split_metrics["validation"], + "test": split_metrics["test"], + } + + for split in ("validation", "test"): + base = configurations[base_configuration_id][split] + for configuration in configurations.values(): + current = configuration[split] + current["delta_vs_base"] = { + "net_sharpe": ( + current["performance"]["sharpe"] + - base["performance"]["sharpe"] + ), + "total_return": ( + current["performance"]["total_return"] + - base["performance"]["total_return"] + ), + "annualized_one_way_turnover": ( + current["implementation"][ + "annualized_one_way_turnover" + ] + - base["implementation"][ + "annualized_one_way_turnover" + ] + ), + "total_cost_drag": ( + current["implementation"]["total_cost_drag"] + - base["implementation"]["total_cost_drag"] + ), + "signal_transitions": ( + current["signal"]["signal_transitions"] + - base["signal"]["signal_transitions"] + ), + } + + split_output: dict[str, Any] = {} + for split in ("validation", "test"): + values = [ + configuration[split] + for configuration in configurations.values() + ] + sharpes = np.asarray( + [value["performance"]["sharpe"] for value in values], + dtype=float, + ) + base_sharpe = float( + configurations[base_configuration_id][split][ + "performance" + ]["sharpe"] + ) + deltas = sharpes - base_sharpe + turnovers = [ + value["implementation"]["annualized_one_way_turnover"] + for value in values + ] + costs = [ + value["implementation"]["total_cost_drag"] + for value in values + ] + transitions = [ + value["signal"]["signal_transitions"] for value in values + ] + base_sign = 1 if base_sharpe > 0 else -1 if base_sharpe < 0 else 0 + signs = np.sign(sharpes) + split_output[split] = { + "configurations": { + configuration_id: configuration[split] + for configuration_id, configuration in configurations.items() + }, + "aggregate": { + "configuration_count": len(values), + "base_net_sharpe": base_sharpe, + "positive_net_sharpe_rate": float( + np.mean(sharpes > 0) + ), + "sign_agreement_with_base_rate": float( + np.mean(signs == base_sign) + ), + "minimum_net_sharpe": float(np.min(sharpes)), + "median_net_sharpe": float(np.median(sharpes)), + "maximum_net_sharpe": float(np.max(sharpes)), + "net_sharpe_std": float(np.std(sharpes, ddof=0)), + "worst_net_sharpe_delta": float(np.min(deltas)), + "best_net_sharpe_delta": float(np.max(deltas)), + "minimum_annualized_one_way_turnover": float( + min(turnovers) + ), + "maximum_annualized_one_way_turnover": float( + max(turnovers) + ), + "minimum_total_cost_drag": float(min(costs)), + "maximum_total_cost_drag": float(max(costs)), + "minimum_signal_transitions": int(min(transitions)), + "maximum_signal_transitions": int(max(transitions)), + }, + } + + policy = { + "method": PARAMETER_NEIGHBORHOOD_METHOD, + "base_configuration_id": base_configuration_id, + "role": "robustness-only", + "selection_authority": "context-only", + "trading_authority": "none", + "configuration_count": len(configurations), + "signal_profiles": [ + { + "id": profile["id"], + "label": profile["label"], + "long_entry": profile["long_entry"], + "long_exit": profile["long_exit"], + "short_exit": profile["short_exit"], + "short_entry": profile["short_entry"], + } + for profile in PARAMETER_SIGNAL_PROFILES + ], + "no_trade_bands": list(bands), + } + metrics = { + "policy": policy, + "validation": split_output["validation"], + "test": split_output["test"], + } + artifact = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "method": PARAMETER_NEIGHBORHOOD_METHOD, + "baseConfigurationId": base_configuration_id, + "signalProfiles": [ + { + "id": profile["id"], + "label": profile["label"], + "longEntry": profile["long_entry"], + "longExit": profile["long_exit"], + "shortExit": profile["short_exit"], + "shortEntry": profile["short_entry"], + } + for profile in PARAMETER_SIGNAL_PROFILES + ], + "noTradeBands": list(bands), + "rows": rows, + } + return metrics, artifact + + +def _translation_robustness( + factor_panel: pd.DataFrame, + close_panel: pd.DataFrame, + volume_panel: pd.DataFrame, + splits: dict[str, pd.DatetimeIndex], + mandate: dict[str, Any], + prediction_population: dict[str, Any], + *, + base_construction: Any, + base_simulation: Any, + risk_covariance_cache: Any, +) -> tuple[dict[str, Any], dict[str, Any] | None]: + """Stress only the causal temporal history used to translate a Factor.""" + + mode = str(prediction_population["evaluation_mode"]) + policy = { + "method": TRANSLATION_ROBUSTNESS_METHOD, + "evaluation_mode": mode, + "role": "robustness-only", + "selection_authority": "context-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + "base_profile_id": "base", + "profiles": [dict(profile) for profile in TRANSLATION_ROBUSTNESS_PROFILES], + "stability_thresholds": { + "minimum_active_state_agreement_rate": ( + TRANSLATION_ACTIVE_AGREEMENT_FLOOR + ), + "maximum_mean_absolute_target_delta": ( + TRANSLATION_TARGET_MAE_CEILING + ), + }, + } + if mode not in TEMPORAL_EVALUATION_MODES: + return ( + { + "applicable": False, + "reason": "cross-sectional-mode-has-no-temporal-window", + "policy": policy, + "diagnosis": { + "selection_split": "validation", + "status": "not-applicable", + "explanation": ( + "Cross-sectional decision scores use the same-timestamp " + "prediction population and have no temporal history window." + ), + }, + "validation": None, + "test": None, + "current": None, + }, + None, + ) + + prediction_assets = list(prediction_population["prediction_assets"]) + constructions: dict[str, Any] = {"base": base_construction} + simulations: dict[str, Any] = {"base": base_simulation} + for profile in TRANSLATION_ROBUSTNESS_PROFILES: + profile_id = str(profile["id"]) + if profile_id == "base": + continue + construction = construct_signal_policy( + factor_panel, + close_panel, + mandate=mandate, + prediction_population=prediction_population, + temporal_translation_window=int(profile["window"]), + temporal_translation_minimum=int(profile["minimum"]), + risk_covariance_cache=risk_covariance_cache, + ) + constructions[profile_id] = construction + simulations[profile_id] = simulate_targets( + construction.targets, + close_panel, + volume_panel, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + + split_metrics: dict[str, Any] = {} + for split in ("validation", "test"): + index = splits[split] + base_states = constructions["base"].states.loc[index, prediction_assets] + base_targets = constructions["base"].targets.loc[index, prediction_assets] + profiles: dict[str, Any] = {} + for profile in TRANSLATION_ROBUSTNESS_PROFILES: + profile_id = str(profile["id"]) + construction = constructions[profile_id] + simulation = simulations[profile_id] + states = construction.states.loc[index, prediction_assets] + targets = construction.targets.loc[index, prediction_assets] + scores = construction.scores.loc[index, prediction_assets] + active_union = states.ne(0) | base_states.ne(0) + active_count = int(active_union.to_numpy().sum()) + state_agreement = ( + float(states.eq(base_states).where(active_union).stack().mean()) + if active_count + else None + ) + target_direction_agreement = ( + float( + np.sign(targets) + .eq(np.sign(base_targets)) + .where(active_union) + .stack() + .mean() + ) + if active_count + else None + ) + profile_implementation = implementation_metrics(simulation, index) + profiles[profile_id] = { + "window_observations": int(profile["window"]), + "minimum_observations": int(profile["minimum"]), + "is_base": bool(profile["is_base"]), + "score_availability_rate": float( + scores.notna().to_numpy().mean() + ), + "active_union_observations": active_count, + "active_state_agreement_with_base_rate": state_agreement, + "active_target_direction_agreement_with_base_rate": ( + target_direction_agreement + ), + "mean_absolute_target_delta_vs_base": float( + (targets - base_targets).abs().to_numpy().mean() + ), + "performance": performance_metrics( + simulation.daily.loc[index, "net_return"], + simulation.daily.loc[index, "benchmark_return"], + ), + "implementation": { + "annualized_one_way_turnover": profile_implementation[ + "annualized_one_way_turnover" + ], + "total_cost_drag": profile_implementation[ + "total_cost_drag" + ], + "rebalance_rate": profile_implementation[ + "rebalance_rate" + ], + }, + } + split_metrics[split] = {"profiles": profiles} + + validation_profiles = split_metrics["validation"]["profiles"] + non_base = [ + validation_profiles[str(profile["id"])] + for profile in TRANSLATION_ROBUSTNESS_PROFILES + if not profile["is_base"] + ] + agreements = [ + float(profile["active_state_agreement_with_base_rate"]) + for profile in non_base + if profile["active_state_agreement_with_base_rate"] is not None + ] + maximum_target_delta = max( + float(profile["mean_absolute_target_delta_vs_base"]) + for profile in non_base + ) + if not agreements: + status = "insufficient-active-targets" + explanation = ( + "No validation target was active under the base or adjacent causal " + "translation windows, so active-path stability is unavailable." + ) + elif ( + min(agreements) >= TRANSLATION_ACTIVE_AGREEMENT_FLOOR + and maximum_target_delta <= TRANSLATION_TARGET_MAE_CEILING + ): + status = "stable-target-path" + explanation = ( + "Validation signal states and target weights remain locally stable " + "across the predeclared causal history windows." + ) + else: + status = "translation-sensitive-target-path" + explanation = ( + "Validation signal states or target weights change materially across " + "the predeclared causal history windows; do not treat one 60-bar " + "target path as structurally robust." + ) + sharpes = [ + float(profile["performance"]["sharpe"]) + for profile in validation_profiles.values() + ] + base_sharpe = float(validation_profiles["base"]["performance"]["sharpe"]) + base_sign = 1 if base_sharpe > 0 else -1 if base_sharpe < 0 else 0 + diagnosis = { + "selection_split": "validation", + "test_enters_diagnosis": False, + "status": status, + "minimum_active_state_agreement_rate": ( + min(agreements) if agreements else None + ), + "maximum_mean_absolute_target_delta": maximum_target_delta, + "net_sharpe_range": [min(sharpes), max(sharpes)], + "net_sharpe_sign_agreement_with_base": all( + (1 if value > 0 else -1 if value < 0 else 0) == base_sign + for value in sharpes + ), + "explanation": explanation, + } + + reconciled_index = base_simulation.daily.index + current_timestamp = reconciled_index[-1] + current = { + "timestamp": timestamp_label(current_timestamp), + "profiles": { + str(profile["id"]): [ + { + "asset": asset, + "score": ( + float(constructions[str(profile["id"])].scores.loc[current_timestamp, asset]) + if pd.notna(constructions[str(profile["id"])].scores.loc[current_timestamp, asset]) + else None + ), + "signal_state": int( + constructions[str(profile["id"])].states.loc[ + current_timestamp, asset + ] + ), + "target_weight": float( + constructions[str(profile["id"])].targets.loc[ + current_timestamp, asset + ] + ), + } + for asset in prediction_assets + ] + for profile in TRANSLATION_ROBUSTNESS_PROFILES + }, + } + + asset_rows: list[dict[str, Any]] = [] + daily_rows: list[dict[str, Any]] = [] + for profile in TRANSLATION_ROBUSTNESS_PROFILES: + profile_id = str(profile["id"]) + construction = constructions[profile_id] + simulation = simulations[profile_id] + for timestamp in reconciled_index: + for asset in prediction_assets: + score = construction.scores.loc[timestamp, asset] + asset_rows.append( + { + "profileId": profile_id, + "timestamp": timestamp_label(timestamp), + "asset": asset, + "score": float(score) if pd.notna(score) else None, + "observations": int( + construction.translation_observations.loc[ + timestamp, asset + ] + ), + "signalState": int(construction.states.loc[timestamp, asset]), + "targetWeight": float( + construction.targets.loc[timestamp, asset] + ), + } + ) + for split in ("validation", "test"): + for timestamp in splits[split]: + row = simulation.daily.loc[timestamp] + daily_rows.append( + { + "profileId": profile_id, + "split": split, + "timestamp": timestamp_label(timestamp), + "netReturn": float(row["net_return"]), + "benchmarkReturn": float(row["benchmark_return"]), + "oneWayTurnover": float(row["one_way_turnover"]), + "cost": float(row["cost"]), + "rebalanced": bool(row["rebalanced"]), + } + ) + artifact = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "method": TRANSLATION_ROBUSTNESS_METHOD, + "evaluationMode": mode, + "predictionAssets": prediction_assets, + "profiles": [dict(profile) for profile in TRANSLATION_ROBUSTNESS_PROFILES], + "assetRows": asset_rows, + "dailyRows": daily_rows, + } + return ( + { + "applicable": True, + "reason": None, + "policy": policy, + "diagnosis": diagnosis, + "validation": split_metrics["validation"], + "test": split_metrics["test"], + "current": current, + }, + artifact, + ) + + +def _evaluate() -> tuple[ + dict[str, Any], + dict[str, Any], + Any, + Any, + pd.DataFrame, + pd.DataFrame, + dict[str, Any], +]: + study, data_root = _load_contract() + mandate = _load_mandate() + factor_claim = _load_factor_claim() + factor_population = _load_factor_population() + if factor_outcome(factor_claim) != FORWARD_RETURN_OUTCOME: + raise JudgeFailure( + "portfolio.factor-outcome", + "Portfolio construction requires a forward-return Factor; a " + "risk forecast has no expected-return or target-weight meaning", + ) + research_horizon = _load_horizon() + implementation_policy = resolve_implementation_policy(mandate) + dataset = study["dataset"] + universe = dataset["universe"] + try: + prediction_population = resolve_prediction_population( + universe, + factor_claim, + factor_population, + ).as_metrics() + validate_population_mandate_compatibility( + prediction_population, + mandate, + ) + except PredictionModeError as error: + raise JudgeFailure(error.code, str(error)) from error + time_range = dataset["time_range"] + module = importlib.import_module("factors.candidate") + + frames: dict[str, pd.DataFrame] = {} + closes: dict[str, pd.Series] = {} + volumes: dict[str, pd.Series] = {} + for asset in universe: + frame = _load_asset( + data_root, + asset, + time_range["start"], + time_range["end"], + ) + frames[asset] = frame + timestamp = pd.DatetimeIndex(frame["timestamp"]) + close = frame["close"].astype(float) + close.index = timestamp + volume = frame["volume"].astype(float) + volume.index = timestamp + closes[asset] = close + volumes[asset] = volume + + try: + panel = build_factor_panel(frames, universe=universe) + factor_evaluation = evaluate_factor(module, panel) + factor_panel = values_to_wide( + panel, + factor_evaluation.values, + universe=universe, + ) + except FactorRuntimeError as error: + raise JudgeFailure(error.code, str(error)) from error + close_panel = pd.DataFrame(closes) + volume_panel = pd.DataFrame(volumes) + forward_returns = close_panel.shift(-1) / close_panel - 1.0 + risk_covariance_cache = build_risk_covariance_cache( + close_panel, + mandate=mandate, + ) + factor_evidence = _daily_factor_evidence( + factor_panel, + forward_returns, + prediction_population, + ) + construction = construct_signal_policy( + factor_panel, + close_panel, + mandate=mandate, + prediction_population=prediction_population, + risk_covariance_cache=risk_covariance_cache, + ) + targets = construction.targets + audit = constraint_audit(targets, mandate=mandate) + if not audit["passed"]: + raise JudgeFailure( + "portfolio.constraints", + "Fixed target construction violated its declared constraints", + ) + base = simulate_targets( + targets, + close_panel, + volume_panel, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + decision_ledger = build_decision_ledger( + construction, + base, + close_panel, + volume_panel, + mandate=mandate, + ) + splits, split_protocol = _split_indices( + pd.DatetimeIndex(factor_panel.index) + ) + + factor_metrics: dict[str, Any] = {} + portfolio_metrics: dict[str, Any] = {} + implementation: dict[str, Any] = {} + for name, index in splits.items(): + factor_metrics[name] = _factor_metrics( + factor_evidence, + index, + factor_panel[prediction_population["prediction_assets"]], + str(prediction_population["evaluation_mode"]), + ) + portfolio_metrics[name] = { + "gross": performance_metrics( + base.daily.loc[index, "gross_return"], + base.daily.loc[index, "benchmark_return"], + ), + "net": performance_metrics( + base.daily.loc[index, "net_return"], + base.daily.loc[index, "benchmark_return"], + ), + } + implementation[name] = implementation_metrics(base, index) + + policy_metrics = { + name: signal_policy_metrics(construction, index) + for name, index in splits.items() + } + attribution = { + name: attribution_metrics(decision_ledger, base, index) + for name, index in splits.items() + } + liquidity_capacity = { + "policy": { + "method": "trailing-average-dollar-volume-capacity-v1", + "adv_window": LIQUIDITY_ADV_WINDOW, + "participation_limits": list( + LIQUIDITY_PARTICIPATION_LIMITS + ), + "reference_nav": implementation_policy["reference_nav"], + "selection_authority": "context-only", + "trading_authority": "none", + }, + **{ + name: liquidity_capacity_metrics( + decision_ledger, + index, + reference_nav=implementation_policy["reference_nav"], + ) + for name, index in splits.items() + }, + } + execution_risk = { + "policy": { + "method": ( + "post-drift-executed-book-volatility-compliance-v1" + ), + "risk_policy": mandate["construction"]["riskPolicy"], + "no_trade_priority": "risk-compliance-first", + "repair": "minimum-proportional-scale-down", + "selection_authority": "context-only", + "trading_authority": "none", + }, + **{ + name: execution_risk_metrics(base, index) + for name, index in splits.items() + }, + } + episode_roles = { + "train": "training", + "validation": "selection", + "test": "visible-audit", + } + episode_frames = { + name: build_position_episodes( + decision_ledger, + index, + split=name, + role=episode_roles[name], + ) + for name, index in splits.items() + } + position_episodes = pd.concat( + episode_frames.values(), + ignore_index=True, + ) + position_lifecycle = { + "policy": { + "method": "split-bounded-executed-position-episodes-v1", + "state": "sign-of-executed-weight", + "boundary": "split-clipped-left-right-censored", + "pnl": ( + "additive-portfolio-return-contribution-after-" + "proportional-trade-cost" + ), + "excursion": ( + "cumulative-net-contribution-from-split-segment-start" + ), + "selection_authority": "context-only", + "trading_authority": "none", + }, + **{ + name: position_episode_metrics( + episode_frames[name], + decision_ledger, + index, + ) + for name, index in splits.items() + }, + } + parameter_neighborhood, parameter_neighborhood_artifact = ( + _parameter_neighborhood( + factor_panel, + close_panel, + volume_panel, + splits, + mandate, + prediction_population, + base_construction=construction, + base_simulation=base, + risk_covariance_cache=risk_covariance_cache, + ) + ) + translation_robustness, translation_robustness_artifact = ( + _translation_robustness( + factor_panel, + close_panel, + volume_panel, + splits, + mandate, + prediction_population, + base_construction=construction, + base_simulation=base, + risk_covariance_cache=risk_covariance_cache, + ) + ) + + validation_net_sharpe = float( + portfolio_metrics["validation"]["net"]["sharpe"] + ) + cost_stress: dict[str, Any] = {} + base_cost_bps = implementation_policy["base_cost_bps"] + adverse_cost_bps = max(25.0, 2.0 * base_cost_bps) + for cost_bps in dict.fromkeys( + (0.0, base_cost_bps, adverse_cost_bps) + ): + simulation = simulate_targets( + targets, + close_panel, + volume_panel, + cost_bps=cost_bps, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + key = f"{cost_bps:g}bps" + cost_stress[key] = { + split: performance_metrics( + simulation.daily.loc[index, "net_return"], + simulation.daily.loc[index, "benchmark_return"], + ) + for split, index in ( + ("validation", splits["validation"]), + ("test", splits["test"]), + ) + } + delayed = simulate_targets( + targets, + close_panel, + volume_panel, + extra_delay=1, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + delay_stress = { + split: performance_metrics( + delayed.daily.loc[index, "net_return"], + delayed.daily.loc[index, "benchmark_return"], + ) + for split, index in ( + ("validation", splits["validation"]), + ("test", splits["test"]), + ) + } + no_hysteresis = construct_signal_policy( + factor_panel, + close_panel, + long_exit=LONG_ENTRY_PERCENTILE, + short_exit=SHORT_ENTRY_PERCENTILE, + mandate=mandate, + prediction_population=prediction_population, + risk_covariance_cache=risk_covariance_cache, + ) + no_hysteresis_simulation = simulate_targets( + no_hysteresis.targets, + close_panel, + volume_panel, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + hysteresis_comparison: dict[str, Any] = {} + for split in ("validation", "test"): + index = splits[split] + governed_policy = policy_metrics[split] + baseline_policy = signal_policy_metrics(no_hysteresis, index) + governed_implementation = implementation[split] + baseline_implementation = implementation_metrics( + no_hysteresis_simulation, + index, + ) + governed_net = portfolio_metrics[split]["net"] + baseline_net = performance_metrics( + no_hysteresis_simulation.daily.loc[index, "net_return"], + no_hysteresis_simulation.daily.loc[index, "benchmark_return"], + ) + baseline_transitions = int(baseline_policy["signal_transitions"]) + transition_reduction = ( + baseline_transitions + - int(governed_policy["signal_transitions"]) + ) + hysteresis_comparison[split] = { + "governed": { + "signal_transitions": governed_policy["signal_transitions"], + "state_change_rate": governed_policy["state_change_rate"], + "annualized_target_one_way_turnover": governed_policy[ + "annualized_target_one_way_turnover" + ], + "annualized_implementation_one_way_turnover": ( + governed_implementation["annualized_one_way_turnover"] + ), + "net_sharpe": governed_net["sharpe"], + }, + "no_hysteresis": { + "signal_transitions": baseline_transitions, + "state_change_rate": baseline_policy["state_change_rate"], + "annualized_target_one_way_turnover": baseline_policy[ + "annualized_target_one_way_turnover" + ], + "annualized_implementation_one_way_turnover": ( + baseline_implementation["annualized_one_way_turnover"] + ), + "net_sharpe": baseline_net["sharpe"], + }, + "transition_reduction": transition_reduction, + "transition_reduction_rate": ( + transition_reduction / baseline_transitions + if baseline_transitions > 0 + else 0.0 + ), + "implementation_turnover_reduction": ( + baseline_implementation["annualized_one_way_turnover"] + - governed_implementation["annualized_one_way_turnover"] + ), + } + ungoverned = construct_signal_policy( + factor_panel, + close_panel, + mandate=mandate, + prediction_population=prediction_population, + apply_risk_governor=False, + risk_covariance_cache=risk_covariance_cache, + ) + ungoverned_simulation = simulate_targets( + ungoverned.targets, + close_panel, + volume_panel, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + risk_governor_comparison: dict[str, Any] = {} + for split in ("validation", "test"): + index = splits[split] + governed_net = portfolio_metrics[split]["net"] + ungoverned_net = performance_metrics( + ungoverned_simulation.daily.loc[index, "net_return"], + ungoverned_simulation.daily.loc[index, "benchmark_return"], + ) + governed_implementation = implementation[split] + ungoverned_implementation = implementation_metrics( + ungoverned_simulation, + index, + ) + risk_governor_comparison[split] = { + "governed": { + "net_sharpe": governed_net["sharpe"], + "annual_volatility": governed_net["annual_volatility"], + "maximum_drawdown": governed_net["maximum_drawdown"], + "average_gross_exposure": governed_implementation[ + "average_gross_exposure" + ], + "risk_limited_dates": policy_metrics[split][ + "risk_limited_dates" + ], + "risk_limited_rate": policy_metrics[split][ + "risk_limited_rate" + ], + "average_active_risk_scale": policy_metrics[split][ + "average_active_risk_scale" + ], + "maximum_pre_governor_annualized_volatility": policy_metrics[ + split + ]["maximum_pre_governor_annualized_volatility"], + "maximum_post_governor_annualized_volatility": policy_metrics[ + split + ]["maximum_post_governor_annualized_volatility"], + }, + "ungoverned_diagnostic": { + "net_sharpe": ungoverned_net["sharpe"], + "annual_volatility": ungoverned_net["annual_volatility"], + "maximum_drawdown": ungoverned_net["maximum_drawdown"], + "average_gross_exposure": ungoverned_implementation[ + "average_gross_exposure" + ], + }, + "net_sharpe_delta": ( + governed_net["sharpe"] - ungoverned_net["sharpe"] + ), + "annual_volatility_delta": ( + governed_net["annual_volatility"] + - ungoverned_net["annual_volatility"] + ), + } + test_index = splits["test"] + contribution = ( + base.weights.loc[test_index] + * forward_returns.loc[test_index] + ).mean() * annualization_periods(test_index) + per_asset_contribution = { + asset: float(value) + for asset, value in contribution.items() + } + metrics = { + "validation_net_sharpe": validation_net_sharpe, + "factor_api": factor_contract(factor_evaluation), + "factor_claim": factor_claim, + "prediction_universe": prediction_population, + "portfolio_mandate": mandate, + "research_horizon": research_horizon, + "factor": factor_metrics, + "portfolio": portfolio_metrics, + "implementation": implementation, + "signal_policy": { + "translation": construction.translation, + "parameters": { + "long_entry_percentile": LONG_ENTRY_PERCENTILE, + "long_exit_percentile": LONG_EXIT_PERCENTILE, + "short_exit_percentile": SHORT_EXIT_PERCENTILE, + "short_entry_percentile": SHORT_ENTRY_PERCENTILE, + "volatility_window": VOLATILITY_WINDOW, + "gross_target": mandate["construction"]["grossLimit"], + "max_abs_weight": mandate["construction"]["maxAbsWeight"], + "no_trade_one_way": implementation_policy[ + "no_trade_one_way" + ], + "decision_schedule": implementation_policy[ + "decision_policy" + ], + }, + "train": policy_metrics["train"], + "validation": policy_metrics["validation"], + "test": policy_metrics["test"], + "hysteresis_comparison": hysteresis_comparison, + }, + "attribution": attribution, + "liquidity_capacity": liquidity_capacity, + "execution_risk": execution_risk, + "position_lifecycle": position_lifecycle, + "parameter_neighborhood": parameter_neighborhood, + "translation_robustness": translation_robustness, + "split_protocol": split_protocol, + "robustness": { + "cost_stress": cost_stress, + "extra_delay": delay_stress, + "risk_governor": { + "policy": mandate["construction"]["riskPolicy"], + "selectionAuthority": "diagnostic-only", + "validation": risk_governor_comparison["validation"], + "test": risk_governor_comparison["test"], + }, + "test_annualized_gross_contribution": per_asset_contribution, + }, + "constraint_audit": audit, + "research_integrity": { + "selection_split": "validation", + "test_role": "visible-diagnostic", + "test_enters_selection": False, + "external_holdout_rule": ( + "required-after-visible-test-and-candidate-iteration" + ), + }, + } + if not math.isfinite(validation_net_sharpe): + raise JudgeFailure("portfolio.non-finite", "Primary score is non-finite") + report = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "dataset": { + "id": dataset["id"], + "version": dataset["version"], + "universe": universe, + "timeRange": time_range, + }, + "factorClaim": factor_claim, + "predictionUniverse": prediction_population, + "portfolioMandate": mandate, + "researchHorizon": research_horizon, + "signalTranslation": construction.translation, + "semantics": { + "simulation": "bar-target-weight", + "decision": "OHLCV and factor known through close t", + "return": "close t to close t+1", + "researchHorizonRole": ( + "fixed question identity and disclosure; sequential one-bar " + "accounting is not a direct multi-bar forecast" + ), + "factorTransform": ( + f"{construction.translation['method']} / " + f"{construction.translation['evaluation_mode']} / " + f"{construction.translation['score_basis']}; percentile state " + "machine with inverse-volatility conviction sizing followed " + "by a causal one-sided portfolio volatility ceiling" + ), + "signalState": ( + "long entry/exit 0.75/0.55; short entry/exit 0.25/0.45; " + "direct reversal at opposite entry" + ), + "portfolio": ( + f"{mandate['construction']['family']} over authorized tradable " + "assets; gross limit " + f"{mandate['construction']['grossLimit']}; max abs weight " + f"{mandate['construction']['maxAbsWeight']}; unused " + "directional budget and risk-governor reductions remain cash" + ), + "riskGovernor": ( + "60-bar trailing covariance through close t; minimum 20 " + "observations; annualized volatility ceiling " + f"{mandate['construction']['riskPolicy']['annualizedVolatilityCeiling']}; " + "scale-down only" + ), + "executionRisk": ( + "the final post-drift book is rechecked through close t; " + "risk compliance bypasses the no-trade band with minimum " + "proportional scale-down" + ), + "positionLifecycle": ( + "contiguous executed-weight signs clipped to fixed splits; " + "linear close/open cost allocated exactly; MFE/MAE is " + "daily cumulative additive return contribution" + ), + "noTrade": ( + "retain drifted book below " + f"{implementation_policy['no_trade_one_way']} one-way turnover" + ), + "turnover": "0.5 * sum(abs(trade weight))", + "cost": ( + "sum(abs(trade weight)) * " + f"{base_cost_bps:g}bps" + ), + "liquidityCapacity": ( + "20-observation trailing average close-times-volume through " + "decision close; exact executed trade weights inverted at " + "1% and 5% participation; contextual only" + ), + "benchmark": mandate["construction"]["benchmark"], + "split": ( + "dataset-fixed chronological 60/20/20 with one-bar " + "boundary purge" + ), + "score": ( + "validation net Sharpe at " + f"{base_cost_bps:g}bps only" + ), + "testRole": ( + "visible diagnostic evidence; never enters candidate selection" + ), + "tradingAuthority": "none", + }, + "fixedParameters": { + "volatilityWindow": VOLATILITY_WINDOW, + "grossTarget": mandate["construction"]["grossLimit"], + "maxAbsWeight": mandate["construction"]["maxAbsWeight"], + "noTradeOneWay": implementation_policy["no_trade_one_way"], + "baseCostBps": base_cost_bps, + "referenceNav": implementation_policy["reference_nav"], + "decisionSchedule": implementation_policy["decision_policy"], + "longEntryPercentile": LONG_ENTRY_PERCENTILE, + "longExitPercentile": LONG_EXIT_PERCENTILE, + "shortExitPercentile": SHORT_EXIT_PERCENTILE, + "shortEntryPercentile": SHORT_ENTRY_PERCENTILE, + "riskCovarianceWindow": RISK_COVARIANCE_WINDOW, + "riskCovarianceMinimum": RISK_COVARIANCE_MINIMUM, + "riskPolicy": mandate["construction"]["riskPolicy"], + "executionRiskMethod": ( + "post-drift-executed-book-volatility-compliance-v1" + ), + "liquidityAdvWindow": LIQUIDITY_ADV_WINDOW, + "liquidityParticipationLimits": list( + LIQUIDITY_PARTICIPATION_LIMITS + ), + }, + "causalityAuditCuts": list(factor_evaluation.causality_cuts), + "splitProtocol": split_protocol, + "metrics": metrics, + } + return ( + metrics, + report, + base, + construction, + decision_ledger, + position_episodes, + parameter_neighborhood_artifact, + translation_robustness_artifact, + ) + + +def main() -> None: + try: + ( + metrics, + report, + simulation, + construction, + decision_ledger, + position_episodes, + parameter_neighborhood, + translation_robustness, + ) = _evaluate() + artifacts = Path(os.environ["AUTOQUANT_ARTIFACTS_DIR"]) + report_path = artifacts / "portfolio-report.json" + report_path.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + daily = simulation.daily.copy() + daily.index = [timestamp_label(value) for value in daily.index] + daily.index.name = "timestamp" + daily.to_csv(artifacts / "daily-portfolio.csv", float_format="%.17g") + proposed_targets = construction.targets.copy() + proposed_targets.index = [ + timestamp_label(value) for value in proposed_targets.index + ] + proposed_targets.index.name = "timestamp" + proposed_targets.to_csv( + artifacts / "proposed-target-weights.csv", + float_format="%.17g", + ) + executed_weights = simulation.weights.copy() + executed_weights.index = [ + timestamp_label(value) for value in executed_weights.index + ] + executed_weights.index.name = "timestamp" + executed_weights.to_csv( + artifacts / "executed-weights.csv", + float_format="%.17g", + ) + decision_artifact = decision_ledger.copy() + decision_artifact["timestamp"] = decision_artifact["timestamp"].map( + timestamp_label + ) + decision_artifact.to_csv( + artifacts / "portfolio-decisions.csv", + index=False, + float_format="%.17g", + ) + episode_artifact = position_episodes.copy() + for column in ( + "entry_timestamp", + "last_earning_timestamp", + "exit_timestamp", + ): + episode_artifact[column] = episode_artifact[column].map( + lambda value: ( + value if pd.isna(value) else timestamp_label(value) + ) + ) + episode_artifact.to_csv( + artifacts / "position-episodes.csv", + index=False, + float_format="%.17g", + ) + (artifacts / "portfolio-parameter-neighborhood.json").write_text( + json.dumps( + parameter_neighborhood, + indent=2, + sort_keys=True, + ) + + "\n", + encoding="utf-8", + ) + if translation_robustness is not None: + (artifacts / "portfolio-translation-robustness.json").write_text( + json.dumps( + translation_robustness, + indent=2, + sort_keys=True, + ) + + "\n", + encoding="utf-8", + ) + declared_artifacts = [ + { + "kind": "portfolio-report", + "path": "portfolio-report.json", + "description": ( + "Timing, construction, split, cost, risk, stress, " + "constraint, and causality evidence" + ), + }, + { + "kind": "portfolio-daily", + "path": "daily-portfolio.csv", + "description": ( + "Daily gross/net/benchmark returns, turnover, costs, " + "exposures, rebalance state, and participation" + ), + }, + { + "kind": "portfolio-targets", + "path": "proposed-target-weights.csv", + "description": ( + "Exact per-date signal-policy proposed target weights" + ), + }, + { + "kind": "portfolio-weights", + "path": "executed-weights.csv", + "description": ( + "Exact post-drift and no-trade-band executed weights" + ), + }, + { + "kind": "portfolio-decisions", + "path": "portfolio-decisions.csv", + "description": ( + "Per-asset signal state, sizing, execution reason, " + "trade, contribution, cost, regime, variance, " + "executed-book risk compliance, and causal " + "liquidity-capacity ledger" + ), + }, + { + "kind": "portfolio-position-episodes", + "path": "position-episodes.csv", + "description": ( + "Split-bounded executed-position episodes with holding, " + "contribution, cost, excursion, censoring, and " + "signal/execution mismatch evidence" + ), + }, + { + "kind": "portfolio-parameter-neighborhood", + "path": "portfolio-parameter-neighborhood.json", + "description": ( + "Exact predeclared local signal-threshold and " + "no-trade-band validation/test paths" + ), + }, + ] + if translation_robustness is not None: + declared_artifacts.append( + { + "kind": "portfolio-translation-robustness", + "path": "portfolio-translation-robustness.json", + "description": ( + "Exact predeclared causal temporal-window score, " + "state, target, and validation/test paths" + ), + } + ) + _write_output( + { + "schema_version": 1, + "status": "succeeded", + "summary": ( + "Causal factor translated into constrained next-bar targets; " + "validation net Sharpe=" + f"{metrics['validation_net_sharpe']:.6f}" + ), + "metrics": metrics, + "artifacts": declared_artifacts, + "errors": [], + } + ) + except (JudgeFailure, PortfolioFailure) as error: + code = getattr(error, "code", "portfolio.failure") + _write_output( + { + "schema_version": 1, + "status": "failed", + "summary": str(error), + "metrics": {}, + "artifacts": [], + "errors": [{"code": code, "message": str(error)}], + } + ) + except Exception as error: + _write_output( + { + "schema_version": 1, + "status": "failed", + "summary": f"Portfolio evaluation raised {type(error).__name__}", + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": "portfolio.exception", + "message": f"{type(error).__name__}: {error}", + } + ], + } + ) + + +if __name__ == "__main__": + main() diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/portfolio_core.py b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/portfolio_core.py new file mode 100644 index 0000000..174c8b2 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/judge-sources/judges/portfolio_core.py @@ -0,0 +1,4178 @@ +"""Fixed causal target construction and portfolio accounting primitives.""" + +from __future__ import annotations + +import math +from bisect import bisect_left, bisect_right, insort +from collections import deque +from dataclasses import dataclass + +import exchange_calendars +import numpy as np +import pandas as pd + +from autoquant.intervals import annualization_periods +from autoquant.prediction_modes import ( + CROSS_SECTIONAL_MODE, + SIGNAL_TRANSLATION_METHOD, + SINGLE_ASSET_TEMPORAL_MODE, + TEMPORAL_SCORE_MINIMUM, + TEMPORAL_SCORE_WINDOW, + TWO_ASSET_RELATIVE_VALUE_MODE, + signal_translation_contract, +) +VOLATILITY_WINDOW = 20 +GROSS_TARGET = 1.0 +SIDE_BUDGET = GROSS_TARGET / 2.0 +MAX_ABS_WEIGHT = 0.30 +NO_TRADE_ONE_WAY = 0.05 +BASE_COST_BPS = 10.0 +REFERENCE_NAV = 1_000_000.0 +DECISION_EVERY_BARS = 1 +DECISION_ANCHOR = "dataset-start" +DEFAULT_DECISION_POLICY = { + "source": "reference-default", + "kind": "every-bars", + "bars": DECISION_EVERY_BARS, + "anchor": DECISION_ANCHOR, +} +LONG_ENTRY_PERCENTILE = 0.75 +LONG_EXIT_PERCENTILE = 0.55 +SHORT_EXIT_PERCENTILE = 0.45 +SHORT_ENTRY_PERCENTILE = 0.25 +RISK_COVARIANCE_WINDOW = 60 +RISK_COVARIANCE_MINIMUM = 20 +LIQUIDITY_ADV_WINDOW = 20 +LIQUIDITY_PARTICIPATION_LIMITS = (0.01, 0.05) +RISK_COMPLIANCE_TOLERANCE = 1e-10 +POSITION_EPISODE_TOLERANCE = 1e-10 +POSITION_EPISODE_COLUMNS = ( + "episode_id", + "split", + "role", + "episode_number", + "asset", + "side", + "entry_timestamp", + "last_earning_timestamp", + "exit_timestamp", + "entry_action", + "exit_action", + "left_censored", + "right_censored", + "complete", + "decision_bars", + "entry_weight", + "last_executed_weight", + "peak_abs_weight", + "average_abs_weight", + "gross_contribution", + "entry_cost", + "holding_cost", + "exit_cost", + "total_cost", + "net_contribution", + "maximum_favorable_excursion", + "maximum_adverse_excursion", + "intent_mismatch_bars", + "no_trade_bars", + "risk_override_bars", +) + + +class PortfolioFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +@dataclass(frozen=True) +class Simulation: + daily: pd.DataFrame + weights: pd.DataFrame + trades: pd.DataFrame + participation: pd.DataFrame + + +@dataclass(frozen=True) +class SignalConstruction: + targets: pd.DataFrame + states: pd.DataFrame + ledger: pd.DataFrame + scores: pd.DataFrame + translation_values: pd.DataFrame + translation_observations: pd.DataFrame + translation: dict[str, object] + + +RiskCovarianceCache = dict[object, tuple[int, np.ndarray | None]] + + +def _causal_empirical_percentile( + values: pd.Series, + *, + window: int = TEMPORAL_SCORE_WINDOW, + minimum: int = TEMPORAL_SCORE_MINIMUM, +) -> tuple[pd.Series, pd.Series]: + """Rank each finite value inside the latest observed causal window.""" + + if minimum < 2 or window < minimum: + raise PortfolioFailure( + "portfolio.translation-window", + "Temporal score window must contain its fixed minimum", + ) + scores = pd.Series(np.nan, index=values.index, dtype=float) + observations = pd.Series(0, index=values.index, dtype=int) + ordered: list[float] = [] + history: deque[float] = deque() + for timestamp, raw in values.items(): + value = float(raw) if pd.notna(raw) else math.nan + if not math.isfinite(value): + continue + history.append(value) + insort(ordered, value) + if len(history) > window: + expired = history.popleft() + position = bisect_left(ordered, expired) + if position >= len(ordered) or ordered[position] != expired: + raise PortfolioFailure( + "portfolio.translation-window", + "Temporal score window could not remove prior value", + ) + ordered.pop(position) + count = len(ordered) + observations.loc[timestamp] = count + if count < minimum: + continue + lower = bisect_left(ordered, value) + upper = bisect_right(ordered, value) + average_one_based_rank = (lower + 1 + upper) / 2.0 + scores.loc[timestamp] = average_one_based_rank / count + return scores, observations + + +def translate_factor_scores( + factors: pd.DataFrame, + prediction_population: dict[str, object] | None = None, + *, + temporal_window: int = TEMPORAL_SCORE_WINDOW, + temporal_minimum: int = TEMPORAL_SCORE_MINIMUM, +) -> tuple[ + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, object], +]: + """Translate causal Factor values into one fixed decision-score surface.""" + + if factors.empty or factors.columns.has_duplicates: + raise PortfolioFailure( + "portfolio.translation-input", + "Factor panel must have rows and unique asset columns", + ) + population = prediction_population or { + "evaluation_mode": CROSS_SECTIONAL_MODE, + "prediction_assets": list(factors.columns), + "context_assets": [], + "authority": "reference-default-research-universe", + "relative_value_pair": None, + } + mode = population.get("evaluation_mode") + prediction_assets = population.get("prediction_assets") + context_assets = population.get("context_assets") + if ( + mode not in { + CROSS_SECTIONAL_MODE, + SINGLE_ASSET_TEMPORAL_MODE, + TWO_ASSET_RELATIVE_VALUE_MODE, + } + or not isinstance(prediction_assets, list) + or not prediction_assets + or len(prediction_assets) != len(set(prediction_assets)) + or any(asset not in factors.columns for asset in prediction_assets) + or not isinstance(context_assets, list) + or set(prediction_assets).intersection(context_assets) + or set(prediction_assets).union(context_assets) != set(factors.columns) + ): + raise PortfolioFailure( + "portfolio.translation-population", + "Prediction population does not partition the Factor panel", + ) + scores = pd.DataFrame(np.nan, index=factors.index, columns=factors.columns) + translation_values = pd.DataFrame( + np.nan, + index=factors.index, + columns=factors.columns, + ) + observations = pd.DataFrame( + 0, + index=factors.index, + columns=factors.columns, + dtype=int, + ) + relative_value_pair = population.get("relative_value_pair") + + if mode == CROSS_SECTIONAL_MODE: + if len(prediction_assets) < 4: + raise PortfolioFailure( + "portfolio.translation-population", + "Cross-sectional translation requires at least four prediction assets", + ) + selected = factors[prediction_assets].where( + np.isfinite(factors[prediction_assets].to_numpy(dtype=float)) + ) + counts = selected.notna().sum(axis=1) + distinct = selected.nunique(axis=1) + available = counts.ge(4) & distinct.ge(2) + ranks = selected.rank(axis=1, method="average") + translated = ranks.sub(1.0).div(counts.sub(1.0), axis=0) + scores.loc[:, prediction_assets] = translated.where(available) + translation_values.loc[:, prediction_assets] = selected + for asset in prediction_assets: + observations.loc[:, asset] = counts.astype(int) + elif mode == SINGLE_ASSET_TEMPORAL_MODE: + if len(prediction_assets) != 1 or relative_value_pair is not None: + raise PortfolioFailure( + "portfolio.translation-population", + "Single-asset temporal translation requires exactly one prediction asset", + ) + asset = prediction_assets[0] + values = factors[asset].where(np.isfinite(factors[asset])) + temporal_scores, temporal_observations = _causal_empirical_percentile( + values, + window=temporal_window, + minimum=temporal_minimum, + ) + scores.loc[:, asset] = temporal_scores + translation_values.loc[:, asset] = values + observations.loc[:, asset] = temporal_observations + else: + if len(prediction_assets) != 2 or not isinstance(relative_value_pair, dict): + raise PortfolioFailure( + "portfolio.translation-population", + "Relative-value translation requires one ordered two-asset pair", + ) + left = relative_value_pair.get("left_asset") + right = relative_value_pair.get("right_asset") + if prediction_assets != [left, right]: + raise PortfolioFailure( + "portfolio.translation-pair", + "Relative-value pair order differs from the prediction population", + ) + spread = (factors[left] - factors[right]).where( + np.isfinite(factors[left]) & np.isfinite(factors[right]) + ) + spread_scores, spread_observations = _causal_empirical_percentile( + spread, + window=temporal_window, + minimum=temporal_minimum, + ) + scores.loc[:, left] = spread_scores + scores.loc[:, right] = 1.0 - spread_scores + translation_values.loc[:, left] = spread + translation_values.loc[:, right] = -spread + observations.loc[:, left] = spread_observations + observations.loc[:, right] = spread_observations + semantics = signal_translation_contract( + population, + temporal_window=temporal_window, + temporal_minimum=temporal_minimum, + ) + return scores, translation_values, observations, semantics + + +def _valid_decision_policy( + value: object, + *, + expected_source: object | None = None, +) -> bool: + if not isinstance(value, dict): + return False + source = value.get("source") + if expected_source is not None and source != expected_source: + return False + if source is not None and source not in { + "caller-supplied", + "reference-default", + }: + return False + source_keys = {"source"} if source is not None else set() + if value.get("kind") == "every-bars": + bars = value.get("bars") + return ( + set(value) == source_keys | {"kind", "bars", "anchor"} + and isinstance(bars, int) + and not isinstance(bars, bool) + and 1 <= bars <= 252 + and value.get("anchor") in {"dataset-start", "session-start"} + ) + return ( + value.get("kind") == "calendar-month-end" + and set(value) == source_keys | {"kind"} + ) + + +def _allocate_capped_side( + strengths: pd.Series, + *, + budget: float = SIDE_BUDGET, + cap: float | pd.Series = MAX_ABS_WEIGHT, +) -> pd.Series: + """Proportionally water-fill one non-negative side under named caps.""" + + clean = strengths.astype(float) + clean = clean[np.isfinite(clean.to_numpy()) & (clean > 0)] + output = pd.Series(0.0, index=strengths.index, dtype=float) + caps = ( + pd.Series(float(cap), index=strengths.index, dtype=float) + if isinstance(cap, (int, float)) and not isinstance(cap, bool) + else cap.reindex(strengths.index).astype(float) + if isinstance(cap, pd.Series) + else pd.Series(dtype=float) + ) + if ( + not caps.index.equals(strengths.index) + or not np.isfinite(caps.to_numpy()).all() + or (caps < 0).any() + ): + raise PortfolioFailure( + "portfolio.asset-caps", + "Per-asset caps must be aligned, finite, and non-negative", + ) + if clean.empty or float(caps.loc[clean.index].sum()) + 1e-12 < budget: + return output + remaining = list(clean.index) + remaining_budget = float(budget) + while remaining: + values = clean.loc[remaining] + total = float(values.sum()) + if total <= 0: + return pd.Series(0.0, index=strengths.index, dtype=float) + proposed = values / total * remaining_budget + capped = proposed[ + proposed > caps.loc[proposed.index] + 1e-12 + ] + if capped.empty: + output.loc[remaining] = proposed + remaining_budget = 0.0 + break + for asset in capped.index: + output.loc[asset] = caps.loc[asset] + remaining.remove(asset) + remaining_budget -= float(caps.loc[asset]) + if remaining_budget < -1e-10: + raise PortfolioFailure( + "portfolio.allocation", + "Capped side allocation exceeded its budget", + ) + if abs(float(output.sum()) - budget) > 1e-9: + return pd.Series(0.0, index=strengths.index, dtype=float) + return output + + +def _allocate_capped_up_to( + strengths: pd.Series, + *, + limit: float, + cap: float | pd.Series, +) -> pd.Series: + """Allocate available directional conviction and leave unused budget in cash.""" + + clean = strengths.astype(float) + clean = clean[np.isfinite(clean.to_numpy()) & (clean > 0)] + if clean.empty: + return pd.Series(0.0, index=strengths.index, dtype=float) + caps = ( + pd.Series(float(cap), index=strengths.index, dtype=float) + if isinstance(cap, (int, float)) and not isinstance(cap, bool) + else cap.reindex(strengths.index).astype(float) + if isinstance(cap, pd.Series) + else pd.Series(dtype=float) + ) + budget = min(float(limit), float(caps.loc[clean.index].sum())) + return _allocate_capped_side(strengths, budget=budget, cap=cap) + + +def _valid_benchmark_contract( + benchmark: object, + universe: list[str], + tradable: list[str], + asset_position_roles: dict[str, str], +) -> bool: + if ( + not isinstance(benchmark, dict) + or set(benchmark) != {"source", "kind", "asset", "weights"} + or benchmark.get("source") + not in {"caller-supplied", "direction-default"} + or benchmark.get("kind") + not in { + "cash", + "fixed-weights", + "equal-weight-long-research-universe", + "equal-weight-long-tradable", + "equal-weight-short-tradable", + "equal-weight-long-capable", + "equal-weight-short-capable", + "single-asset-long", + } + or not isinstance(benchmark.get("weights"), dict) + or set(benchmark["weights"]) != set(universe) + or any( + not isinstance(benchmark["weights"][asset], (int, float)) + or isinstance(benchmark["weights"][asset], bool) + or not math.isfinite(float(benchmark["weights"][asset])) + for asset in universe + ) + ): + return False + kind = benchmark["kind"] + benchmark_asset = benchmark["asset"] + if kind == "cash": + expected = {asset: 0.0 for asset in universe} + valid_asset = benchmark_asset is None + elif kind == "fixed-weights": + expected = { + asset: float(benchmark["weights"][asset]) + for asset in universe + } + valid_asset = ( + benchmark_asset is None + and all(0.0 <= weight <= 1.0 for weight in expected.values()) + and any(weight > 0.0 for weight in expected.values()) + and math.isclose( + sum(expected.values()), + 1.0, + rel_tol=0.0, + abs_tol=1e-9, + ) + ) + elif kind == "equal-weight-long-research-universe": + expected = { + asset: 1.0 / len(universe) for asset in universe + } + valid_asset = benchmark_asset is None + elif kind in { + "equal-weight-long-tradable", + "equal-weight-short-tradable", + }: + sign = 1.0 if kind == "equal-weight-long-tradable" else -1.0 + tradable_set = set(tradable) + expected = { + asset: ( + sign / len(tradable) if asset in tradable_set else 0.0 + ) + for asset in universe + } + valid_asset = benchmark_asset is None + elif kind in { + "equal-weight-long-capable", + "equal-weight-short-capable", + }: + long_side = kind == "equal-weight-long-capable" + capable = [ + asset + for asset in tradable + if asset_position_roles[asset] + in ( + {"long-only", "two-sided"} + if long_side + else {"short-only", "two-sided"} + ) + ] + if not capable: + return False + sign = 1.0 if long_side else -1.0 + capable_set = set(capable) + expected = { + asset: ( + sign / len(capable) if asset in capable_set else 0.0 + ) + for asset in universe + } + valid_asset = benchmark_asset is None + else: + valid_asset = ( + isinstance(benchmark_asset, str) + and benchmark_asset in universe + ) + expected = { + asset: 1.0 if asset == benchmark_asset else 0.0 + for asset in universe + } + if kind == "cash": + source_matches = True + elif kind in {"fixed-weights", "single-asset-long"}: + source_matches = benchmark["source"] == "caller-supplied" + else: + source_matches = benchmark["source"] == "direction-default" + return ( + valid_asset + and source_matches + and all( + math.isclose( + float(benchmark["weights"][asset]), + expected[asset], + rel_tol=0.0, + abs_tol=1e-12, + ) + for asset in universe + ) + ) + + +def _resolve_mandate( + columns: pd.Index, + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Resolve the copied Judge's fixed position contract.""" + + universe = [str(column) for column in columns] + if mandate is None: + return { + "id": "legacy-dollar-neutral", + "direction": "research-only", + "family": "dollar-neutral", + "gross_limit": GROSS_TARGET, + "max_abs_weight": MAX_ABS_WEIGHT, + "asset_max_abs_weights": { + asset: MAX_ABS_WEIGHT for asset in universe + }, + "asset_position_roles": { + asset: "two-sided" for asset in universe + }, + "long_gross_limit": GROSS_TARGET / 2.0, + "short_gross_limit": GROSS_TARGET / 2.0, + "tradable_assets": universe, + "context_assets": [], + "benchmark": { + "source": "direction-default", + "kind": "equal-weight-long-research-universe", + "asset": None, + "weights": { + asset: 1.0 / len(universe) + for asset in universe + }, + }, + "implementation_policy": { + "base_cost_bps": BASE_COST_BPS, + "no_trade_one_way": NO_TRADE_ONE_WAY, + "reference_nav": REFERENCE_NAV, + "decision_policy": dict(DEFAULT_DECISION_POLICY), + }, + "risk_policy": None, + } + source = mandate.get("source") + construction = mandate.get("construction") + if not isinstance(source, dict) or not isinstance(construction, dict): + raise PortfolioFailure( + "mandate.contract", + "Portfolio Mandate source and construction must be objects", + ) + research = mandate.get("researchUniverse") + tradable = mandate.get("tradableAssets") + context = mandate.get("contextAssets") + if research != universe: + raise PortfolioFailure( + "mandate.universe", + "Portfolio Mandate research universe differs from the Study panel", + ) + if ( + not isinstance(tradable, list) + or not tradable + or not all(isinstance(asset, str) for asset in tradable) + or not isinstance(context, list) + or not all(isinstance(asset, str) for asset in context) + or set(tradable) | set(context) != set(universe) + or set(tradable) & set(context) + ): + raise PortfolioFailure( + "mandate.assets", + "Portfolio Mandate must partition research and tradable assets", + ) + direction = source.get("direction") + family = construction.get("family") + gross_limit = construction.get("grossLimit") + max_abs_weight = construction.get("maxAbsWeight") + asset_max_abs_weights = construction.get( + "assetMaxAbsWeights" + ) + asset_position_roles = construction.get("assetPositionRoles") + long_gross_limit = construction.get("longGrossLimit") + short_gross_limit = construction.get("shortGrossLimit") + benchmark = construction.get("benchmark") + risk_policy = construction.get("riskPolicy") + implementation = mandate.get("implementationPolicy") + if ( + direction + not in {"long", "short", "long-short", "relative-value", "research-only"} + or family not in { + "long-cash", + "short-cash", + "dollar-neutral", + "asset-role", + } + or not isinstance(gross_limit, (int, float)) + or isinstance(gross_limit, bool) + or not 0 < float(gross_limit) <= 2 + or not isinstance(max_abs_weight, (int, float)) + or isinstance(max_abs_weight, bool) + or not 0 < float(max_abs_weight) <= float(gross_limit) + or not isinstance(asset_max_abs_weights, dict) + or set(asset_max_abs_weights) != set(universe) + or any( + not isinstance(asset_max_abs_weights[asset], (int, float)) + or isinstance(asset_max_abs_weights[asset], bool) + or not math.isfinite( + float(asset_max_abs_weights[asset]) + ) + or ( + not 0 + < float(asset_max_abs_weights[asset]) + <= float(max_abs_weight) + if asset in tradable + else abs(float(asset_max_abs_weights[asset])) > 1e-12 + ) + for asset in universe + ) + or not isinstance(asset_position_roles, dict) + or set(asset_position_roles) != set(universe) + or any( + asset_position_roles[asset] + not in { + "long-only", + "short-only", + "two-sided", + "context-only", + } + or ( + asset_position_roles[asset] == "context-only" + if asset in tradable + else asset_position_roles[asset] != "context-only" + ) + for asset in universe + ) + or not isinstance(long_gross_limit, (int, float)) + or isinstance(long_gross_limit, bool) + or not math.isfinite(float(long_gross_limit)) + or not 0 <= float(long_gross_limit) <= float(gross_limit) + or not isinstance(short_gross_limit, (int, float)) + or isinstance(short_gross_limit, bool) + or not math.isfinite(float(short_gross_limit)) + or not 0 <= float(short_gross_limit) <= float(gross_limit) + or float(long_gross_limit) + float(short_gross_limit) + > float(gross_limit) + 1e-12 + or not _valid_benchmark_contract( + benchmark, + universe, + tradable, + asset_position_roles, + ) + or benchmark["source"] != source.get("benchmarkPolicy") + or not isinstance(risk_policy, dict) + or not isinstance(implementation, dict) + or set(implementation) + != { + "baseCostBps", + "noTradeOneWay", + "referenceNav", + "decisionPolicy", + "costModel", + "capacityModel", + } + or implementation.get("costModel") + != "linear-traded-notional-v1" + or implementation.get("capacityModel") + != "trailing-dollar-volume-participation-v1" + or not isinstance(implementation.get("baseCostBps"), (int, float)) + or isinstance(implementation.get("baseCostBps"), bool) + or not 0 <= float(implementation["baseCostBps"]) <= 1000 + or not isinstance(implementation.get("noTradeOneWay"), (int, float)) + or isinstance(implementation.get("noTradeOneWay"), bool) + or not 0 <= float(implementation["noTradeOneWay"]) <= 1 + or not isinstance(implementation.get("referenceNav"), (int, float)) + or isinstance(implementation.get("referenceNav"), bool) + or not 0 < float(implementation["referenceNav"]) <= 1e12 + or not _valid_decision_policy( + implementation.get("decisionPolicy"), + expected_source=source.get("portfolioPolicy"), + ) + or set(risk_policy) + != { + "method", + "annualizedVolatilityCeiling", + "covarianceWindow", + "minimumObservations", + "annualizationPeriods", + "scaleUp", + } + or risk_policy.get("method") + != "trailing-covariance-volatility-ceiling-v1" + or not isinstance( + risk_policy.get("annualizedVolatilityCeiling"), + (int, float), + ) + or isinstance( + risk_policy.get("annualizedVolatilityCeiling"), + bool, + ) + or not 0 + < float(risk_policy["annualizedVolatilityCeiling"]) + <= 1 + or not isinstance(risk_policy.get("covarianceWindow"), int) + or isinstance(risk_policy.get("covarianceWindow"), bool) + or risk_policy["covarianceWindow"] < 2 + or not isinstance(risk_policy.get("minimumObservations"), int) + or isinstance(risk_policy.get("minimumObservations"), bool) + or not 2 + <= risk_policy["minimumObservations"] + <= risk_policy["covarianceWindow"] + or not isinstance(risk_policy.get("annualizationPeriods"), int) + or isinstance(risk_policy.get("annualizationPeriods"), bool) + or risk_policy["annualizationPeriods"] < 1 + or risk_policy.get("scaleUp") is not False + ): + raise PortfolioFailure( + "mandate.construction", + "Portfolio Mandate contains unsupported construction semantics", + ) + return { + "id": str(mandate.get("id")), + "direction": str(direction), + "family": str(family), + "gross_limit": float(gross_limit), + "max_abs_weight": float(max_abs_weight), + "asset_max_abs_weights": { + asset: float(asset_max_abs_weights[asset]) + for asset in universe + }, + "asset_position_roles": { + asset: str(asset_position_roles[asset]) + for asset in universe + }, + "long_gross_limit": float(long_gross_limit), + "short_gross_limit": float(short_gross_limit), + "tradable_assets": list(tradable), + "context_assets": list(context), + "benchmark": { + "source": str(benchmark["source"]), + "kind": str(benchmark["kind"]), + "asset": benchmark["asset"], + "weights": { + asset: float(benchmark["weights"][asset]) + for asset in universe + }, + }, + "implementation_policy": { + "base_cost_bps": float(implementation["baseCostBps"]), + "no_trade_one_way": float(implementation["noTradeOneWay"]), + "reference_nav": float(implementation["referenceNav"]), + "decision_policy": dict(implementation["decisionPolicy"]), + }, + "risk_policy": { + "method": str(risk_policy["method"]), + "annualized_volatility_ceiling": float( + risk_policy["annualizedVolatilityCeiling"] + ), + "covariance_window": int(risk_policy["covarianceWindow"]), + "minimum_observations": int( + risk_policy["minimumObservations"] + ), + "annualization_periods": int( + risk_policy["annualizationPeriods"] + ), + "scale_up": False, + }, + } + + +def resolve_implementation_policy( + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Resolve fixed accounting assumptions without requiring an asset panel.""" + + if mandate is None: + return { + "base_cost_bps": BASE_COST_BPS, + "no_trade_one_way": NO_TRADE_ONE_WAY, + "reference_nav": REFERENCE_NAV, + "decision_policy": dict(DEFAULT_DECISION_POLICY), + } + implementation = mandate.get("implementationPolicy") + if not isinstance(implementation, dict): + raise PortfolioFailure( + "mandate.implementation", + "Portfolio Mandate implementation policy must be an object", + ) + result = { + "base_cost_bps": implementation.get("baseCostBps"), + "no_trade_one_way": implementation.get("noTradeOneWay"), + "reference_nav": implementation.get("referenceNav"), + "decision_policy": implementation.get("decisionPolicy"), + } + if ( + not isinstance(result["base_cost_bps"], (int, float)) + or isinstance(result["base_cost_bps"], bool) + or not 0 <= float(result["base_cost_bps"]) <= 1000 + or not isinstance(result["no_trade_one_way"], (int, float)) + or isinstance(result["no_trade_one_way"], bool) + or not 0 <= float(result["no_trade_one_way"]) <= 1 + or not isinstance(result["reference_nav"], (int, float)) + or isinstance(result["reference_nav"], bool) + or not 0 < float(result["reference_nav"]) <= 1e12 + or not _valid_decision_policy(result["decision_policy"]) + ): + raise PortfolioFailure( + "mandate.implementation", + "Portfolio Mandate implementation policy is invalid", + ) + return { + "base_cost_bps": float(result["base_cost_bps"]), + "no_trade_one_way": float(result["no_trade_one_way"]), + "reference_nav": float(result["reference_nav"]), + "decision_policy": dict(result["decision_policy"]), + } + + +def resolve_portfolio_mandate( + columns: pd.Index, + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Validate and resolve one fixed Mandate once for repeated accounting.""" + + return _resolve_mandate(columns, mandate) + + +def decision_schedule_sessions( + index: pd.Index, + decision_policy: dict[str, object], +) -> pd.Series: + """Return the immutable schedule group for every complete-panel row.""" + + if not index.is_unique or not _valid_decision_policy(decision_policy): + raise PortfolioFailure( + "portfolio.decision-schedule", + "Decision schedule requires a unique index and valid policy", + ) + if decision_policy["kind"] == "calendar-month-end": + timestamps = pd.DatetimeIndex(index) + if timestamps.tz is not None: + timestamps = timestamps.tz_convert("America/New_York").tz_localize(None) + return pd.Series( + timestamps.to_period("M").astype(str), + index=index, + dtype="string", + name="decision_session", + ) + decision_anchor = str(decision_policy["anchor"]) + if decision_anchor == "dataset-start": + return pd.Series( + "dataset", + index=index, + dtype="string", + name="decision_session", + ) + timestamps = pd.DatetimeIndex(index) + if timestamps.tz is None: + raise PortfolioFailure( + "portfolio.decision-anchor", + "session-start requires timezone-aware XNYS base closes", + ) + return pd.Series( + timestamps.tz_convert("UTC").strftime("%Y-%m-%d"), + index=index, + dtype="string", + name="decision_session", + ) + + +def decision_schedule_mask( + index: pd.Index, + decision_policy: dict[str, object], +) -> pd.Series: + """Return one immutable complete-panel anchor-aware decision mask.""" + + if not index.is_unique or not _valid_decision_policy(decision_policy): + raise PortfolioFailure( + "portfolio.decision-cadence", + "Decision cadence requires a unique index and valid policy", + ) + if decision_policy["kind"] == "calendar-month-end": + timestamps = pd.DatetimeIndex(index) + if timestamps.empty: + return pd.Series( + [], + index=index, + dtype=bool, + name="decision_eligible", + ) + if timestamps.tz is not None: + local_dates = timestamps.tz_convert( + "America/New_York" + ).tz_localize(None).normalize() + else: + local_dates = timestamps.normalize() + month_end = local_dates.max() + pd.offsets.MonthEnd(0) + calendar = exchange_calendars.get_calendar( + "XNYS", + start=str(local_dates.min().date()), + end=str(month_end.date()), + ) + official_sessions = pd.DatetimeIndex(calendar.sessions) + if official_sessions.tz is not None: + official_sessions = official_sessions.tz_localize(None) + official_sessions = official_sessions.normalize() + official_month_ends = ( + pd.Series(official_sessions, index=official_sessions.to_period("M")) + .groupby(level=0, sort=True) + .max() + ) + eligible_dates = set(official_month_ends.tolist()) + return pd.Series( + local_dates.isin(eligible_dates), + index=index, + dtype=bool, + name="decision_eligible", + ) + decision_every_bars = int(decision_policy["bars"]) + sessions = decision_schedule_sessions(index, decision_policy) + ordinal = sessions.groupby(sessions, sort=False).cumcount() + return pd.Series( + ordinal.mod(decision_every_bars).eq(0).to_numpy(), + index=index, + dtype=bool, + name="decision_eligible", + ) + + +def _govern_portfolio_risk( + raw_targets: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + resolved: dict[str, object], + *, + enabled: bool, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> tuple[pd.Series, dict[str, float | int | str]]: + """Apply one causal, one-sided portfolio-volatility ceiling.""" + + gross = float(raw_targets.abs().sum()) + policy = resolved["risk_policy"] + if gross <= 1e-12: + return raw_targets.copy(), { + "status": "flat", + "observations": 0, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ( + float(policy["annualized_volatility_ceiling"]) + if isinstance(policy, dict) + else 0.0 + ), + "scale": 1.0, + } + if policy is None: + return raw_targets.copy(), { + "status": "legacy_none", + "observations": 0, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": 0.0, + "scale": 1.0, + } + assert isinstance(policy, dict) + covariance_values: np.ndarray | None + if risk_covariance_cache is not None: + cached = risk_covariance_cache.get(timestamp) + if cached is None: + raise PortfolioFailure( + "portfolio.risk-cache", + "Risk covariance cache is missing a decision timestamp", + ) + observations, covariance_values = cached + if ( + covariance_values is not None + and covariance_values.shape + != (len(raw_targets), len(raw_targets)) + ): + raise PortfolioFailure( + "portfolio.risk-cache", + "Risk covariance cache shape is invalid", + ) + else: + history = ( + close_returns.loc[:timestamp] + .tail(int(policy["covariance_window"])) + .dropna(how="any") + ) + observations = int(len(history)) + covariance = history.cov(ddof=0).reindex( + index=raw_targets.index, + columns=raw_targets.index, + ) + covariance_values = ( + None + if covariance.isna().any().any() + else covariance.to_numpy(dtype=float) + ) + minimum = int(policy["minimum_observations"]) + ceiling = float(policy["annualized_volatility_ceiling"]) + if observations < minimum: + return raw_targets * 0.0, { + "status": "insufficient_history", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + if covariance_values is None: + return raw_targets * 0.0, { + "status": "invalid_covariance", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + vector = raw_targets.to_numpy(dtype=float) + variance = float(vector @ covariance_values @ vector) + if not math.isfinite(variance) or variance < -1e-12: + return raw_targets * 0.0, { + "status": "invalid_covariance", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + forecast = math.sqrt( + max(variance, 0.0) * int(policy["annualization_periods"]) + ) + scale = ( + min(1.0, ceiling / forecast) + if enabled and forecast > 1e-12 + else 1.0 + ) + governed = raw_targets * scale + return governed, { + "status": ( + "diagnostic_disabled" + if not enabled + else "volatility_limited" + if scale < 1.0 - 1e-12 + else "within_ceiling" + ), + "observations": observations, + "pre_annualized_volatility": forecast, + "post_annualized_volatility": forecast * scale, + "annualized_volatility_ceiling": ceiling, + "scale": scale, + } + + +def build_risk_covariance_cache( + closes: pd.DataFrame, + *, + mandate: dict[str, object] | None, +) -> RiskCovarianceCache: + """Precompute one causal covariance panel for repeated fixed simulations.""" + + resolved = _resolve_mandate(closes.columns, mandate) + policy = resolved["risk_policy"] + if policy is None: + return {} + assert isinstance(policy, dict) + close_returns = closes.pct_change(fill_method=None) + cache: RiskCovarianceCache = {} + window = int(policy["covariance_window"]) + minimum = int(policy["minimum_observations"]) + return_values = close_returns.to_numpy(dtype=float) + for row_number, timestamp in enumerate(closes.index): + history = return_values[ + max(0, row_number - window + 1) : row_number + 1 + ] + history = history[np.isfinite(history).all(axis=1)] + observations = int(history.shape[0]) + covariance_values: np.ndarray | None = None + if observations >= minimum: + covariance_values = np.atleast_2d( + np.cov( + history, + rowvar=False, + ddof=0, + ) + ) + if ( + covariance_values.shape + != (len(closes.columns), len(closes.columns)) + or not np.isfinite(covariance_values).all() + ): + covariance_values = None + cache[timestamp] = (observations, covariance_values) + return cache + + +def _repair_executed_mandate_constraints( + weights: pd.Series, + resolved: dict[str, object], +) -> tuple[pd.Series, dict[str, float | bool | str]]: + """Scale down a chosen book until every hard Mandate constraint holds.""" + + original = weights.to_numpy(dtype=float, copy=False) + roles = resolved["asset_position_roles"] + caps = resolved["asset_max_abs_weights"] + assert isinstance(roles, dict) + assert isinstance(caps, dict) + role_values = resolved.get("_constraint_role_values") + cap_values = resolved.get("_constraint_cap_values") + if ( + not isinstance(role_values, tuple) + or len(role_values) != len(weights) + or not isinstance(cap_values, np.ndarray) + or cap_values.shape != (len(weights),) + ): + role_values = tuple( + str(roles[str(asset)]) for asset in weights.index + ) + cap_values = np.asarray( + [float(caps[str(asset)]) for asset in weights.index], + dtype=float, + ) + resolved["_constraint_role_values"] = role_values + resolved["_constraint_cap_values"] = cap_values + family = str(resolved["family"]) + gross_limit = float(resolved["gross_limit"]) + long_limit = float(resolved["long_gross_limit"]) + short_limit = float(resolved["short_gross_limit"]) + repaired_values = original.copy() + for position, role in enumerate(role_values): + cap = float(cap_values[position]) + value = float(repaired_values[position]) + if role == "context-only": + value = 0.0 + elif role == "long-only": + value = max(value, 0.0) + elif role == "short-only": + value = min(value, 0.0) + repaired_values[position] = min(max(value, -cap), cap) + + def scale_side(positive: bool, limit: float) -> None: + mask = repaired_values > 0.0 if positive else repaired_values < 0.0 + exposure = float( + repaired_values[mask].sum() + if positive + else -repaired_values[mask].sum() + ) + if exposure > limit + 1e-15: + repaired_values[mask] *= limit / exposure + + if family == "dollar-neutral": + positive = repaired_values > 0.0 + negative = repaired_values < 0.0 + long_exposure = float(repaired_values[positive].sum()) + short_exposure = float(-repaired_values[negative].sum()) + funded_side = min( + long_exposure, + short_exposure, + long_limit, + short_limit, + gross_limit / 2.0, + ) + if funded_side <= 1e-15: + repaired_values.fill(0.0) + else: + repaired_values[positive] *= funded_side / long_exposure + repaired_values[negative] *= funded_side / short_exposure + else: + scale_side(True, long_limit) + scale_side(False, short_limit) + gross = float(np.abs(repaired_values).sum()) + if gross > gross_limit + 1e-15: + repaired_values *= gross_limit / gross + + gross = float(np.abs(repaired_values).sum()) + long_exposure = float(repaired_values[repaired_values > 0.0].sum()) + short_exposure = float(-repaired_values[repaired_values < 0.0].sum()) + net = float(repaired_values.sum()) + cap_excess = float( + np.maximum(np.abs(repaired_values) - cap_values, 0.0).max( + initial=0.0 + ) + ) + context_exposure = max( + ( + abs(float(repaired_values[position])) + for position, role in enumerate(role_values) + if role == "context-only" + ), + default=0.0, + ) + role_error = max( + ( + max(0.0, -float(repaired_values[position])) + if role == "long-only" + else max(0.0, float(repaired_values[position])) + if role == "short-only" + else 0.0 + for position, role in enumerate(role_values) + ), + default=0.0, + ) + net_error = ( + abs(net) + if family == "dollar-neutral" + else abs(net - gross) + if family == "long-cash" + else abs(net + gross) + if family == "short-cash" + else 0.0 + ) + maximum_error = max( + 0.0, + gross - gross_limit, + long_exposure - long_limit, + short_exposure - short_limit, + cap_excess, + context_exposure, + role_error, + net_error, + ) + if maximum_error > 1e-10: + raise PortfolioFailure( + "portfolio.executed-constraint-breach", + "Final executed book cannot be repaired to the Portfolio Mandate", + ) + repair_one_way = 0.5 * float(np.abs(repaired_values - original).sum()) + repaired = ( + pd.Series(repaired_values, index=weights.index, dtype=float) + if repair_one_way > 1e-12 + else weights.astype(float, copy=False) + ) + return repaired, { + "status": ( + "constraint_repaired" + if repair_one_way > 1e-12 + else "within_constraints" + ), + "repaired": repair_one_way > 1e-12, + "repair_one_way": repair_one_way, + "maximum_error": maximum_error, + } + + +def execute_risk_compliant_book( + pretrade: pd.Series, + proposed: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + *, + mandate: dict[str, object] | None, + no_trade_one_way: float = NO_TRADE_ONE_WAY, + ordinary_rebalance_allowed: bool = True, + risk_covariance_cache: RiskCovarianceCache | None = None, + _resolved_mandate: dict[str, object] | None = None, + _state_only: bool = False, +) -> tuple[pd.Series, dict[str, object]]: + """Choose the final book, with risk compliance outranking no-trade.""" + + if ( + not pretrade.index.equals(proposed.index) + or list(close_returns.columns) != list(pretrade.index) + or timestamp not in close_returns.index + or not 0 <= no_trade_one_way <= 1 + or not isinstance(ordinary_rebalance_allowed, bool) + or not isinstance(_state_only, bool) + ): + raise PortfolioFailure( + "portfolio.execution-risk", + "Invalid executed-book risk inputs", + ) + if not np.isfinite(pretrade.to_numpy(dtype=float)).all() or not np.isfinite( + proposed.to_numpy(dtype=float) + ).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Executed-book risk inputs contain non-finite weights", + ) + resolved = ( + _resolved_mandate + if _resolved_mandate is not None + else _resolve_mandate(pretrade.index, mandate) + ) + if _state_only and not ordinary_rebalance_allowed: + constraint_book, _ = _repair_executed_mandate_constraints( + pretrade, + resolved, + ) + current, _ = _govern_portfolio_risk( + constraint_book, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + return current, {} + pretrade_risk: dict[str, object] | None = None + if not _state_only: + _, pretrade_risk = _govern_portfolio_risk( + pretrade, + close_returns, + timestamp, + resolved, + enabled=False, + risk_covariance_cache=risk_covariance_cache, + ) + constrained_proposed, _ = _repair_executed_mandate_constraints( + proposed, + resolved, + ) + runtime_proposed, proposed_risk = _govern_portfolio_risk( + constrained_proposed, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + proposed_delta = runtime_proposed - pretrade + proposed_one_way = 0.5 * float(proposed_delta.abs().sum()) + ordinary_rebalance = ( + ordinary_rebalance_allowed + and proposed_one_way + 1e-12 >= no_trade_one_way + ) + ordinary_book = runtime_proposed if ordinary_rebalance else pretrade + constraint_book, constraint_evidence = ( + _repair_executed_mandate_constraints( + ordinary_book, + resolved, + ) + ) + if ordinary_rebalance: + current = constraint_book + execution_risk = proposed_risk + else: + current, execution_risk = _govern_portfolio_risk( + constraint_book, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + risk_repair_trade = current - constraint_book + risk_repaired = bool(risk_repair_trade.abs().sum() > 1e-12) + constraint_repaired = bool(constraint_evidence["repaired"]) + risk_override = risk_repaired and not ordinary_rebalance + constraint_override = constraint_repaired and not ordinary_rebalance + actual_trade = current - pretrade + rebalanced = bool(actual_trade.abs().sum() > 1e-12) + if _state_only: + return current, {} + assert pretrade_risk is not None + raw_status = str(execution_risk["status"]) + if risk_repaired: + status = ( + f"{raw_status}_fail_flat" + if raw_status in {"insufficient_history", "invalid_covariance"} + else "risk_repaired" + ) + elif constraint_repaired: + status = "constraint_repaired" + else: + status = raw_status + if risk_override and constraint_override: + reason = "mandate_and_risk_override" + elif constraint_override: + reason = "mandate_constraint_override" + elif risk_override: + reason = "risk_ceiling_override" + elif risk_repaired: + reason = "target_risk_repair" + elif constraint_repaired: + reason = "target_constraint_repair" + elif ordinary_rebalance: + reason = "rebalance_threshold_met" + elif not ordinary_rebalance_allowed: + reason = "decision_schedule_hold" + else: + reason = "portfolio_no_trade_band" + + final_forecast = float(execution_risk["post_annualized_volatility"]) + ceiling = float(execution_risk["annualized_volatility_ceiling"]) + policy = resolved["risk_policy"] + forecast_available = ( + isinstance(policy, dict) + and raw_status + not in {"insufficient_history", "invalid_covariance"} + ) + if ( + forecast_available + and final_forecast + > ceiling + RISK_COMPLIANCE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.risk-breach", + "Final executed book exceeds the volatility ceiling", + ) + result: dict[str, object] = { + "status": status, + "forecast_available": forecast_available, + "observations": int(execution_risk["observations"]), + "pretrade_forecast_annualized": float( + pretrade_risk["pre_annualized_volatility"] + ), + "proposed_forecast_pre_annualized": float( + proposed_risk["pre_annualized_volatility"] + ), + "proposed_forecast_post_annualized": float( + proposed_risk["post_annualized_volatility"] + ), + "executed_forecast_annualized": final_forecast, + "annualized_volatility_ceiling": ceiling, + "proposed_runtime_scale": float(proposed_risk["scale"]), + "risk_repair_scale": float(execution_risk["scale"]), + "proposed_one_way": proposed_one_way, + "ordinary_rebalance": ordinary_rebalance, + "decision_eligible": ordinary_rebalance_allowed, + "risk_rebalance_override": risk_override, + "constraint_rebalance_override": constraint_override, + "constraint_repair_one_way": float( + constraint_evidence["repair_one_way"] + ), + "executed_constraint_maximum_error": float( + constraint_evidence["maximum_error"] + ), + "rebalanced": rebalanced, + "execution_reason": reason, + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Executed-book risk evidence contains invalid values", + ) + return current, result + + +def _signal_transition( + previous: int, + score: float | None, + *, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + if score is None or not math.isfinite(score): + return ( + 0, + "unavailable_flat" if previous == 0 else "unavailable_reset", + ) + if previous == 0: + if score >= long_entry: + return 1, "enter_long" + if score <= short_entry: + return -1, "enter_short" + return 0, "stay_flat" + if previous == 1: + if score <= short_entry: + return -1, "reverse_long_to_short" + if score < long_exit: + return 0, "exit_long" + return 1, "hold_long" + if previous == -1: + if score >= long_entry: + return 1, "reverse_short_to_long" + if score > short_exit: + return 0, "exit_short" + return -1, "hold_short" + raise PortfolioFailure("portfolio.state", "Unknown prior signal state") + + +def _directional_signal_transition( + previous: int, + score: float | None, + *, + family: str, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + if family == "dollar-neutral": + return _signal_transition( + previous, + score, + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + if score is None or not math.isfinite(score): + return ( + 0, + "unavailable_flat" if previous == 0 else "unavailable_reset", + ) + if family == "long-cash": + if previous == 1: + return (1, "hold_long") if score >= long_exit else (0, "exit_long") + return (1, "enter_long") if score >= long_entry else (0, "stay_flat") + if family == "short-cash": + if previous == -1: + return ( + (-1, "hold_short") + if score <= short_exit + else (0, "exit_short") + ) + return ( + (-1, "enter_short") + if score <= short_entry + else (0, "stay_flat") + ) + raise PortfolioFailure("mandate.family", "Unknown Portfolio Mandate family") + + +def _position_role_signal_transition( + previous: int, + score: float | None, + *, + role: str, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + family = { + "long-only": "long-cash", + "short-only": "short-cash", + "two-sided": "dollar-neutral", + }.get(role) + if family is None: + if role == "context-only": + return 0, "context_only" + raise PortfolioFailure( + "mandate.asset-position-role", + "Unknown asset position role", + ) + return _directional_signal_transition( + previous, + score, + family=family, + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + + +def _weight_action(previous: float, current: float) -> str: + tolerance = 1e-12 + previous_zero = abs(previous) <= tolerance + current_zero = abs(current) <= tolerance + if previous_zero and current_zero: + return "stay_flat" + if previous_zero: + return "open_long" if current > 0 else "open_short" + if current_zero: + return "close_long" if previous > 0 else "close_short" + if previous * current < 0: + return ( + "reverse_long_to_short" + if previous > 0 + else "reverse_short_to_long" + ) + if abs(previous - current) <= tolerance: + return "hold_long" if current > 0 else "hold_short" + return "resize_long" if current > 0 else "resize_short" + + +def construct_signal_policy( + factors: pd.DataFrame, + closes: pd.DataFrame, + *, + volatility_window: int = VOLATILITY_WINDOW, + gross_target: float = GROSS_TARGET, + max_abs_weight: float = MAX_ABS_WEIGHT, + long_entry: float = LONG_ENTRY_PERCENTILE, + long_exit: float = LONG_EXIT_PERCENTILE, + short_exit: float = SHORT_EXIT_PERCENTILE, + short_entry: float = SHORT_ENTRY_PERCENTILE, + mandate: dict[str, object] | None = None, + prediction_population: dict[str, object] | None = None, + temporal_translation_window: int = TEMPORAL_SCORE_WINDOW, + temporal_translation_minimum: int = TEMPORAL_SCORE_MINIMUM, + apply_risk_governor: bool = True, + risk_covariance_cache: RiskCovarianceCache | None = None, + include_ledger: bool = True, +) -> SignalConstruction: + """Turn causal factor ranks into persistent intent and target weights.""" + + if not factors.index.equals(closes.index) or list(factors.columns) != list( + closes.columns + ): + raise PortfolioFailure( + "portfolio.alignment", + "Factor and close panels must have identical index and columns", + ) + resolved = _resolve_mandate(factors.columns, mandate) + gross_target = float(resolved["gross_limit"]) + max_abs_weight = float(resolved["max_abs_weight"]) + asset_caps = pd.Series( + resolved["asset_max_abs_weights"], + index=factors.columns, + dtype=float, + ) + asset_position_roles = { + str(asset): str(role) + for asset, role in resolved["asset_position_roles"].items() + } + long_gross_limit = float(resolved["long_gross_limit"]) + short_gross_limit = float(resolved["short_gross_limit"]) + family = str(resolved["family"]) + tradable_assets = set(resolved["tradable_assets"]) + if ( + volatility_window < 2 + or not 0 < gross_target <= 2 + or not 0 < max_abs_weight <= gross_target + or ( + family == "dollar-neutral" + and max_abs_weight > gross_target / 2 + ) + or not ( + 0.0 + <= short_entry + <= short_exit + < long_exit + <= long_entry + <= 1.0 + ) + or not isinstance(include_ledger, bool) + ): + raise PortfolioFailure( + "portfolio.parameters", + "Invalid fixed signal-policy parameters", + ) + returns = closes.pct_change(fill_method=None) + volatility = ( + returns.rolling( + volatility_window, + min_periods=volatility_window, + ) + .std(ddof=0) + .clip(lower=1e-6) + ) + side_budget = gross_target / 2.0 + targets = pd.DataFrame(0.0, index=factors.index, columns=factors.columns) + states = pd.DataFrame(0, index=factors.index, columns=factors.columns) + prior_states = pd.Series(0, index=factors.columns, dtype=int) + prior_targets = pd.Series(0.0, index=factors.columns, dtype=float) + ledger_rows: list[dict[str, object]] = [] + decision_policy = dict( + resolved["implementation_policy"]["decision_policy"] + ) + decision_schedule_kind = str(decision_policy["kind"]) + decision_every_bars = decision_policy.get("bars") + decision_anchor = decision_policy.get("anchor") + decision_sessions = decision_schedule_sessions( + factors.index, + decision_policy, + ) + decision_mask = decision_schedule_mask( + factors.index, + decision_policy, + ) + finite_volatility = pd.DataFrame( + np.isfinite(volatility.to_numpy(dtype=float)), + index=volatility.index, + columns=volatility.columns, + ) + ( + score_panel, + translation_values, + translation_observations, + translation_semantics, + ) = translate_factor_scores( + factors, + prediction_population, + temporal_window=temporal_translation_window, + temporal_minimum=temporal_translation_minimum, + ) + insufficient_score_status = ( + "insufficient_temporal_history" + if translation_semantics["evaluation_mode"] + == SINGLE_ASSET_TEMPORAL_MODE + else "insufficient_cross_section" + ) + valid_panel = score_panel.notna() & volatility.notna() & finite_volatility + sufficient_rows = valid_panel.any(axis=1) + + for timestamp in factors.index: + decision_eligible = bool(decision_mask.loc[timestamp]) + row_factor = ( + factors.loc[timestamp].astype(float) + if include_ledger + else None + ) + row_volatility = volatility.loc[timestamp].astype(float) + scores = score_panel.loc[timestamp].where(valid_panel.loc[timestamp]) + translated_scores = score_panel.loc[timestamp] + sufficient = bool(sufficient_rows.loc[timestamp]) + + current_states = pd.Series(0, index=factors.columns, dtype=int) + events: dict[str, str] = {} + convictions = pd.Series(0.0, index=factors.columns, dtype=float) + strengths = pd.Series(0.0, index=factors.columns, dtype=float) + if decision_eligible: + for asset in factors.columns: + if str(asset) not in tradable_assets: + current_states.loc[asset] = 0 + events[str(asset)] = "context_only" + continue + raw_score = scores.loc[asset] + score = ( + float(raw_score) + if math.isfinite(raw_score) + else None + ) + state, event = _position_role_signal_transition( + int(prior_states.loc[asset]), + score, + role=asset_position_roles[str(asset)], + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + current_states.loc[asset] = state + events[str(asset)] = event + if state != 0 and score is not None: + conviction = 2.0 * abs(score - 0.5) + convictions.loc[asset] = conviction + strengths.loc[asset] = ( + conviction / float(row_volatility.loc[asset]) + ) + else: + current_states = prior_states.copy() + events = { + str(asset): ( + "decision_schedule_hold" + if str(asset) in tradable_assets + else "context_only" + ) + for asset in factors.columns + } + for asset in factors.columns: + score = scores.loc[asset] + state = int(current_states.loc[asset]) + volatility_value = row_volatility.loc[asset] + if ( + str(asset) in tradable_assets + and state != 0 + and math.isfinite(score) + and math.isfinite(volatility_value) + ): + conviction = 2.0 * abs(float(score) - 0.5) + convictions.loc[asset] = conviction + strengths.loc[asset] = ( + conviction / float(volatility_value) + ) + + if not decision_eligible: + current_targets = prior_targets.copy() + allocation_status = "decision_schedule_hold" + elif family == "dollar-neutral": + if ( + translation_semantics["evaluation_mode"] + == TWO_ASSET_RELATIVE_VALUE_MODE + ): + pair = translation_semantics["relative_value_pair"] + assert isinstance(pair, dict) + left = str(pair["left_asset"]) + right = str(pair["right_asset"]) + left_state = int(current_states.loc[left]) + right_state = int(current_states.loc[right]) + paired = ( + abs(left_state) == 1 + and right_state == -left_state + ) + pair_budget = min( + side_budget, + float(asset_caps.loc[left]), + float(asset_caps.loc[right]), + ) + current_targets = pd.Series( + 0.0, + index=factors.columns, + dtype=float, + ) + if paired: + current_targets.loc[left] = left_state * pair_budget + current_targets.loc[right] = right_state * pair_budget + allocation_status = ( + "insufficient_temporal_history" + if not sufficient + else ( + "no_permitted_signal" + if not paired + else ( + "allocated" + if math.isclose( + pair_budget, + side_budget, + rel_tol=0.0, + abs_tol=1e-9, + ) + else "allocated_with_cash" + ) + ) + ) + else: + long_weights = _allocate_capped_side( + strengths.where(current_states.eq(1), 0.0), + budget=side_budget, + cap=asset_caps, + ) + short_weights = _allocate_capped_side( + strengths.where(current_states.eq(-1), 0.0), + budget=side_budget, + cap=asset_caps, + ) + allocated = ( + abs(float(long_weights.sum()) - side_budget) <= 1e-9 + and abs(float(short_weights.sum()) - side_budget) <= 1e-9 + ) + current_targets = ( + long_weights - short_weights + if allocated + else pd.Series(0.0, index=factors.columns, dtype=float) + ) + allocation_status = ( + "allocated" + if allocated + else ( + "insufficient_cross_section" + if not sufficient + else "insufficient_side_breadth" + ) + ) + elif family == "asset-role": + long_weights = _allocate_capped_up_to( + strengths.where(current_states.eq(1), 0.0), + limit=long_gross_limit, + cap=asset_caps, + ) + short_weights = _allocate_capped_up_to( + strengths.where(current_states.eq(-1), 0.0), + limit=short_gross_limit, + cap=asset_caps, + ) + current_targets = long_weights - short_weights + used_long = float(long_weights.sum()) + used_short = float(short_weights.sum()) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if used_long + used_short <= 1e-12 + else ( + "allocated" + if math.isclose( + used_long, + long_gross_limit, + rel_tol=0.0, + abs_tol=1e-9, + ) + and math.isclose( + used_short, + short_gross_limit, + rel_tol=0.0, + abs_tol=1e-9, + ) + else "allocated_with_cash" + ) + ) + ) + elif family == "long-cash": + current_targets = _allocate_capped_up_to( + strengths.where(current_states.eq(1), 0.0), + limit=gross_target, + cap=asset_caps, + ) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if float(current_targets.abs().sum()) <= 1e-12 + else ( + "allocated" + if abs(float(current_targets.sum()) - gross_target) + <= 1e-9 + else "allocated_with_cash" + ) + ) + ) + elif family == "short-cash": + current_targets = -_allocate_capped_up_to( + strengths.where(current_states.eq(-1), 0.0), + limit=gross_target, + cap=asset_caps, + ) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if float(current_targets.abs().sum()) <= 1e-12 + else ( + "allocated" + if abs(float(current_targets.sum()) + gross_target) + <= 1e-9 + else "allocated_with_cash" + ) + ) + ) + else: + current_targets = pd.Series( + 0.0, + index=factors.columns, + dtype=float, + ) + allocation_status = "invalid_mandate" + pre_governor_targets = current_targets.copy() + if decision_eligible: + current_targets, risk_governor = _govern_portfolio_risk( + pre_governor_targets, + returns, + timestamp, + resolved, + enabled=apply_risk_governor, + risk_covariance_cache=risk_covariance_cache, + ) + else: + pre_governor_targets = prior_targets.copy() + _, risk_governor = _govern_portfolio_risk( + pre_governor_targets, + returns, + timestamp, + resolved, + enabled=False, + risk_covariance_cache=risk_covariance_cache, + ) + current_targets = prior_targets.copy() + diagonal_risk = current_targets.abs() * row_volatility.fillna(0.0) + diagonal_total = float(diagonal_risk.sum()) + diagonal_share = ( + diagonal_risk / diagonal_total + if diagonal_total > 1e-12 + else pd.Series(0.0, index=factors.columns, dtype=float) + ) + targets.loc[timestamp] = current_targets + states.loc[timestamp] = current_states + if include_ledger: + assert row_factor is not None + for asset in factors.columns: + score = scores.loc[asset] + translation_score = translated_scores.loc[asset] + volatility_value = row_volatility.loc[asset] + factor_value = row_factor.loc[asset] + translation_value = translation_values.loc[timestamp, asset] + translation_count = translation_observations.loc[ + timestamp, + asset, + ] + target = float(current_targets.loc[asset]) + previous_target = float(prior_targets.loc[asset]) + ledger_rows.append( + { + "timestamp": timestamp, + "asset": str(asset), + "factor": ( + float(factor_value) + if math.isfinite(factor_value) + else np.nan + ), + "percentile_score": ( + float(score) if math.isfinite(score) else np.nan + ), + "translation_score": ( + float(translation_score) + if math.isfinite(translation_score) + else np.nan + ), + "translation_value": ( + float(translation_value) + if math.isfinite(translation_value) + else np.nan + ), + "translation_observations": int(translation_count), + "translation_method": translation_semantics["method"], + "evaluation_mode": translation_semantics[ + "evaluation_mode" + ], + "prior_signal_state": int( + prior_states.loc[asset] + ), + "signal_state": int(current_states.loc[asset]), + "signal_event": events[str(asset)], + "decision_eligible": decision_eligible, + "decision_schedule_kind": decision_schedule_kind, + "decision_every_bars": decision_every_bars, + "decision_anchor": decision_anchor, + "decision_session": str( + decision_sessions.loc[timestamp] + ), + "tradable": str(asset) in tradable_assets, + "permitted_direction": family, + "position_role": asset_position_roles[str(asset)], + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + "mandate_id": str(resolved["id"]), + "conviction": float(convictions.loc[asset]), + "trailing_volatility": ( + float(volatility_value) + if math.isfinite(volatility_value) + else np.nan + ), + "risk_strength": float(strengths.loc[asset]), + "allocation_status": ( + allocation_status + if str(asset) in tradable_assets + else "context_only" + ), + "pre_governor_target_weight": float( + pre_governor_targets.loc[asset] + ), + "risk_governor_status": str( + risk_governor["status"] + ), + "risk_estimation_observations": int( + risk_governor["observations"] + ), + "risk_forecast_pre_annualized": float( + risk_governor["pre_annualized_volatility"] + ), + "risk_forecast_post_annualized": float( + risk_governor["post_annualized_volatility"] + ), + "risk_volatility_ceiling_annualized": float( + risk_governor[ + "annualized_volatility_ceiling" + ] + ), + "risk_governor_scale": float( + risk_governor["scale"] + ), + "prior_target_weight": previous_target, + "proposed_target_weight": target, + "target_delta": target - previous_target, + "target_action": _weight_action( + previous_target, + target, + ), + "diagonal_risk_budget_share": float( + diagonal_share.loc[asset] + ), + } + ) + prior_states = current_states + prior_targets = current_targets + + return SignalConstruction( + targets=targets, + states=states, + ledger=pd.DataFrame(ledger_rows), + scores=score_panel, + translation_values=translation_values, + translation_observations=translation_observations, + translation=translation_semantics, + ) + + +def drift_weights( + previous: pd.Series, + realized_returns: pd.Series, +) -> pd.Series: + """Drift prior close targets through the just-realized asset return.""" + + aligned_returns = realized_returns.reindex(previous.index).fillna(0.0).astype(float) + gross_return = float((previous * aligned_returns).sum()) + denominator = 1.0 + gross_return + if not math.isfinite(denominator) or denominator <= 1e-9: + raise PortfolioFailure( + "portfolio.bankrupt", + "Portfolio drift denominator is non-positive", + ) + drifted = previous * (1.0 + aligned_returns) / denominator + if not np.isfinite(drifted.to_numpy()).all(): + raise PortfolioFailure("portfolio.non-finite", "Drift produced non-finite weights") + return drifted + + +def simulate_targets( + targets: pd.DataFrame, + closes: pd.DataFrame, + volumes: pd.DataFrame, + *, + cost_bps: float | None = None, + no_trade_one_way: float | None = None, + reference_nav: float | None = None, + extra_delay: int = 0, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> Simulation: + """Execute close targets, then credit only the following close return.""" + + if ( + not targets.index.equals(closes.index) + or not targets.index.equals(volumes.index) + or list(targets.columns) != list(closes.columns) + or list(targets.columns) != list(volumes.columns) + ): + raise PortfolioFailure( + "portfolio.alignment", + "Targets, closes, and volumes must share one panel shape", + ) + implementation = resolve_implementation_policy(mandate) + cost_bps = ( + implementation["base_cost_bps"] + if cost_bps is None + else float(cost_bps) + ) + no_trade_one_way = ( + implementation["no_trade_one_way"] + if no_trade_one_way is None + else float(no_trade_one_way) + ) + reference_nav = ( + implementation["reference_nav"] + if reference_nav is None + else float(reference_nav) + ) + decision_policy = dict(implementation["decision_policy"]) + decision_schedule_kind = str(decision_policy["kind"]) + decision_every_bars = decision_policy.get("bars") + decision_anchor = decision_policy.get("anchor") + decision_sessions = decision_schedule_sessions( + targets.index, + decision_policy, + ) + if cost_bps < 0 or not 0 <= no_trade_one_way <= 1 or reference_nav <= 0: + raise PortfolioFailure( + "portfolio.parameters", + "Invalid accounting parameters", + ) + if not isinstance(extra_delay, int) or extra_delay < 0: + raise PortfolioFailure("portfolio.delay", "extra_delay must be non-negative") + resolved = _resolve_mandate(targets.columns, mandate) + benchmark = resolved["benchmark"] + benchmark_weights = pd.Series( + benchmark["weights"], + index=targets.columns, + dtype=float, + ) + proposed_targets = targets.shift(extra_delay).fillna(0.0) + decision_mask = decision_schedule_mask( + targets.index, + decision_policy, + ).shift(extra_delay, fill_value=False) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + target_values = proposed_targets.to_numpy(dtype=float) + close_return_values = close_returns.to_numpy(dtype=float) + forward_return_values = forward_returns.to_numpy(dtype=float) + close_values = closes.to_numpy(dtype=float) + volume_values = volumes.to_numpy(dtype=float) + executed_values = np.zeros(targets.shape, dtype=float) + trade_values = np.zeros(targets.shape, dtype=float) + participation_values = np.zeros(targets.shape, dtype=float) + daily_rows: list[dict[str, object]] = [] + prior_values = np.zeros(len(targets.columns), dtype=float) + + for row_number, timestamp in enumerate(targets.index): + decision_eligible = bool(decision_mask.loc[timestamp]) + if row_number == 0: + pretrade_values = np.zeros_like(prior_values) + else: + realized = np.nan_to_num( + close_return_values[row_number], + nan=0.0, + ) + gross_realized = float(prior_values @ realized) + denominator = 1.0 + gross_realized + if not math.isfinite(denominator) or denominator <= 1e-9: + raise PortfolioFailure( + "portfolio.bankrupt", + "Portfolio drift denominator is non-positive", + ) + pretrade_values = ( + prior_values * (1.0 + realized) / denominator + ) + pretrade = pd.Series( + pretrade_values, + index=targets.columns, + dtype=float, + ) + proposed = pd.Series( + np.nan_to_num(target_values[row_number], nan=0.0), + index=targets.columns, + dtype=float, + ) + current, execution_risk = execute_risk_compliant_book( + pretrade, + proposed, + close_returns, + timestamp, + mandate=mandate, + no_trade_one_way=no_trade_one_way, + ordinary_rebalance_allowed=decision_eligible, + risk_covariance_cache=risk_covariance_cache, + _resolved_mandate=resolved, + ) + rebalance = bool(execution_risk["rebalanced"]) + current_values = current.to_numpy(dtype=float) + row_trade_values = current_values - pretrade_values + traded_notional = float(np.abs(row_trade_values).sum()) + one_way_turnover = 0.5 * traded_notional + cost = traded_notional * cost_bps / 10_000.0 + next_returns = np.nan_to_num( + forward_return_values[row_number], + nan=0.0, + ) + gross_return = float(current_values @ next_returns) + net_return = gross_return - cost + benchmark_return = float( + benchmark_weights.to_numpy(dtype=float) @ next_returns + ) + dollar_volume = ( + close_values[row_number] * volume_values[row_number] + ) + row_participation = np.divide( + np.abs(row_trade_values) * reference_nav, + dollar_volume, + out=np.zeros_like(row_trade_values), + where=dollar_volume != 0.0, + ) + if not all( + math.isfinite(value) + for value in ( + gross_return, + net_return, + benchmark_return, + cost, + one_way_turnover, + traded_notional, + ) + ): + raise PortfolioFailure( + "portfolio.non-finite", + "Accounting produced non-finite values", + ) + executed_values[row_number] = current_values + trade_values[row_number] = row_trade_values + participation_values[row_number] = row_participation + daily_rows.append( + { + "gross_return": gross_return, + "net_return": net_return, + "benchmark_return": benchmark_return, + "one_way_turnover": one_way_turnover, + "traded_notional": traded_notional, + "cost": cost, + "gross_exposure": float(np.abs(current_values).sum()), + "net_exposure": float(current_values.sum()), + "cash_weight": 1.0 - float(np.abs(current_values).sum()), + "max_abs_weight": float(np.abs(current_values).max()), + "concentration_hhi": float((current_values**2).sum()), + "rebalanced": rebalance, + "decision_eligible": decision_eligible, + "decision_schedule_kind": decision_schedule_kind, + "decision_every_bars": decision_every_bars, + "decision_anchor": decision_anchor, + "decision_session": str( + decision_sessions.loc[timestamp] + ), + "execution_reason": str( + execution_risk["execution_reason"] + ), + "execution_risk_status": str(execution_risk["status"]), + "execution_risk_forecast_available": bool( + execution_risk["forecast_available"] + ), + "execution_risk_observations": int( + execution_risk["observations"] + ), + "pretrade_risk_forecast_annualized": float( + execution_risk["pretrade_forecast_annualized"] + ), + "proposed_risk_forecast_pre_annualized": float( + execution_risk[ + "proposed_forecast_pre_annualized" + ] + ), + "proposed_risk_forecast_post_annualized": float( + execution_risk[ + "proposed_forecast_post_annualized" + ] + ), + "executed_risk_forecast_annualized": float( + execution_risk["executed_forecast_annualized"] + ), + "execution_risk_ceiling_annualized": float( + execution_risk[ + "annualized_volatility_ceiling" + ] + ), + "proposed_runtime_risk_scale": float( + execution_risk["proposed_runtime_scale"] + ), + "execution_risk_repair_scale": float( + execution_risk["risk_repair_scale"] + ), + "proposed_one_way_turnover": float( + execution_risk["proposed_one_way"] + ), + "ordinary_rebalance": bool( + execution_risk["ordinary_rebalance"] + ), + "risk_rebalance_override": bool( + execution_risk["risk_rebalance_override"] + ), + "constraint_rebalance_override": bool( + execution_risk["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + execution_risk["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + execution_risk["executed_constraint_maximum_error"] + ), + "max_participation": float(row_participation.max()), + "mean_participation": float(row_participation.mean()), + } + ) + prior_values = current_values + daily = pd.DataFrame(daily_rows, index=targets.index) + executed = pd.DataFrame( + executed_values, + index=targets.index, + columns=targets.columns, + ) + trades = pd.DataFrame( + trade_values, + index=targets.index, + columns=targets.columns, + ) + participation = pd.DataFrame( + participation_values, + index=targets.index, + columns=targets.columns, + ) + valid = forward_returns.notna().any(axis=1) + return Simulation( + daily=daily.loc[valid].copy(), + weights=executed.loc[valid].copy(), + trades=trades.loc[valid].copy(), + participation=participation.loc[valid].copy(), + ) + + +def causal_market_regimes(closes: pd.DataFrame) -> pd.Series: + market_return = closes.pct_change(fill_method=None).mean(axis=1) + trailing_direction = ( + (1.0 + market_return) + .rolling(20, min_periods=20) + .apply(np.prod, raw=True) + - 1.0 + ) + trailing_volatility = market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0) + lagged_threshold = trailing_volatility.shift(1).rolling( + 60, + min_periods=20, + ).median() + labels = pd.Series("unavailable", index=closes.index, dtype="object") + valid = ( + trailing_direction.notna() + & trailing_volatility.notna() + & lagged_threshold.notna() + ) + for timestamp in closes.index[valid]: + direction = "up" if trailing_direction.loc[timestamp] >= 0 else "down" + volatility = ( + "stressed" + if trailing_volatility.loc[timestamp] + > lagged_threshold.loc[timestamp] + else "calm" + ) + labels.loc[timestamp] = f"{direction}-{volatility}" + return labels + + +def build_decision_ledger( + construction: SignalConstruction, + simulation: Simulation, + closes: pd.DataFrame, + volumes: pd.DataFrame, + *, + cost_bps: float | None = None, + reference_nav: float | None = None, + liquidity_adv_window: int = LIQUIDITY_ADV_WINDOW, + mandate: dict[str, object] | None = None, +) -> pd.DataFrame: + """Join signal intent, target sizing, execution, and attribution evidence.""" + + if ( + not construction.targets.index.equals(closes.index) + or not closes.index.equals(volumes.index) + or list(construction.targets.columns) != list(closes.columns) + or list(closes.columns) != list(volumes.columns) + or not simulation.weights.index.equals(simulation.daily.index) + or list(simulation.weights.columns) != list(closes.columns) + ): + raise PortfolioFailure( + "portfolio.alignment", + "Construction, simulation, and closes are not aligned", + ) + implementation = resolve_implementation_policy(mandate) + cost_bps = ( + implementation["base_cost_bps"] + if cost_bps is None + else float(cost_bps) + ) + reference_nav = ( + implementation["reference_nav"] + if reference_nav is None + else float(reference_nav) + ) + if reference_nav <= 0 or cost_bps < 0 or liquidity_adv_window < 2: + raise PortfolioFailure( + "portfolio.parameters", + "Invalid liquidity-capacity parameters", + ) + policy = construction.ledger.set_index(["timestamp", "asset"]) + if not policy.index.is_unique: + raise PortfolioFailure( + "portfolio.ledger", + "Signal construction ledger keys must be unique", + ) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + dollar_volume = closes.astype(float) * volumes.astype(float) + causal_adv = dollar_volume.rolling( + liquidity_adv_window, + min_periods=liquidity_adv_window, + ).mean() + regimes = causal_market_regimes(closes) + prior = pd.Series(0.0, index=closes.columns, dtype=float) + rows: list[dict[str, object]] = [] + for row_number, timestamp in enumerate(simulation.daily.index): + pretrade = ( + pd.Series(0.0, index=closes.columns, dtype=float) + if row_number == 0 + else drift_weights(prior, close_returns.loc[timestamp]) + ) + executed = simulation.weights.loc[timestamp].astype(float) + trade = simulation.trades.loc[timestamp].astype(float) + active_trade = trade.abs() > 1e-12 + adv_row = causal_adv.loc[timestamp].astype(float) + if not bool(active_trade.any()): + capacity_status = "no_trade" + portfolio_capacity = { + limit: 0.0 + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + binding_asset: str | None = None + elif ( + adv_row.loc[active_trade].isna().any() + or (adv_row.loc[active_trade] <= 0).any() + ): + capacity_status = "insufficient_adv_history" + portfolio_capacity = { + limit: 0.0 + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + binding_asset = None + else: + capacity_status = "available" + conservative_asset_capacity = ( + LIQUIDITY_PARTICIPATION_LIMITS[0] + * adv_row.loc[active_trade] + / trade.loc[active_trade].abs() + ) + binding_asset = str(conservative_asset_capacity.idxmin()) + portfolio_capacity = { + limit: float( + ( + limit + * adv_row.loc[active_trade] + / trade.loc[active_trade].abs() + ).min() + ) + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + next_return = forward_returns.loc[timestamp].fillna(0.0).astype(float) + history = close_returns.loc[:timestamp].tail(RISK_COVARIANCE_WINDOW) + history = history.dropna(how="all") + component_variance = pd.Series( + 0.0, + index=closes.columns, + dtype=float, + ) + portfolio_variance = 0.0 + if len(history) >= RISK_COVARIANCE_MINIMUM: + covariance = history.cov( + min_periods=RISK_COVARIANCE_MINIMUM, + ddof=0, + ).reindex(index=closes.columns, columns=closes.columns).fillna(0.0) + marginal = covariance.dot(executed) + component_variance = executed * marginal + portfolio_variance = float(component_variance.sum()) + variance_share = ( + component_variance / portfolio_variance + if portfolio_variance > 1e-18 + else pd.Series(0.0, index=closes.columns, dtype=float) + ) + portfolio_row = simulation.daily.loc[timestamp] + for asset in closes.columns: + policy_row = policy.loc[(timestamp, str(asset))] + asset_trade = float(trade.loc[asset]) + asset_adv = ( + float(adv_row.loc[asset]) + if math.isfinite(float(adv_row.loc[asset])) + else 0.0 + ) + reference_participation = ( + abs(asset_trade) * reference_nav / asset_adv + if ( + capacity_status == "available" + and abs(asset_trade) > 1e-12 + and asset_adv > 0 + ) + else 0.0 + ) + asset_capacity = { + limit: ( + limit * asset_adv / abs(asset_trade) + if ( + capacity_status == "available" + and abs(asset_trade) > 1e-12 + and asset_adv > 0 + ) + else 0.0 + ) + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + gross_contribution = float( + executed.loc[asset] * next_return.loc[asset] + ) + cost_contribution = ( + abs(asset_trade) * cost_bps / 10_000.0 + ) + executed_weight = float(executed.loc[asset]) + rows.append( + { + **policy_row.to_dict(), + "timestamp": timestamp, + "asset": str(asset), + "regime": str(regimes.loc[timestamp]), + "pretrade_weight": float(pretrade.loc[asset]), + "executed_weight": executed_weight, + "executed_state": ( + 1 + if executed_weight > 1e-12 + else -1 + if executed_weight < -1e-12 + else 0 + ), + "trade_weight": asset_trade, + "execution_action": _weight_action( + float(pretrade.loc[asset]), + executed_weight, + ), + "execution_reason": str( + portfolio_row["execution_reason"] + ), + "execution_risk_status": str( + portfolio_row["execution_risk_status"] + ), + "execution_risk_forecast_available": bool( + portfolio_row[ + "execution_risk_forecast_available" + ] + ), + "execution_risk_observations": int( + portfolio_row["execution_risk_observations"] + ), + "pretrade_risk_forecast_annualized": float( + portfolio_row[ + "pretrade_risk_forecast_annualized" + ] + ), + "proposed_risk_forecast_pre_annualized": float( + portfolio_row[ + "proposed_risk_forecast_pre_annualized" + ] + ), + "proposed_risk_forecast_post_annualized": float( + portfolio_row[ + "proposed_risk_forecast_post_annualized" + ] + ), + "executed_risk_forecast_annualized": float( + portfolio_row[ + "executed_risk_forecast_annualized" + ] + ), + "execution_risk_ceiling_annualized": float( + portfolio_row[ + "execution_risk_ceiling_annualized" + ] + ), + "proposed_runtime_risk_scale": float( + portfolio_row["proposed_runtime_risk_scale"] + ), + "execution_risk_repair_scale": float( + portfolio_row["execution_risk_repair_scale"] + ), + "proposed_one_way_turnover": float( + portfolio_row["proposed_one_way_turnover"] + ), + "ordinary_rebalance": bool( + portfolio_row["ordinary_rebalance"] + ), + "risk_rebalance_override": bool( + portfolio_row["risk_rebalance_override"] + ), + "constraint_rebalance_override": bool( + portfolio_row["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + portfolio_row["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + portfolio_row[ + "executed_constraint_maximum_error" + ] + ), + "liquidity_capacity_status": capacity_status, + "liquidity_adv_observations": ( + liquidity_adv_window + if math.isfinite(float(adv_row.loc[asset])) + else 0 + ), + "causal_adv_dollar_volume": asset_adv, + "reference_nav_adv_participation": ( + reference_participation + ), + "asset_capacity_nav_1pct": asset_capacity[0.01], + "asset_capacity_nav_5pct": asset_capacity[0.05], + "portfolio_capacity_nav_1pct": portfolio_capacity[0.01], + "portfolio_capacity_nav_5pct": portfolio_capacity[0.05], + "capacity_binding_asset": ( + binding_asset is not None + and str(asset) == binding_asset + ), + "asset_forward_return": float(next_return.loc[asset]), + "gross_return_contribution": gross_contribution, + "cost_contribution": cost_contribution, + "net_return_contribution": ( + gross_contribution - cost_contribution + ), + "one_way_turnover_contribution": 0.5 + * abs(asset_trade), + "component_variance": float( + component_variance.loc[asset] + ), + "variance_contribution_share": float( + variance_share.loc[asset] + ), + "portfolio_variance": portfolio_variance, + "portfolio_gross_return": float( + portfolio_row["gross_return"] + ), + "portfolio_cost": float(portfolio_row["cost"]), + "portfolio_net_return": float( + portfolio_row["net_return"] + ), + "portfolio_traded_notional": float( + portfolio_row["traded_notional"] + ), + } + ) + prior = executed + result = pd.DataFrame(rows) + required_numeric = result[ + [ + "conviction", + "risk_strength", + "pre_governor_target_weight", + "risk_estimation_observations", + "risk_forecast_pre_annualized", + "risk_forecast_post_annualized", + "risk_volatility_ceiling_annualized", + "risk_governor_scale", + "prior_target_weight", + "proposed_target_weight", + "target_delta", + "diagonal_risk_budget_share", + "pretrade_weight", + "executed_weight", + "trade_weight", + "execution_risk_observations", + "pretrade_risk_forecast_annualized", + "proposed_risk_forecast_pre_annualized", + "proposed_risk_forecast_post_annualized", + "executed_risk_forecast_annualized", + "execution_risk_ceiling_annualized", + "proposed_runtime_risk_scale", + "execution_risk_repair_scale", + "proposed_one_way_turnover", + "constraint_repair_one_way", + "executed_constraint_maximum_error", + "liquidity_adv_observations", + "causal_adv_dollar_volume", + "reference_nav_adv_participation", + "asset_capacity_nav_1pct", + "asset_capacity_nav_5pct", + "portfolio_capacity_nav_1pct", + "portfolio_capacity_nav_5pct", + "asset_forward_return", + "gross_return_contribution", + "cost_contribution", + "net_return_contribution", + "one_way_turnover_contribution", + "component_variance", + "variance_contribution_share", + "portfolio_variance", + "portfolio_gross_return", + "portfolio_cost", + "portfolio_net_return", + "portfolio_traded_notional", + ] + ].to_numpy(dtype=float) + if not np.isfinite(required_numeric).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Decision ledger contains non-finite numeric evidence", + ) + return result + + +def _position_state(weight: float) -> int: + return 1 if weight > 1e-12 else -1 if weight < -1e-12 else 0 + + +def build_position_episodes( + ledger: pd.DataFrame, + index: pd.Index, + *, + split: str, + role: str, +) -> pd.DataFrame: + """Reconstruct exact split-bounded executed-position episodes.""" + + required = { + "timestamp", + "asset", + "signal_state", + "pretrade_weight", + "executed_weight", + "executed_state", + "trade_weight", + "execution_action", + "execution_reason", + "risk_rebalance_override", + "gross_return_contribution", + "cost_contribution", + } + if ( + not isinstance(split, str) + or not split + or role not in {"training", "selection", "visible-audit"} + or len(index) == 0 + or not required.issubset(ledger.columns) + ): + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode inputs are incomplete", + ) + timestamps = pd.DatetimeIndex(index) + if not timestamps.is_monotonic_increasing or not timestamps.is_unique: + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode index must be unique and chronological", + ) + selected = ledger[ledger["timestamp"].isin(timestamps)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode split has no decision rows", + ) + selected = selected.sort_values(["asset", "timestamp"]).reset_index( + drop=True + ) + expected_dates = set(timestamps) + if any( + len(group) != len(timestamps) + or set(pd.DatetimeIndex(group["timestamp"])) != expected_dates + for _, group in selected.groupby("asset", sort=True) + ): + raise PortfolioFailure( + "portfolio.position-episodes", + "Every asset must cover the complete split", + ) + + output: list[dict[str, object]] = [] + for asset, asset_rows in selected.groupby("asset", sort=True): + sequence = 0 + current: dict[str, object] | None = None + + def open_episode( + state: int, + timestamp: pd.Timestamp, + *, + left_censored: bool, + action: str, + entry_weight: float, + entry_cost: float, + ) -> dict[str, object]: + nonlocal sequence + sequence += 1 + return { + "episode_id": f"{split}:{asset}:{sequence:04d}", + "split": split, + "role": role, + "episode_number": sequence, + "asset": str(asset), + "side": "long" if state == 1 else "short", + "entry_timestamp": timestamp, + "last_earning_timestamp": pd.NaT, + "exit_timestamp": pd.NaT, + "entry_action": action, + "exit_action": "split_boundary_carry", + "left_censored": left_censored, + "right_censored": False, + "decision_bars": 0, + "entry_weight": entry_weight, + "last_executed_weight": 0.0, + "_weight_sum": 0.0, + "peak_abs_weight": 0.0, + "gross_contribution": 0.0, + "entry_cost": entry_cost, + "holding_cost": 0.0, + "exit_cost": 0.0, + "_path": [], + "intent_mismatch_bars": 0, + "no_trade_bars": 0, + "risk_override_bars": 0, + } + + def close_episode( + episode: dict[str, object], + *, + right_censored: bool, + timestamp: pd.Timestamp | None, + action: str, + ) -> None: + episode["right_censored"] = right_censored + episode["exit_timestamp"] = ( + pd.NaT if right_censored else timestamp + ) + episode["exit_action"] = action + costs = ( + float(episode["entry_cost"]) + + float(episode["holding_cost"]) + + float(episode["exit_cost"]) + ) + gross = float(episode["gross_contribution"]) + path = np.cumsum( + np.asarray(episode.pop("_path"), dtype=float) + ) + net = gross - costs + if path.size and not math.isclose( + float(path[-1]), + net, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ): + raise PortfolioFailure( + "portfolio.position-episode-reconciliation", + "Episode contribution path does not reconcile", + ) + bars = int(episode["decision_bars"]) + weight_sum = float(episode.pop("_weight_sum")) + episode["average_abs_weight"] = ( + weight_sum / bars if bars else 0.0 + ) + episode["total_cost"] = costs + episode["net_contribution"] = net + episode["maximum_favorable_excursion"] = ( + max(0.0, float(path.max())) if path.size else 0.0 + ) + episode["maximum_adverse_excursion"] = ( + min(0.0, float(path.min())) if path.size else 0.0 + ) + episode["complete"] = not bool( + episode["left_censored"] + or episode["right_censored"] + ) + output.append(episode) + + first = True + for raw in asset_rows.to_dict("records"): + timestamp = pd.Timestamp(raw["timestamp"]) + pretrade_weight = float(raw["pretrade_weight"]) + executed_weight = float(raw["executed_weight"]) + trade_weight = float(raw["trade_weight"]) + pretrade_state = _position_state(pretrade_weight) + executed_state = int(raw["executed_state"]) + if ( + executed_state != _position_state(executed_weight) + or not math.isclose( + trade_weight, + executed_weight - pretrade_weight, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ) + ): + raise PortfolioFailure( + "portfolio.position-episode-state", + "Executed state or trade differs from the weight transition", + ) + if first and pretrade_state != 0: + current = open_episode( + pretrade_state, + timestamp, + left_censored=True, + action="split_boundary_carry", + entry_weight=pretrade_weight, + entry_cost=0.0, + ) + first = False + current_state = ( + 1 + if current is not None and current["side"] == "long" + else -1 + if current is not None + else 0 + ) + if current_state != pretrade_state: + raise PortfolioFailure( + "portfolio.position-episode-state", + "Pretrade weight does not match the open episode", + ) + + cost = float(raw["cost_contribution"]) + if cost < -POSITION_EPISODE_TOLERANCE: + raise PortfolioFailure( + "portfolio.position-episode-cost", + "Episode source cost cannot be negative", + ) + cost_for_current = 0.0 + if current_state == executed_state: + if current is None: + if ( + abs(trade_weight) > POSITION_EPISODE_TOLERANCE + or cost > POSITION_EPISODE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.position-episode-cost", + "Flat-to-flat row cannot contain trade cost", + ) + else: + current["holding_cost"] = ( + float(current["holding_cost"]) + cost + ) + cost_for_current = cost + else: + close_notional = ( + abs(pretrade_weight) if current_state else 0.0 + ) + open_notional = ( + abs(executed_weight) if executed_state else 0.0 + ) + transition_notional = close_notional + open_notional + if ( + not math.isclose( + abs(trade_weight), + transition_notional, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ) + or transition_notional <= POSITION_EPISODE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.position-episode-trade", + "Episode transition notional does not reconcile", + ) + close_cost = cost * close_notional / transition_notional + open_cost = cost - close_cost + if current is not None: + current["exit_cost"] = ( + float(current["exit_cost"]) + close_cost + ) + if int(current["decision_bars"]) == 0: + current["last_executed_weight"] = pretrade_weight + current["_path"].append(-close_cost) + close_episode( + current, + right_censored=False, + timestamp=timestamp, + action=str(raw["execution_action"]), + ) + current = None + if executed_state: + current = open_episode( + executed_state, + timestamp, + left_censored=False, + action=str(raw["execution_action"]), + entry_weight=executed_weight, + entry_cost=open_cost, + ) + cost_for_current = open_cost + + gross = float(raw["gross_return_contribution"]) + if current is None: + if abs(gross) > POSITION_EPISODE_TOLERANCE: + raise PortfolioFailure( + "portfolio.position-episode-contribution", + "Flat executed state cannot earn gross contribution", + ) + continue + current["gross_contribution"] = ( + float(current["gross_contribution"]) + gross + ) + current["_path"].append(gross - cost_for_current) + current["decision_bars"] = int(current["decision_bars"]) + 1 + current["last_earning_timestamp"] = timestamp + current["last_executed_weight"] = executed_weight + current["_weight_sum"] = ( + float(current["_weight_sum"]) + abs(executed_weight) + ) + current["peak_abs_weight"] = max( + float(current["peak_abs_weight"]), + abs(executed_weight), + ) + if int(raw["signal_state"]) != executed_state: + current["intent_mismatch_bars"] = ( + int(current["intent_mismatch_bars"]) + 1 + ) + if str(raw["execution_reason"]) == "portfolio_no_trade_band": + current["no_trade_bars"] = ( + int(current["no_trade_bars"]) + 1 + ) + if bool(raw["risk_rebalance_override"]): + current["risk_override_bars"] = ( + int(current["risk_override_bars"]) + 1 + ) + + if current is not None: + close_episode( + current, + right_censored=True, + timestamp=None, + action="split_boundary_carry", + ) + + if not output: + return pd.DataFrame(columns=POSITION_EPISODE_COLUMNS) + result = pd.DataFrame(output) + result = result.loc[:, POSITION_EPISODE_COLUMNS] + numeric = result[ + [ + "episode_number", + "decision_bars", + "entry_weight", + "last_executed_weight", + "peak_abs_weight", + "average_abs_weight", + "gross_contribution", + "entry_cost", + "holding_cost", + "exit_cost", + "total_cost", + "net_contribution", + "maximum_favorable_excursion", + "maximum_adverse_excursion", + "intent_mismatch_bars", + "no_trade_bars", + "risk_override_bars", + ] + ].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Position episodes contain non-finite evidence", + ) + return result + + +def position_episode_metrics( + episodes: pd.DataFrame, + ledger: pd.DataFrame, + index: pd.Index, +) -> dict[str, object]: + """Aggregate split-bounded episode diagnostics and exact reconciliation.""" + + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Position-lifecycle split has no decision rows", + ) + active = episodes[episodes["decision_bars"].astype(int) > 0].copy() + complete = active[active["complete"].astype(bool)].copy() + winners = complete[ + complete["net_contribution"] > POSITION_EPISODE_TOLERANCE + ] + losers = complete[ + complete["net_contribution"] < -POSITION_EPISODE_TOLERANCE + ] + average_win = ( + float(winners["net_contribution"].mean()) + if len(winners) + else 0.0 + ) + average_loss = ( + float(losers["net_contribution"].mean()) + if len(losers) + else 0.0 + ) + gross_profit = float(winners["net_contribution"].sum()) + gross_loss = abs(float(losers["net_contribution"].sum())) + + def group_metrics(group: pd.DataFrame) -> dict[str, object]: + active_group = group[group["decision_bars"].astype(int) > 0] + complete_group = active_group[ + active_group["complete"].astype(bool) + ] + return { + "segments": int(len(group)), + "active_segments": int(len(active_group)), + "complete_episodes": int(len(complete_group)), + "decision_bars": int(active_group["decision_bars"].sum()), + "total_gross_contribution": float( + group["gross_contribution"].sum() + ), + "total_cost": float(group["total_cost"].sum()), + "total_net_contribution": float( + group["net_contribution"].sum() + ), + "complete_episode_win_rate": ( + float( + ( + complete_group["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(complete_group) + else 0.0 + ), + } + + episode_gross = float(episodes["gross_contribution"].sum()) + episode_cost = float(episodes["total_cost"].sum()) + episode_net = float(episodes["net_contribution"].sum()) + ledger_gross = float(selected["gross_return_contribution"].sum()) + ledger_cost = float(selected["cost_contribution"].sum()) + ledger_net = float(selected["net_return_contribution"].sum()) + reconciliation = { + "passed": ( + abs(episode_gross - ledger_gross) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_cost - ledger_cost) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_net - ledger_net) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_net - (episode_gross - episode_cost)) + <= POSITION_EPISODE_TOLERANCE + ), + "gross_contribution_error": abs(episode_gross - ledger_gross), + "cost_error": abs(episode_cost - ledger_cost), + "net_contribution_error": abs(episode_net - ledger_net), + "episode_identity_error": abs( + episode_net - (episode_gross - episode_cost) + ), + } + if not reconciliation["passed"]: + raise PortfolioFailure( + "portfolio.position-episode-reconciliation", + "Position episodes do not reconcile the decision ledger", + ) + + total_bars = int(active["decision_bars"].sum()) + result: dict[str, object] = { + "status": "available" if len(active) else "no_positions", + "segments": int(len(episodes)), + "active_segments": int(len(active)), + "complete_episodes": int(len(complete)), + "left_censored_segments": int( + episodes["left_censored"].astype(bool).sum() + ), + "right_censored_segments": int( + episodes["right_censored"].astype(bool).sum() + ), + "long_segments": int((episodes["side"] == "long").sum()), + "short_segments": int((episodes["side"] == "short").sum()), + "decision_bars": total_bars, + "segment_positive_rate": ( + float( + ( + active["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(active) + else 0.0 + ), + "complete_episode_win_rate": ( + float( + ( + complete["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(complete) + else 0.0 + ), + "average_complete_holding_bars": ( + float(complete["decision_bars"].mean()) + if len(complete) + else 0.0 + ), + "median_complete_holding_bars": ( + float(complete["decision_bars"].median()) + if len(complete) + else 0.0 + ), + "average_complete_win_contribution": average_win, + "average_complete_loss_contribution": average_loss, + "complete_payoff_ratio": ( + average_win / abs(average_loss) + if average_win > 0 and average_loss < 0 + else 0.0 + ), + "complete_profit_factor": ( + gross_profit / gross_loss + if gross_profit > 0 and gross_loss > 0 + else 0.0 + ), + "average_segment_mfe": ( + float(active["maximum_favorable_excursion"].mean()) + if len(active) + else 0.0 + ), + "average_segment_mae": ( + float(active["maximum_adverse_excursion"].mean()) + if len(active) + else 0.0 + ), + "intent_mismatch_bars": int( + active["intent_mismatch_bars"].sum() + ), + "intent_mismatch_rate": ( + float(active["intent_mismatch_bars"].sum() / total_bars) + if total_bars + else 0.0 + ), + "no_trade_bars": int(active["no_trade_bars"].sum()), + "no_trade_bar_rate": ( + float(active["no_trade_bars"].sum() / total_bars) + if total_bars + else 0.0 + ), + "risk_override_bars": int( + active["risk_override_bars"].sum() + ), + "total_gross_contribution": episode_gross, + "total_cost": episode_cost, + "total_net_contribution": episode_net, + "entry_action_counts": { + str(key): int(value) + for key, value in episodes["entry_action"].value_counts().items() + }, + "exit_action_counts": { + str(key): int(value) + for key, value in episodes["exit_action"].value_counts().items() + }, + "by_asset": { + str(name): group_metrics(group) + for name, group in episodes.groupby("asset", sort=True) + }, + "by_side": { + str(name): group_metrics(group) + for name, group in episodes.groupby("side", sort=True) + }, + "reconciliation": reconciliation, + } + numeric: list[float] = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + numeric.extend(float(value) for value in reconciliation.values()) + if not all(math.isfinite(value) for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Position-lifecycle metrics contain non-finite values", + ) + return result + + +def execution_risk_metrics( + simulation: Simulation, + index: pd.Index, +) -> dict[str, object]: + """Summarize final-book compliance with the causal volatility ceiling.""" + + daily = simulation.daily.loc[index].copy() + required = { + "execution_reason", + "execution_risk_status", + "execution_risk_forecast_available", + "pretrade_risk_forecast_annualized", + "executed_risk_forecast_annualized", + "execution_risk_ceiling_annualized", + "proposed_one_way_turnover", + "risk_rebalance_override", + "gross_exposure", + } + if daily.empty or not required.issubset(daily.columns): + raise PortfolioFailure( + "portfolio.execution-risk", + "Execution-risk split evidence is incomplete", + ) + active = daily[ + (daily["gross_exposure"].abs() > 1e-12) + | (daily["proposed_one_way_turnover"].abs() > 1e-12) + | daily["risk_rebalance_override"].astype(bool) + ] + available = active[ + active["execution_risk_forecast_available"].astype(bool) + ] + unavailable = active[ + ~active["execution_risk_forecast_available"].astype(bool) + ] + pretrade_breach = available[ + available["pretrade_risk_forecast_annualized"] + > available["execution_risk_ceiling_annualized"] + + RISK_COMPLIANCE_TOLERANCE + ] + executed_breach = available[ + available["executed_risk_forecast_annualized"] + > available["execution_risk_ceiling_annualized"] + + RISK_COMPLIANCE_TOLERANCE + ] + overrides = active[active["risk_rebalance_override"].astype(bool)] + ceiling_error = ( + available["executed_risk_forecast_annualized"] + - available["execution_risk_ceiling_annualized"] + ).clip(lower=0.0) + result: dict[str, object] = { + "status": ( + "available" + if not available.empty + else "no_active_dates" + if active.empty + else "forecast_unavailable" + ), + "dates": int(len(daily)), + "active_dates": int(len(active)), + "forecast_available_dates": int(len(available)), + "forecast_unavailable_dates": int(len(unavailable)), + "forecast_coverage": ( + float(len(available) / len(active)) + if len(active) + else 0.0 + ), + "pretrade_breach_dates": int(len(pretrade_breach)), + "pretrade_breach_rate": ( + float(len(pretrade_breach) / len(available)) + if len(available) + else 0.0 + ), + "risk_rebalance_override_dates": int(len(overrides)), + "risk_rebalance_override_rate": ( + float(len(overrides) / len(active)) + if len(active) + else 0.0 + ), + "executed_breach_dates": int(len(executed_breach)), + "executed_breach_rate": ( + float(len(executed_breach) / len(available)) + if len(available) + else 0.0 + ), + "mean_executed_forecast_annualized": ( + float(available["executed_risk_forecast_annualized"].mean()) + if len(available) + else 0.0 + ), + "maximum_executed_forecast_annualized": ( + float(available["executed_risk_forecast_annualized"].max()) + if len(available) + else 0.0 + ), + "maximum_ceiling_error": ( + float(ceiling_error.max()) if len(ceiling_error) else 0.0 + ), + "status_counts": { + str(key): int(value) + for key, value in daily[ + "execution_risk_status" + ].value_counts().items() + }, + "execution_reason_counts": { + str(key): int(value) + for key, value in daily[ + "execution_reason" + ].value_counts().items() + }, + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Execution-risk metrics contain invalid values", + ) + if int(result["executed_breach_dates"]): + raise PortfolioFailure( + "portfolio.risk-breach", + "Final executed-book path contains a volatility-ceiling breach", + ) + return result + + +def liquidity_capacity_metrics( + ledger: pd.DataFrame, + index: pd.Index, + *, + reference_nav: float = REFERENCE_NAV, +) -> dict[str, object]: + """Aggregate the exact per-date OHLCV participation-capacity envelope.""" + + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Liquidity-capacity split has no decision rows", + ) + dates = ( + selected.groupby("timestamp", sort=True) + .agg( + status=("liquidity_capacity_status", "first"), + capacity_1pct=("portfolio_capacity_nav_1pct", "first"), + capacity_5pct=("portfolio_capacity_nav_5pct", "first"), + ) + ) + trade_dates = dates[dates["status"].ne("no_trade")] + available = trade_dates[trade_dates["status"].eq("available")] + unavailable = trade_dates[ + trade_dates["status"].eq("insufficient_adv_history") + ] + if len(trade_dates): + coverage = float(len(available) / len(trade_dates)) + else: + coverage = 0.0 + + def summarize(column: str) -> dict[str, float | int | str]: + values = available[column].astype(float) + if values.empty: + return { + "status": "unavailable", + "observations": 0, + "minimum_nav": 0.0, + "tenth_percentile_nav": 0.0, + "median_nav": 0.0, + "reference_nav_breach_rate": 0.0, + } + return { + "status": "available", + "observations": int(len(values)), + "minimum_nav": float(values.min()), + "tenth_percentile_nav": float(values.quantile(0.10)), + "median_nav": float(values.median()), + "reference_nav_breach_rate": float( + (values + 1e-12 < reference_nav).mean() + ), + } + + binding = selected[ + selected["capacity_binding_asset"].astype(bool) + & selected["timestamp"].isin(available.index) + ] + result: dict[str, object] = { + "status": ( + "available" + if not available.empty + else "no_trades" + if trade_dates.empty + else "insufficient_adv_history" + ), + "trade_dates": int(len(trade_dates)), + "available_trade_dates": int(len(available)), + "unavailable_trade_dates": int(len(unavailable)), + "trade_date_coverage": coverage, + "binding_asset_counts_1pct": { + str(asset): int(count) + for asset, count in binding["asset"].value_counts().items() + }, + "capacity_1pct": summarize("capacity_1pct"), + "capacity_5pct": summarize("capacity_5pct"), + } + numeric: list[float] = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + for key in ("capacity_1pct", "capacity_5pct"): + numeric.extend( + float(value) + for value in result[key].values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ) + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Liquidity-capacity metrics contain invalid values", + ) + return result + + +def signal_policy_metrics( + construction: SignalConstruction, + index: pd.Index, +) -> dict[str, object]: + selected = construction.ledger[ + construction.ledger["timestamp"].isin(index) + ].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Signal policy split has no decision rows", + ) + timestamps = int(selected["timestamp"].nunique()) + annual_periods = annualization_periods(index) + events = { + str(key): int(value) + for key, value in selected["signal_event"].value_counts().items() + } + actions = { + str(key): int(value) + for key, value in selected["target_action"].value_counts().items() + } + allocation = { + str(key): int(value) + for key, value in selected["allocation_status"].value_counts().items() + } + risk_by_timestamp = ( + selected.groupby("timestamp", sort=True) + .agg( + status=("risk_governor_status", "first"), + scale=("risk_governor_scale", "first"), + observations=("risk_estimation_observations", "first"), + pre=("risk_forecast_pre_annualized", "first"), + post=("risk_forecast_post_annualized", "first"), + ceiling=("risk_volatility_ceiling_annualized", "first"), + ) + ) + cadence_by_timestamp = ( + selected.groupby("timestamp", sort=True) + .agg( + eligible=("decision_eligible", "first"), + kind=("decision_schedule_kind", "first"), + every_bars=("decision_every_bars", "first"), + anchor=("decision_anchor", "first"), + ) + ) + active_risk = risk_by_timestamp[ + ~risk_by_timestamp["status"].isin({"flat", "legacy_none"}) + ] + state_counts = ( + selected.groupby("timestamp")["signal_state"] + .value_counts() + .unstack(fill_value=0) + ) + target_turnover = ( + selected.assign(abs_delta=selected["target_delta"].abs()) + .groupby("timestamp")["abs_delta"] + .sum() + * 0.5 + ) + transitions = int( + selected["prior_signal_state"].ne(selected["signal_state"]).sum() + ) + result: dict[str, object] = { + "decision_rows": int(len(selected)), + "timestamps": timestamps, + "decision_eligible_timestamps": int( + cadence_by_timestamp["eligible"].sum() + ), + "decision_eligible_rate": float( + cadence_by_timestamp["eligible"].mean() + ), + "decision_schedule": ( + { + "kind": "calendar-month-end", + } + if str(cadence_by_timestamp["kind"].iloc[0]) + == "calendar-month-end" + else { + "kind": "every-bars", + "bars": int(cadence_by_timestamp["every_bars"].iloc[0]), + "anchor": str(cadence_by_timestamp["anchor"].iloc[0]), + } + ), + "scheduled_hold_timestamps": int( + (~cadence_by_timestamp["eligible"]).sum() + ), + "signal_transitions": transitions, + "state_change_rate": float(transitions / len(selected)), + "entries": int( + selected["signal_event"].isin( + {"enter_long", "enter_short"} + ).sum() + ), + "exits": int( + selected["signal_event"].isin({"exit_long", "exit_short"}).sum() + ), + "reversals": int( + selected["signal_event"].isin( + {"reverse_long_to_short", "reverse_short_to_long"} + ).sum() + ), + "signal_event_counts": events, + "target_action_counts": actions, + "allocation_status_counts": allocation, + "risk_governor_status_counts": { + str(key): int(value) + for key, value in risk_by_timestamp["status"].value_counts().items() + }, + "risk_limited_dates": int( + risk_by_timestamp["status"].eq("volatility_limited").sum() + ), + "risk_limited_rate": float( + risk_by_timestamp["status"].eq("volatility_limited").mean() + ), + "risk_unavailable_dates": int( + risk_by_timestamp["status"] + .isin({"insufficient_history", "invalid_covariance"}) + .sum() + ), + "average_active_risk_scale": ( + float(active_risk["scale"].mean()) + if not active_risk.empty + else 1.0 + ), + "maximum_pre_governor_annualized_volatility": float( + risk_by_timestamp["pre"].max() + ), + "maximum_post_governor_annualized_volatility": float( + risk_by_timestamp["post"].max() + ), + "average_long_intents": ( + float(state_counts[1].mean()) if 1 in state_counts else 0.0 + ), + "average_short_intents": ( + float(state_counts[-1].mean()) if -1 in state_counts else 0.0 + ), + "average_flat_intents": ( + float(state_counts[0].mean()) if 0 in state_counts else 0.0 + ), + "mean_target_one_way_turnover": float(target_turnover.mean()), + "annualized_target_one_way_turnover": float( + target_turnover.mean() * annual_periods + ), + "average_gross_proposed_target": float( + selected.groupby("timestamp")["proposed_target_weight"] + .apply(lambda values: values.abs().sum()) + .mean() + ), + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Signal policy metrics contain non-finite values", + ) + return result + + +def attribution_metrics( + ledger: pd.DataFrame, + simulation: Simulation, + index: pd.Index, +) -> dict[str, object]: + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Attribution split has no decision rows", + ) + timestamps = int(selected["timestamp"].nunique()) + annual_periods = annualization_periods(index) + + def aggregate(group: pd.DataFrame) -> dict[str, float | int]: + return { + "observations": int(len(group)), + "total_gross_contribution": float( + group["gross_return_contribution"].sum() + ), + "annualized_gross_contribution": float( + group["gross_return_contribution"].sum() + / timestamps + * annual_periods + ), + "total_cost_contribution": float( + group["cost_contribution"].sum() + ), + "total_net_contribution": float( + group["net_return_contribution"].sum() + ), + "annualized_net_contribution": float( + group["net_return_contribution"].sum() + / timestamps + * annual_periods + ), + "total_one_way_turnover_contribution": float( + group["one_way_turnover_contribution"].sum() + ), + "average_absolute_executed_weight": float( + group["executed_weight"].abs().mean() + ), + "mean_variance_contribution_share": float( + group["variance_contribution_share"].mean() + ), + } + + by_asset = { + str(name): aggregate(group) + for name, group in selected.groupby("asset", sort=True) + } + state_names = {-1: "short", 0: "flat", 1: "long"} + by_signal_state = { + state_names[int(name)]: aggregate(group) + for name, group in selected.groupby("signal_state", sort=True) + } + by_regime = { + str(name): aggregate(group) + for name, group in selected.groupby("regime", sort=True) + } + asset_net = pd.Series( + { + asset: values["total_net_contribution"] + for asset, values in by_asset.items() + }, + dtype=float, + ) + absolute_total = float(asset_net.abs().sum()) + contribution_shares = ( + asset_net.abs() / absolute_total + if absolute_total > 1e-12 + else pd.Series(0.0, index=asset_net.index, dtype=float) + ) + asset_risk = pd.Series( + { + asset: values["mean_variance_contribution_share"] + for asset, values in by_asset.items() + }, + dtype=float, + ) + absolute_risk_total = float(asset_risk.abs().sum()) + risk_shares = ( + asset_risk.abs() / absolute_risk_total + if absolute_risk_total > 1e-12 + else pd.Series(0.0, index=asset_risk.index, dtype=float) + ) + + gross_error = 0.0 + cost_error = 0.0 + net_error = 0.0 + traded_error = 0.0 + risk_share_error = 0.0 + risk_dates = 0 + for timestamp, group in selected.groupby("timestamp", sort=True): + portfolio = simulation.daily.loc[timestamp] + gross_error = max( + gross_error, + abs( + float(group["gross_return_contribution"].sum()) + - float(portfolio["gross_return"]) + ), + ) + cost_error = max( + cost_error, + abs( + float(group["cost_contribution"].sum()) + - float(portfolio["cost"]) + ), + ) + net_error = max( + net_error, + abs( + float(group["net_return_contribution"].sum()) + - float(portfolio["net_return"]) + ), + ) + traded_error = max( + traded_error, + abs( + float(group["trade_weight"].abs().sum()) + - float(portfolio["traded_notional"]) + ), + ) + if float(group["portfolio_variance"].iloc[0]) > 1e-18: + risk_dates += 1 + risk_share_error = max( + risk_share_error, + abs(float(group["variance_contribution_share"].sum()) - 1.0), + ) + tolerance = 1e-10 + reconciliation = { + "passed": ( + gross_error <= tolerance + and cost_error <= tolerance + and net_error <= tolerance + and traded_error <= tolerance + and risk_share_error <= tolerance + ), + "maximum_gross_return_error": gross_error, + "maximum_cost_error": cost_error, + "maximum_net_return_error": net_error, + "maximum_traded_notional_error": traded_error, + "maximum_variance_share_error": risk_share_error, + "variance_attributed_dates": risk_dates, + } + return { + "reconciliation": reconciliation, + "concentration": { + "maximum_absolute_net_contribution_share": ( + float(contribution_shares.max()) + if not contribution_shares.empty + else 0.0 + ), + "absolute_net_contribution_hhi": float( + contribution_shares.pow(2).sum() + ), + "maximum_absolute_variance_contribution_share": ( + float(risk_shares.max()) if not risk_shares.empty else 0.0 + ), + "absolute_variance_contribution_hhi": float( + risk_shares.pow(2).sum() + ), + }, + "by_asset": by_asset, + "by_signal_state": by_signal_state, + "by_regime": by_regime, + } + + +def performance_metrics( + returns: pd.Series, + benchmark: pd.Series, + *, + annual_periods: int | None = None, +) -> dict[str, float | int]: + pair = pd.DataFrame( + {"returns": returns, "benchmark": benchmark} + ).dropna() + if len(pair) < 20: + raise PortfolioFailure( + "portfolio.population", + "Portfolio split has fewer than 20 valid observations", + ) + values = pair["returns"].astype(float) + benchmark_values = pair["benchmark"].astype(float) + annual_periods = ( + annualization_periods(values.index) + if annual_periods is None + else annual_periods + ) + mean = float(values.mean()) + std = float(values.std(ddof=0)) + standardized = ( + (values - mean) / std + if std > 1e-12 + else pd.Series(0.0, index=values.index, dtype=float) + ) + return_skewness = ( + float(standardized.pow(3).mean()) + if std > 1e-12 + else 0.0 + ) + return_kurtosis = ( + float(standardized.pow(4).mean()) + if std > 1e-12 + else 3.0 + ) + annual_volatility = std * math.sqrt(annual_periods) + total_growth = float((1.0 + values).prod()) + annual_return = ( + total_growth ** (annual_periods / len(values)) - 1.0 + if total_growth > 0 + else -1.0 + ) + sharpe = mean / std * math.sqrt(annual_periods) if std > 1e-12 else 0.0 + downside = np.minimum(values.to_numpy(dtype=float), 0.0) + downside_deviation = float(np.sqrt(np.mean(np.square(downside)))) + sortino = ( + mean / downside_deviation * math.sqrt(annual_periods) + if downside_deviation > 1e-12 + else 0.0 + ) + equity = (1.0 + values).cumprod() + drawdown = equity / equity.cummax() - 1.0 + maximum_drawdown = float(drawdown.min()) + calmar = ( + annual_return / abs(maximum_drawdown) + if maximum_drawdown < -1e-12 + else 0.0 + ) + tail_count = max(1, math.ceil(len(values) * 0.05)) + expected_shortfall = -float(values.nsmallest(tail_count).mean()) + benchmark_variance = float(benchmark_values.var(ddof=0)) + beta = ( + float(values.cov(benchmark_values, ddof=0)) / benchmark_variance + if benchmark_variance > 1e-12 + else 0.0 + ) + active = values - benchmark_values + tracking_error_daily = float(active.std(ddof=0)) + tracking_error = tracking_error_daily * math.sqrt(annual_periods) + active_annual_return = float(active.mean()) * annual_periods + information_ratio = ( + float(active.mean()) / tracking_error_daily * math.sqrt(annual_periods) + if tracking_error_daily > 1e-12 + else 0.0 + ) + result: dict[str, float | int] = { + "observations": int(len(values)), + "annualization_periods": int(annual_periods), + "total_return": total_growth - 1.0, + "annual_return": annual_return, + "annual_volatility": annual_volatility, + "sharpe": sharpe, + "period_sharpe": mean / std if std > 1e-12 else 0.0, + "return_skewness": return_skewness, + "return_kurtosis": return_kurtosis, + "sortino": sortino, + "maximum_drawdown": maximum_drawdown, + "calmar": calmar, + "expected_shortfall_95": expected_shortfall, + "positive_rate": float((values > 0).mean()), + "benchmark_beta": beta, + "active_annual_return": active_annual_return, + "tracking_error": tracking_error, + "information_ratio": information_ratio, + } + if not all( + math.isfinite(float(value)) + for value in result.values() + ): + raise PortfolioFailure( + "portfolio.non-finite", + "Performance metrics contain non-finite values", + ) + return result + + +def implementation_metrics( + simulation: Simulation, + index: pd.Index, + *, + annual_periods: int | None = None, +) -> dict[str, float]: + daily = simulation.daily.loc[index] + weights = simulation.weights.loc[index] + annual_periods = ( + annualization_periods(daily.index) + if annual_periods is None + else annual_periods + ) + result = { + "mean_one_way_turnover": float(daily["one_way_turnover"].mean()), + "annualized_one_way_turnover": float( + daily["one_way_turnover"].mean() * annual_periods + ), + "mean_traded_notional": float(daily["traded_notional"].mean()), + "total_cost_drag": float(daily["cost"].sum()), + "rebalance_rate": float(daily["rebalanced"].mean()), + "no_trade_rate": float((~daily["rebalanced"]).mean()), + "average_gross_exposure": float(daily["gross_exposure"].mean()), + "maximum_gross_exposure": float(daily["gross_exposure"].max()), + "average_abs_net_exposure": float(daily["net_exposure"].abs().mean()), + "maximum_abs_net_exposure": float(daily["net_exposure"].abs().max()), + "average_max_abs_weight": float(daily["max_abs_weight"].mean()), + "maximum_abs_weight": float(weights.abs().max().max()), + "average_concentration_hhi": float(daily["concentration_hhi"].mean()), + "mean_volume_participation": float(daily["mean_participation"].mean()), + "maximum_volume_participation": float(daily["max_participation"].max()), + } + if not all(math.isfinite(value) for value in result.values()): + raise PortfolioFailure( + "portfolio.non-finite", + "Implementation metrics contain non-finite values", + ) + return result + + +def constraint_audit( + targets: pd.DataFrame, + *, + gross_target: float = GROSS_TARGET, + max_abs_weight: float = MAX_ABS_WEIGHT, + mandate: dict[str, object] | None = None, +) -> dict[str, object]: + resolved = _resolve_mandate(targets.columns, mandate) + gross_target = float(resolved["gross_limit"]) + max_abs_weight = float(resolved["max_abs_weight"]) + asset_caps = pd.Series( + resolved["asset_max_abs_weights"], + index=targets.columns, + dtype=float, + ) + family = str(resolved["family"]) + asset_position_roles = { + str(asset): str(role) + for asset, role in resolved["asset_position_roles"].items() + } + long_gross_limit = float(resolved["long_gross_limit"]) + short_gross_limit = float(resolved["short_gross_limit"]) + tradable = list(resolved["tradable_assets"]) + context = list(resolved["context_assets"]) + active = targets[targets.abs().sum(axis=1) > 1e-12] + if active.empty: + if family == "dollar-neutral": + raise PortfolioFailure( + "portfolio.no-targets", + "No active targets were constructed", + ) + return { + "passed": True, + "mandate_id": resolved["id"], + "family": family, + "tradable_assets": tradable, + "context_assets": context, + "active_dates": 0, + "maximum_gross_error": 0.0, + "maximum_gross_exposure": 0.0, + "maximum_abs_net_target": 0.0, + "maximum_net_rule_error": 0.0, + "maximum_opposite_exposure": 0.0, + "maximum_context_weight": 0.0, + "maximum_tradable_gross": 0.0, + "maximum_abs_target_weight": 0.0, + "maximum_asset_cap_excess": 0.0, + "asset_max_abs_weights": { + asset: float(asset_caps.loc[asset]) + for asset in targets.columns + }, + "asset_position_roles": asset_position_roles, + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + } + gross = active.abs().sum(axis=1) + net = active.sum(axis=1) + if family == "dollar-neutral": + gross_error = ( + float((gross - gross_target).clip(lower=0.0).max()) + if resolved["risk_policy"] is not None + else float((gross - gross_target).abs().max()) + ) + net_rule_error = float(net.abs().max()) + opposite_exposure = 0.0 + elif family == "long-cash": + gross_error = float((gross - gross_target).clip(lower=0.0).max()) + net_rule_error = float((net - gross).abs().max()) + opposite_exposure = float((-active.clip(upper=0.0)).sum(axis=1).max()) + elif family == "short-cash": + gross_error = float((gross - gross_target).clip(lower=0.0).max()) + net_rule_error = float((net + gross).abs().max()) + opposite_exposure = float(active.clip(lower=0.0).sum(axis=1).max()) + elif family == "asset-role": + long_exposure = active.clip(lower=0.0).sum(axis=1) + short_exposure = (-active.clip(upper=0.0)).sum(axis=1) + gross_error = float( + pd.concat( + [ + (long_exposure - long_gross_limit).clip(lower=0.0), + (short_exposure - short_gross_limit).clip(lower=0.0), + (gross - gross_target).clip(lower=0.0), + ], + axis=1, + ).max().max() + ) + forbidden: list[float] = [] + for asset in active.columns: + role = asset_position_roles[str(asset)] + if role == "long-only": + forbidden.append(float((-active[asset].clip(upper=0.0)).max())) + elif role == "short-only": + forbidden.append(float(active[asset].clip(lower=0.0).max())) + elif role == "context-only": + forbidden.append(float(active[asset].abs().max())) + opposite_exposure = max(forbidden, default=0.0) + net_rule_error = 0.0 + else: + raise PortfolioFailure("mandate.family", "Unknown mandate family") + maximum_weight = float(active.abs().max().max()) + cap_excess = active.abs().subtract(asset_caps, axis="columns") + maximum_asset_cap_excess = float( + cap_excess.clip(lower=0.0).max().max() + ) + context_weight = ( + float(active[context].abs().max().max()) + if context + else 0.0 + ) + tradable_gross = float(active[tradable].abs().sum(axis=1).max()) + passed = ( + gross_error <= 1e-8 + and net_rule_error <= 1e-8 + and opposite_exposure <= 1e-8 + and context_weight <= 1e-8 + and maximum_weight <= max_abs_weight + 1e-8 + and maximum_asset_cap_excess <= 1e-8 + ) + return { + "passed": passed, + "mandate_id": resolved["id"], + "family": family, + "tradable_assets": tradable, + "context_assets": context, + "active_dates": int(len(active)), + "maximum_gross_error": gross_error, + "maximum_gross_exposure": float(gross.max()), + "maximum_abs_net_target": float(net.abs().max()), + "maximum_net_rule_error": net_rule_error, + "maximum_opposite_exposure": opposite_exposure, + "maximum_context_weight": context_weight, + "maximum_tradable_gross": tradable_gross, + "maximum_abs_target_weight": maximum_weight, + "maximum_asset_cap_excess": maximum_asset_cap_excess, + "asset_max_abs_weights": { + asset: float(asset_caps.loc[asset]) + for asset in targets.columns + }, + "asset_position_roles": asset_position_roles, + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + } diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/program.md b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/program.md new file mode 100644 index 0000000..7dd3bbe --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/program.md @@ -0,0 +1,129 @@ +# OHLCV Portfolio Construction Study + +## Question + +Can one causal panel factor become a stable cross-asset portfolio after fixed +sizing, position caps, drift-aware turnover, costs, and chronological +out-of-sample evaluation? + +## Editable closure + +Edit only `factors/**`. Keep the API: + +```python +def compute_factor(panel: pandas.DataFrame) -> pandas.Series: + ... +``` + +The factor receives the complete Study universe as long-form OHLCV rows with +`asset` and `timestamp`. It may combine within-asset history and +contemporaneous cross-asset context, must return one numeric aligned Series, +and may not use any future timestamp. + +Before choosing a hypothesis, inspect `candidateContract` from +`aq orient . --json` or `aq study inspect . --study +ohlcv-portfolio-quality --json`. It is the authority for this Project's actual +base interval, completed feature intervals, panel columns, component metadata +fields, and legal component roles. Conditional higher-interval branches in +reusable candidate code do not prove that this Project supplies those columns. +If components are declared, roles are exactly `cross-sectional-score` or +`timestamp-context`; the latter must be one shared value across assets at a +timestamp. + +Before each edit, run `aq orient . --json` and read the current verified +`researchAgenda`. Its move must still target `factors/**`: sizing, caps, +Mandate, covariance risk, no-trade, and cost remain fixed evaluation pressure. +The agenda is a validation-only experiment brief, not an executable action or +permission to select from visible test audit. + +## Fixed portfolio contract + +- `strategies/factor-population.json` fixes which assets supply Factor target + observations and scores; it grants no position authority; +- `strategies/portfolio-mandate.json` fixes tradable versus context assets, + permitted direction, cash, gross/net, cap, and a structured benchmark; +- benchmark return uses only the Mandate's complete fixed weight vector; a + context-only benchmark asset never gains position authority; +- factor rank becomes only a mandate-permitted percentile state; +- enter long/short at `0.75 / 0.25` and exit at `0.55 / 0.45`; +- size conviction by inverse trailing 20-bar volatility; +- directional requests allocate only their permitted side and retain unused + gross budget in cash; +- long-short/relative-value require the Portfolio Mandate's exact gross and + equal funded long/short sides; +- an explicit asset-role Mandate applies long-only, short-only, two-sided, or + context-only signal state per asset, allocates each active side only up to + its locked gross-side limit, and leaves unused side capacity cash; +- context-only assets remain flat with zero target; +- maximum absolute target weight comes from the Portfolio Mandate; +- the Portfolio Mandate's decision schedule uses its locked dataset-start or + verified XNYS session-start anchor: signal state and governed targets may + advance only on eligible base bars, and remain frozen between them; +- forecast portfolio volatility from up to 60 complete trailing return rows + through decision close `t`, with at least 20 observations; +- uniformly scale raw targets down to the Portfolio Mandate's annualized + volatility ceiling and never scale exposure up; +- retain the drifted book below the Portfolio Mandate's one-way no-trade + threshold; +- recheck that chosen post-drift book against the same causal covariance + ceiling; risk outranks no-trade and may apply only the minimum proportional + scale-down; +- an ineligible decision bar forbids ordinary rebalancing, but the same + every-bar risk check may still flatten or proportionally scale down an + unsafe drifted book; +- cost every bought/sold dollar at the Portfolio Mandate's base cost; +- estimate causal ADV from 20 `close × volume` observations through decision + close and invert exact trade weights at 1%/5% participation; +- signal at close `t` earns only close `t` to close `t+1`; +- dataset-fixed purged 60/20/20 train/validation/test; +- primary score is validation net Sharpe only; +- mandatory 0/10/25bps, one-extra-bar-delay, and no-hysteresis comparisons. +- mandatory five-profile × three-band mechanical parameter neighborhood; + every cell is context only and cannot select a parameter or candidate. + +The Judge owns every rule above. Do not edit `judges/**`, +`strategies/portfolio-mandate.json`, the Study, program, or dataset while +comparing candidates. + +Test metrics are visible diagnostic evidence and never enter KEEP/REVERT. +Changing a candidate after inspecting them consumes their holdout value; use a +new external period or dataset before a production-grade claim. + +## Evidence discipline + +Inspect factor, signal-state, portfolio/risk, implementation, attribution, +constraint, and robustness layers. Read `portfolio-decisions.csv` when a +conclusion depends on one asset or date. Reconcile raw and governed targets, +forecast volatility, scale, and status. Then reconcile the actual executed +book's forecast coverage, pretrade breaches, risk-only no-trade overrides, +executed breaches, and execution reason; inspect how often the ceiling binds +and whether cash exposure is signal-driven or risk-driven. Reconcile capacity +to the exact trade weight, causal ADV, and binding asset; treat missing-history +dates as unavailable, not liquid. The 1% p10 envelope is contextual and cannot +select a candidate. Read `position-episodes.csv` to inspect contiguous +executed long/short states, exact entry/resize/exit/reversal costs, holding +bars, additive contribution, MFE/MAE, and intent mismatch. Keep left/right +censored segments out of complete-episode win/payoff interpretation. This +lifecycle evidence is contextual and cannot select a candidate. The +`portfolio-parameter-neighborhood.json` artifact preserves exact +validation/test return, turnover, cost, rebalance, and signal-transition paths. +Use it to detect a one-point result, not to choose the strongest cell. The +`portfolio-translation-robustness.json` artifact, when the Factor claim is +temporal or two-asset relative value, preserves the fixed 40/60/120 causal +history-window score, state, target, return, turnover, and cost paths. Use it +to detect translation sensitivity; never select a stress window or change the +ordinary 60/20 contract. Cross-sectional Runs correctly omit that artifact. +The +ungoverned comparison is diagnostic only and cannot +select a candidate. A higher primary score is not enough when coverage +collapses, hysteresis adds no value, +concentration rises, the risk governor constantly suppresses an unstable raw +book, turnover/costs dominate, the capacity envelope is too small or poorly +covered, delayed performance reverses, attribution fails to reconcile, or one +asset explains the result. Also inspect the complete +Project-family trial count, probabilistic/deflated Sharpe, expected maximum +Sharpe from strategy search, and minimum track record. Those diagnostics do +not rewrite KEEP/REVERT and cannot be reset by starting another Session. + +This is a synthetic bar-target-weight simulation, not an L2 fill model, order +instruction, or live-trading recommendation. diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/study.json b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/study.json new file mode 100644 index 0000000..84fba61 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/inputs/study.json @@ -0,0 +1,60 @@ +{ + "dataset": { + "asset_class": "synthetic-multi-asset", + "id": "synthetic-ohlcv-research-desk-fixture", + "paths": [ + "ohlcv/**" + ], + "time_range": { + "end": "2025-08-11", + "start": "2024-01-02" + }, + "universe": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "version": "v1" + }, + "dependencies": { + "paths": [ + "strategies/factor-claim.json", + "strategies/factor-population.json", + "strategies/portfolio-mandate.json", + "strategies/research-horizon.json" + ] + }, + "description": "Translate the shared candidate factor into constrained, costed target weights", + "editable": { + "paths": [ + "factors/**" + ] + }, + "id": "ohlcv-portfolio-quality", + "judge": { + "arguments": [], + "entrypoint": "judges/ohlcv_portfolio.py", + "kind": "python", + "paths": [ + "judges/ohlcv_portfolio.py", + "judges/portfolio_core.py" + ], + "timeout_seconds": 180 + }, + "name": "OHLCV Portfolio Quality", + "objective": { + "direction": "maximize", + "metric": "validation_net_sharpe", + "minimum_improvement": 0.05 + }, + "program": "program.md", + "schema_version": 1, + "subject": { + "kind": "factor", + "name": "portfolio-factor", + "version": "working" + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/judge-output.json b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/judge-output.json new file mode 100644 index 0000000..7d654db --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/judge-output.json @@ -0,0 +1,3608 @@ +{ + "artifacts": [ + { + "description": "Timing, construction, split, cost, risk, stress, constraint, and causality evidence", + "kind": "portfolio-report", + "path": "portfolio-report.json" + }, + { + "description": "Daily gross/net/benchmark returns, turnover, costs, exposures, rebalance state, and participation", + "kind": "portfolio-daily", + "path": "daily-portfolio.csv" + }, + { + "description": "Exact per-date signal-policy proposed target weights", + "kind": "portfolio-targets", + "path": "proposed-target-weights.csv" + }, + { + "description": "Exact post-drift and no-trade-band executed weights", + "kind": "portfolio-weights", + "path": "executed-weights.csv" + }, + { + "description": "Per-asset signal state, 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"purgedBoundaryRows": 1, + "signalEnd": "2024-12-17", + "start": "2024-01-02", + "targetEnd": "2024-12-18" + }, + "validation": { + "eligibleSignalRows": 83, + "end": "2025-04-15", + "purgedBoundaryRows": 1, + "signalEnd": "2025-04-14", + "start": "2024-12-19", + "targetEnd": "2025-04-15" + } + }, + "targetCrossesBoundary": false + }, + "translation_robustness": { + "applicable": false, + "current": null, + "diagnosis": { + "explanation": "Cross-sectional decision scores use the same-timestamp prediction population and have no temporal history window.", + "selection_split": "validation", + "status": "not-applicable" + }, + "policy": { + "base_profile_id": "base", + "evaluation_mode": "cross-sectional", + "method": "predeclared-temporal-translation-window-stability-v1", + "profiles": [ + { + "id": "short-history", + "is_base": false, + "label": "Short history", + "minimum": 20, + "window": 40 + }, + { + "id": "base", + "is_base": true, + "label": "Base", + "minimum": 20, + "window": 60 + }, + { + "id": "long-history", + "is_base": false, + "label": "Long history", + "minimum": 20, + "window": 120 + } + ], + "role": "robustness-only", + "selection_authority": "context-only", + "stability_thresholds": { + "maximum_mean_absolute_target_delta": 0.05, + "minimum_active_state_agreement_rate": 0.8 + }, + "test_enters_diagnosis": false, + "trading_authority": "none" + }, + "reason": "cross-sectional-mode-has-no-temporal-window", + "test": null, + "validation": null + }, + "validation_net_sharpe": -2.656120004139166 + }, + "objective": { + "direction": "maximize", + "metric": "validation_net_sharpe", + "minimumImprovement": 0.05 + }, + "project": { + "id": "sample-research-desk", + "name": "Sample Research Desk" + }, + "schemaVersion": 1, + "startedAt": "2026-08-02T16:20:58.022613+00:00", + "status": "succeeded", + "study": { + "hash": "cd39867c134c489ad75e85f1d162206b6348a2292043e77b9f7db8b32467844d", + "id": "ohlcv-portfolio-quality", + "name": "OHLCV Portfolio Quality", + "programHash": "a15db6ab34a2bfbb30b990dc9ca523e4d60871ea5f0c54a8c54161b62d4cf9ab" + }, + "studyInputHash": "7ffa6ce4421fbb8db66fcbdacf16a5d88e2f0b8248979906b7b010cdc5dc1014", + "subject": { + "kind": "factor", + "name": "portfolio-factor", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourcePaths": [ + "factors/candidate.py" + ], + "version": "working" + }, + "summary": "Causal factor translated into constrained next-bar targets; validation net Sharpe=-2.656120" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/sources/factors/candidate.py b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/sources/factors/candidate.py new file mode 100644 index 0000000..6ee5eeb --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/sources/factors/candidate.py @@ -0,0 +1,128 @@ +"""Agent-editable baseline factor for the OHLCV Portfolio Lab.""" + +from __future__ import annotations + +import pandas as pd + + +AVAILABLE_FEATURE_INTERVALS = [] + +_BASE_COMPONENT = { + "base_momentum_10": { + "label": "10-base-bar momentum", + "role": "cross-sectional-score", + "intervals": ["base"], + "hypothesis": ( + "Recent relative strength persists over the next base bar." + ), + }, +} + +_FEATURE_COMPONENTS = { + "3h": ( + "momentum_3h_4", + 4, + { + "label": "Four completed 3-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["3h"], + "hypothesis": ( + "Short intraday trend persists beyond the latest completed " + "3-hour bar." + ), + }, + ), + "12h": ( + "momentum_12h_2", + 2, + { + "label": "Two completed 12-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["12h"], + "hypothesis": "Half-day trend filters noisy base-bar momentum.", + }, + ), + "1d": ( + "momentum_1d_3", + 3, + { + "label": "Three completed daily bars momentum", + "role": "cross-sectional-score", + "intervals": ["1d"], + "hypothesis": ( + "Multi-day relative strength persists at the next base close." + ), + }, + ), +} + +FACTOR_COMPONENTS = dict(_BASE_COMPONENT) +for _interval in AVAILABLE_FEATURE_INTERVALS: + if _interval in _FEATURE_COMPONENTS: + _name, _periods, _declaration = _FEATURE_COMPONENTS[_interval] + FACTOR_COMPONENTS[_name] = _declaration + + +def _completed_bar_return( + panel: pd.DataFrame, + interval: str, + periods: int, +) -> pd.Series: + close_column = f"close__{interval}" + bar_column = f"bar_close__{interval}" + if close_column not in panel or bar_column not in panel: + return pd.Series(float("nan"), index=panel.index, dtype=float) + completed = panel.loc[ + panel[bar_column].notna(), + ["asset", bar_column, close_column], + ].drop_duplicates(["asset", bar_column], keep="first") + completed["return"] = completed.groupby( + "asset", + sort=False, + )[close_column].pct_change(periods, fill_method=None) + lookup = completed.set_index( + ["asset", bar_column], + )["return"] + keys = pd.MultiIndex.from_frame( + panel.loc[:, ["asset", bar_column]], + ) + return pd.Series( + lookup.reindex(keys).to_numpy(dtype=float), + index=panel.index, + dtype=float, + ) + + +def compute_factor_components(panel: pd.DataFrame) -> pd.DataFrame: + """Declare causal components without changing downstream factor authority.""" + + components = { + "base_momentum_10": panel.groupby( + "asset", + sort=False, + )["close"].pct_change(10, fill_method=None), + } + for interval in AVAILABLE_FEATURE_INTERVALS: + if interval in _FEATURE_COMPONENTS: + name, periods, _declaration = _FEATURE_COMPONENTS[interval] + components[name] = _completed_bar_return( + panel, + interval, + periods, + ) + return pd.DataFrame(components, index=panel.index) + + +def compute_factor(panel: pd.DataFrame) -> pd.Series: + """Return causal relative multi-horizon momentum for construction. + + The fixed Judge owns signal state/hysteresis, conviction/volatility sizing, + target and executed weights, attribution, delay, drift, costs, benchmark, + splits, metrics, and stress tests. Change only this factor while testing + one falsifiable hypothesis at a time. + """ + + components = compute_factor_components(panel) + raw = components.mean(axis=1, skipna=True) + market_center = raw.groupby(panel["timestamp"], sort=False).transform("mean") + return (raw - market_center).rename("relative_multi_horizon_momentum") diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/stderr.txt b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/stderr.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/stdout.txt b/projects/sample-research-desk/runs/run-20260802T162058022613Z-db461a08e771/stdout.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/artifacts/policy-actions.csv b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/artifacts/policy-actions.csv new file mode 100644 index 0000000..5315898 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/artifacts/policy-actions.csv @@ -0,0 +1,781 @@ +fold,seed,split,timestamp,action,decision_eligible,decision_schedule_kind,decision_every_bars,decision_anchor,decision_session,reward,gross_return,net_return,one_way_turnover,cost,execution_risk_status,execution_risk_forecast_available,execution_risk_observations,pretrade_risk_forecast_annualized,executed_risk_forecast_annualized,execution_risk_ceiling_annualized,risk_rebalance_override,execution_reason,gross_exposure,proposed_one_way_turnover,constraint_rebalance_override,constraint_repair_one_way,executed_constraint_maximum_error 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"fb4efdfcd1533816ce67fdc58f6d924ffd750944126a8d64e0c57255a8b5e22a", + "ohlcv/FOXTROT.csv": "72f715db8f40793ffd1327def2d92c40adc6f9ca48b501b92a32cf744c7a23ec", + "ohlcv/README.md": "8911f42951ab7a291ebd3e8ad6193bdbc52e79418f00dbe4b2cda8b3d3a00277" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/factors/candidate.py b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/factors/candidate.py new file mode 100644 index 0000000..6ee5eeb --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/factors/candidate.py @@ -0,0 +1,128 @@ +"""Agent-editable baseline factor for the OHLCV Portfolio Lab.""" + +from __future__ import annotations + +import pandas as pd + + +AVAILABLE_FEATURE_INTERVALS = [] + +_BASE_COMPONENT = { + "base_momentum_10": { + "label": "10-base-bar momentum", + "role": "cross-sectional-score", + "intervals": ["base"], + "hypothesis": ( + "Recent relative strength persists over the next base bar." + ), + }, +} + +_FEATURE_COMPONENTS = { + "3h": ( + "momentum_3h_4", + 4, + { + "label": "Four completed 3-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["3h"], + "hypothesis": ( + "Short intraday trend persists beyond the latest completed " + "3-hour bar." + ), + }, + ), + "12h": ( + "momentum_12h_2", + 2, + { + "label": "Two completed 12-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["12h"], + "hypothesis": "Half-day trend filters noisy base-bar momentum.", + }, + ), + "1d": ( + "momentum_1d_3", + 3, + { + "label": "Three completed daily bars momentum", + "role": "cross-sectional-score", + "intervals": ["1d"], + "hypothesis": ( + "Multi-day relative strength persists at the next base close." + ), + }, + ), +} + +FACTOR_COMPONENTS = dict(_BASE_COMPONENT) +for _interval in AVAILABLE_FEATURE_INTERVALS: + if _interval in _FEATURE_COMPONENTS: + _name, _periods, _declaration = _FEATURE_COMPONENTS[_interval] + FACTOR_COMPONENTS[_name] = _declaration + + +def _completed_bar_return( + panel: pd.DataFrame, + interval: str, + periods: int, +) -> pd.Series: + close_column = f"close__{interval}" + bar_column = f"bar_close__{interval}" + if close_column not in panel or bar_column not in panel: + return pd.Series(float("nan"), index=panel.index, dtype=float) + completed = panel.loc[ + panel[bar_column].notna(), + ["asset", bar_column, close_column], + ].drop_duplicates(["asset", bar_column], keep="first") + completed["return"] = completed.groupby( + "asset", + sort=False, + )[close_column].pct_change(periods, fill_method=None) + lookup = completed.set_index( + ["asset", bar_column], + )["return"] + keys = pd.MultiIndex.from_frame( + panel.loc[:, ["asset", bar_column]], + ) + return pd.Series( + lookup.reindex(keys).to_numpy(dtype=float), + index=panel.index, + dtype=float, + ) + + +def compute_factor_components(panel: pd.DataFrame) -> pd.DataFrame: + """Declare causal components without changing downstream factor authority.""" + + components = { + "base_momentum_10": panel.groupby( + "asset", + sort=False, + )["close"].pct_change(10, fill_method=None), + } + for interval in AVAILABLE_FEATURE_INTERVALS: + if interval in _FEATURE_COMPONENTS: + name, periods, _declaration = _FEATURE_COMPONENTS[interval] + components[name] = _completed_bar_return( + panel, + interval, + periods, + ) + return pd.DataFrame(components, index=panel.index) + + +def compute_factor(panel: pd.DataFrame) -> pd.Series: + """Return causal relative multi-horizon momentum for construction. + + The fixed Judge owns signal state/hysteresis, conviction/volatility sizing, + target and executed weights, attribution, delay, drift, costs, benchmark, + splits, metrics, and stress tests. Change only this factor while testing + one falsifiable hypothesis at a time. + """ + + components = compute_factor_components(panel) + raw = components.mean(axis=1, skipna=True) + market_center = raw.groupby(panel["timestamp"], sort=False).transform("mean") + return (raw - market_center).rename("relative_multi_horizon_momentum") diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-claim.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-claim.json new file mode 100644 index 0000000..bca8f07 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-claim.json @@ -0,0 +1,16 @@ +{ + "authority": "quantitative-decision-support", + "claim": "novel-factor", + "id": "factor-claim-a002cb5137e5253b", + "kind": "autoquant-factor-claim", + "knownStyle": null, + "schemaVersion": 1, + "selectionAuthority": "validation-only", + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "testRole": "visible-audit", + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-population.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-population.json new file mode 100644 index 0000000..b19f0db --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/factor-population.json @@ -0,0 +1,43 @@ +{ + "assetPredictionRoles": { + "ALPHA": "prediction", + "BRAVO": "prediction", + "CHARLIE": "prediction", + "DELTA": "prediction", + "ECHO": "prediction", + "FOXTROT": "prediction" + }, + "authority": "factor-claim-complete-research-universe", + "claim": "novel-factor", + "contextAssets": [], + "evaluationAuthority": "factor-evaluation-only", + "evaluationMode": "cross-sectional", + "id": "factor-population-78adf037945d08ec", + "kind": "autoquant-factor-population", + "outcome": "forward-return", + "portfolioAuthority": "none", + "predictionAssets": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "relativeValuePair": null, + "researchUniverse": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "schemaVersion": 1, + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/portfolio-mandate.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/portfolio-mandate.json new file mode 100644 index 0000000..ca5cbde --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/portfolio-mandate.json @@ -0,0 +1,92 @@ +{ + "authority": "quantitative-decision-support", + "construction": { + "assetMaxAbsWeights": { + "ALPHA": 0.3, + "BRAVO": 0.3, + "CHARLIE": 0.3, + "DELTA": 0.3, + "ECHO": 0.3, + "FOXTROT": 0.3 + }, + "assetPositionRoles": { + "ALPHA": "two-sided", + "BRAVO": "two-sided", + "CHARLIE": "two-sided", + "DELTA": "two-sided", + "ECHO": "two-sided", + "FOXTROT": "two-sided" + }, + "benchmark": { + "asset": null, + "kind": "equal-weight-long-research-universe", + "source": "direction-default", + "weights": { + "ALPHA": 0.16666666666666666, + "BRAVO": 0.16666666666666666, + "CHARLIE": 0.16666666666666666, + "DELTA": 0.16666666666666666, + "ECHO": 0.16666666666666666, + "FOXTROT": 0.16666666666666666 + } + }, + "cashAllowed": true, + "family": "dollar-neutral", + "grossLimit": 1.0, + "longGrossLimit": 0.5, + "maxAbsWeight": 0.3, + "netRule": "zero", + "riskPolicy": { + "annualizationPeriods": 252, + "annualizedVolatilityCeiling": 0.15, + "covarianceWindow": 60, + "method": "trailing-covariance-volatility-ceiling-v1", + "minimumObservations": 20, + "scaleUp": false + }, + "shortAllowed": true, + "shortGrossLimit": 0.5 + }, + "contextAssets": [], + "id": "mandate-678fa6043016024d", + "implementationPolicy": { + "baseCostBps": 10.0, + "capacityModel": "trailing-dollar-volume-participation-v1", + "costModel": "linear-traded-notional-v1", + "decisionPolicy": { + "anchor": "dataset-start", + "bars": 1, + "kind": "every-bars", + "source": "reference-default" + }, + "noTradeOneWay": 0.05, + "referenceNav": 1000000.0 + }, + "kind": "autoquant-portfolio-mandate", + "researchUniverse": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "schemaVersion": 1, + "source": { + "assetPositionRoles": "direction-derived", + "benchmarkPolicy": "direction-default", + "direction": "research-only", + "kind": "template-default", + "portfolioPolicy": "reference-default", + "requestHash": null + }, + "tradableAssets": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/research-horizon.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/research-horizon.json new file mode 100644 index 0000000..f660e08 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/dependency-sources/strategies/research-horizon.json @@ -0,0 +1,24 @@ +{ + "authority": "quantitative-decision-support", + "diagnosticForwardBars": [ + 1, + 5, + 10 + ], + "id": "horizon-c3a43b664e7f6568", + "kind": "autoquant-research-horizon", + "primaryForwardBars": 1, + "schemaVersion": 1, + "selectionAuthority": { + "diagnostics": "context-only", + "primary": "validation-only" + }, + "source": { + "horizon": "reference decision-bar horizon", + "horizonPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "targetSemantics": "close-t-to-close-t-plus-n-decision-bars", + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/identity.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/identity.json new file mode 100644 index 0000000..32a1b12 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/identity.json @@ -0,0 +1,44 @@ +{ + "datasetHash": "0d740a8f0f285f69eae626fc1e76e9d60d57b42acd03842708ed3675570e97f7", + "datasetSourceHashes": { + "ohlcv/ALPHA.csv": "18724414024a549eff1f8fa509c922827d8d9e6d1e4c6bd322e99935801bfdb3", + "ohlcv/BRAVO.csv": "e7ff2864b8270b40c4327b3e83379b3225c2216d732aa4c256547ff85c395ee7", + "ohlcv/CHARLIE.csv": 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"d6dfb8b951e8816a396f346ffc073188d02142ecbaa097815817083dc3a1266d", + "sourceHashes": { + "models/candidate.py": "c96095b7e9ff1a45cd1b32c88565654722cc98b3d0ceb7fde0b5a18d2d3369a4" + }, + "studyHash": "554c8666ae72af7f45196d01a7834ed000d37368068538013f75ef68300eeec5", + "studyInputHash": "4f84736c6e8080fa7cdc2e2aae4e69ce8bff63c2a560b95bdfa1660a3b936413" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/ohlcv_rl_factor.py b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/ohlcv_rl_factor.py new file mode 100644 index 0000000..1809427 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/ohlcv_rl_factor.py @@ -0,0 +1,2623 @@ +"""Fixed governed RL factor-policy Judge for the reference laboratory.""" + +from __future__ import annotations + +import copy +import importlib +import json +import math +import os +from pathlib import Path +from typing import Any, Callable + +import numpy as np +import pandas as pd + +from autoquant.factor_claims import ( + FACTOR_CLAIM, + FORWARD_RETURN_OUTCOME, + factor_outcome, + load_factor_claim, +) +from autoquant.factor_runtime import ( + FactorRuntimeError, + build_factor_panel, + evaluate_factor, + factor_contract, + values_to_wide, +) +from autoquant.intervals import ( + IntervalContractError, + annualization_periods, + load_multi_interval_asset, + timestamp_label, +) +from autoquant.horizons import ( + RESEARCH_HORIZON, + load_research_horizon, +) +from autoquant.mandates import ( + PORTFOLIO_MANDATE, + load_portfolio_mandate, +) +from autoquant.prediction_modes import ( + FACTOR_POPULATION, + PredictionModeError, + load_factor_population, + resolve_prediction_population, + validate_population_mandate_compatibility, +) +from judges.portfolio_core import ( + build_risk_covariance_cache, + constraint_audit, + resolve_implementation_policy, + translate_factor_scores, +) +from judges.rl_core import ( + ACTIONS, + CONTEXTUAL_RIDGE_ITERATIONS, + DISCOUNT, + EPISODES, + EPSILON_END, + EPSILON_START, + EXPERTS, + FEATURE_ABS_LIMIT, + LEARNING_RATE, + POLICY_STATE_COLUMNS, + RISK_AVERSION, + SEEDS, + PolicyFailure, + build_action_targets, + build_policy_state, + build_raw_states, + chronological_folds, + compact_opportunity_rows, + fixed_selector, + one_step_action_opportunities, + q_selector, + ridge_selector, + rollout_metrics, + rollout_policy, + training_policy_net_sharpe, + train_contextual_ridge, + train_q_policy, +) + + +REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume") +MIN_OBSERVATIONS = 240 +MAX_FEATURES = 32 +FACTOR_OPPORTUNITY_POLICY = { + "method": "actual-pretrade-one-step-governed-action-audit-v1", + "path_propagation": "selected-policy-only", + "oracle_role": "ex-post-audit-upper-bound", + "selection_authority": "context-only", + "trading_authority": "none", +} +INCREMENTAL_ATTRIBUTION_POLICY = { + "method": "selected-baseline-full-path-active-attribution-v1", + "comparison_path": "independent-full-rollouts", + "baseline_selection": "validation-only-per-fold", + "test_role": "visible-diagnostic", + "selection_authority": "context-only", + "trading_authority": "none", +} +LEARNING_CONTRACT = { + "method": "fixed-after-train-only-blocked-stability-audit-v1", + "development_selection_scope": ( + "reference-fixture-outer-train-only-70/30-blocked" + ), + "candidate_configurations": 5, + "selection_order": [ + "maximize-minimum-seed-advantage-vs-contextual-ridge", + "maximize-mean-seed-advantage-vs-contextual-ridge", + "minimize-within-fold-seed-dispersion", + "minimize-pairwise-action-mismatch", + ], + "runtime_policy": "harness-fixed-before-study-validation", + "validation_role": "post-freeze-selection-evidence", + "test_role": "visible-diagnostic", + "trading_authority": "none", +} + + +class JudgeFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +class TrialFailures(JudgeFailure): + def __init__(self, failures: list[dict[str, Any]]): + self.failures = failures + super().__init__( + "policy.seed-failures", + f"{len(failures)} declared fold/seed trials failed", + ) + + +def _write_output(value: dict[str, Any]) -> None: + Path(os.environ["AUTOQUANT_RUN_OUTPUT"]).write_text( + json.dumps(value, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _load_contract() -> tuple[dict[str, Any], Path]: + study = json.loads( + Path(os.environ["AUTOQUANT_STUDY_PATH"]).read_text(encoding="utf-8") + ) + data_root = Path(os.environ["AUTOQUANT_DATA_ROOT"]).resolve() + if not data_root.is_dir(): + raise JudgeFailure("dataset.root", "AUTOQUANT_DATA_ROOT is not a directory") + return study, data_root + + +def _load_mandate() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / PORTFOLIO_MANDATE + try: + return load_portfolio_mandate(path) + except Exception as error: + raise JudgeFailure( + "mandate.invalid", + f"Invalid fixed Portfolio Mandate: {error}", + ) from error + + +def _load_factor_claim() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_CLAIM + try: + return load_factor_claim(path) + except Exception as error: + raise JudgeFailure( + "factor-claim.invalid", + f"Invalid fixed Factor claim: {error}", + ) from error + + +def _load_factor_population() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_POPULATION + try: + return load_factor_population(path) + except Exception as error: + raise JudgeFailure( + "factor-population.invalid", + f"Invalid fixed Factor population: {error}", + ) from error + + +def _load_horizon() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / RESEARCH_HORIZON + try: + return load_research_horizon(path) + except Exception as error: + raise JudgeFailure( + "horizon.invalid", + f"Invalid fixed Horizon Mandate: {error}", + ) from error + + +def _load_asset(data_root: Path, asset: str, start: str, end: str) -> pd.DataFrame: + try: + multi_interval = load_multi_interval_asset( + data_root, + asset, + start=start, + end=end, + ) + except IntervalContractError as error: + raise JudgeFailure(error.code, str(error)) from error + if multi_interval is not None: + if len(multi_interval) < MIN_OBSERVATIONS: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than {MIN_OBSERVATIONS} base observations", + ) + return multi_interval + source = (data_root / "ohlcv" / f"{asset}.csv").resolve() + if data_root not in source.parents or not source.is_file(): + raise JudgeFailure("dataset.asset", f"Missing confined OHLCV file for {asset}") + frame = pd.read_csv(source) + if tuple(frame.columns) != REQUIRED_COLUMNS: + raise JudgeFailure( + "dataset.columns", + f"{asset} columns must be exactly {', '.join(REQUIRED_COLUMNS)}", + ) + frame["timestamp"] = pd.to_datetime( + frame["timestamp"], + format="%Y-%m-%d", + errors="raise", + ) + if ( + frame["timestamp"].duplicated().any() + or not frame["timestamp"].is_monotonic_increasing + ): + raise JudgeFailure( + "dataset.time-order", + f"{asset} timestamps must be unique and chronological", + ) + for column in REQUIRED_COLUMNS[1:]: + frame[column] = pd.to_numeric(frame[column], errors="raise") + numeric = frame[list(REQUIRED_COLUMNS[1:])].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise JudgeFailure("dataset.non-finite", f"{asset} contains non-finite OHLCV") + if (frame[["open", "high", "low", "close", "volume"]] <= 0).any().any(): + raise JudgeFailure("dataset.non-positive", f"{asset} contains non-positive OHLCV") + if ( + (frame["high"] < frame[["open", "close"]].max(axis=1)).any() + or (frame["low"] > frame[["open", "close"]].min(axis=1)).any() + ): + raise JudgeFailure("dataset.bar-shape", f"{asset} contains invalid bars") + selected = frame[ + (frame["timestamp"] >= pd.Timestamp(start)) + & (frame["timestamp"] <= pd.Timestamp(end)) + ].copy() + if len(selected) < MIN_OBSERVATIONS: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than {MIN_OBSERVATIONS} observations", + ) + return selected.reset_index(drop=True) + + +def _candidate_encoder( + module: Any, +) -> tuple[list[str], Callable[[dict[str, float]], np.ndarray]]: + raw_names = getattr(module, "FEATURE_NAMES", None) + if ( + not isinstance(raw_names, (tuple, list)) + or not raw_names + or len(raw_names) > MAX_FEATURES + or any(not isinstance(name, str) or not name for name in raw_names) + or len(set(raw_names)) != len(raw_names) + ): + raise JudgeFailure( + "policy.features", + f"FEATURE_NAMES must contain 1 to {MAX_FEATURES} unique strings", + ) + names = list(raw_names) + candidate = getattr(module, "encode_state", None) + if not callable(candidate): + raise JudgeFailure( + "policy.api", + "models.candidate must export callable encode_state(state)", + ) + + def encode(state: dict[str, float]) -> np.ndarray: + before = copy.deepcopy(state) + try: + first = candidate(state) + second = candidate(state) + except Exception as error: + raise PolicyFailure( + "policy.encoder", + f"encode_state raised {type(error).__name__}: {error}", + ) from error + if state != before: + raise PolicyFailure( + "policy.mutation", + "encode_state mutated the fixed causal state", + ) + if not isinstance(first, (tuple, list, np.ndarray)) or not isinstance( + second, + (tuple, list, np.ndarray), + ): + raise PolicyFailure( + "policy.type", + "encode_state must return a list, tuple, or numpy array", + ) + try: + first_array = np.asarray(first, dtype=float) + second_array = np.asarray(second, dtype=float) + except (TypeError, ValueError) as error: + raise PolicyFailure( + "policy.numeric", + f"Encoded features must be numeric: {error}", + ) from error + if first_array.shape != (len(names),) or second_array.shape != ( + len(names), + ): + raise PolicyFailure( + "policy.alignment", + "Encoded vector length must exactly match FEATURE_NAMES", + ) + if ( + not np.isfinite(first_array).all() + or not np.isfinite(second_array).all() + ): + raise PolicyFailure( + "policy.non-finite", + "Encoded state contains a non-finite feature", + ) + if ( + np.abs(first_array).max() > FEATURE_ABS_LIMIT + or np.abs(second_array).max() > FEATURE_ABS_LIMIT + ): + raise PolicyFailure( + "policy.bounds", + f"Encoded features must be within ±{FEATURE_ABS_LIMIT:g}", + ) + if not np.array_equal(first_array, second_array): + raise PolicyFailure( + "policy.nondeterministic", + "encode_state returned different values for one fixed state", + ) + return first_array + + return names, encode + + +def _panels( + study: dict[str, Any], + data_root: Path, +) -> tuple[dict[str, pd.DataFrame], pd.DataFrame, pd.DataFrame, pd.DataFrame]: + dataset = study["dataset"] + time_range = dataset["time_range"] + frames: dict[str, pd.DataFrame] = {} + opens: dict[str, pd.Series] = {} + closes: dict[str, pd.Series] = {} + volumes: dict[str, pd.Series] = {} + for asset in dataset["universe"]: + frame = _load_asset( + data_root, + asset, + time_range["start"], + time_range["end"], + ) + frames[asset] = frame + index = pd.DatetimeIndex(frame["timestamp"]) + for target, column in ( + (opens, "open"), + (closes, "close"), + (volumes, "volume"), + ): + values = frame[column].astype(float) + values.index = index + target[asset] = values + return frames, pd.DataFrame(opens), pd.DataFrame(closes), pd.DataFrame(volumes) + + +def _factor_panels( + opens: pd.DataFrame, + closes: pd.DataFrame, + volumes: pd.DataFrame, + candidate: pd.DataFrame, +) -> dict[str, pd.DataFrame]: + return { + "candidate": candidate, + "activity": np.log( + volumes + / volumes.rolling(20, min_periods=20).mean() + ), + "intraday": closes / opens - 1.0, + "reversal": -closes.pct_change(fill_method=None), + } + + +def _fixed_baselines( + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + split: dict[str, pd.Index], + mandate: dict[str, Any], + risk_covariance_cache, +) -> tuple[dict[str, Any], dict[str, Any], str, dict[str, Any]]: + fixed: dict[str, Any] = {} + fixed_rollouts: dict[str, dict[str, Any]] = {} + training_scores: dict[str, float] = {} + for action in ACTIONS: + training_scores[action] = training_policy_net_sharpe( + fixed_selector(action), + raw_states, + action_targets, + closes, + split["train"], + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + fixed_rollouts[action] = { + name: rollout_policy( + fixed_selector(action), + raw_states, + action_targets, + closes, + volumes, + index, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + for name, index in ( + ("validation", split["validation"]), + ("test", split["test"]), + ) + } + fixed[action] = { + name: rollout_metrics(rollout) + for name, rollout in fixed_rollouts[action].items() + } + selected_action = max(training_scores, key=training_scores.__getitem__) + ridge_model = train_contextual_ridge( + raw_states, + action_targets, + closes, + volumes, + split["train"], + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + ridge_rollouts = { + name: rollout_policy( + ridge_selector(ridge_model), + raw_states, + action_targets, + closes, + volumes, + index, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + for name, index in ( + ("validation", split["validation"]), + ("test", split["test"]), + ) + } + ridge = { + name: rollout_metrics(rollout) + for name, rollout in ridge_rollouts.items() + } + result = { + "fixed_factor_or_blend": fixed, + "best_training_expert": { + "selected": selected_action, + "training_net_sharpe": training_scores, + "validation": fixed[selected_action]["validation"], + "test": fixed[selected_action]["test"], + }, + "contextual_ridge": ridge, + } + validation_candidates = { + **{ + f"fixed:{action}": float(metrics["validation"]["net"]["sharpe"]) + for action, metrics in fixed.items() + }, + "best-training-expert": float( + result["best_training_expert"]["validation"]["net"]["sharpe"] + ), + "contextual-ridge": float(ridge["validation"]["net"]["sharpe"]), + } + best_name = max(validation_candidates, key=validation_candidates.__getitem__) + if best_name.startswith("fixed:"): + selected_rollouts = fixed_rollouts[best_name.split(":", 1)[1]] + elif best_name == "best-training-expert": + selected_rollouts = fixed_rollouts[selected_action] + else: + selected_rollouts = ridge_rollouts + return result, ridge_model, best_name, selected_rollouts + + +def _baseline_split( + baselines: dict[str, Any], + name: str, + split: str, +) -> dict[str, Any]: + if name.startswith("fixed:"): + return baselines["fixed_factor_or_blend"][name.split(":", 1)[1]][split] + if name == "best-training-expert": + return baselines["best_training_expert"][split] + return baselines["contextual_ridge"][split] + + +def _rollout_action_rows( + fold: str, + seed: int, + split: str, + rollout, +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for timestamp in rollout.actions.index: + daily = rollout.simulation.daily.loc[timestamp] + rows.append( + { + "fold": fold, + "seed": seed, + "split": split, + "timestamp": timestamp_label(timestamp), + "action": rollout.actions.loc[timestamp], + "decision_eligible": bool( + daily["decision_eligible"] + ), + "decision_schedule_kind": str( + daily["decision_schedule_kind"] + ), + "decision_every_bars": daily["decision_every_bars"], + "decision_anchor": daily["decision_anchor"], + "decision_session": str(daily["decision_session"]), + "reward": float(daily["reward"]), + "gross_return": float(daily["gross_return"]), + "net_return": float(daily["net_return"]), + "one_way_turnover": float(daily["one_way_turnover"]), + "cost": float(daily["cost"]), + "execution_risk_status": str( + daily["execution_risk_status"] + ), + "execution_risk_forecast_available": bool( + daily["execution_risk_forecast_available"] + ), + "execution_risk_observations": int( + daily["execution_risk_observations"] + ), + "pretrade_risk_forecast_annualized": float( + daily["pretrade_risk_forecast_annualized"] + ), + "executed_risk_forecast_annualized": float( + daily["executed_risk_forecast_annualized"] + ), + "execution_risk_ceiling_annualized": float( + daily["execution_risk_ceiling_annualized"] + ), + "risk_rebalance_override": bool( + daily["risk_rebalance_override"] + ), + "execution_reason": str(daily["execution_reason"]), + "gross_exposure": float(daily["gross_exposure"]), + "proposed_one_way_turnover": float( + daily["proposed_one_way_turnover"] + ), + "constraint_rebalance_override": bool( + daily["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + daily["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + daily["executed_constraint_maximum_error"] + ), + } + ) + return rows + + +def _rollout_rationale_rows( + fold: str, + seed: int, + split: str, + rollout, + encoder: Callable[[dict[str, float]], np.ndarray], + feature_names: list[str], + weights: np.ndarray, +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + previous_action = "balanced" + for timestamp in rollout.actions.index: + daily = rollout.simulation.daily.loc[timestamp] + decision_eligible = bool(daily["decision_eligible"]) + state = { + field: float(rollout.states.loc[timestamp, field]) + for field in POLICY_STATE_COLUMNS + } + if not math.isclose( + state[f"previous_{previous_action}"], + 1.0, + rel_tol=0.0, + abs_tol=1e-12, + ): + raise JudgeFailure( + "policy.rationale-state", + "Rollout state previous action does not reconcile", + ) + encoded = encoder(state) + q_values = weights @ encoded + ranked = sorted( + range(len(ACTIONS)), + key=lambda index: (-float(q_values[index]), index), + ) + actual_action = str(rollout.actions.loc[timestamp]) + if decision_eligible: + selected_index, runner_up_index = ranked[:2] + selection_reason = "q-argmax" + else: + selected_index = ACTIONS.index(actual_action) + runner_up_index = next( + index + for index in ranked + if index != selected_index + ) + selection_reason = "decision-schedule-hold" + selected_action = ACTIONS[selected_index] + runner_up_action = ACTIONS[runner_up_index] + if selected_action != actual_action: + raise JudgeFailure( + "policy.rationale-action", + "Rationale availability differs from the policy rollout", + ) + contributions = ( + weights[selected_index] - weights[runner_up_index] + ) * encoded + margin = float( + q_values[selected_index] - q_values[runner_up_index] + ) + if not math.isclose( + float(contributions.sum()), + margin, + rel_tol=0.0, + abs_tol=1e-10, + ): + raise JudgeFailure( + "policy.rationale-contribution", + "Feature contributions do not reproduce the Q margin", + ) + dominant_index = max( + range(len(feature_names)), + key=lambda index: (abs(float(contributions[index])), -index), + ) + row = { + "fold": fold, + "seed": seed, + "split": split, + "timestamp": timestamp_label(timestamp), + "decisionEligible": decision_eligible, + "decisionSchedule": ( + {"kind": "calendar-month-end"} + if daily["decision_schedule_kind"] + == "calendar-month-end" + else { + "kind": "every-bars", + "bars": int(daily["decision_every_bars"]), + "anchor": str(daily["decision_anchor"]), + } + ), + "decisionSession": str(daily["decision_session"]), + "selectionReason": selection_reason, + "previousAction": previous_action, + "selectedAction": selected_action, + "runnerUpAction": runner_up_action, + "rawState": { + field: float(state[field]) + for field in POLICY_STATE_COLUMNS + }, + "encodedFeatures": { + name: float(encoded[index]) + for index, name in enumerate(feature_names) + }, + "qValues": { + action: float(q_values[index]) + for index, action in enumerate(ACTIONS) + }, + "actionMargin": margin, + "tieForBest": abs(margin) <= 1e-12, + "marginContributions": { + name: float(contributions[index]) + for index, name in enumerate(feature_names) + }, + "dominantMarginFeature": feature_names[dominant_index], + "dominantMarginContribution": float( + contributions[dominant_index] + ), + } + numeric = [ + *row["rawState"].values(), + *row["encodedFeatures"].values(), + *row["qValues"].values(), + row["actionMargin"], + *row["marginContributions"].values(), + row["dominantMarginContribution"], + ] + if not all(math.isfinite(float(value)) for value in numeric): + raise JudgeFailure( + "policy.rationale-non-finite", + "Policy rationale contains non-finite evidence", + ) + rows.append(row) + previous_action = selected_action + return rows + + +def _rollout_opportunity_rows( + fold: str, + seed: int, + split: str, + rollout, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + mandate: dict[str, Any], + risk_covariance_cache, +) -> list[dict[str, Any]]: + rows = one_step_action_opportunities( + rollout, + action_targets, + closes, + volumes, + rollout.actions.index, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + return [ + { + **row, + "fold": fold, + "seed": seed, + "split": split, + "timestamp": timestamp_label(row["timestamp"]), + } + for row in rows + ] + + +def _rollout_incremental_rows( + fold: str, + seed: int, + split: str, + baseline_name: str, + policy_rollout, + baseline_rollout, + raw_states: pd.DataFrame, + closes: pd.DataFrame, + volatility_threshold: float, +) -> list[dict[str, Any]]: + index = policy_rollout.actions.index + if ( + not baseline_rollout.actions.index.equals(index) + or not policy_rollout.simulation.daily.index.equals(index) + or not baseline_rollout.simulation.daily.index.equals(index) + or not policy_rollout.simulation.weights.index.equals(index) + or not baseline_rollout.simulation.weights.index.equals(index) + ): + raise JudgeFailure( + "policy.incremental-identity", + "Policy and selected baseline paths do not share one chronology", + ) + forward_returns = closes.shift(-1) / closes - 1.0 + rows: list[dict[str, Any]] = [] + previous_policy_action: str | None = None + previous_baseline_action: str | None = None + for timestamp in index: + policy_daily = policy_rollout.simulation.daily.loc[timestamp] + baseline_daily = baseline_rollout.simulation.daily.loc[timestamp] + policy_action = str(policy_rollout.actions.loc[timestamp]) + baseline_action = str(baseline_rollout.actions.loc[timestamp]) + forward = forward_returns.loc[timestamp].fillna(0.0) + contribution = ( + policy_rollout.simulation.weights.loc[timestamp] + - baseline_rollout.simulation.weights.loc[timestamp] + ) * forward + gross_delta = float( + policy_daily["gross_return"] - baseline_daily["gross_return"] + ) + incremental_cost = float( + policy_daily["cost"] - baseline_daily["cost"] + ) + net_delta = float( + policy_daily["net_return"] - baseline_daily["net_return"] + ) + contribution_error = abs(float(contribution.sum()) - gross_delta) + identity_error = abs(gross_delta - incremental_cost - net_delta) + if max(contribution_error, identity_error) > 1e-12: + raise JudgeFailure( + "policy.incremental-reconciliation", + "Incremental portfolio attribution does not reconcile", + ) + raw = raw_states.loc[timestamp] + volume_value = float(raw["volume_regime"]) + trend_value = float(raw["market_return_5"]) + volatility_value = float(raw["market_volatility_20"]) + row = { + "fold": fold, + "seed": seed, + "split": split, + "timestamp": timestamp_label(timestamp), + "baselineName": baseline_name, + "policyAction": policy_action, + "baselineAction": baseline_action, + "policySwitched": ( + previous_policy_action is not None + and policy_action != previous_policy_action + ), + "baselineSwitched": ( + previous_baseline_action is not None + and baseline_action != previous_baseline_action + ), + "actionsDiffer": policy_action != baseline_action, + "volumeRegimeValue": volume_value, + "marketReturn5": trend_value, + "marketVolatility20": volatility_value, + "volumeRegime": ( + "below-trend" if volume_value < 0.0 else "above-trend" + ), + "marketTrend": ( + "negative" if trend_value < 0.0 else "nonnegative" + ), + "volatilityRegime": ( + "low" + if volatility_value < volatility_threshold + else "high" + ), + "policyGrossReturn": float(policy_daily["gross_return"]), + "baselineGrossReturn": float(baseline_daily["gross_return"]), + "grossActiveReturn": gross_delta, + "policyNetReturn": float(policy_daily["net_return"]), + "baselineNetReturn": float(baseline_daily["net_return"]), + "incrementalCost": incremental_cost, + "netActiveReturn": net_delta, + "policyReward": float(policy_daily["reward"]), + "baselineReward": float(baseline_daily["reward"]), + "rewardDelta": float( + policy_daily["reward"] - baseline_daily["reward"] + ), + "policyOneWayTurnover": float( + policy_daily["one_way_turnover"] + ), + "baselineOneWayTurnover": float( + baseline_daily["one_way_turnover"] + ), + "oneWayTurnoverDelta": float( + policy_daily["one_way_turnover"] + - baseline_daily["one_way_turnover"] + ), + "assetGrossContribution": { + asset: float(contribution[asset]) + for asset in closes.columns + }, + } + numeric = [ + value + for key, value in row.items() + if isinstance(value, (int, float)) + and not isinstance(value, bool) + and key != "seed" + ] + if not all(math.isfinite(float(value)) for value in numeric): + raise JudgeFailure( + "policy.incremental-non-finite", + "Incremental attribution contains non-finite values", + ) + rows.append(row) + previous_policy_action = policy_action + previous_baseline_action = baseline_action + return rows + + +def _relative_path_statistics( + rows: list[dict[str, Any]], +) -> dict[str, float]: + active = np.asarray( + [float(row["netActiveReturn"]) for row in rows], + dtype=float, + ) + policy = np.asarray( + [float(row["policyNetReturn"]) for row in rows], + dtype=float, + ) + baseline = np.asarray( + [float(row["baselineNetReturn"]) for row in rows], + dtype=float, + ) + periods = annualization_periods( + pd.to_datetime([row["timestamp"] for row in rows]) + ) + annualized_active_return = float(active.mean() * periods) + tracking_error = float(active.std(ddof=0) * math.sqrt(periods)) + relative_path = np.cumprod((1.0 + policy) / (1.0 + baseline)) + running_peak = np.maximum.accumulate( + np.maximum(relative_path, 1.0) + ) + relative_drawdown = relative_path / running_peak - 1.0 + return { + "annualized_active_return": annualized_active_return, + "annualized_tracking_error": tracking_error, + "information_ratio": ( + annualized_active_return / tracking_error + if tracking_error > 1e-15 + else 0.0 + ), + "relative_total_return": float(relative_path[-1] - 1.0), + "relative_maximum_drawdown": float(relative_drawdown.min()), + } + + +def _incremental_bucket( + rows: list[dict[str, Any]], +) -> dict[str, Any]: + net = [float(row["netActiveReturn"]) for row in rows] + active = [value for value in net if abs(value) > 1e-12] + return { + "decisions": len(rows), + "active_decisions": len(active), + "active_decision_rate": len(active) / len(rows), + "mean_gross_active_return": float( + np.mean([row["grossActiveReturn"] for row in rows]) + ), + "total_gross_active_return": float( + sum(row["grossActiveReturn"] for row in rows) + ), + "total_incremental_cost": float( + sum(row["incrementalCost"] for row in rows) + ), + "mean_net_active_return": float(np.mean(net)), + "total_net_active_return": float(sum(net)), + "active_win_rate": float(np.mean([value > 0.0 for value in net])), + "conditional_active_win_rate": ( + float(np.mean([value > 0.0 for value in active])) + if active + else 0.0 + ), + } + + +def _incremental_attribution_metrics( + rows: list[dict[str, Any]], + split: str, + assets: list[str], +) -> dict[str, Any]: + selected = [row for row in rows if row["split"] == split] + if not selected: + raise JudgeFailure( + "policy.incremental-coverage", + "Incremental attribution split has no decisions", + ) + groups: dict[tuple[str, int], list[dict[str, Any]]] = {} + for row in selected: + groups.setdefault((row["fold"], int(row["seed"])), []).append(row) + base = _incremental_bucket(selected) + net = [float(row["netActiveReturn"]) for row in selected] + path_stats = [_relative_path_statistics(group) for group in groups.values()] + by_asset = { + asset: { + "total_gross_active_contribution": float( + sum(row["assetGrossContribution"][asset] for row in selected) + ), + "mean_trial_total_gross_active_contribution": float( + sum(row["assetGrossContribution"][asset] for row in selected) + / len(groups) + ), + "mean_gross_active_contribution": float( + np.mean( + [ + row["assetGrossContribution"][asset] + for row in selected + ] + ) + ), + } + for asset in assets + } + regime_fields = { + "volume": "volumeRegime", + "trend": "marketTrend", + "volatility": "volatilityRegime", + } + by_regime: dict[str, dict[str, Any]] = {} + regime_counts: dict[str, int] = {} + for dimension, field in regime_fields.items(): + buckets = sorted({str(row[field]) for row in selected}) + regime_counts[dimension] = 0 + for bucket in buckets: + group = [row for row in selected if row[field] == bucket] + by_regime[f"{dimension}:{bucket}"] = { + "dimension": dimension, + "bucket": bucket, + **_incremental_bucket(group), + } + regime_counts[dimension] += len(group) + by_action_pair: dict[str, dict[str, Any]] = {} + for policy_action, baseline_action in sorted( + { + (str(row["policyAction"]), str(row["baselineAction"])) + for row in selected + } + ): + group = [ + row + for row in selected + if row["policyAction"] == policy_action + and row["baselineAction"] == baseline_action + ] + by_action_pair[f"{policy_action}->{baseline_action}"] = { + "policy_action": policy_action, + "baseline_action": baseline_action, + **_incremental_bucket(group), + } + by_switch_state: dict[str, dict[str, Any]] = {} + for policy_switched in (False, True): + for baseline_switched in (False, True): + group = [ + row + for row in selected + if bool(row["policySwitched"]) == policy_switched + and bool(row["baselineSwitched"]) == baseline_switched + ] + if not group: + continue + key = ( + ("policy-switch" if policy_switched else "policy-hold") + + "/" + + ( + "baseline-switch" + if baseline_switched + else "baseline-hold" + ) + ) + by_switch_state[key] = { + "policy_switched": policy_switched, + "baseline_switched": baseline_switched, + **_incremental_bucket(group), + } + trial_metrics = [] + for (fold, seed), group in groups.items(): + trial_metrics.append( + { + "fold": fold, + "seed": seed, + "baseline_name": str(group[0]["baselineName"]), + "observations": len(group), + **_incremental_bucket(group), + **_relative_path_statistics(group), + } + ) + reconciliation = { + "row_count": len(selected), + "trial_path_count": len(groups), + "gross_cost_net_error": max( + abs( + float(row["grossActiveReturn"]) + - float(row["incrementalCost"]) + - float(row["netActiveReturn"]) + ) + for row in selected + ), + "asset_gross_error": max( + abs( + sum(row["assetGrossContribution"].values()) + - float(row["grossActiveReturn"]) + ) + for row in selected + ), + "asset_total_error": abs( + sum( + value["total_gross_active_contribution"] + for value in by_asset.values() + ) + - base["total_gross_active_return"] + ), + "action_pair_count_error": abs( + sum(value["decisions"] for value in by_action_pair.values()) + - len(selected) + ), + "switch_state_count_error": abs( + sum(value["decisions"] for value in by_switch_state.values()) + - len(selected) + ), + "regime_count_error": max( + abs(count - len(selected)) for count in regime_counts.values() + ), + } + reconciliation["passed"] = ( + max( + float(value) + for key, value in reconciliation.items() + if key not in {"row_count", "trial_path_count"} + ) + <= 1e-10 + ) + return { + "status": "available", + "decisions": len(selected), + "trial_paths": len(groups), + **base, + "mean_trial_total_gross_active_return": float( + np.mean( + [ + item["total_gross_active_return"] + for item in trial_metrics + ] + ) + ), + "mean_trial_total_incremental_cost": float( + np.mean( + [item["total_incremental_cost"] for item in trial_metrics] + ) + ), + "mean_trial_total_net_active_return": float( + np.mean( + [item["total_net_active_return"] for item in trial_metrics] + ) + ), + "annualized_active_return": float( + np.mean( + [item["annualized_active_return"] for item in path_stats] + ) + ), + "annualized_tracking_error": float( + np.mean( + [item["annualized_tracking_error"] for item in path_stats] + ) + ), + "information_ratio": float( + np.mean([item["information_ratio"] for item in path_stats]) + ), + "relative_total_return": float( + np.mean([item["relative_total_return"] for item in path_stats]) + ), + "relative_maximum_drawdown": float( + np.mean( + [item["relative_maximum_drawdown"] for item in path_stats] + ) + ), + "p05_net_active_return": _linear_percentile(net, 0.05), + "median_net_active_return": float(np.median(net)), + "p95_net_active_return": _linear_percentile(net, 0.95), + "worst_net_active_return": float(min(net)), + "best_net_active_return": float(max(net)), + "actions_differ_rate": float( + np.mean([row["actionsDiffer"] for row in selected]) + ), + "policy_switch_rate": float( + np.mean([row["policySwitched"] for row in selected]) + ), + "baseline_switch_rate": float( + np.mean([row["baselineSwitched"] for row in selected]) + ), + "by_asset": by_asset, + "by_regime": by_regime, + "by_action_pair": by_action_pair, + "by_switch_state": by_switch_state, + "trials": trial_metrics, + "reconciliation": reconciliation, + } + + +def _linear_percentile(values: list[float], percentile: float) -> float: + ordered = sorted(float(value) for value in values) + position = (len(ordered) - 1) * percentile + lower = int(math.floor(position)) + upper = int(math.ceil(position)) + if lower == upper: + return ordered[lower] + weight = position - lower + return ordered[lower] * (1.0 - weight) + ordered[upper] * weight + + +def _factor_opportunity_metrics( + rows: list[dict[str, Any]], + split: str, +) -> dict[str, Any]: + selected = [row for row in rows if row["split"] == split] + if not selected: + raise JudgeFailure( + "policy.opportunity-coverage", + "Factor opportunity split has no decisions", + ) + groups: dict[tuple[str, int], list[dict[str, Any]]] = {} + for row in selected: + groups.setdefault((row["fold"], int(row["seed"])), []).append(row) + decisions = len(selected) + regrets = [float(row["realizedRegret"]) for row in selected] + selected_rewards = [float(row["selectedReward"]) for row in selected] + oracle_rewards = [float(row["oracleReward"]) for row in selected] + selected_ranks = [int(row["selectedRank"]) for row in selected] + oracle_hits = sum(bool(row["oracleHit"]) for row in selected) + by_action: dict[str, dict[str, Any]] = {} + for action in ACTIONS: + selected_count = sum( + row["selectedAction"] == action for row in selected + ) + oracle_count = sum(row["oracleAction"] == action for row in selected) + evidence = [row["actions"][action] for row in selected] + by_action[action] = { + "selected_decisions": selected_count, + "selected_frequency": selected_count / decisions, + "oracle_decisions": oracle_count, + "oracle_frequency": oracle_count / decisions, + "mean_local_reward": float( + np.mean([item["reward"] for item in evidence]) + ), + "mean_one_way_turnover": float( + np.mean([item["oneWayTurnover"] for item in evidence]) + ), + "total_cost": float(sum(item["cost"] for item in evidence)), + "risk_repair_rate": float( + np.mean([item["riskRebalanceOverride"] for item in evidence]) + ), + } + candidate_oracle = [ + row for row in selected if row["oracleAction"] == "candidate" + ] + candidate_missed = [ + row + for row in candidate_oracle + if row["selectedAction"] != "candidate" + ] + candidate_captured = len(candidate_oracle) - len(candidate_missed) + candidate_vs_selected = [ + float(row["candidateMinusSelectedReward"]) for row in selected + ] + candidate_vs_balanced = [ + float(row["candidateMinusBalancedReward"]) for row in selected + ] + trials = [] + for (fold, seed), group in groups.items(): + group_candidate_oracle = sum( + row["oracleAction"] == "candidate" for row in group + ) + group_candidate_missed = sum( + row["oracleAction"] == "candidate" + and row["selectedAction"] != "candidate" + for row in group + ) + trials.append( + { + "fold": fold, + "seed": seed, + "decisions": len(group), + "oracle_hits": sum(row["oracleHit"] for row in group), + "oracle_hit_rate": float( + np.mean([row["oracleHit"] for row in group]) + ), + "mean_selected_rank": float( + np.mean([row["selectedRank"] for row in group]) + ), + "mean_realized_regret": float( + np.mean([row["realizedRegret"] for row in group]) + ), + "candidate_oracle_rate": ( + group_candidate_oracle / len(group) + ), + "candidate_missed_opportunity_rate": ( + group_candidate_missed / len(group) + ), + } + ) + reconciliation = { + "decision_rows": decisions, + "action_evaluations": sum( + len(row["actions"]) for row in selected + ), + "action_evaluation_count_error": abs( + sum(len(row["actions"]) for row in selected) + - decisions * len(ACTIONS) + ), + "selected_frequency_error": abs( + sum(item["selected_frequency"] for item in by_action.values()) + - 1.0 + ), + "oracle_frequency_error": abs( + sum(item["oracle_frequency"] for item in by_action.values()) + - 1.0 + ), + "regret_identity_error": max( + abs( + float(row["oracleReward"]) + - float(row["selectedReward"]) + - float(row["realizedRegret"]) + ) + for row in selected + ), + "candidate_selected_delta_error": max( + abs( + float(row["actions"]["candidate"]["reward"]) + - float(row["selectedReward"]) + - float(row["candidateMinusSelectedReward"]) + ) + for row in selected + ), + "candidate_balanced_delta_error": max( + abs( + float(row["actions"]["candidate"]["reward"]) + - float(row["actions"]["balanced"]["reward"]) + - float(row["candidateMinusBalancedReward"]) + ) + for row in selected + ), + "negative_regret_error": max( + max(0.0, -float(row["realizedRegret"])) + for row in selected + ), + "selected_action_reconciliation_error": 0.0, + } + reconciliation["passed"] = ( + reconciliation["action_evaluations"] + == decisions * len(ACTIONS) + and max( + float(value) + for key, value in reconciliation.items() + if key + not in { + "decision_rows", + "action_evaluations", + } + ) + <= 1e-10 + ) + return { + "status": "available", + "decisions": decisions, + "trial_paths": len(groups), + "oracle_hit_decisions": oracle_hits, + "oracle_hit_rate": oracle_hits / decisions, + "mean_selected_rank": float(np.mean(selected_ranks)), + "mean_selected_reward": float(np.mean(selected_rewards)), + "mean_oracle_reward": float(np.mean(oracle_rewards)), + "total_realized_regret": float(sum(regrets)), + "mean_realized_regret": float(np.mean(regrets)), + "median_realized_regret": float(np.median(regrets)), + "p90_realized_regret": _linear_percentile(regrets, 0.90), + "maximum_realized_regret": float(max(regrets)), + "positive_regret_rate": float( + np.mean([regret > 1e-12 for regret in regrets]) + ), + "by_action": by_action, + "candidate": { + "selected_decisions": by_action["candidate"][ + "selected_decisions" + ], + "selected_frequency": by_action["candidate"][ + "selected_frequency" + ], + "oracle_decisions": len(candidate_oracle), + "oracle_frequency": len(candidate_oracle) / decisions, + "captured_oracle_decisions": candidate_captured, + "oracle_capture_rate": ( + candidate_captured / len(candidate_oracle) + if candidate_oracle + else 0.0 + ), + "missed_opportunity_decisions": len(candidate_missed), + "missed_opportunity_rate": len(candidate_missed) / decisions, + "mean_reward": by_action["candidate"]["mean_local_reward"], + "mean_vs_selected_reward": float( + np.mean(candidate_vs_selected) + ), + "mean_vs_balanced_reward": float( + np.mean(candidate_vs_balanced) + ), + "win_rate_vs_balanced": float( + np.mean([value > 1e-12 for value in candidate_vs_balanced]) + ), + }, + "trials": trials, + "reconciliation": reconciliation, + } + + +def _policy_behavior_metrics( + rationale_rows: list[dict[str, Any]], + action_rows: list[dict[str, Any]], + split: str, + feature_names: list[str], +) -> dict[str, Any]: + selected_rationales = [ + row for row in rationale_rows if row["split"] == split + ] + selected_actions = [ + row for row in action_rows if row["split"] == split + ] + action_by_key = { + ( + row["fold"], + int(row["seed"]), + row["split"], + row["timestamp"], + ): row + for row in selected_actions + } + if ( + not selected_rationales + or len(action_by_key) != len(selected_actions) + or len(selected_rationales) != len(selected_actions) + ): + raise JudgeFailure( + "policy.rationale-coverage", + "Policy rationale and action coverage differ", + ) + groups: dict[tuple[str, int], list[dict[str, Any]]] = {} + for row in selected_rationales: + key = (row["fold"], int(row["seed"])) + groups.setdefault(key, []).append(row) + action = action_by_key.get( + ( + row["fold"], + int(row["seed"]), + row["split"], + row["timestamp"], + ) + ) + if action is None or action["action"] != row["selectedAction"]: + raise JudgeFailure( + "policy.rationale-action", + "Policy rationale differs from the action ledger", + ) + + run_lengths: list[int] = [] + runs_by_action = {action: [] for action in ACTIONS} + transitions = 0 + comparable_decisions = 0 + trial_metrics: list[dict[str, Any]] = [] + for (fold, seed), rows in groups.items(): + lengths: list[int] = [] + current_action = rows[0]["selectedAction"] + current_length = 0 + trial_transitions = 0 + for row in rows: + if row["selectedAction"] != current_action: + lengths.append(current_length) + runs_by_action[current_action].append(current_length) + current_action = row["selectedAction"] + current_length = 0 + trial_transitions += 1 + current_length += 1 + lengths.append(current_length) + runs_by_action[current_action].append(current_length) + run_lengths.extend(lengths) + transitions += trial_transitions + comparable_decisions += max(0, len(rows) - 1) + margins = [float(row["actionMargin"]) for row in rows] + trial_metrics.append( + { + "fold": fold, + "seed": seed, + "decisions": len(rows), + "action_runs": len(lengths), + "transitions": trial_transitions, + "transition_rate": ( + trial_transitions / (len(rows) - 1) + if len(rows) > 1 + else 0.0 + ), + "mean_action_run_length": float(np.mean(lengths)), + "mean_action_margin": float(np.mean(margins)), + "tie_rate": float( + np.mean([row["tieForBest"] for row in rows]) + ), + } + ) + + decisions = len(selected_rationales) + margins = [ + float(row["actionMargin"]) for row in selected_rationales + ] + by_action: dict[str, dict[str, Any]] = {} + for action_name in ACTIONS: + rationale_group = [ + row + for row in selected_rationales + if row["selectedAction"] == action_name + ] + action_group = [ + action_by_key[ + ( + row["fold"], + int(row["seed"]), + row["split"], + row["timestamp"], + ) + ] + for row in rationale_group + ] + action_runs = runs_by_action[action_name] + by_action[action_name] = { + "decisions": len(rationale_group), + "frequency": len(rationale_group) / decisions, + "action_runs": len(action_runs), + "mean_action_run_length": ( + float(np.mean(action_runs)) if action_runs else 0.0 + ), + "mean_action_margin": ( + float( + np.mean( + [row["actionMargin"] for row in rationale_group] + ) + ) + if rationale_group + else 0.0 + ), + "mean_reward": ( + float(np.mean([row["reward"] for row in action_group])) + if action_group + else 0.0 + ), + "mean_net_return": ( + float( + np.mean([row["net_return"] for row in action_group]) + ) + if action_group + else 0.0 + ), + "mean_one_way_turnover": ( + float( + np.mean( + [row["one_way_turnover"] for row in action_group] + ) + ) + if action_group + else 0.0 + ), + "total_cost": float( + sum(row["cost"] for row in action_group) + ), + } + by_feature: dict[str, dict[str, Any]] = {} + for feature_name in feature_names: + contributions = [ + float(row["marginContributions"][feature_name]) + for row in selected_rationales + ] + dominant = sum( + row["dominantMarginFeature"] == feature_name + for row in selected_rationales + ) + by_feature[feature_name] = { + "dominant_decisions": dominant, + "dominant_rate": dominant / decisions, + "mean_signed_margin_contribution": float( + np.mean(contributions) + ), + "mean_absolute_margin_contribution": float( + np.mean(np.abs(contributions)) + ), + } + total_reward = float(sum(row["reward"] for row in selected_actions)) + total_net_return = float( + sum(row["net_return"] for row in selected_actions) + ) + total_cost = float(sum(row["cost"] for row in selected_actions)) + mean_turnover = float( + np.mean([row["one_way_turnover"] for row in selected_actions]) + ) + reconciliation = { + "rationale_rows": len(selected_rationales), + "action_rows": len(selected_actions), + "decision_count_error": abs( + sum(item["decisions"] for item in by_action.values()) + - decisions + ), + "frequency_error": abs( + sum(item["frequency"] for item in by_action.values()) - 1.0 + ), + "reward_error": abs( + sum( + item["mean_reward"] * item["decisions"] + for item in by_action.values() + ) + - total_reward + ), + "net_return_error": abs( + sum( + item["mean_net_return"] * item["decisions"] + for item in by_action.values() + ) + - total_net_return + ), + "turnover_error": abs( + sum( + item["mean_one_way_turnover"] * item["frequency"] + for item in by_action.values() + ) + - mean_turnover + ), + "cost_error": abs( + sum(item["total_cost"] for item in by_action.values()) + - total_cost + ), + "transition_run_error": abs( + len(run_lengths) - transitions - len(groups) + ), + "dominant_feature_rate_error": abs( + sum(item["dominant_rate"] for item in by_feature.values()) + - 1.0 + ), + } + reconciliation["passed"] = ( + reconciliation["rationale_rows"] + == reconciliation["action_rows"] + == decisions + and max( + float(value) + for key, value in reconciliation.items() + if key not in {"rationale_rows", "action_rows"} + ) + <= 1e-10 + ) + return { + "status": "available", + "decisions": decisions, + "trial_paths": len(groups), + "transitions": transitions, + "transition_rate": ( + transitions / comparable_decisions + if comparable_decisions + else 0.0 + ), + "retention_rate": ( + 1.0 - transitions / comparable_decisions + if comparable_decisions + else 1.0 + ), + "action_runs": len(run_lengths), + "mean_action_run_length": float(np.mean(run_lengths)), + "median_action_run_length": float(np.median(run_lengths)), + "maximum_action_run_length": int(max(run_lengths)), + "single_bar_run_rate": float( + np.mean([length == 1 for length in run_lengths]) + ), + "mean_action_margin": float(np.mean(margins)), + "median_action_margin": float(np.median(margins)), + "minimum_action_margin": float(min(margins)), + "maximum_action_margin": float(max(margins)), + "tie_decisions": int( + sum(row["tieForBest"] for row in selected_rationales) + ), + "tie_rate": float( + np.mean( + [row["tieForBest"] for row in selected_rationales] + ) + ), + "total_reward": total_reward, + "total_net_return": total_net_return, + "mean_one_way_turnover": mean_turnover, + "total_cost": total_cost, + "by_action": by_action, + "by_feature": by_feature, + "trials": trial_metrics, + "reconciliation": reconciliation, + } + + +def _aggregate(values: list[float]) -> dict[str, float | int]: + array = np.asarray(values, dtype=float) + if not len(array) or not np.isfinite(array).all(): + raise JudgeFailure( + "policy.aggregate", + "Cannot aggregate missing or non-finite policy evidence", + ) + return { + "observations": int(len(array)), + "mean": float(array.mean()), + "standard_deviation": float(array.std(ddof=0)), + "minimum": float(array.min()), + "maximum": float(array.max()), + } + + +def _aggregate_execution_risk_trials( + folds: dict[str, Any], + split: str, +) -> dict[str, float | int | str]: + items = [ + seed[split]["execution_risk"] + for fold in folds.values() + for seed in fold["seeds"].values() + if seed.get("status") == "succeeded" + ] + active = sum(int(item["active_dates"]) for item in items) + available = sum( + int(item["forecast_available_dates"]) for item in items + ) + executed_breaches = sum( + int(item["executed_breach_dates"]) for item in items + ) + if executed_breaches: + raise JudgeFailure( + "policy.risk-breach", + "A governed RL rollout contains an executed-book risk breach", + ) + return { + "status": "available" if available else "forecast_unavailable", + "trial_paths": len(items), + "active_dates": active, + "forecast_available_dates": available, + "forecast_coverage": available / active if active else 0.0, + "pretrade_breach_dates": sum( + int(item["pretrade_breach_dates"]) for item in items + ), + "risk_rebalance_override_dates": sum( + int(item["risk_rebalance_override_dates"]) + for item in items + ), + "executed_breach_dates": executed_breaches, + "maximum_executed_forecast_annualized": max( + ( + float(item["maximum_executed_forecast_annualized"]) + for item in items + ), + default=0.0, + ), + "maximum_ceiling_error": max( + (float(item["maximum_ceiling_error"]) for item in items), + default=0.0, + ), + } + + +def _evaluate() -> tuple[ + dict[str, Any], + dict[str, Any], + dict[str, Any], + dict[str, Any], + list[dict[str, Any]], + dict[str, Any], + dict[str, Any], + dict[str, Any], +]: + study, data_root = _load_contract() + mandate = _load_mandate() + factor_claim = _load_factor_claim() + factor_population = _load_factor_population() + if factor_outcome(factor_claim) != FORWARD_RETURN_OUTCOME: + raise JudgeFailure( + "rl.factor-outcome", + "Governed RL requires a forward-return Factor and Portfolio " + "baseline; a risk forecast has no action-score meaning", + ) + research_horizon = _load_horizon() + implementation_policy = resolve_implementation_policy(mandate) + model_module = importlib.import_module("models.candidate") + factor_module = importlib.import_module("factors.candidate") + feature_names, encoder = _candidate_encoder(model_module) + frames, opens, closes, volumes = _panels(study, data_root) + universe = study["dataset"]["universe"] + try: + prediction_population = resolve_prediction_population( + universe, + factor_claim, + factor_population, + ).as_metrics() + validate_population_mandate_compatibility( + prediction_population, + mandate, + ) + except PredictionModeError as error: + raise JudgeFailure(error.code, str(error)) from error + try: + factor_panel = build_factor_panel(frames, universe=universe) + factor_evaluation = evaluate_factor( + factor_module, + factor_panel, + ) + candidate_panel = values_to_wide( + factor_panel, + factor_evaluation.values, + universe=universe, + ) + except FactorRuntimeError as error: + raise JudgeFailure(error.code, str(error)) from error + action_targets = build_action_targets( + _factor_panels(opens, closes, volumes, candidate_panel), + closes, + mandate=mandate, + prediction_population=prediction_population, + ) + _, _, _, signal_translation = translate_factor_scores( + candidate_panel, + prediction_population, + ) + risk_covariance_cache = build_risk_covariance_cache( + closes, + mandate=mandate, + ) + audits = { + action: constraint_audit(targets, mandate=mandate) + for action, targets in action_targets.items() + } + if not all(audit["passed"] for audit in audits.values()): + raise JudgeFailure( + "policy.constraints", + "A fixed action violated portfolio target constraints", + ) + raw_states = build_raw_states( + closes, + volumes, + action_targets, + ) + forward_valid = (closes.shift(-1) / closes - 1.0).notna().all(axis=1) + active = pd.Series(True, index=closes.index) + if mandate["construction"]["family"] == "dollar-neutral": + for targets in action_targets.values(): + active &= targets.abs().sum(axis=1) > 1e-12 + valid = raw_states.notna().all(axis=1) & forward_valid & active + active_index = closes.index[valid] + folds = chronological_folds(active_index) + + sample_positions = sorted( + {0, len(active_index) // 2, len(active_index) - 1} + ) + zero_pretrade = pd.Series( + 0.0, + index=closes.columns, + dtype=float, + ) + for position in sample_positions: + for previous_action in ACTIONS: + encoder( + build_policy_state( + raw_states.loc[active_index[position]], + previous_action, + zero_pretrade, + { + action: action_targets[action].loc[ + active_index[position] + ] + for action in ACTIONS + }, + ) + ) + + fold_metrics: dict[str, Any] = {} + baseline_metrics: dict[str, Any] = {} + policy_models: dict[str, Any] = {} + training_history: dict[str, Any] = {} + action_rows: list[dict[str, Any]] = [] + rationale_rows: list[dict[str, Any]] = [] + opportunity_rows: list[dict[str, Any]] = [] + incremental_rows: list[dict[str, Any]] = [] + incremental_thresholds: dict[str, dict[str, float]] = {} + failures: list[dict[str, Any]] = [] + validation_sharpes: list[float] = [] + test_sharpes: list[float] = [] + validation_advantages: list[float] = [] + test_advantages: list[float] = [] + candidate_validation_sharpes: list[float] = [] + candidate_test_sharpes: list[float] = [] + validation_advantages_vs_candidate: list[float] = [] + test_advantages_vs_candidate: list[float] = [] + validation_candidate_action_frequencies: list[float] = [] + + for fold_name, split in folds.items(): + ( + baselines, + ridge_model, + best_baseline, + selected_baseline_rollouts, + ) = _fixed_baselines( + raw_states, + action_targets, + closes, + volumes, + split, + mandate, + risk_covariance_cache, + ) + volatility_threshold = float( + raw_states.loc[split["train"], "market_volatility_20"].median() + ) + if not math.isfinite(volatility_threshold): + raise JudgeFailure( + "policy.incremental-threshold", + "Train-frozen volatility threshold is non-finite", + ) + incremental_thresholds[fold_name] = { + "marketVolatility20Median": volatility_threshold, + } + baseline_metrics[fold_name] = { + **baselines, + "best_validation_policy": best_baseline, + } + best_validation = float( + _baseline_split( + baselines, + best_baseline, + "validation", + )["net"]["sharpe"] + ) + matching_test = float( + _baseline_split( + baselines, + best_baseline, + "test", + )["net"]["sharpe"] + ) + candidate_validation = float( + baselines["fixed_factor_or_blend"]["candidate"]["validation"][ + "net" + ]["sharpe"] + ) + candidate_test = float( + baselines["fixed_factor_or_blend"]["candidate"]["test"]["net"][ + "sharpe" + ] + ) + candidate_validation_sharpes.append(candidate_validation) + candidate_test_sharpes.append(candidate_test) + seed_metrics: dict[str, Any] = {} + policy_models[fold_name] = { + "contextualRidgeBaseline": ridge_model, + "seeds": {}, + } + training_history[fold_name] = {} + fold_validation: list[float] = [] + fold_test: list[float] = [] + for seed in SEEDS: + try: + trained = train_q_policy( + encoder, + len(feature_names), + raw_states, + action_targets, + closes, + volumes, + split["train"], + seed=seed, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + selector = q_selector(trained.weights, encoder) + validation_rollout = rollout_policy( + selector, + raw_states, + action_targets, + closes, + volumes, + split["validation"], + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + test_rollout = rollout_policy( + selector, + raw_states, + action_targets, + closes, + volumes, + split["test"], + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + validation = rollout_metrics(validation_rollout) + test = rollout_metrics(test_rollout) + validation_sharpe = float(validation["net"]["sharpe"]) + test_sharpe = float(test["net"]["sharpe"]) + fold_validation.append(validation_sharpe) + fold_test.append(test_sharpe) + validation_sharpes.append(validation_sharpe) + test_sharpes.append(test_sharpe) + validation_candidate_action_frequencies.append( + float(validation["action_frequency"]["candidate"]) + ) + seed_metrics[str(seed)] = { + "status": "succeeded", + "validation": validation, + "test": test, + } + policy_models[fold_name]["seeds"][str(seed)] = { + "weights": trained.weights.tolist(), + } + training_history[fold_name][str(seed)] = trained.history + action_rows.extend( + _rollout_action_rows( + fold_name, + seed, + "validation", + validation_rollout, + ) + ) + rationale_rows.extend( + _rollout_rationale_rows( + fold_name, + seed, + "validation", + validation_rollout, + encoder, + feature_names, + trained.weights, + ) + ) + opportunity_rows.extend( + _rollout_opportunity_rows( + fold_name, + seed, + "validation", + validation_rollout, + action_targets, + closes, + volumes, + mandate, + risk_covariance_cache, + ) + ) + incremental_rows.extend( + _rollout_incremental_rows( + fold_name, + seed, + "validation", + best_baseline, + validation_rollout, + selected_baseline_rollouts["validation"], + raw_states, + closes, + volatility_threshold, + ) + ) + action_rows.extend( + _rollout_action_rows( + fold_name, + seed, + "test", + test_rollout, + ) + ) + rationale_rows.extend( + _rollout_rationale_rows( + fold_name, + seed, + "test", + test_rollout, + encoder, + feature_names, + trained.weights, + ) + ) + opportunity_rows.extend( + _rollout_opportunity_rows( + fold_name, + seed, + "test", + test_rollout, + action_targets, + closes, + volumes, + mandate, + risk_covariance_cache, + ) + ) + incremental_rows.extend( + _rollout_incremental_rows( + fold_name, + seed, + "test", + best_baseline, + test_rollout, + selected_baseline_rollouts["test"], + raw_states, + closes, + volatility_threshold, + ) + ) + except Exception as error: + code = getattr(error, "code", "policy.seed-failed") + failure = { + "fold": fold_name, + "seed": seed, + "code": code, + "message": f"{type(error).__name__}: {error}", + } + failures.append(failure) + seed_metrics[str(seed)] = { + "status": "failed", + "error": failure, + } + if not fold_validation: + fold_metrics[fold_name] = { + "ranges": { + name: { + "start": timestamp_label(index[0]), + "end": timestamp_label(index[-1]), + "observations": len(index), + } + for name, index in split.items() + }, + "seeds": seed_metrics, + "aggregate": { + "status": "all-seeds-failed", + "best_validation_baseline": best_baseline, + }, + } + continue + validation_advantage = float(np.mean(fold_validation)) - best_validation + test_advantage = float(np.mean(fold_test)) - matching_test + validation_advantage_vs_candidate = ( + float(np.mean(fold_validation)) - candidate_validation + ) + test_advantage_vs_candidate = float(np.mean(fold_test)) - candidate_test + validation_advantages.append(validation_advantage) + test_advantages.append(test_advantage) + validation_advantages_vs_candidate.append( + validation_advantage_vs_candidate + ) + test_advantages_vs_candidate.append(test_advantage_vs_candidate) + fold_metrics[fold_name] = { + "ranges": { + name: { + "start": timestamp_label(index[0]), + "end": timestamp_label(index[-1]), + "observations": len(index), + } + for name, index in split.items() + }, + "seeds": seed_metrics, + "aggregate": { + "validation_net_sharpe": _aggregate(fold_validation), + "test_net_sharpe": _aggregate(fold_test), + "validation_advantage_vs_best_baseline": validation_advantage, + "test_advantage_vs_validation_selected_baseline": test_advantage, + "validation_advantage_vs_candidate_factor": ( + validation_advantage_vs_candidate + ), + "test_advantage_vs_candidate_factor": test_advantage_vs_candidate, + "best_validation_baseline": best_baseline, + }, + } + + total_trials = len(folds) * len(SEEDS) + if failures: + raise TrialFailures(failures) + policy_rationale = { + "policy": { + "method": ( + "linear-q-chosen-vs-runner-up-decomposition-v1" + ), + "action_runs": ( + "contiguous-actions-clipped-to-fold-seed-split" + ), + "q_scale": "uncalibrated-linear-model-score", + "feature_contribution": ( + "exact-linear-chosen-minus-runner-decomposition" + ), + "realized_outcomes": ( + "descriptive-endogenous-action-conditioning" + ), + "selection_authority": "context-only", + "trading_authority": "none", + }, + "validation": _policy_behavior_metrics( + rationale_rows, + action_rows, + "validation", + feature_names, + ), + "test": _policy_behavior_metrics( + rationale_rows, + action_rows, + "test", + feature_names, + ), + } + execution_risk = { + "policy": { + "method": ( + "post-drift-executed-book-volatility-compliance-v1" + ), + "risk_policy": mandate["construction"]["riskPolicy"], + "no_trade_priority": "risk-compliance-first", + "repair": "minimum-proportional-scale-down", + "selection_authority": "context-only", + "trading_authority": "none", + }, + "validation": _aggregate_execution_risk_trials( + fold_metrics, + "validation", + ), + "test": _aggregate_execution_risk_trials( + fold_metrics, + "test", + ), + } + factor_opportunity = { + "policy": FACTOR_OPPORTUNITY_POLICY, + "validation": _factor_opportunity_metrics( + opportunity_rows, + "validation", + ), + "test": _factor_opportunity_metrics( + opportunity_rows, + "test", + ), + } + incremental_attribution = { + "policy": INCREMENTAL_ATTRIBUTION_POLICY, + "validation": _incremental_attribution_metrics( + incremental_rows, + "validation", + list(closes.columns), + ), + "test": _incremental_attribution_metrics( + incremental_rows, + "test", + list(closes.columns), + ), + } + metrics = { + "validation_mean_net_sharpe": float(np.mean(validation_sharpes)), + "factor_api": factor_contract(factor_evaluation), + "factor_claim": factor_claim, + "prediction_universe": prediction_population, + "signal_translation": signal_translation, + "portfolio_mandate": mandate, + "research_horizon": research_horizon, + "rl": { + "aggregate": { + "validation_net_sharpe": _aggregate(validation_sharpes), + "test_net_sharpe": _aggregate(test_sharpes), + "failure_rate": len(failures) / total_trials, + "failures": failures, + }, + "folds": fold_metrics, + }, + "baselines": baseline_metrics, + "comparison": { + "mean_validation_advantage_vs_best_baseline": float( + np.mean(validation_advantages) + ), + "mean_test_advantage_vs_validation_selected_baseline": float( + np.mean(test_advantages) + ), + "candidate_factor_validation_net_sharpe": _aggregate( + candidate_validation_sharpes + ), + "candidate_factor_test_net_sharpe": _aggregate( + candidate_test_sharpes + ), + "mean_validation_advantage_vs_candidate_factor": float( + np.mean(validation_advantages_vs_candidate) + ), + "mean_test_advantage_vs_candidate_factor": float( + np.mean(test_advantages_vs_candidate) + ), + "mean_validation_candidate_action_frequency": float( + np.mean(validation_candidate_action_frequencies) + ), + }, + "policy_rationale": policy_rationale, + "factor_opportunity": factor_opportunity, + "incremental_attribution": incremental_attribution, + "execution_risk": execution_risk, + "constraint_audit": audits, + "configuration": { + "portfolioMandateId": mandate["id"], + "researchHorizonId": research_horizon["id"], + "actions": list(ACTIONS), + "factorExperts": list(EXPERTS), + "rawStateFields": list(POLICY_STATE_COLUMNS), + "featureNames": feature_names, + "seeds": list(SEEDS), + "folds": list(folds), + "episodes": EPISODES, + "learningRate": LEARNING_RATE, + "discount": DISCOUNT, + "epsilonStart": EPSILON_START, + "epsilonEnd": EPSILON_END, + "contextualRidgeIterations": ( + CONTEXTUAL_RIDGE_ITERATIONS + ), + "contextualRidgeMethod": ( + "iterative-same-pretrade-contextual-ridge-v1" + ), + "contextualRidgeLabelScope": "train-only", + "contextualRidgeAnchorAction": "balanced", + "riskAversion": RISK_AVERSION, + "costBps": implementation_policy["base_cost_bps"], + "noTradeOneWay": implementation_policy["no_trade_one_way"], + "referenceNav": implementation_policy["reference_nav"], + "decisionSchedule": implementation_policy["decision_policy"], + "executionRiskMethod": ( + "post-drift-executed-book-volatility-compliance-v1" + ), + "executionRiskPriority": "risk-compliance-first", + "factorOpportunityMethod": FACTOR_OPPORTUNITY_POLICY["method"], + "incrementalAttributionMethod": ( + INCREMENTAL_ATTRIBUTION_POLICY["method"] + ), + "learningContract": LEARNING_CONTRACT, + }, + "research_integrity": { + "selection_split": "validation", + "test_role": "visible-diagnostic", + "test_enters_selection": False, + "external_holdout_rule": ( + "required-after-visible-test-and-candidate-iteration" + ), + "learning_configuration": LEARNING_CONTRACT, + "factor_dependency": { + "module": "factors.candidate", + "mode": "content-locked-study-dependency", + "causalityAuditCuts": list( + factor_evaluation.causality_cuts + ), + }, + }, + } + if not math.isfinite(metrics["validation_mean_net_sharpe"]): + raise JudgeFailure("policy.non-finite", "Primary score is non-finite") + dataset = study["dataset"] + report = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "dataset": { + "id": dataset["id"], + "version": dataset["version"], + "universe": dataset["universe"], + "timeRange": dataset["time_range"], + }, + "portfolioMandate": mandate, + "factorClaim": factor_claim, + "predictionUniverse": prediction_population, + "signalTranslation": signal_translation, + "researchHorizon": research_horizon, + "semantics": { + "simulation": "governed-factor-mixture-q-policy", + "state": "fixed causal scalars known through close t", + "action": ( + "candidate factor, one of three reference factors, or their " + "equal-weight blend at close t" + ), + "return": "close t to close t+1", + "researchHorizonRole": ( + "fixed question identity and disclosure; one-step RL rewards " + "are not direct multi-bar forecasts" + ), + "reward": ( + "net return after " + f"{implementation_policy['base_cost_bps']:g}bps cost minus " + "0.10 * gross_return^2" + ), + "policyRationale": ( + "exact linear chosen-versus-runner-up Q-margin " + "decomposition; Q scores are uncalibrated and contextual" + ), + "factorOpportunity": ( + "all fixed sleeves evaluated for one next bar from the " + "selected policy path's exact shared pretrade book; the " + "ex-post oracle is an audit upper bound only" + ), + "incrementalAttribution": ( + "independent full-path RL minus validation-selected " + "mechanical baseline; gross edge minus incremental cost " + "reconciles net active return" + ), + "learningConfiguration": ( + "episodes, learning rate, discount, and exploration were " + "frozen by a reference-fixture outer-train-only blocked " + "stability audit before Study validation; validation and " + "test do not tune them" + ), + "executionRisk": ( + "every selected sleeve is rechecked after drift; risk " + "compliance bypasses the no-trade band with minimum " + "proportional scale-down" + ), + "objective": "mean validation net Sharpe across every successful seed/fold", + "testRole": "reported audit evidence; never enters promotion", + "testVisibilityWarning": ( + "Repeated candidate changes after inspecting test metrics consume " + "their holdout value; use a new external holdout for production claims." + ), + "tradingAuthority": "none", + }, + "dependencies": { + "numpy": np.__version__, + "pandas": pd.__version__, + }, + "metrics": metrics, + } + models = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "featureNames": feature_names, + "configuration": metrics["configuration"], + "models": policy_models, + } + histories = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "histories": training_history, + } + rationales = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "method": ( + "linear-q-chosen-vs-runner-up-decomposition-v1" + ), + "actions": list(ACTIONS), + "rawStateFields": list(POLICY_STATE_COLUMNS), + "featureNames": feature_names, + "rows": rationale_rows, + } + opportunities = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "method": FACTOR_OPPORTUNITY_POLICY["method"], + "actions": list(ACTIONS), + "assets": list(closes.columns), + "reward": ( + "net-return-after-" + f"{implementation_policy['base_cost_bps']:g}bps-cost-minus-" + "0.10-times-gross-return-squared" + ), + "policy": FACTOR_OPPORTUNITY_POLICY, + "rows": compact_opportunity_rows(opportunity_rows), + } + incremental = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "method": INCREMENTAL_ATTRIBUTION_POLICY["method"], + "assets": list(closes.columns), + "policy": INCREMENTAL_ATTRIBUTION_POLICY, + "thresholds": incremental_thresholds, + "rows": incremental_rows, + } + return ( + metrics, + report, + models, + histories, + action_rows, + rationales, + opportunities, + incremental, + ) + + +def main() -> None: + try: + ( + metrics, + report, + models, + histories, + action_rows, + rationales, + opportunities, + incremental, + ) = _evaluate() + artifacts = Path(os.environ["AUTOQUANT_ARTIFACTS_DIR"]) + (artifacts / "rl-report.json").write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + (artifacts / "policy-models.json").write_text( + json.dumps(models, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + (artifacts / "training-history.json").write_text( + json.dumps(histories, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + pd.DataFrame(action_rows).to_csv( + artifacts / "policy-actions.csv", + index=False, + float_format="%.17g", + ) + (artifacts / "policy-rationales.json").write_text( + json.dumps(rationales, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + (artifacts / "policy-opportunities.json").write_text( + json.dumps( + opportunities, + separators=(",", ":"), + sort_keys=True, + ) + + "\n", + encoding="utf-8", + ) + (artifacts / "policy-incremental-attribution.json").write_text( + json.dumps(incremental, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + _write_output( + { + "schema_version": 1, + "status": "succeeded", + "summary": ( + "Governed factor-mixture policy evaluated across all fixed " + "folds/seeds; validation mean net Sharpe=" + f"{metrics['validation_mean_net_sharpe']:.6f}" + ), + "metrics": metrics, + "artifacts": [ + { + "kind": "rl-report", + "path": "rl-report.json", + "description": ( + "State/action/reward semantics, folds, seeds, baselines, " + "comparisons, warnings, and complete metrics" + ), + }, + { + "kind": "policy-models", + "path": "policy-models.json", + "description": ( + "Exact candidate feature names, configuration, Q weights, " + "and contextual-ridge baseline parameters" + ), + }, + { + "kind": "training-history", + "path": "training-history.json", + "description": "Every episode for every declared fold and seed", + }, + { + "kind": "policy-actions", + "path": "policy-actions.csv", + "description": ( + "Timestamped validation/test actions, rewards, returns, " + "turnover, costs, and executed-book risk compliance" + ), + }, + { + "kind": "policy-rationales", + "path": "policy-rationales.json", + "description": ( + "Exact raw state, encoded features, action Q values, " + "runner-up margins, and linear feature contributions" + ), + }, + { + "kind": "policy-opportunities", + "path": "policy-opportunities.json", + "description": ( + "Same-pretrade one-step governed action books, " + "rewards, local oracle rank/regret, and candidate " + "factor opportunity evidence" + ), + }, + { + "kind": "policy-incremental-attribution", + "path": "policy-incremental-attribution.json", + "description": ( + "Full-path active return, cost, regime, action, " + "and asset attribution versus each fold's " + "validation-selected mechanical baseline" + ), + }, + ], + "errors": [], + } + ) + except TrialFailures as error: + _write_output( + { + "schema_version": 1, + "status": "failed", + "summary": str(error), + "metrics": { + "declared_trials": len(SEEDS) * 2, + "failed_trials": len(error.failures), + }, + "artifacts": [], + "errors": [ + { + "code": item["code"], + "message": ( + f"{item['fold']} seed {item['seed']}: " + f"{item['message']}" + ), + } + for item in error.failures + ], + } + ) + except (JudgeFailure, PolicyFailure) as error: + _write_output( + { + "schema_version": 1, + "status": "failed", + "summary": str(error), + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": getattr(error, "code", "policy.failure"), + "message": str(error), + } + ], + } + ) + except Exception as error: + _write_output( + { + "schema_version": 1, + "status": "failed", + "summary": f"RL evaluation raised {type(error).__name__}", + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": "policy.exception", + "message": f"{type(error).__name__}: {error}", + } + ], + } + ) + + +if __name__ == "__main__": + main() diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/portfolio_core.py b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/portfolio_core.py new file mode 100644 index 0000000..174c8b2 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/portfolio_core.py @@ -0,0 +1,4178 @@ +"""Fixed causal target construction and portfolio accounting primitives.""" + +from __future__ import annotations + +import math +from bisect import bisect_left, bisect_right, insort +from collections import deque +from dataclasses import dataclass + +import exchange_calendars +import numpy as np +import pandas as pd + +from autoquant.intervals import annualization_periods +from autoquant.prediction_modes import ( + CROSS_SECTIONAL_MODE, + SIGNAL_TRANSLATION_METHOD, + SINGLE_ASSET_TEMPORAL_MODE, + TEMPORAL_SCORE_MINIMUM, + TEMPORAL_SCORE_WINDOW, + TWO_ASSET_RELATIVE_VALUE_MODE, + signal_translation_contract, +) +VOLATILITY_WINDOW = 20 +GROSS_TARGET = 1.0 +SIDE_BUDGET = GROSS_TARGET / 2.0 +MAX_ABS_WEIGHT = 0.30 +NO_TRADE_ONE_WAY = 0.05 +BASE_COST_BPS = 10.0 +REFERENCE_NAV = 1_000_000.0 +DECISION_EVERY_BARS = 1 +DECISION_ANCHOR = "dataset-start" +DEFAULT_DECISION_POLICY = { + "source": "reference-default", + "kind": "every-bars", + "bars": DECISION_EVERY_BARS, + "anchor": DECISION_ANCHOR, +} +LONG_ENTRY_PERCENTILE = 0.75 +LONG_EXIT_PERCENTILE = 0.55 +SHORT_EXIT_PERCENTILE = 0.45 +SHORT_ENTRY_PERCENTILE = 0.25 +RISK_COVARIANCE_WINDOW = 60 +RISK_COVARIANCE_MINIMUM = 20 +LIQUIDITY_ADV_WINDOW = 20 +LIQUIDITY_PARTICIPATION_LIMITS = (0.01, 0.05) +RISK_COMPLIANCE_TOLERANCE = 1e-10 +POSITION_EPISODE_TOLERANCE = 1e-10 +POSITION_EPISODE_COLUMNS = ( + "episode_id", + "split", + "role", + "episode_number", + "asset", + "side", + "entry_timestamp", + "last_earning_timestamp", + "exit_timestamp", + "entry_action", + "exit_action", + "left_censored", + "right_censored", + "complete", + "decision_bars", + "entry_weight", + "last_executed_weight", + "peak_abs_weight", + "average_abs_weight", + "gross_contribution", + "entry_cost", + "holding_cost", + "exit_cost", + "total_cost", + "net_contribution", + "maximum_favorable_excursion", + "maximum_adverse_excursion", + "intent_mismatch_bars", + "no_trade_bars", + "risk_override_bars", +) + + +class PortfolioFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +@dataclass(frozen=True) +class Simulation: + daily: pd.DataFrame + weights: pd.DataFrame + trades: pd.DataFrame + participation: pd.DataFrame + + +@dataclass(frozen=True) +class SignalConstruction: + targets: pd.DataFrame + states: pd.DataFrame + ledger: pd.DataFrame + scores: pd.DataFrame + translation_values: pd.DataFrame + translation_observations: pd.DataFrame + translation: dict[str, object] + + +RiskCovarianceCache = dict[object, tuple[int, np.ndarray | None]] + + +def _causal_empirical_percentile( + values: pd.Series, + *, + window: int = TEMPORAL_SCORE_WINDOW, + minimum: int = TEMPORAL_SCORE_MINIMUM, +) -> tuple[pd.Series, pd.Series]: + """Rank each finite value inside the latest observed causal window.""" + + if minimum < 2 or window < minimum: + raise PortfolioFailure( + "portfolio.translation-window", + "Temporal score window must contain its fixed minimum", + ) + scores = pd.Series(np.nan, index=values.index, dtype=float) + observations = pd.Series(0, index=values.index, dtype=int) + ordered: list[float] = [] + history: deque[float] = deque() + for timestamp, raw in values.items(): + value = float(raw) if pd.notna(raw) else math.nan + if not math.isfinite(value): + continue + history.append(value) + insort(ordered, value) + if len(history) > window: + expired = history.popleft() + position = bisect_left(ordered, expired) + if position >= len(ordered) or ordered[position] != expired: + raise PortfolioFailure( + "portfolio.translation-window", + "Temporal score window could not remove prior value", + ) + ordered.pop(position) + count = len(ordered) + observations.loc[timestamp] = count + if count < minimum: + continue + lower = bisect_left(ordered, value) + upper = bisect_right(ordered, value) + average_one_based_rank = (lower + 1 + upper) / 2.0 + scores.loc[timestamp] = average_one_based_rank / count + return scores, observations + + +def translate_factor_scores( + factors: pd.DataFrame, + prediction_population: dict[str, object] | None = None, + *, + temporal_window: int = TEMPORAL_SCORE_WINDOW, + temporal_minimum: int = TEMPORAL_SCORE_MINIMUM, +) -> tuple[ + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, object], +]: + """Translate causal Factor values into one fixed decision-score surface.""" + + if factors.empty or factors.columns.has_duplicates: + raise PortfolioFailure( + "portfolio.translation-input", + "Factor panel must have rows and unique asset columns", + ) + population = prediction_population or { + "evaluation_mode": CROSS_SECTIONAL_MODE, + "prediction_assets": list(factors.columns), + "context_assets": [], + "authority": "reference-default-research-universe", + "relative_value_pair": None, + } + mode = population.get("evaluation_mode") + prediction_assets = population.get("prediction_assets") + context_assets = population.get("context_assets") + if ( + mode not in { + CROSS_SECTIONAL_MODE, + SINGLE_ASSET_TEMPORAL_MODE, + TWO_ASSET_RELATIVE_VALUE_MODE, + } + or not isinstance(prediction_assets, list) + or not prediction_assets + or len(prediction_assets) != len(set(prediction_assets)) + or any(asset not in factors.columns for asset in prediction_assets) + or not isinstance(context_assets, list) + or set(prediction_assets).intersection(context_assets) + or set(prediction_assets).union(context_assets) != set(factors.columns) + ): + raise PortfolioFailure( + "portfolio.translation-population", + "Prediction population does not partition the Factor panel", + ) + scores = pd.DataFrame(np.nan, index=factors.index, columns=factors.columns) + translation_values = pd.DataFrame( + np.nan, + index=factors.index, + columns=factors.columns, + ) + observations = pd.DataFrame( + 0, + index=factors.index, + columns=factors.columns, + dtype=int, + ) + relative_value_pair = population.get("relative_value_pair") + + if mode == CROSS_SECTIONAL_MODE: + if len(prediction_assets) < 4: + raise PortfolioFailure( + "portfolio.translation-population", + "Cross-sectional translation requires at least four prediction assets", + ) + selected = factors[prediction_assets].where( + np.isfinite(factors[prediction_assets].to_numpy(dtype=float)) + ) + counts = selected.notna().sum(axis=1) + distinct = selected.nunique(axis=1) + available = counts.ge(4) & distinct.ge(2) + ranks = selected.rank(axis=1, method="average") + translated = ranks.sub(1.0).div(counts.sub(1.0), axis=0) + scores.loc[:, prediction_assets] = translated.where(available) + translation_values.loc[:, prediction_assets] = selected + for asset in prediction_assets: + observations.loc[:, asset] = counts.astype(int) + elif mode == SINGLE_ASSET_TEMPORAL_MODE: + if len(prediction_assets) != 1 or relative_value_pair is not None: + raise PortfolioFailure( + "portfolio.translation-population", + "Single-asset temporal translation requires exactly one prediction asset", + ) + asset = prediction_assets[0] + values = factors[asset].where(np.isfinite(factors[asset])) + temporal_scores, temporal_observations = _causal_empirical_percentile( + values, + window=temporal_window, + minimum=temporal_minimum, + ) + scores.loc[:, asset] = temporal_scores + translation_values.loc[:, asset] = values + observations.loc[:, asset] = temporal_observations + else: + if len(prediction_assets) != 2 or not isinstance(relative_value_pair, dict): + raise PortfolioFailure( + "portfolio.translation-population", + "Relative-value translation requires one ordered two-asset pair", + ) + left = relative_value_pair.get("left_asset") + right = relative_value_pair.get("right_asset") + if prediction_assets != [left, right]: + raise PortfolioFailure( + "portfolio.translation-pair", + "Relative-value pair order differs from the prediction population", + ) + spread = (factors[left] - factors[right]).where( + np.isfinite(factors[left]) & np.isfinite(factors[right]) + ) + spread_scores, spread_observations = _causal_empirical_percentile( + spread, + window=temporal_window, + minimum=temporal_minimum, + ) + scores.loc[:, left] = spread_scores + scores.loc[:, right] = 1.0 - spread_scores + translation_values.loc[:, left] = spread + translation_values.loc[:, right] = -spread + observations.loc[:, left] = spread_observations + observations.loc[:, right] = spread_observations + semantics = signal_translation_contract( + population, + temporal_window=temporal_window, + temporal_minimum=temporal_minimum, + ) + return scores, translation_values, observations, semantics + + +def _valid_decision_policy( + value: object, + *, + expected_source: object | None = None, +) -> bool: + if not isinstance(value, dict): + return False + source = value.get("source") + if expected_source is not None and source != expected_source: + return False + if source is not None and source not in { + "caller-supplied", + "reference-default", + }: + return False + source_keys = {"source"} if source is not None else set() + if value.get("kind") == "every-bars": + bars = value.get("bars") + return ( + set(value) == source_keys | {"kind", "bars", "anchor"} + and isinstance(bars, int) + and not isinstance(bars, bool) + and 1 <= bars <= 252 + and value.get("anchor") in {"dataset-start", "session-start"} + ) + return ( + value.get("kind") == "calendar-month-end" + and set(value) == source_keys | {"kind"} + ) + + +def _allocate_capped_side( + strengths: pd.Series, + *, + budget: float = SIDE_BUDGET, + cap: float | pd.Series = MAX_ABS_WEIGHT, +) -> pd.Series: + """Proportionally water-fill one non-negative side under named caps.""" + + clean = strengths.astype(float) + clean = clean[np.isfinite(clean.to_numpy()) & (clean > 0)] + output = pd.Series(0.0, index=strengths.index, dtype=float) + caps = ( + pd.Series(float(cap), index=strengths.index, dtype=float) + if isinstance(cap, (int, float)) and not isinstance(cap, bool) + else cap.reindex(strengths.index).astype(float) + if isinstance(cap, pd.Series) + else pd.Series(dtype=float) + ) + if ( + not caps.index.equals(strengths.index) + or not np.isfinite(caps.to_numpy()).all() + or (caps < 0).any() + ): + raise PortfolioFailure( + "portfolio.asset-caps", + "Per-asset caps must be aligned, finite, and non-negative", + ) + if clean.empty or float(caps.loc[clean.index].sum()) + 1e-12 < budget: + return output + remaining = list(clean.index) + remaining_budget = float(budget) + while remaining: + values = clean.loc[remaining] + total = float(values.sum()) + if total <= 0: + return pd.Series(0.0, index=strengths.index, dtype=float) + proposed = values / total * remaining_budget + capped = proposed[ + proposed > caps.loc[proposed.index] + 1e-12 + ] + if capped.empty: + output.loc[remaining] = proposed + remaining_budget = 0.0 + break + for asset in capped.index: + output.loc[asset] = caps.loc[asset] + remaining.remove(asset) + remaining_budget -= float(caps.loc[asset]) + if remaining_budget < -1e-10: + raise PortfolioFailure( + "portfolio.allocation", + "Capped side allocation exceeded its budget", + ) + if abs(float(output.sum()) - budget) > 1e-9: + return pd.Series(0.0, index=strengths.index, dtype=float) + return output + + +def _allocate_capped_up_to( + strengths: pd.Series, + *, + limit: float, + cap: float | pd.Series, +) -> pd.Series: + """Allocate available directional conviction and leave unused budget in cash.""" + + clean = strengths.astype(float) + clean = clean[np.isfinite(clean.to_numpy()) & (clean > 0)] + if clean.empty: + return pd.Series(0.0, index=strengths.index, dtype=float) + caps = ( + pd.Series(float(cap), index=strengths.index, dtype=float) + if isinstance(cap, (int, float)) and not isinstance(cap, bool) + else cap.reindex(strengths.index).astype(float) + if isinstance(cap, pd.Series) + else pd.Series(dtype=float) + ) + budget = min(float(limit), float(caps.loc[clean.index].sum())) + return _allocate_capped_side(strengths, budget=budget, cap=cap) + + +def _valid_benchmark_contract( + benchmark: object, + universe: list[str], + tradable: list[str], + asset_position_roles: dict[str, str], +) -> bool: + if ( + not isinstance(benchmark, dict) + or set(benchmark) != {"source", "kind", "asset", "weights"} + or benchmark.get("source") + not in {"caller-supplied", "direction-default"} + or benchmark.get("kind") + not in { + "cash", + "fixed-weights", + "equal-weight-long-research-universe", + "equal-weight-long-tradable", + "equal-weight-short-tradable", + "equal-weight-long-capable", + "equal-weight-short-capable", + "single-asset-long", + } + or not isinstance(benchmark.get("weights"), dict) + or set(benchmark["weights"]) != set(universe) + or any( + not isinstance(benchmark["weights"][asset], (int, float)) + or isinstance(benchmark["weights"][asset], bool) + or not math.isfinite(float(benchmark["weights"][asset])) + for asset in universe + ) + ): + return False + kind = benchmark["kind"] + benchmark_asset = benchmark["asset"] + if kind == "cash": + expected = {asset: 0.0 for asset in universe} + valid_asset = benchmark_asset is None + elif kind == "fixed-weights": + expected = { + asset: float(benchmark["weights"][asset]) + for asset in universe + } + valid_asset = ( + benchmark_asset is None + and all(0.0 <= weight <= 1.0 for weight in expected.values()) + and any(weight > 0.0 for weight in expected.values()) + and math.isclose( + sum(expected.values()), + 1.0, + rel_tol=0.0, + abs_tol=1e-9, + ) + ) + elif kind == "equal-weight-long-research-universe": + expected = { + asset: 1.0 / len(universe) for asset in universe + } + valid_asset = benchmark_asset is None + elif kind in { + "equal-weight-long-tradable", + "equal-weight-short-tradable", + }: + sign = 1.0 if kind == "equal-weight-long-tradable" else -1.0 + tradable_set = set(tradable) + expected = { + asset: ( + sign / len(tradable) if asset in tradable_set else 0.0 + ) + for asset in universe + } + valid_asset = benchmark_asset is None + elif kind in { + "equal-weight-long-capable", + "equal-weight-short-capable", + }: + long_side = kind == "equal-weight-long-capable" + capable = [ + asset + for asset in tradable + if asset_position_roles[asset] + in ( + {"long-only", "two-sided"} + if long_side + else {"short-only", "two-sided"} + ) + ] + if not capable: + return False + sign = 1.0 if long_side else -1.0 + capable_set = set(capable) + expected = { + asset: ( + sign / len(capable) if asset in capable_set else 0.0 + ) + for asset in universe + } + valid_asset = benchmark_asset is None + else: + valid_asset = ( + isinstance(benchmark_asset, str) + and benchmark_asset in universe + ) + expected = { + asset: 1.0 if asset == benchmark_asset else 0.0 + for asset in universe + } + if kind == "cash": + source_matches = True + elif kind in {"fixed-weights", "single-asset-long"}: + source_matches = benchmark["source"] == "caller-supplied" + else: + source_matches = benchmark["source"] == "direction-default" + return ( + valid_asset + and source_matches + and all( + math.isclose( + float(benchmark["weights"][asset]), + expected[asset], + rel_tol=0.0, + abs_tol=1e-12, + ) + for asset in universe + ) + ) + + +def _resolve_mandate( + columns: pd.Index, + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Resolve the copied Judge's fixed position contract.""" + + universe = [str(column) for column in columns] + if mandate is None: + return { + "id": "legacy-dollar-neutral", + "direction": "research-only", + "family": "dollar-neutral", + "gross_limit": GROSS_TARGET, + "max_abs_weight": MAX_ABS_WEIGHT, + "asset_max_abs_weights": { + asset: MAX_ABS_WEIGHT for asset in universe + }, + "asset_position_roles": { + asset: "two-sided" for asset in universe + }, + "long_gross_limit": GROSS_TARGET / 2.0, + "short_gross_limit": GROSS_TARGET / 2.0, + "tradable_assets": universe, + "context_assets": [], + "benchmark": { + "source": "direction-default", + "kind": "equal-weight-long-research-universe", + "asset": None, + "weights": { + asset: 1.0 / len(universe) + for asset in universe + }, + }, + "implementation_policy": { + "base_cost_bps": BASE_COST_BPS, + "no_trade_one_way": NO_TRADE_ONE_WAY, + "reference_nav": REFERENCE_NAV, + "decision_policy": dict(DEFAULT_DECISION_POLICY), + }, + "risk_policy": None, + } + source = mandate.get("source") + construction = mandate.get("construction") + if not isinstance(source, dict) or not isinstance(construction, dict): + raise PortfolioFailure( + "mandate.contract", + "Portfolio Mandate source and construction must be objects", + ) + research = mandate.get("researchUniverse") + tradable = mandate.get("tradableAssets") + context = mandate.get("contextAssets") + if research != universe: + raise PortfolioFailure( + "mandate.universe", + "Portfolio Mandate research universe differs from the Study panel", + ) + if ( + not isinstance(tradable, list) + or not tradable + or not all(isinstance(asset, str) for asset in tradable) + or not isinstance(context, list) + or not all(isinstance(asset, str) for asset in context) + or set(tradable) | set(context) != set(universe) + or set(tradable) & set(context) + ): + raise PortfolioFailure( + "mandate.assets", + "Portfolio Mandate must partition research and tradable assets", + ) + direction = source.get("direction") + family = construction.get("family") + gross_limit = construction.get("grossLimit") + max_abs_weight = construction.get("maxAbsWeight") + asset_max_abs_weights = construction.get( + "assetMaxAbsWeights" + ) + asset_position_roles = construction.get("assetPositionRoles") + long_gross_limit = construction.get("longGrossLimit") + short_gross_limit = construction.get("shortGrossLimit") + benchmark = construction.get("benchmark") + risk_policy = construction.get("riskPolicy") + implementation = mandate.get("implementationPolicy") + if ( + direction + not in {"long", "short", "long-short", "relative-value", "research-only"} + or family not in { + "long-cash", + "short-cash", + "dollar-neutral", + "asset-role", + } + or not isinstance(gross_limit, (int, float)) + or isinstance(gross_limit, bool) + or not 0 < float(gross_limit) <= 2 + or not isinstance(max_abs_weight, (int, float)) + or isinstance(max_abs_weight, bool) + or not 0 < float(max_abs_weight) <= float(gross_limit) + or not isinstance(asset_max_abs_weights, dict) + or set(asset_max_abs_weights) != set(universe) + or any( + not isinstance(asset_max_abs_weights[asset], (int, float)) + or isinstance(asset_max_abs_weights[asset], bool) + or not math.isfinite( + float(asset_max_abs_weights[asset]) + ) + or ( + not 0 + < float(asset_max_abs_weights[asset]) + <= float(max_abs_weight) + if asset in tradable + else abs(float(asset_max_abs_weights[asset])) > 1e-12 + ) + for asset in universe + ) + or not isinstance(asset_position_roles, dict) + or set(asset_position_roles) != set(universe) + or any( + asset_position_roles[asset] + not in { + "long-only", + "short-only", + "two-sided", + "context-only", + } + or ( + asset_position_roles[asset] == "context-only" + if asset in tradable + else asset_position_roles[asset] != "context-only" + ) + for asset in universe + ) + or not isinstance(long_gross_limit, (int, float)) + or isinstance(long_gross_limit, bool) + or not math.isfinite(float(long_gross_limit)) + or not 0 <= float(long_gross_limit) <= float(gross_limit) + or not isinstance(short_gross_limit, (int, float)) + or isinstance(short_gross_limit, bool) + or not math.isfinite(float(short_gross_limit)) + or not 0 <= float(short_gross_limit) <= float(gross_limit) + or float(long_gross_limit) + float(short_gross_limit) + > float(gross_limit) + 1e-12 + or not _valid_benchmark_contract( + benchmark, + universe, + tradable, + asset_position_roles, + ) + or benchmark["source"] != source.get("benchmarkPolicy") + or not isinstance(risk_policy, dict) + or not isinstance(implementation, dict) + or set(implementation) + != { + "baseCostBps", + "noTradeOneWay", + "referenceNav", + "decisionPolicy", + "costModel", + "capacityModel", + } + or implementation.get("costModel") + != "linear-traded-notional-v1" + or implementation.get("capacityModel") + != "trailing-dollar-volume-participation-v1" + or not isinstance(implementation.get("baseCostBps"), (int, float)) + or isinstance(implementation.get("baseCostBps"), bool) + or not 0 <= float(implementation["baseCostBps"]) <= 1000 + or not isinstance(implementation.get("noTradeOneWay"), (int, float)) + or isinstance(implementation.get("noTradeOneWay"), bool) + or not 0 <= float(implementation["noTradeOneWay"]) <= 1 + or not isinstance(implementation.get("referenceNav"), (int, float)) + or isinstance(implementation.get("referenceNav"), bool) + or not 0 < float(implementation["referenceNav"]) <= 1e12 + or not _valid_decision_policy( + implementation.get("decisionPolicy"), + expected_source=source.get("portfolioPolicy"), + ) + or set(risk_policy) + != { + "method", + "annualizedVolatilityCeiling", + "covarianceWindow", + "minimumObservations", + "annualizationPeriods", + "scaleUp", + } + or risk_policy.get("method") + != "trailing-covariance-volatility-ceiling-v1" + or not isinstance( + risk_policy.get("annualizedVolatilityCeiling"), + (int, float), + ) + or isinstance( + risk_policy.get("annualizedVolatilityCeiling"), + bool, + ) + or not 0 + < float(risk_policy["annualizedVolatilityCeiling"]) + <= 1 + or not isinstance(risk_policy.get("covarianceWindow"), int) + or isinstance(risk_policy.get("covarianceWindow"), bool) + or risk_policy["covarianceWindow"] < 2 + or not isinstance(risk_policy.get("minimumObservations"), int) + or isinstance(risk_policy.get("minimumObservations"), bool) + or not 2 + <= risk_policy["minimumObservations"] + <= risk_policy["covarianceWindow"] + or not isinstance(risk_policy.get("annualizationPeriods"), int) + or isinstance(risk_policy.get("annualizationPeriods"), bool) + or risk_policy["annualizationPeriods"] < 1 + or risk_policy.get("scaleUp") is not False + ): + raise PortfolioFailure( + "mandate.construction", + "Portfolio Mandate contains unsupported construction semantics", + ) + return { + "id": str(mandate.get("id")), + "direction": str(direction), + "family": str(family), + "gross_limit": float(gross_limit), + "max_abs_weight": float(max_abs_weight), + "asset_max_abs_weights": { + asset: float(asset_max_abs_weights[asset]) + for asset in universe + }, + "asset_position_roles": { + asset: str(asset_position_roles[asset]) + for asset in universe + }, + "long_gross_limit": float(long_gross_limit), + "short_gross_limit": float(short_gross_limit), + "tradable_assets": list(tradable), + "context_assets": list(context), + "benchmark": { + "source": str(benchmark["source"]), + "kind": str(benchmark["kind"]), + "asset": benchmark["asset"], + "weights": { + asset: float(benchmark["weights"][asset]) + for asset in universe + }, + }, + "implementation_policy": { + "base_cost_bps": float(implementation["baseCostBps"]), + "no_trade_one_way": float(implementation["noTradeOneWay"]), + "reference_nav": float(implementation["referenceNav"]), + "decision_policy": dict(implementation["decisionPolicy"]), + }, + "risk_policy": { + "method": str(risk_policy["method"]), + "annualized_volatility_ceiling": float( + risk_policy["annualizedVolatilityCeiling"] + ), + "covariance_window": int(risk_policy["covarianceWindow"]), + "minimum_observations": int( + risk_policy["minimumObservations"] + ), + "annualization_periods": int( + risk_policy["annualizationPeriods"] + ), + "scale_up": False, + }, + } + + +def resolve_implementation_policy( + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Resolve fixed accounting assumptions without requiring an asset panel.""" + + if mandate is None: + return { + "base_cost_bps": BASE_COST_BPS, + "no_trade_one_way": NO_TRADE_ONE_WAY, + "reference_nav": REFERENCE_NAV, + "decision_policy": dict(DEFAULT_DECISION_POLICY), + } + implementation = mandate.get("implementationPolicy") + if not isinstance(implementation, dict): + raise PortfolioFailure( + "mandate.implementation", + "Portfolio Mandate implementation policy must be an object", + ) + result = { + "base_cost_bps": implementation.get("baseCostBps"), + "no_trade_one_way": implementation.get("noTradeOneWay"), + "reference_nav": implementation.get("referenceNav"), + "decision_policy": implementation.get("decisionPolicy"), + } + if ( + not isinstance(result["base_cost_bps"], (int, float)) + or isinstance(result["base_cost_bps"], bool) + or not 0 <= float(result["base_cost_bps"]) <= 1000 + or not isinstance(result["no_trade_one_way"], (int, float)) + or isinstance(result["no_trade_one_way"], bool) + or not 0 <= float(result["no_trade_one_way"]) <= 1 + or not isinstance(result["reference_nav"], (int, float)) + or isinstance(result["reference_nav"], bool) + or not 0 < float(result["reference_nav"]) <= 1e12 + or not _valid_decision_policy(result["decision_policy"]) + ): + raise PortfolioFailure( + "mandate.implementation", + "Portfolio Mandate implementation policy is invalid", + ) + return { + "base_cost_bps": float(result["base_cost_bps"]), + "no_trade_one_way": float(result["no_trade_one_way"]), + "reference_nav": float(result["reference_nav"]), + "decision_policy": dict(result["decision_policy"]), + } + + +def resolve_portfolio_mandate( + columns: pd.Index, + mandate: dict[str, object] | None, +) -> dict[str, object]: + """Validate and resolve one fixed Mandate once for repeated accounting.""" + + return _resolve_mandate(columns, mandate) + + +def decision_schedule_sessions( + index: pd.Index, + decision_policy: dict[str, object], +) -> pd.Series: + """Return the immutable schedule group for every complete-panel row.""" + + if not index.is_unique or not _valid_decision_policy(decision_policy): + raise PortfolioFailure( + "portfolio.decision-schedule", + "Decision schedule requires a unique index and valid policy", + ) + if decision_policy["kind"] == "calendar-month-end": + timestamps = pd.DatetimeIndex(index) + if timestamps.tz is not None: + timestamps = timestamps.tz_convert("America/New_York").tz_localize(None) + return pd.Series( + timestamps.to_period("M").astype(str), + index=index, + dtype="string", + name="decision_session", + ) + decision_anchor = str(decision_policy["anchor"]) + if decision_anchor == "dataset-start": + return pd.Series( + "dataset", + index=index, + dtype="string", + name="decision_session", + ) + timestamps = pd.DatetimeIndex(index) + if timestamps.tz is None: + raise PortfolioFailure( + "portfolio.decision-anchor", + "session-start requires timezone-aware XNYS base closes", + ) + return pd.Series( + timestamps.tz_convert("UTC").strftime("%Y-%m-%d"), + index=index, + dtype="string", + name="decision_session", + ) + + +def decision_schedule_mask( + index: pd.Index, + decision_policy: dict[str, object], +) -> pd.Series: + """Return one immutable complete-panel anchor-aware decision mask.""" + + if not index.is_unique or not _valid_decision_policy(decision_policy): + raise PortfolioFailure( + "portfolio.decision-cadence", + "Decision cadence requires a unique index and valid policy", + ) + if decision_policy["kind"] == "calendar-month-end": + timestamps = pd.DatetimeIndex(index) + if timestamps.empty: + return pd.Series( + [], + index=index, + dtype=bool, + name="decision_eligible", + ) + if timestamps.tz is not None: + local_dates = timestamps.tz_convert( + "America/New_York" + ).tz_localize(None).normalize() + else: + local_dates = timestamps.normalize() + month_end = local_dates.max() + pd.offsets.MonthEnd(0) + calendar = exchange_calendars.get_calendar( + "XNYS", + start=str(local_dates.min().date()), + end=str(month_end.date()), + ) + official_sessions = pd.DatetimeIndex(calendar.sessions) + if official_sessions.tz is not None: + official_sessions = official_sessions.tz_localize(None) + official_sessions = official_sessions.normalize() + official_month_ends = ( + pd.Series(official_sessions, index=official_sessions.to_period("M")) + .groupby(level=0, sort=True) + .max() + ) + eligible_dates = set(official_month_ends.tolist()) + return pd.Series( + local_dates.isin(eligible_dates), + index=index, + dtype=bool, + name="decision_eligible", + ) + decision_every_bars = int(decision_policy["bars"]) + sessions = decision_schedule_sessions(index, decision_policy) + ordinal = sessions.groupby(sessions, sort=False).cumcount() + return pd.Series( + ordinal.mod(decision_every_bars).eq(0).to_numpy(), + index=index, + dtype=bool, + name="decision_eligible", + ) + + +def _govern_portfolio_risk( + raw_targets: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + resolved: dict[str, object], + *, + enabled: bool, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> tuple[pd.Series, dict[str, float | int | str]]: + """Apply one causal, one-sided portfolio-volatility ceiling.""" + + gross = float(raw_targets.abs().sum()) + policy = resolved["risk_policy"] + if gross <= 1e-12: + return raw_targets.copy(), { + "status": "flat", + "observations": 0, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ( + float(policy["annualized_volatility_ceiling"]) + if isinstance(policy, dict) + else 0.0 + ), + "scale": 1.0, + } + if policy is None: + return raw_targets.copy(), { + "status": "legacy_none", + "observations": 0, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": 0.0, + "scale": 1.0, + } + assert isinstance(policy, dict) + covariance_values: np.ndarray | None + if risk_covariance_cache is not None: + cached = risk_covariance_cache.get(timestamp) + if cached is None: + raise PortfolioFailure( + "portfolio.risk-cache", + "Risk covariance cache is missing a decision timestamp", + ) + observations, covariance_values = cached + if ( + covariance_values is not None + and covariance_values.shape + != (len(raw_targets), len(raw_targets)) + ): + raise PortfolioFailure( + "portfolio.risk-cache", + "Risk covariance cache shape is invalid", + ) + else: + history = ( + close_returns.loc[:timestamp] + .tail(int(policy["covariance_window"])) + .dropna(how="any") + ) + observations = int(len(history)) + covariance = history.cov(ddof=0).reindex( + index=raw_targets.index, + columns=raw_targets.index, + ) + covariance_values = ( + None + if covariance.isna().any().any() + else covariance.to_numpy(dtype=float) + ) + minimum = int(policy["minimum_observations"]) + ceiling = float(policy["annualized_volatility_ceiling"]) + if observations < minimum: + return raw_targets * 0.0, { + "status": "insufficient_history", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + if covariance_values is None: + return raw_targets * 0.0, { + "status": "invalid_covariance", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + vector = raw_targets.to_numpy(dtype=float) + variance = float(vector @ covariance_values @ vector) + if not math.isfinite(variance) or variance < -1e-12: + return raw_targets * 0.0, { + "status": "invalid_covariance", + "observations": observations, + "pre_annualized_volatility": 0.0, + "post_annualized_volatility": 0.0, + "annualized_volatility_ceiling": ceiling, + "scale": 0.0, + } + forecast = math.sqrt( + max(variance, 0.0) * int(policy["annualization_periods"]) + ) + scale = ( + min(1.0, ceiling / forecast) + if enabled and forecast > 1e-12 + else 1.0 + ) + governed = raw_targets * scale + return governed, { + "status": ( + "diagnostic_disabled" + if not enabled + else "volatility_limited" + if scale < 1.0 - 1e-12 + else "within_ceiling" + ), + "observations": observations, + "pre_annualized_volatility": forecast, + "post_annualized_volatility": forecast * scale, + "annualized_volatility_ceiling": ceiling, + "scale": scale, + } + + +def build_risk_covariance_cache( + closes: pd.DataFrame, + *, + mandate: dict[str, object] | None, +) -> RiskCovarianceCache: + """Precompute one causal covariance panel for repeated fixed simulations.""" + + resolved = _resolve_mandate(closes.columns, mandate) + policy = resolved["risk_policy"] + if policy is None: + return {} + assert isinstance(policy, dict) + close_returns = closes.pct_change(fill_method=None) + cache: RiskCovarianceCache = {} + window = int(policy["covariance_window"]) + minimum = int(policy["minimum_observations"]) + return_values = close_returns.to_numpy(dtype=float) + for row_number, timestamp in enumerate(closes.index): + history = return_values[ + max(0, row_number - window + 1) : row_number + 1 + ] + history = history[np.isfinite(history).all(axis=1)] + observations = int(history.shape[0]) + covariance_values: np.ndarray | None = None + if observations >= minimum: + covariance_values = np.atleast_2d( + np.cov( + history, + rowvar=False, + ddof=0, + ) + ) + if ( + covariance_values.shape + != (len(closes.columns), len(closes.columns)) + or not np.isfinite(covariance_values).all() + ): + covariance_values = None + cache[timestamp] = (observations, covariance_values) + return cache + + +def _repair_executed_mandate_constraints( + weights: pd.Series, + resolved: dict[str, object], +) -> tuple[pd.Series, dict[str, float | bool | str]]: + """Scale down a chosen book until every hard Mandate constraint holds.""" + + original = weights.to_numpy(dtype=float, copy=False) + roles = resolved["asset_position_roles"] + caps = resolved["asset_max_abs_weights"] + assert isinstance(roles, dict) + assert isinstance(caps, dict) + role_values = resolved.get("_constraint_role_values") + cap_values = resolved.get("_constraint_cap_values") + if ( + not isinstance(role_values, tuple) + or len(role_values) != len(weights) + or not isinstance(cap_values, np.ndarray) + or cap_values.shape != (len(weights),) + ): + role_values = tuple( + str(roles[str(asset)]) for asset in weights.index + ) + cap_values = np.asarray( + [float(caps[str(asset)]) for asset in weights.index], + dtype=float, + ) + resolved["_constraint_role_values"] = role_values + resolved["_constraint_cap_values"] = cap_values + family = str(resolved["family"]) + gross_limit = float(resolved["gross_limit"]) + long_limit = float(resolved["long_gross_limit"]) + short_limit = float(resolved["short_gross_limit"]) + repaired_values = original.copy() + for position, role in enumerate(role_values): + cap = float(cap_values[position]) + value = float(repaired_values[position]) + if role == "context-only": + value = 0.0 + elif role == "long-only": + value = max(value, 0.0) + elif role == "short-only": + value = min(value, 0.0) + repaired_values[position] = min(max(value, -cap), cap) + + def scale_side(positive: bool, limit: float) -> None: + mask = repaired_values > 0.0 if positive else repaired_values < 0.0 + exposure = float( + repaired_values[mask].sum() + if positive + else -repaired_values[mask].sum() + ) + if exposure > limit + 1e-15: + repaired_values[mask] *= limit / exposure + + if family == "dollar-neutral": + positive = repaired_values > 0.0 + negative = repaired_values < 0.0 + long_exposure = float(repaired_values[positive].sum()) + short_exposure = float(-repaired_values[negative].sum()) + funded_side = min( + long_exposure, + short_exposure, + long_limit, + short_limit, + gross_limit / 2.0, + ) + if funded_side <= 1e-15: + repaired_values.fill(0.0) + else: + repaired_values[positive] *= funded_side / long_exposure + repaired_values[negative] *= funded_side / short_exposure + else: + scale_side(True, long_limit) + scale_side(False, short_limit) + gross = float(np.abs(repaired_values).sum()) + if gross > gross_limit + 1e-15: + repaired_values *= gross_limit / gross + + gross = float(np.abs(repaired_values).sum()) + long_exposure = float(repaired_values[repaired_values > 0.0].sum()) + short_exposure = float(-repaired_values[repaired_values < 0.0].sum()) + net = float(repaired_values.sum()) + cap_excess = float( + np.maximum(np.abs(repaired_values) - cap_values, 0.0).max( + initial=0.0 + ) + ) + context_exposure = max( + ( + abs(float(repaired_values[position])) + for position, role in enumerate(role_values) + if role == "context-only" + ), + default=0.0, + ) + role_error = max( + ( + max(0.0, -float(repaired_values[position])) + if role == "long-only" + else max(0.0, float(repaired_values[position])) + if role == "short-only" + else 0.0 + for position, role in enumerate(role_values) + ), + default=0.0, + ) + net_error = ( + abs(net) + if family == "dollar-neutral" + else abs(net - gross) + if family == "long-cash" + else abs(net + gross) + if family == "short-cash" + else 0.0 + ) + maximum_error = max( + 0.0, + gross - gross_limit, + long_exposure - long_limit, + short_exposure - short_limit, + cap_excess, + context_exposure, + role_error, + net_error, + ) + if maximum_error > 1e-10: + raise PortfolioFailure( + "portfolio.executed-constraint-breach", + "Final executed book cannot be repaired to the Portfolio Mandate", + ) + repair_one_way = 0.5 * float(np.abs(repaired_values - original).sum()) + repaired = ( + pd.Series(repaired_values, index=weights.index, dtype=float) + if repair_one_way > 1e-12 + else weights.astype(float, copy=False) + ) + return repaired, { + "status": ( + "constraint_repaired" + if repair_one_way > 1e-12 + else "within_constraints" + ), + "repaired": repair_one_way > 1e-12, + "repair_one_way": repair_one_way, + "maximum_error": maximum_error, + } + + +def execute_risk_compliant_book( + pretrade: pd.Series, + proposed: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + *, + mandate: dict[str, object] | None, + no_trade_one_way: float = NO_TRADE_ONE_WAY, + ordinary_rebalance_allowed: bool = True, + risk_covariance_cache: RiskCovarianceCache | None = None, + _resolved_mandate: dict[str, object] | None = None, + _state_only: bool = False, +) -> tuple[pd.Series, dict[str, object]]: + """Choose the final book, with risk compliance outranking no-trade.""" + + if ( + not pretrade.index.equals(proposed.index) + or list(close_returns.columns) != list(pretrade.index) + or timestamp not in close_returns.index + or not 0 <= no_trade_one_way <= 1 + or not isinstance(ordinary_rebalance_allowed, bool) + or not isinstance(_state_only, bool) + ): + raise PortfolioFailure( + "portfolio.execution-risk", + "Invalid executed-book risk inputs", + ) + if not np.isfinite(pretrade.to_numpy(dtype=float)).all() or not np.isfinite( + proposed.to_numpy(dtype=float) + ).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Executed-book risk inputs contain non-finite weights", + ) + resolved = ( + _resolved_mandate + if _resolved_mandate is not None + else _resolve_mandate(pretrade.index, mandate) + ) + if _state_only and not ordinary_rebalance_allowed: + constraint_book, _ = _repair_executed_mandate_constraints( + pretrade, + resolved, + ) + current, _ = _govern_portfolio_risk( + constraint_book, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + return current, {} + pretrade_risk: dict[str, object] | None = None + if not _state_only: + _, pretrade_risk = _govern_portfolio_risk( + pretrade, + close_returns, + timestamp, + resolved, + enabled=False, + risk_covariance_cache=risk_covariance_cache, + ) + constrained_proposed, _ = _repair_executed_mandate_constraints( + proposed, + resolved, + ) + runtime_proposed, proposed_risk = _govern_portfolio_risk( + constrained_proposed, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + proposed_delta = runtime_proposed - pretrade + proposed_one_way = 0.5 * float(proposed_delta.abs().sum()) + ordinary_rebalance = ( + ordinary_rebalance_allowed + and proposed_one_way + 1e-12 >= no_trade_one_way + ) + ordinary_book = runtime_proposed if ordinary_rebalance else pretrade + constraint_book, constraint_evidence = ( + _repair_executed_mandate_constraints( + ordinary_book, + resolved, + ) + ) + if ordinary_rebalance: + current = constraint_book + execution_risk = proposed_risk + else: + current, execution_risk = _govern_portfolio_risk( + constraint_book, + close_returns, + timestamp, + resolved, + enabled=True, + risk_covariance_cache=risk_covariance_cache, + ) + risk_repair_trade = current - constraint_book + risk_repaired = bool(risk_repair_trade.abs().sum() > 1e-12) + constraint_repaired = bool(constraint_evidence["repaired"]) + risk_override = risk_repaired and not ordinary_rebalance + constraint_override = constraint_repaired and not ordinary_rebalance + actual_trade = current - pretrade + rebalanced = bool(actual_trade.abs().sum() > 1e-12) + if _state_only: + return current, {} + assert pretrade_risk is not None + raw_status = str(execution_risk["status"]) + if risk_repaired: + status = ( + f"{raw_status}_fail_flat" + if raw_status in {"insufficient_history", "invalid_covariance"} + else "risk_repaired" + ) + elif constraint_repaired: + status = "constraint_repaired" + else: + status = raw_status + if risk_override and constraint_override: + reason = "mandate_and_risk_override" + elif constraint_override: + reason = "mandate_constraint_override" + elif risk_override: + reason = "risk_ceiling_override" + elif risk_repaired: + reason = "target_risk_repair" + elif constraint_repaired: + reason = "target_constraint_repair" + elif ordinary_rebalance: + reason = "rebalance_threshold_met" + elif not ordinary_rebalance_allowed: + reason = "decision_schedule_hold" + else: + reason = "portfolio_no_trade_band" + + final_forecast = float(execution_risk["post_annualized_volatility"]) + ceiling = float(execution_risk["annualized_volatility_ceiling"]) + policy = resolved["risk_policy"] + forecast_available = ( + isinstance(policy, dict) + and raw_status + not in {"insufficient_history", "invalid_covariance"} + ) + if ( + forecast_available + and final_forecast + > ceiling + RISK_COMPLIANCE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.risk-breach", + "Final executed book exceeds the volatility ceiling", + ) + result: dict[str, object] = { + "status": status, + "forecast_available": forecast_available, + "observations": int(execution_risk["observations"]), + "pretrade_forecast_annualized": float( + pretrade_risk["pre_annualized_volatility"] + ), + "proposed_forecast_pre_annualized": float( + proposed_risk["pre_annualized_volatility"] + ), + "proposed_forecast_post_annualized": float( + proposed_risk["post_annualized_volatility"] + ), + "executed_forecast_annualized": final_forecast, + "annualized_volatility_ceiling": ceiling, + "proposed_runtime_scale": float(proposed_risk["scale"]), + "risk_repair_scale": float(execution_risk["scale"]), + "proposed_one_way": proposed_one_way, + "ordinary_rebalance": ordinary_rebalance, + "decision_eligible": ordinary_rebalance_allowed, + "risk_rebalance_override": risk_override, + "constraint_rebalance_override": constraint_override, + "constraint_repair_one_way": float( + constraint_evidence["repair_one_way"] + ), + "executed_constraint_maximum_error": float( + constraint_evidence["maximum_error"] + ), + "rebalanced": rebalanced, + "execution_reason": reason, + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Executed-book risk evidence contains invalid values", + ) + return current, result + + +def _signal_transition( + previous: int, + score: float | None, + *, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + if score is None or not math.isfinite(score): + return ( + 0, + "unavailable_flat" if previous == 0 else "unavailable_reset", + ) + if previous == 0: + if score >= long_entry: + return 1, "enter_long" + if score <= short_entry: + return -1, "enter_short" + return 0, "stay_flat" + if previous == 1: + if score <= short_entry: + return -1, "reverse_long_to_short" + if score < long_exit: + return 0, "exit_long" + return 1, "hold_long" + if previous == -1: + if score >= long_entry: + return 1, "reverse_short_to_long" + if score > short_exit: + return 0, "exit_short" + return -1, "hold_short" + raise PortfolioFailure("portfolio.state", "Unknown prior signal state") + + +def _directional_signal_transition( + previous: int, + score: float | None, + *, + family: str, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + if family == "dollar-neutral": + return _signal_transition( + previous, + score, + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + if score is None or not math.isfinite(score): + return ( + 0, + "unavailable_flat" if previous == 0 else "unavailable_reset", + ) + if family == "long-cash": + if previous == 1: + return (1, "hold_long") if score >= long_exit else (0, "exit_long") + return (1, "enter_long") if score >= long_entry else (0, "stay_flat") + if family == "short-cash": + if previous == -1: + return ( + (-1, "hold_short") + if score <= short_exit + else (0, "exit_short") + ) + return ( + (-1, "enter_short") + if score <= short_entry + else (0, "stay_flat") + ) + raise PortfolioFailure("mandate.family", "Unknown Portfolio Mandate family") + + +def _position_role_signal_transition( + previous: int, + score: float | None, + *, + role: str, + long_entry: float, + long_exit: float, + short_exit: float, + short_entry: float, +) -> tuple[int, str]: + family = { + "long-only": "long-cash", + "short-only": "short-cash", + "two-sided": "dollar-neutral", + }.get(role) + if family is None: + if role == "context-only": + return 0, "context_only" + raise PortfolioFailure( + "mandate.asset-position-role", + "Unknown asset position role", + ) + return _directional_signal_transition( + previous, + score, + family=family, + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + + +def _weight_action(previous: float, current: float) -> str: + tolerance = 1e-12 + previous_zero = abs(previous) <= tolerance + current_zero = abs(current) <= tolerance + if previous_zero and current_zero: + return "stay_flat" + if previous_zero: + return "open_long" if current > 0 else "open_short" + if current_zero: + return "close_long" if previous > 0 else "close_short" + if previous * current < 0: + return ( + "reverse_long_to_short" + if previous > 0 + else "reverse_short_to_long" + ) + if abs(previous - current) <= tolerance: + return "hold_long" if current > 0 else "hold_short" + return "resize_long" if current > 0 else "resize_short" + + +def construct_signal_policy( + factors: pd.DataFrame, + closes: pd.DataFrame, + *, + volatility_window: int = VOLATILITY_WINDOW, + gross_target: float = GROSS_TARGET, + max_abs_weight: float = MAX_ABS_WEIGHT, + long_entry: float = LONG_ENTRY_PERCENTILE, + long_exit: float = LONG_EXIT_PERCENTILE, + short_exit: float = SHORT_EXIT_PERCENTILE, + short_entry: float = SHORT_ENTRY_PERCENTILE, + mandate: dict[str, object] | None = None, + prediction_population: dict[str, object] | None = None, + temporal_translation_window: int = TEMPORAL_SCORE_WINDOW, + temporal_translation_minimum: int = TEMPORAL_SCORE_MINIMUM, + apply_risk_governor: bool = True, + risk_covariance_cache: RiskCovarianceCache | None = None, + include_ledger: bool = True, +) -> SignalConstruction: + """Turn causal factor ranks into persistent intent and target weights.""" + + if not factors.index.equals(closes.index) or list(factors.columns) != list( + closes.columns + ): + raise PortfolioFailure( + "portfolio.alignment", + "Factor and close panels must have identical index and columns", + ) + resolved = _resolve_mandate(factors.columns, mandate) + gross_target = float(resolved["gross_limit"]) + max_abs_weight = float(resolved["max_abs_weight"]) + asset_caps = pd.Series( + resolved["asset_max_abs_weights"], + index=factors.columns, + dtype=float, + ) + asset_position_roles = { + str(asset): str(role) + for asset, role in resolved["asset_position_roles"].items() + } + long_gross_limit = float(resolved["long_gross_limit"]) + short_gross_limit = float(resolved["short_gross_limit"]) + family = str(resolved["family"]) + tradable_assets = set(resolved["tradable_assets"]) + if ( + volatility_window < 2 + or not 0 < gross_target <= 2 + or not 0 < max_abs_weight <= gross_target + or ( + family == "dollar-neutral" + and max_abs_weight > gross_target / 2 + ) + or not ( + 0.0 + <= short_entry + <= short_exit + < long_exit + <= long_entry + <= 1.0 + ) + or not isinstance(include_ledger, bool) + ): + raise PortfolioFailure( + "portfolio.parameters", + "Invalid fixed signal-policy parameters", + ) + returns = closes.pct_change(fill_method=None) + volatility = ( + returns.rolling( + volatility_window, + min_periods=volatility_window, + ) + .std(ddof=0) + .clip(lower=1e-6) + ) + side_budget = gross_target / 2.0 + targets = pd.DataFrame(0.0, index=factors.index, columns=factors.columns) + states = pd.DataFrame(0, index=factors.index, columns=factors.columns) + prior_states = pd.Series(0, index=factors.columns, dtype=int) + prior_targets = pd.Series(0.0, index=factors.columns, dtype=float) + ledger_rows: list[dict[str, object]] = [] + decision_policy = dict( + resolved["implementation_policy"]["decision_policy"] + ) + decision_schedule_kind = str(decision_policy["kind"]) + decision_every_bars = decision_policy.get("bars") + decision_anchor = decision_policy.get("anchor") + decision_sessions = decision_schedule_sessions( + factors.index, + decision_policy, + ) + decision_mask = decision_schedule_mask( + factors.index, + decision_policy, + ) + finite_volatility = pd.DataFrame( + np.isfinite(volatility.to_numpy(dtype=float)), + index=volatility.index, + columns=volatility.columns, + ) + ( + score_panel, + translation_values, + translation_observations, + translation_semantics, + ) = translate_factor_scores( + factors, + prediction_population, + temporal_window=temporal_translation_window, + temporal_minimum=temporal_translation_minimum, + ) + insufficient_score_status = ( + "insufficient_temporal_history" + if translation_semantics["evaluation_mode"] + == SINGLE_ASSET_TEMPORAL_MODE + else "insufficient_cross_section" + ) + valid_panel = score_panel.notna() & volatility.notna() & finite_volatility + sufficient_rows = valid_panel.any(axis=1) + + for timestamp in factors.index: + decision_eligible = bool(decision_mask.loc[timestamp]) + row_factor = ( + factors.loc[timestamp].astype(float) + if include_ledger + else None + ) + row_volatility = volatility.loc[timestamp].astype(float) + scores = score_panel.loc[timestamp].where(valid_panel.loc[timestamp]) + translated_scores = score_panel.loc[timestamp] + sufficient = bool(sufficient_rows.loc[timestamp]) + + current_states = pd.Series(0, index=factors.columns, dtype=int) + events: dict[str, str] = {} + convictions = pd.Series(0.0, index=factors.columns, dtype=float) + strengths = pd.Series(0.0, index=factors.columns, dtype=float) + if decision_eligible: + for asset in factors.columns: + if str(asset) not in tradable_assets: + current_states.loc[asset] = 0 + events[str(asset)] = "context_only" + continue + raw_score = scores.loc[asset] + score = ( + float(raw_score) + if math.isfinite(raw_score) + else None + ) + state, event = _position_role_signal_transition( + int(prior_states.loc[asset]), + score, + role=asset_position_roles[str(asset)], + long_entry=long_entry, + long_exit=long_exit, + short_exit=short_exit, + short_entry=short_entry, + ) + current_states.loc[asset] = state + events[str(asset)] = event + if state != 0 and score is not None: + conviction = 2.0 * abs(score - 0.5) + convictions.loc[asset] = conviction + strengths.loc[asset] = ( + conviction / float(row_volatility.loc[asset]) + ) + else: + current_states = prior_states.copy() + events = { + str(asset): ( + "decision_schedule_hold" + if str(asset) in tradable_assets + else "context_only" + ) + for asset in factors.columns + } + for asset in factors.columns: + score = scores.loc[asset] + state = int(current_states.loc[asset]) + volatility_value = row_volatility.loc[asset] + if ( + str(asset) in tradable_assets + and state != 0 + and math.isfinite(score) + and math.isfinite(volatility_value) + ): + conviction = 2.0 * abs(float(score) - 0.5) + convictions.loc[asset] = conviction + strengths.loc[asset] = ( + conviction / float(volatility_value) + ) + + if not decision_eligible: + current_targets = prior_targets.copy() + allocation_status = "decision_schedule_hold" + elif family == "dollar-neutral": + if ( + translation_semantics["evaluation_mode"] + == TWO_ASSET_RELATIVE_VALUE_MODE + ): + pair = translation_semantics["relative_value_pair"] + assert isinstance(pair, dict) + left = str(pair["left_asset"]) + right = str(pair["right_asset"]) + left_state = int(current_states.loc[left]) + right_state = int(current_states.loc[right]) + paired = ( + abs(left_state) == 1 + and right_state == -left_state + ) + pair_budget = min( + side_budget, + float(asset_caps.loc[left]), + float(asset_caps.loc[right]), + ) + current_targets = pd.Series( + 0.0, + index=factors.columns, + dtype=float, + ) + if paired: + current_targets.loc[left] = left_state * pair_budget + current_targets.loc[right] = right_state * pair_budget + allocation_status = ( + "insufficient_temporal_history" + if not sufficient + else ( + "no_permitted_signal" + if not paired + else ( + "allocated" + if math.isclose( + pair_budget, + side_budget, + rel_tol=0.0, + abs_tol=1e-9, + ) + else "allocated_with_cash" + ) + ) + ) + else: + long_weights = _allocate_capped_side( + strengths.where(current_states.eq(1), 0.0), + budget=side_budget, + cap=asset_caps, + ) + short_weights = _allocate_capped_side( + strengths.where(current_states.eq(-1), 0.0), + budget=side_budget, + cap=asset_caps, + ) + allocated = ( + abs(float(long_weights.sum()) - side_budget) <= 1e-9 + and abs(float(short_weights.sum()) - side_budget) <= 1e-9 + ) + current_targets = ( + long_weights - short_weights + if allocated + else pd.Series(0.0, index=factors.columns, dtype=float) + ) + allocation_status = ( + "allocated" + if allocated + else ( + "insufficient_cross_section" + if not sufficient + else "insufficient_side_breadth" + ) + ) + elif family == "asset-role": + long_weights = _allocate_capped_up_to( + strengths.where(current_states.eq(1), 0.0), + limit=long_gross_limit, + cap=asset_caps, + ) + short_weights = _allocate_capped_up_to( + strengths.where(current_states.eq(-1), 0.0), + limit=short_gross_limit, + cap=asset_caps, + ) + current_targets = long_weights - short_weights + used_long = float(long_weights.sum()) + used_short = float(short_weights.sum()) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if used_long + used_short <= 1e-12 + else ( + "allocated" + if math.isclose( + used_long, + long_gross_limit, + rel_tol=0.0, + abs_tol=1e-9, + ) + and math.isclose( + used_short, + short_gross_limit, + rel_tol=0.0, + abs_tol=1e-9, + ) + else "allocated_with_cash" + ) + ) + ) + elif family == "long-cash": + current_targets = _allocate_capped_up_to( + strengths.where(current_states.eq(1), 0.0), + limit=gross_target, + cap=asset_caps, + ) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if float(current_targets.abs().sum()) <= 1e-12 + else ( + "allocated" + if abs(float(current_targets.sum()) - gross_target) + <= 1e-9 + else "allocated_with_cash" + ) + ) + ) + elif family == "short-cash": + current_targets = -_allocate_capped_up_to( + strengths.where(current_states.eq(-1), 0.0), + limit=gross_target, + cap=asset_caps, + ) + allocation_status = ( + insufficient_score_status + if not sufficient + else ( + "no_permitted_signal" + if float(current_targets.abs().sum()) <= 1e-12 + else ( + "allocated" + if abs(float(current_targets.sum()) + gross_target) + <= 1e-9 + else "allocated_with_cash" + ) + ) + ) + else: + current_targets = pd.Series( + 0.0, + index=factors.columns, + dtype=float, + ) + allocation_status = "invalid_mandate" + pre_governor_targets = current_targets.copy() + if decision_eligible: + current_targets, risk_governor = _govern_portfolio_risk( + pre_governor_targets, + returns, + timestamp, + resolved, + enabled=apply_risk_governor, + risk_covariance_cache=risk_covariance_cache, + ) + else: + pre_governor_targets = prior_targets.copy() + _, risk_governor = _govern_portfolio_risk( + pre_governor_targets, + returns, + timestamp, + resolved, + enabled=False, + risk_covariance_cache=risk_covariance_cache, + ) + current_targets = prior_targets.copy() + diagonal_risk = current_targets.abs() * row_volatility.fillna(0.0) + diagonal_total = float(diagonal_risk.sum()) + diagonal_share = ( + diagonal_risk / diagonal_total + if diagonal_total > 1e-12 + else pd.Series(0.0, index=factors.columns, dtype=float) + ) + targets.loc[timestamp] = current_targets + states.loc[timestamp] = current_states + if include_ledger: + assert row_factor is not None + for asset in factors.columns: + score = scores.loc[asset] + translation_score = translated_scores.loc[asset] + volatility_value = row_volatility.loc[asset] + factor_value = row_factor.loc[asset] + translation_value = translation_values.loc[timestamp, asset] + translation_count = translation_observations.loc[ + timestamp, + asset, + ] + target = float(current_targets.loc[asset]) + previous_target = float(prior_targets.loc[asset]) + ledger_rows.append( + { + "timestamp": timestamp, + "asset": str(asset), + "factor": ( + float(factor_value) + if math.isfinite(factor_value) + else np.nan + ), + "percentile_score": ( + float(score) if math.isfinite(score) else np.nan + ), + "translation_score": ( + float(translation_score) + if math.isfinite(translation_score) + else np.nan + ), + "translation_value": ( + float(translation_value) + if math.isfinite(translation_value) + else np.nan + ), + "translation_observations": int(translation_count), + "translation_method": translation_semantics["method"], + "evaluation_mode": translation_semantics[ + "evaluation_mode" + ], + "prior_signal_state": int( + prior_states.loc[asset] + ), + "signal_state": int(current_states.loc[asset]), + "signal_event": events[str(asset)], + "decision_eligible": decision_eligible, + "decision_schedule_kind": decision_schedule_kind, + "decision_every_bars": decision_every_bars, + "decision_anchor": decision_anchor, + "decision_session": str( + decision_sessions.loc[timestamp] + ), + "tradable": str(asset) in tradable_assets, + "permitted_direction": family, + "position_role": asset_position_roles[str(asset)], + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + "mandate_id": str(resolved["id"]), + "conviction": float(convictions.loc[asset]), + "trailing_volatility": ( + float(volatility_value) + if math.isfinite(volatility_value) + else np.nan + ), + "risk_strength": float(strengths.loc[asset]), + "allocation_status": ( + allocation_status + if str(asset) in tradable_assets + else "context_only" + ), + "pre_governor_target_weight": float( + pre_governor_targets.loc[asset] + ), + "risk_governor_status": str( + risk_governor["status"] + ), + "risk_estimation_observations": int( + risk_governor["observations"] + ), + "risk_forecast_pre_annualized": float( + risk_governor["pre_annualized_volatility"] + ), + "risk_forecast_post_annualized": float( + risk_governor["post_annualized_volatility"] + ), + "risk_volatility_ceiling_annualized": float( + risk_governor[ + "annualized_volatility_ceiling" + ] + ), + "risk_governor_scale": float( + risk_governor["scale"] + ), + "prior_target_weight": previous_target, + "proposed_target_weight": target, + "target_delta": target - previous_target, + "target_action": _weight_action( + previous_target, + target, + ), + "diagonal_risk_budget_share": float( + diagonal_share.loc[asset] + ), + } + ) + prior_states = current_states + prior_targets = current_targets + + return SignalConstruction( + targets=targets, + states=states, + ledger=pd.DataFrame(ledger_rows), + scores=score_panel, + translation_values=translation_values, + translation_observations=translation_observations, + translation=translation_semantics, + ) + + +def drift_weights( + previous: pd.Series, + realized_returns: pd.Series, +) -> pd.Series: + """Drift prior close targets through the just-realized asset return.""" + + aligned_returns = realized_returns.reindex(previous.index).fillna(0.0).astype(float) + gross_return = float((previous * aligned_returns).sum()) + denominator = 1.0 + gross_return + if not math.isfinite(denominator) or denominator <= 1e-9: + raise PortfolioFailure( + "portfolio.bankrupt", + "Portfolio drift denominator is non-positive", + ) + drifted = previous * (1.0 + aligned_returns) / denominator + if not np.isfinite(drifted.to_numpy()).all(): + raise PortfolioFailure("portfolio.non-finite", "Drift produced non-finite weights") + return drifted + + +def simulate_targets( + targets: pd.DataFrame, + closes: pd.DataFrame, + volumes: pd.DataFrame, + *, + cost_bps: float | None = None, + no_trade_one_way: float | None = None, + reference_nav: float | None = None, + extra_delay: int = 0, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> Simulation: + """Execute close targets, then credit only the following close return.""" + + if ( + not targets.index.equals(closes.index) + or not targets.index.equals(volumes.index) + or list(targets.columns) != list(closes.columns) + or list(targets.columns) != list(volumes.columns) + ): + raise PortfolioFailure( + "portfolio.alignment", + "Targets, closes, and volumes must share one panel shape", + ) + implementation = resolve_implementation_policy(mandate) + cost_bps = ( + implementation["base_cost_bps"] + if cost_bps is None + else float(cost_bps) + ) + no_trade_one_way = ( + implementation["no_trade_one_way"] + if no_trade_one_way is None + else float(no_trade_one_way) + ) + reference_nav = ( + implementation["reference_nav"] + if reference_nav is None + else float(reference_nav) + ) + decision_policy = dict(implementation["decision_policy"]) + decision_schedule_kind = str(decision_policy["kind"]) + decision_every_bars = decision_policy.get("bars") + decision_anchor = decision_policy.get("anchor") + decision_sessions = decision_schedule_sessions( + targets.index, + decision_policy, + ) + if cost_bps < 0 or not 0 <= no_trade_one_way <= 1 or reference_nav <= 0: + raise PortfolioFailure( + "portfolio.parameters", + "Invalid accounting parameters", + ) + if not isinstance(extra_delay, int) or extra_delay < 0: + raise PortfolioFailure("portfolio.delay", "extra_delay must be non-negative") + resolved = _resolve_mandate(targets.columns, mandate) + benchmark = resolved["benchmark"] + benchmark_weights = pd.Series( + benchmark["weights"], + index=targets.columns, + dtype=float, + ) + proposed_targets = targets.shift(extra_delay).fillna(0.0) + decision_mask = decision_schedule_mask( + targets.index, + decision_policy, + ).shift(extra_delay, fill_value=False) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + target_values = proposed_targets.to_numpy(dtype=float) + close_return_values = close_returns.to_numpy(dtype=float) + forward_return_values = forward_returns.to_numpy(dtype=float) + close_values = closes.to_numpy(dtype=float) + volume_values = volumes.to_numpy(dtype=float) + executed_values = np.zeros(targets.shape, dtype=float) + trade_values = np.zeros(targets.shape, dtype=float) + participation_values = np.zeros(targets.shape, dtype=float) + daily_rows: list[dict[str, object]] = [] + prior_values = np.zeros(len(targets.columns), dtype=float) + + for row_number, timestamp in enumerate(targets.index): + decision_eligible = bool(decision_mask.loc[timestamp]) + if row_number == 0: + pretrade_values = np.zeros_like(prior_values) + else: + realized = np.nan_to_num( + close_return_values[row_number], + nan=0.0, + ) + gross_realized = float(prior_values @ realized) + denominator = 1.0 + gross_realized + if not math.isfinite(denominator) or denominator <= 1e-9: + raise PortfolioFailure( + "portfolio.bankrupt", + "Portfolio drift denominator is non-positive", + ) + pretrade_values = ( + prior_values * (1.0 + realized) / denominator + ) + pretrade = pd.Series( + pretrade_values, + index=targets.columns, + dtype=float, + ) + proposed = pd.Series( + np.nan_to_num(target_values[row_number], nan=0.0), + index=targets.columns, + dtype=float, + ) + current, execution_risk = execute_risk_compliant_book( + pretrade, + proposed, + close_returns, + timestamp, + mandate=mandate, + no_trade_one_way=no_trade_one_way, + ordinary_rebalance_allowed=decision_eligible, + risk_covariance_cache=risk_covariance_cache, + _resolved_mandate=resolved, + ) + rebalance = bool(execution_risk["rebalanced"]) + current_values = current.to_numpy(dtype=float) + row_trade_values = current_values - pretrade_values + traded_notional = float(np.abs(row_trade_values).sum()) + one_way_turnover = 0.5 * traded_notional + cost = traded_notional * cost_bps / 10_000.0 + next_returns = np.nan_to_num( + forward_return_values[row_number], + nan=0.0, + ) + gross_return = float(current_values @ next_returns) + net_return = gross_return - cost + benchmark_return = float( + benchmark_weights.to_numpy(dtype=float) @ next_returns + ) + dollar_volume = ( + close_values[row_number] * volume_values[row_number] + ) + row_participation = np.divide( + np.abs(row_trade_values) * reference_nav, + dollar_volume, + out=np.zeros_like(row_trade_values), + where=dollar_volume != 0.0, + ) + if not all( + math.isfinite(value) + for value in ( + gross_return, + net_return, + benchmark_return, + cost, + one_way_turnover, + traded_notional, + ) + ): + raise PortfolioFailure( + "portfolio.non-finite", + "Accounting produced non-finite values", + ) + executed_values[row_number] = current_values + trade_values[row_number] = row_trade_values + participation_values[row_number] = row_participation + daily_rows.append( + { + "gross_return": gross_return, + "net_return": net_return, + "benchmark_return": benchmark_return, + "one_way_turnover": one_way_turnover, + "traded_notional": traded_notional, + "cost": cost, + "gross_exposure": float(np.abs(current_values).sum()), + "net_exposure": float(current_values.sum()), + "cash_weight": 1.0 - float(np.abs(current_values).sum()), + "max_abs_weight": float(np.abs(current_values).max()), + "concentration_hhi": float((current_values**2).sum()), + "rebalanced": rebalance, + "decision_eligible": decision_eligible, + "decision_schedule_kind": decision_schedule_kind, + "decision_every_bars": decision_every_bars, + "decision_anchor": decision_anchor, + "decision_session": str( + decision_sessions.loc[timestamp] + ), + "execution_reason": str( + execution_risk["execution_reason"] + ), + "execution_risk_status": str(execution_risk["status"]), + "execution_risk_forecast_available": bool( + execution_risk["forecast_available"] + ), + "execution_risk_observations": int( + execution_risk["observations"] + ), + "pretrade_risk_forecast_annualized": float( + execution_risk["pretrade_forecast_annualized"] + ), + "proposed_risk_forecast_pre_annualized": float( + execution_risk[ + "proposed_forecast_pre_annualized" + ] + ), + "proposed_risk_forecast_post_annualized": float( + execution_risk[ + "proposed_forecast_post_annualized" + ] + ), + "executed_risk_forecast_annualized": float( + execution_risk["executed_forecast_annualized"] + ), + "execution_risk_ceiling_annualized": float( + execution_risk[ + "annualized_volatility_ceiling" + ] + ), + "proposed_runtime_risk_scale": float( + execution_risk["proposed_runtime_scale"] + ), + "execution_risk_repair_scale": float( + execution_risk["risk_repair_scale"] + ), + "proposed_one_way_turnover": float( + execution_risk["proposed_one_way"] + ), + "ordinary_rebalance": bool( + execution_risk["ordinary_rebalance"] + ), + "risk_rebalance_override": bool( + execution_risk["risk_rebalance_override"] + ), + "constraint_rebalance_override": bool( + execution_risk["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + execution_risk["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + execution_risk["executed_constraint_maximum_error"] + ), + "max_participation": float(row_participation.max()), + "mean_participation": float(row_participation.mean()), + } + ) + prior_values = current_values + daily = pd.DataFrame(daily_rows, index=targets.index) + executed = pd.DataFrame( + executed_values, + index=targets.index, + columns=targets.columns, + ) + trades = pd.DataFrame( + trade_values, + index=targets.index, + columns=targets.columns, + ) + participation = pd.DataFrame( + participation_values, + index=targets.index, + columns=targets.columns, + ) + valid = forward_returns.notna().any(axis=1) + return Simulation( + daily=daily.loc[valid].copy(), + weights=executed.loc[valid].copy(), + trades=trades.loc[valid].copy(), + participation=participation.loc[valid].copy(), + ) + + +def causal_market_regimes(closes: pd.DataFrame) -> pd.Series: + market_return = closes.pct_change(fill_method=None).mean(axis=1) + trailing_direction = ( + (1.0 + market_return) + .rolling(20, min_periods=20) + .apply(np.prod, raw=True) + - 1.0 + ) + trailing_volatility = market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0) + lagged_threshold = trailing_volatility.shift(1).rolling( + 60, + min_periods=20, + ).median() + labels = pd.Series("unavailable", index=closes.index, dtype="object") + valid = ( + trailing_direction.notna() + & trailing_volatility.notna() + & lagged_threshold.notna() + ) + for timestamp in closes.index[valid]: + direction = "up" if trailing_direction.loc[timestamp] >= 0 else "down" + volatility = ( + "stressed" + if trailing_volatility.loc[timestamp] + > lagged_threshold.loc[timestamp] + else "calm" + ) + labels.loc[timestamp] = f"{direction}-{volatility}" + return labels + + +def build_decision_ledger( + construction: SignalConstruction, + simulation: Simulation, + closes: pd.DataFrame, + volumes: pd.DataFrame, + *, + cost_bps: float | None = None, + reference_nav: float | None = None, + liquidity_adv_window: int = LIQUIDITY_ADV_WINDOW, + mandate: dict[str, object] | None = None, +) -> pd.DataFrame: + """Join signal intent, target sizing, execution, and attribution evidence.""" + + if ( + not construction.targets.index.equals(closes.index) + or not closes.index.equals(volumes.index) + or list(construction.targets.columns) != list(closes.columns) + or list(closes.columns) != list(volumes.columns) + or not simulation.weights.index.equals(simulation.daily.index) + or list(simulation.weights.columns) != list(closes.columns) + ): + raise PortfolioFailure( + "portfolio.alignment", + "Construction, simulation, and closes are not aligned", + ) + implementation = resolve_implementation_policy(mandate) + cost_bps = ( + implementation["base_cost_bps"] + if cost_bps is None + else float(cost_bps) + ) + reference_nav = ( + implementation["reference_nav"] + if reference_nav is None + else float(reference_nav) + ) + if reference_nav <= 0 or cost_bps < 0 or liquidity_adv_window < 2: + raise PortfolioFailure( + "portfolio.parameters", + "Invalid liquidity-capacity parameters", + ) + policy = construction.ledger.set_index(["timestamp", "asset"]) + if not policy.index.is_unique: + raise PortfolioFailure( + "portfolio.ledger", + "Signal construction ledger keys must be unique", + ) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + dollar_volume = closes.astype(float) * volumes.astype(float) + causal_adv = dollar_volume.rolling( + liquidity_adv_window, + min_periods=liquidity_adv_window, + ).mean() + regimes = causal_market_regimes(closes) + prior = pd.Series(0.0, index=closes.columns, dtype=float) + rows: list[dict[str, object]] = [] + for row_number, timestamp in enumerate(simulation.daily.index): + pretrade = ( + pd.Series(0.0, index=closes.columns, dtype=float) + if row_number == 0 + else drift_weights(prior, close_returns.loc[timestamp]) + ) + executed = simulation.weights.loc[timestamp].astype(float) + trade = simulation.trades.loc[timestamp].astype(float) + active_trade = trade.abs() > 1e-12 + adv_row = causal_adv.loc[timestamp].astype(float) + if not bool(active_trade.any()): + capacity_status = "no_trade" + portfolio_capacity = { + limit: 0.0 + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + binding_asset: str | None = None + elif ( + adv_row.loc[active_trade].isna().any() + or (adv_row.loc[active_trade] <= 0).any() + ): + capacity_status = "insufficient_adv_history" + portfolio_capacity = { + limit: 0.0 + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + binding_asset = None + else: + capacity_status = "available" + conservative_asset_capacity = ( + LIQUIDITY_PARTICIPATION_LIMITS[0] + * adv_row.loc[active_trade] + / trade.loc[active_trade].abs() + ) + binding_asset = str(conservative_asset_capacity.idxmin()) + portfolio_capacity = { + limit: float( + ( + limit + * adv_row.loc[active_trade] + / trade.loc[active_trade].abs() + ).min() + ) + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + next_return = forward_returns.loc[timestamp].fillna(0.0).astype(float) + history = close_returns.loc[:timestamp].tail(RISK_COVARIANCE_WINDOW) + history = history.dropna(how="all") + component_variance = pd.Series( + 0.0, + index=closes.columns, + dtype=float, + ) + portfolio_variance = 0.0 + if len(history) >= RISK_COVARIANCE_MINIMUM: + covariance = history.cov( + min_periods=RISK_COVARIANCE_MINIMUM, + ddof=0, + ).reindex(index=closes.columns, columns=closes.columns).fillna(0.0) + marginal = covariance.dot(executed) + component_variance = executed * marginal + portfolio_variance = float(component_variance.sum()) + variance_share = ( + component_variance / portfolio_variance + if portfolio_variance > 1e-18 + else pd.Series(0.0, index=closes.columns, dtype=float) + ) + portfolio_row = simulation.daily.loc[timestamp] + for asset in closes.columns: + policy_row = policy.loc[(timestamp, str(asset))] + asset_trade = float(trade.loc[asset]) + asset_adv = ( + float(adv_row.loc[asset]) + if math.isfinite(float(adv_row.loc[asset])) + else 0.0 + ) + reference_participation = ( + abs(asset_trade) * reference_nav / asset_adv + if ( + capacity_status == "available" + and abs(asset_trade) > 1e-12 + and asset_adv > 0 + ) + else 0.0 + ) + asset_capacity = { + limit: ( + limit * asset_adv / abs(asset_trade) + if ( + capacity_status == "available" + and abs(asset_trade) > 1e-12 + and asset_adv > 0 + ) + else 0.0 + ) + for limit in LIQUIDITY_PARTICIPATION_LIMITS + } + gross_contribution = float( + executed.loc[asset] * next_return.loc[asset] + ) + cost_contribution = ( + abs(asset_trade) * cost_bps / 10_000.0 + ) + executed_weight = float(executed.loc[asset]) + rows.append( + { + **policy_row.to_dict(), + "timestamp": timestamp, + "asset": str(asset), + "regime": str(regimes.loc[timestamp]), + "pretrade_weight": float(pretrade.loc[asset]), + "executed_weight": executed_weight, + "executed_state": ( + 1 + if executed_weight > 1e-12 + else -1 + if executed_weight < -1e-12 + else 0 + ), + "trade_weight": asset_trade, + "execution_action": _weight_action( + float(pretrade.loc[asset]), + executed_weight, + ), + "execution_reason": str( + portfolio_row["execution_reason"] + ), + "execution_risk_status": str( + portfolio_row["execution_risk_status"] + ), + "execution_risk_forecast_available": bool( + portfolio_row[ + "execution_risk_forecast_available" + ] + ), + "execution_risk_observations": int( + portfolio_row["execution_risk_observations"] + ), + "pretrade_risk_forecast_annualized": float( + portfolio_row[ + "pretrade_risk_forecast_annualized" + ] + ), + "proposed_risk_forecast_pre_annualized": float( + portfolio_row[ + "proposed_risk_forecast_pre_annualized" + ] + ), + "proposed_risk_forecast_post_annualized": float( + portfolio_row[ + "proposed_risk_forecast_post_annualized" + ] + ), + "executed_risk_forecast_annualized": float( + portfolio_row[ + "executed_risk_forecast_annualized" + ] + ), + "execution_risk_ceiling_annualized": float( + portfolio_row[ + "execution_risk_ceiling_annualized" + ] + ), + "proposed_runtime_risk_scale": float( + portfolio_row["proposed_runtime_risk_scale"] + ), + "execution_risk_repair_scale": float( + portfolio_row["execution_risk_repair_scale"] + ), + "proposed_one_way_turnover": float( + portfolio_row["proposed_one_way_turnover"] + ), + "ordinary_rebalance": bool( + portfolio_row["ordinary_rebalance"] + ), + "risk_rebalance_override": bool( + portfolio_row["risk_rebalance_override"] + ), + "constraint_rebalance_override": bool( + portfolio_row["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + portfolio_row["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + portfolio_row[ + "executed_constraint_maximum_error" + ] + ), + "liquidity_capacity_status": capacity_status, + "liquidity_adv_observations": ( + liquidity_adv_window + if math.isfinite(float(adv_row.loc[asset])) + else 0 + ), + "causal_adv_dollar_volume": asset_adv, + "reference_nav_adv_participation": ( + reference_participation + ), + "asset_capacity_nav_1pct": asset_capacity[0.01], + "asset_capacity_nav_5pct": asset_capacity[0.05], + "portfolio_capacity_nav_1pct": portfolio_capacity[0.01], + "portfolio_capacity_nav_5pct": portfolio_capacity[0.05], + "capacity_binding_asset": ( + binding_asset is not None + and str(asset) == binding_asset + ), + "asset_forward_return": float(next_return.loc[asset]), + "gross_return_contribution": gross_contribution, + "cost_contribution": cost_contribution, + "net_return_contribution": ( + gross_contribution - cost_contribution + ), + "one_way_turnover_contribution": 0.5 + * abs(asset_trade), + "component_variance": float( + component_variance.loc[asset] + ), + "variance_contribution_share": float( + variance_share.loc[asset] + ), + "portfolio_variance": portfolio_variance, + "portfolio_gross_return": float( + portfolio_row["gross_return"] + ), + "portfolio_cost": float(portfolio_row["cost"]), + "portfolio_net_return": float( + portfolio_row["net_return"] + ), + "portfolio_traded_notional": float( + portfolio_row["traded_notional"] + ), + } + ) + prior = executed + result = pd.DataFrame(rows) + required_numeric = result[ + [ + "conviction", + "risk_strength", + "pre_governor_target_weight", + "risk_estimation_observations", + "risk_forecast_pre_annualized", + "risk_forecast_post_annualized", + "risk_volatility_ceiling_annualized", + "risk_governor_scale", + "prior_target_weight", + "proposed_target_weight", + "target_delta", + "diagonal_risk_budget_share", + "pretrade_weight", + "executed_weight", + "trade_weight", + "execution_risk_observations", + "pretrade_risk_forecast_annualized", + "proposed_risk_forecast_pre_annualized", + "proposed_risk_forecast_post_annualized", + "executed_risk_forecast_annualized", + "execution_risk_ceiling_annualized", + "proposed_runtime_risk_scale", + "execution_risk_repair_scale", + "proposed_one_way_turnover", + "constraint_repair_one_way", + "executed_constraint_maximum_error", + "liquidity_adv_observations", + "causal_adv_dollar_volume", + "reference_nav_adv_participation", + "asset_capacity_nav_1pct", + "asset_capacity_nav_5pct", + "portfolio_capacity_nav_1pct", + "portfolio_capacity_nav_5pct", + "asset_forward_return", + "gross_return_contribution", + "cost_contribution", + "net_return_contribution", + "one_way_turnover_contribution", + "component_variance", + "variance_contribution_share", + "portfolio_variance", + "portfolio_gross_return", + "portfolio_cost", + "portfolio_net_return", + "portfolio_traded_notional", + ] + ].to_numpy(dtype=float) + if not np.isfinite(required_numeric).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Decision ledger contains non-finite numeric evidence", + ) + return result + + +def _position_state(weight: float) -> int: + return 1 if weight > 1e-12 else -1 if weight < -1e-12 else 0 + + +def build_position_episodes( + ledger: pd.DataFrame, + index: pd.Index, + *, + split: str, + role: str, +) -> pd.DataFrame: + """Reconstruct exact split-bounded executed-position episodes.""" + + required = { + "timestamp", + "asset", + "signal_state", + "pretrade_weight", + "executed_weight", + "executed_state", + "trade_weight", + "execution_action", + "execution_reason", + "risk_rebalance_override", + "gross_return_contribution", + "cost_contribution", + } + if ( + not isinstance(split, str) + or not split + or role not in {"training", "selection", "visible-audit"} + or len(index) == 0 + or not required.issubset(ledger.columns) + ): + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode inputs are incomplete", + ) + timestamps = pd.DatetimeIndex(index) + if not timestamps.is_monotonic_increasing or not timestamps.is_unique: + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode index must be unique and chronological", + ) + selected = ledger[ledger["timestamp"].isin(timestamps)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.position-episodes", + "Position-episode split has no decision rows", + ) + selected = selected.sort_values(["asset", "timestamp"]).reset_index( + drop=True + ) + expected_dates = set(timestamps) + if any( + len(group) != len(timestamps) + or set(pd.DatetimeIndex(group["timestamp"])) != expected_dates + for _, group in selected.groupby("asset", sort=True) + ): + raise PortfolioFailure( + "portfolio.position-episodes", + "Every asset must cover the complete split", + ) + + output: list[dict[str, object]] = [] + for asset, asset_rows in selected.groupby("asset", sort=True): + sequence = 0 + current: dict[str, object] | None = None + + def open_episode( + state: int, + timestamp: pd.Timestamp, + *, + left_censored: bool, + action: str, + entry_weight: float, + entry_cost: float, + ) -> dict[str, object]: + nonlocal sequence + sequence += 1 + return { + "episode_id": f"{split}:{asset}:{sequence:04d}", + "split": split, + "role": role, + "episode_number": sequence, + "asset": str(asset), + "side": "long" if state == 1 else "short", + "entry_timestamp": timestamp, + "last_earning_timestamp": pd.NaT, + "exit_timestamp": pd.NaT, + "entry_action": action, + "exit_action": "split_boundary_carry", + "left_censored": left_censored, + "right_censored": False, + "decision_bars": 0, + "entry_weight": entry_weight, + "last_executed_weight": 0.0, + "_weight_sum": 0.0, + "peak_abs_weight": 0.0, + "gross_contribution": 0.0, + "entry_cost": entry_cost, + "holding_cost": 0.0, + "exit_cost": 0.0, + "_path": [], + "intent_mismatch_bars": 0, + "no_trade_bars": 0, + "risk_override_bars": 0, + } + + def close_episode( + episode: dict[str, object], + *, + right_censored: bool, + timestamp: pd.Timestamp | None, + action: str, + ) -> None: + episode["right_censored"] = right_censored + episode["exit_timestamp"] = ( + pd.NaT if right_censored else timestamp + ) + episode["exit_action"] = action + costs = ( + float(episode["entry_cost"]) + + float(episode["holding_cost"]) + + float(episode["exit_cost"]) + ) + gross = float(episode["gross_contribution"]) + path = np.cumsum( + np.asarray(episode.pop("_path"), dtype=float) + ) + net = gross - costs + if path.size and not math.isclose( + float(path[-1]), + net, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ): + raise PortfolioFailure( + "portfolio.position-episode-reconciliation", + "Episode contribution path does not reconcile", + ) + bars = int(episode["decision_bars"]) + weight_sum = float(episode.pop("_weight_sum")) + episode["average_abs_weight"] = ( + weight_sum / bars if bars else 0.0 + ) + episode["total_cost"] = costs + episode["net_contribution"] = net + episode["maximum_favorable_excursion"] = ( + max(0.0, float(path.max())) if path.size else 0.0 + ) + episode["maximum_adverse_excursion"] = ( + min(0.0, float(path.min())) if path.size else 0.0 + ) + episode["complete"] = not bool( + episode["left_censored"] + or episode["right_censored"] + ) + output.append(episode) + + first = True + for raw in asset_rows.to_dict("records"): + timestamp = pd.Timestamp(raw["timestamp"]) + pretrade_weight = float(raw["pretrade_weight"]) + executed_weight = float(raw["executed_weight"]) + trade_weight = float(raw["trade_weight"]) + pretrade_state = _position_state(pretrade_weight) + executed_state = int(raw["executed_state"]) + if ( + executed_state != _position_state(executed_weight) + or not math.isclose( + trade_weight, + executed_weight - pretrade_weight, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ) + ): + raise PortfolioFailure( + "portfolio.position-episode-state", + "Executed state or trade differs from the weight transition", + ) + if first and pretrade_state != 0: + current = open_episode( + pretrade_state, + timestamp, + left_censored=True, + action="split_boundary_carry", + entry_weight=pretrade_weight, + entry_cost=0.0, + ) + first = False + current_state = ( + 1 + if current is not None and current["side"] == "long" + else -1 + if current is not None + else 0 + ) + if current_state != pretrade_state: + raise PortfolioFailure( + "portfolio.position-episode-state", + "Pretrade weight does not match the open episode", + ) + + cost = float(raw["cost_contribution"]) + if cost < -POSITION_EPISODE_TOLERANCE: + raise PortfolioFailure( + "portfolio.position-episode-cost", + "Episode source cost cannot be negative", + ) + cost_for_current = 0.0 + if current_state == executed_state: + if current is None: + if ( + abs(trade_weight) > POSITION_EPISODE_TOLERANCE + or cost > POSITION_EPISODE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.position-episode-cost", + "Flat-to-flat row cannot contain trade cost", + ) + else: + current["holding_cost"] = ( + float(current["holding_cost"]) + cost + ) + cost_for_current = cost + else: + close_notional = ( + abs(pretrade_weight) if current_state else 0.0 + ) + open_notional = ( + abs(executed_weight) if executed_state else 0.0 + ) + transition_notional = close_notional + open_notional + if ( + not math.isclose( + abs(trade_weight), + transition_notional, + rel_tol=0.0, + abs_tol=POSITION_EPISODE_TOLERANCE, + ) + or transition_notional <= POSITION_EPISODE_TOLERANCE + ): + raise PortfolioFailure( + "portfolio.position-episode-trade", + "Episode transition notional does not reconcile", + ) + close_cost = cost * close_notional / transition_notional + open_cost = cost - close_cost + if current is not None: + current["exit_cost"] = ( + float(current["exit_cost"]) + close_cost + ) + if int(current["decision_bars"]) == 0: + current["last_executed_weight"] = pretrade_weight + current["_path"].append(-close_cost) + close_episode( + current, + right_censored=False, + timestamp=timestamp, + action=str(raw["execution_action"]), + ) + current = None + if executed_state: + current = open_episode( + executed_state, + timestamp, + left_censored=False, + action=str(raw["execution_action"]), + entry_weight=executed_weight, + entry_cost=open_cost, + ) + cost_for_current = open_cost + + gross = float(raw["gross_return_contribution"]) + if current is None: + if abs(gross) > POSITION_EPISODE_TOLERANCE: + raise PortfolioFailure( + "portfolio.position-episode-contribution", + "Flat executed state cannot earn gross contribution", + ) + continue + current["gross_contribution"] = ( + float(current["gross_contribution"]) + gross + ) + current["_path"].append(gross - cost_for_current) + current["decision_bars"] = int(current["decision_bars"]) + 1 + current["last_earning_timestamp"] = timestamp + current["last_executed_weight"] = executed_weight + current["_weight_sum"] = ( + float(current["_weight_sum"]) + abs(executed_weight) + ) + current["peak_abs_weight"] = max( + float(current["peak_abs_weight"]), + abs(executed_weight), + ) + if int(raw["signal_state"]) != executed_state: + current["intent_mismatch_bars"] = ( + int(current["intent_mismatch_bars"]) + 1 + ) + if str(raw["execution_reason"]) == "portfolio_no_trade_band": + current["no_trade_bars"] = ( + int(current["no_trade_bars"]) + 1 + ) + if bool(raw["risk_rebalance_override"]): + current["risk_override_bars"] = ( + int(current["risk_override_bars"]) + 1 + ) + + if current is not None: + close_episode( + current, + right_censored=True, + timestamp=None, + action="split_boundary_carry", + ) + + if not output: + return pd.DataFrame(columns=POSITION_EPISODE_COLUMNS) + result = pd.DataFrame(output) + result = result.loc[:, POSITION_EPISODE_COLUMNS] + numeric = result[ + [ + "episode_number", + "decision_bars", + "entry_weight", + "last_executed_weight", + "peak_abs_weight", + "average_abs_weight", + "gross_contribution", + "entry_cost", + "holding_cost", + "exit_cost", + "total_cost", + "net_contribution", + "maximum_favorable_excursion", + "maximum_adverse_excursion", + "intent_mismatch_bars", + "no_trade_bars", + "risk_override_bars", + ] + ].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise PortfolioFailure( + "portfolio.non-finite", + "Position episodes contain non-finite evidence", + ) + return result + + +def position_episode_metrics( + episodes: pd.DataFrame, + ledger: pd.DataFrame, + index: pd.Index, +) -> dict[str, object]: + """Aggregate split-bounded episode diagnostics and exact reconciliation.""" + + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Position-lifecycle split has no decision rows", + ) + active = episodes[episodes["decision_bars"].astype(int) > 0].copy() + complete = active[active["complete"].astype(bool)].copy() + winners = complete[ + complete["net_contribution"] > POSITION_EPISODE_TOLERANCE + ] + losers = complete[ + complete["net_contribution"] < -POSITION_EPISODE_TOLERANCE + ] + average_win = ( + float(winners["net_contribution"].mean()) + if len(winners) + else 0.0 + ) + average_loss = ( + float(losers["net_contribution"].mean()) + if len(losers) + else 0.0 + ) + gross_profit = float(winners["net_contribution"].sum()) + gross_loss = abs(float(losers["net_contribution"].sum())) + + def group_metrics(group: pd.DataFrame) -> dict[str, object]: + active_group = group[group["decision_bars"].astype(int) > 0] + complete_group = active_group[ + active_group["complete"].astype(bool) + ] + return { + "segments": int(len(group)), + "active_segments": int(len(active_group)), + "complete_episodes": int(len(complete_group)), + "decision_bars": int(active_group["decision_bars"].sum()), + "total_gross_contribution": float( + group["gross_contribution"].sum() + ), + "total_cost": float(group["total_cost"].sum()), + "total_net_contribution": float( + group["net_contribution"].sum() + ), + "complete_episode_win_rate": ( + float( + ( + complete_group["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(complete_group) + else 0.0 + ), + } + + episode_gross = float(episodes["gross_contribution"].sum()) + episode_cost = float(episodes["total_cost"].sum()) + episode_net = float(episodes["net_contribution"].sum()) + ledger_gross = float(selected["gross_return_contribution"].sum()) + ledger_cost = float(selected["cost_contribution"].sum()) + ledger_net = float(selected["net_return_contribution"].sum()) + reconciliation = { + "passed": ( + abs(episode_gross - ledger_gross) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_cost - ledger_cost) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_net - ledger_net) + <= POSITION_EPISODE_TOLERANCE + and abs(episode_net - (episode_gross - episode_cost)) + <= POSITION_EPISODE_TOLERANCE + ), + "gross_contribution_error": abs(episode_gross - ledger_gross), + "cost_error": abs(episode_cost - ledger_cost), + "net_contribution_error": abs(episode_net - ledger_net), + "episode_identity_error": abs( + episode_net - (episode_gross - episode_cost) + ), + } + if not reconciliation["passed"]: + raise PortfolioFailure( + "portfolio.position-episode-reconciliation", + "Position episodes do not reconcile the decision ledger", + ) + + total_bars = int(active["decision_bars"].sum()) + result: dict[str, object] = { + "status": "available" if len(active) else "no_positions", + "segments": int(len(episodes)), + "active_segments": int(len(active)), + "complete_episodes": int(len(complete)), + "left_censored_segments": int( + episodes["left_censored"].astype(bool).sum() + ), + "right_censored_segments": int( + episodes["right_censored"].astype(bool).sum() + ), + "long_segments": int((episodes["side"] == "long").sum()), + "short_segments": int((episodes["side"] == "short").sum()), + "decision_bars": total_bars, + "segment_positive_rate": ( + float( + ( + active["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(active) + else 0.0 + ), + "complete_episode_win_rate": ( + float( + ( + complete["net_contribution"] + > POSITION_EPISODE_TOLERANCE + ).mean() + ) + if len(complete) + else 0.0 + ), + "average_complete_holding_bars": ( + float(complete["decision_bars"].mean()) + if len(complete) + else 0.0 + ), + "median_complete_holding_bars": ( + float(complete["decision_bars"].median()) + if len(complete) + else 0.0 + ), + "average_complete_win_contribution": average_win, + "average_complete_loss_contribution": average_loss, + "complete_payoff_ratio": ( + average_win / abs(average_loss) + if average_win > 0 and average_loss < 0 + else 0.0 + ), + "complete_profit_factor": ( + gross_profit / gross_loss + if gross_profit > 0 and gross_loss > 0 + else 0.0 + ), + "average_segment_mfe": ( + float(active["maximum_favorable_excursion"].mean()) + if len(active) + else 0.0 + ), + "average_segment_mae": ( + float(active["maximum_adverse_excursion"].mean()) + if len(active) + else 0.0 + ), + "intent_mismatch_bars": int( + active["intent_mismatch_bars"].sum() + ), + "intent_mismatch_rate": ( + float(active["intent_mismatch_bars"].sum() / total_bars) + if total_bars + else 0.0 + ), + "no_trade_bars": int(active["no_trade_bars"].sum()), + "no_trade_bar_rate": ( + float(active["no_trade_bars"].sum() / total_bars) + if total_bars + else 0.0 + ), + "risk_override_bars": int( + active["risk_override_bars"].sum() + ), + "total_gross_contribution": episode_gross, + "total_cost": episode_cost, + "total_net_contribution": episode_net, + "entry_action_counts": { + str(key): int(value) + for key, value in episodes["entry_action"].value_counts().items() + }, + "exit_action_counts": { + str(key): int(value) + for key, value in episodes["exit_action"].value_counts().items() + }, + "by_asset": { + str(name): group_metrics(group) + for name, group in episodes.groupby("asset", sort=True) + }, + "by_side": { + str(name): group_metrics(group) + for name, group in episodes.groupby("side", sort=True) + }, + "reconciliation": reconciliation, + } + numeric: list[float] = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + numeric.extend(float(value) for value in reconciliation.values()) + if not all(math.isfinite(value) for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Position-lifecycle metrics contain non-finite values", + ) + return result + + +def execution_risk_metrics( + simulation: Simulation, + index: pd.Index, +) -> dict[str, object]: + """Summarize final-book compliance with the causal volatility ceiling.""" + + daily = simulation.daily.loc[index].copy() + required = { + "execution_reason", + "execution_risk_status", + "execution_risk_forecast_available", + "pretrade_risk_forecast_annualized", + "executed_risk_forecast_annualized", + "execution_risk_ceiling_annualized", + "proposed_one_way_turnover", + "risk_rebalance_override", + "gross_exposure", + } + if daily.empty or not required.issubset(daily.columns): + raise PortfolioFailure( + "portfolio.execution-risk", + "Execution-risk split evidence is incomplete", + ) + active = daily[ + (daily["gross_exposure"].abs() > 1e-12) + | (daily["proposed_one_way_turnover"].abs() > 1e-12) + | daily["risk_rebalance_override"].astype(bool) + ] + available = active[ + active["execution_risk_forecast_available"].astype(bool) + ] + unavailable = active[ + ~active["execution_risk_forecast_available"].astype(bool) + ] + pretrade_breach = available[ + available["pretrade_risk_forecast_annualized"] + > available["execution_risk_ceiling_annualized"] + + RISK_COMPLIANCE_TOLERANCE + ] + executed_breach = available[ + available["executed_risk_forecast_annualized"] + > available["execution_risk_ceiling_annualized"] + + RISK_COMPLIANCE_TOLERANCE + ] + overrides = active[active["risk_rebalance_override"].astype(bool)] + ceiling_error = ( + available["executed_risk_forecast_annualized"] + - available["execution_risk_ceiling_annualized"] + ).clip(lower=0.0) + result: dict[str, object] = { + "status": ( + "available" + if not available.empty + else "no_active_dates" + if active.empty + else "forecast_unavailable" + ), + "dates": int(len(daily)), + "active_dates": int(len(active)), + "forecast_available_dates": int(len(available)), + "forecast_unavailable_dates": int(len(unavailable)), + "forecast_coverage": ( + float(len(available) / len(active)) + if len(active) + else 0.0 + ), + "pretrade_breach_dates": int(len(pretrade_breach)), + "pretrade_breach_rate": ( + float(len(pretrade_breach) / len(available)) + if len(available) + else 0.0 + ), + "risk_rebalance_override_dates": int(len(overrides)), + "risk_rebalance_override_rate": ( + float(len(overrides) / len(active)) + if len(active) + else 0.0 + ), + "executed_breach_dates": int(len(executed_breach)), + "executed_breach_rate": ( + float(len(executed_breach) / len(available)) + if len(available) + else 0.0 + ), + "mean_executed_forecast_annualized": ( + float(available["executed_risk_forecast_annualized"].mean()) + if len(available) + else 0.0 + ), + "maximum_executed_forecast_annualized": ( + float(available["executed_risk_forecast_annualized"].max()) + if len(available) + else 0.0 + ), + "maximum_ceiling_error": ( + float(ceiling_error.max()) if len(ceiling_error) else 0.0 + ), + "status_counts": { + str(key): int(value) + for key, value in daily[ + "execution_risk_status" + ].value_counts().items() + }, + "execution_reason_counts": { + str(key): int(value) + for key, value in daily[ + "execution_reason" + ].value_counts().items() + }, + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Execution-risk metrics contain invalid values", + ) + if int(result["executed_breach_dates"]): + raise PortfolioFailure( + "portfolio.risk-breach", + "Final executed-book path contains a volatility-ceiling breach", + ) + return result + + +def liquidity_capacity_metrics( + ledger: pd.DataFrame, + index: pd.Index, + *, + reference_nav: float = REFERENCE_NAV, +) -> dict[str, object]: + """Aggregate the exact per-date OHLCV participation-capacity envelope.""" + + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Liquidity-capacity split has no decision rows", + ) + dates = ( + selected.groupby("timestamp", sort=True) + .agg( + status=("liquidity_capacity_status", "first"), + capacity_1pct=("portfolio_capacity_nav_1pct", "first"), + capacity_5pct=("portfolio_capacity_nav_5pct", "first"), + ) + ) + trade_dates = dates[dates["status"].ne("no_trade")] + available = trade_dates[trade_dates["status"].eq("available")] + unavailable = trade_dates[ + trade_dates["status"].eq("insufficient_adv_history") + ] + if len(trade_dates): + coverage = float(len(available) / len(trade_dates)) + else: + coverage = 0.0 + + def summarize(column: str) -> dict[str, float | int | str]: + values = available[column].astype(float) + if values.empty: + return { + "status": "unavailable", + "observations": 0, + "minimum_nav": 0.0, + "tenth_percentile_nav": 0.0, + "median_nav": 0.0, + "reference_nav_breach_rate": 0.0, + } + return { + "status": "available", + "observations": int(len(values)), + "minimum_nav": float(values.min()), + "tenth_percentile_nav": float(values.quantile(0.10)), + "median_nav": float(values.median()), + "reference_nav_breach_rate": float( + (values + 1e-12 < reference_nav).mean() + ), + } + + binding = selected[ + selected["capacity_binding_asset"].astype(bool) + & selected["timestamp"].isin(available.index) + ] + result: dict[str, object] = { + "status": ( + "available" + if not available.empty + else "no_trades" + if trade_dates.empty + else "insufficient_adv_history" + ), + "trade_dates": int(len(trade_dates)), + "available_trade_dates": int(len(available)), + "unavailable_trade_dates": int(len(unavailable)), + "trade_date_coverage": coverage, + "binding_asset_counts_1pct": { + str(asset): int(count) + for asset, count in binding["asset"].value_counts().items() + }, + "capacity_1pct": summarize("capacity_1pct"), + "capacity_5pct": summarize("capacity_5pct"), + } + numeric: list[float] = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + for key in ("capacity_1pct", "capacity_5pct"): + numeric.extend( + float(value) + for value in result[key].values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ) + if not all(math.isfinite(value) and value >= 0 for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Liquidity-capacity metrics contain invalid values", + ) + return result + + +def signal_policy_metrics( + construction: SignalConstruction, + index: pd.Index, +) -> dict[str, object]: + selected = construction.ledger[ + construction.ledger["timestamp"].isin(index) + ].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Signal policy split has no decision rows", + ) + timestamps = int(selected["timestamp"].nunique()) + annual_periods = annualization_periods(index) + events = { + str(key): int(value) + for key, value in selected["signal_event"].value_counts().items() + } + actions = { + str(key): int(value) + for key, value in selected["target_action"].value_counts().items() + } + allocation = { + str(key): int(value) + for key, value in selected["allocation_status"].value_counts().items() + } + risk_by_timestamp = ( + selected.groupby("timestamp", sort=True) + .agg( + status=("risk_governor_status", "first"), + scale=("risk_governor_scale", "first"), + observations=("risk_estimation_observations", "first"), + pre=("risk_forecast_pre_annualized", "first"), + post=("risk_forecast_post_annualized", "first"), + ceiling=("risk_volatility_ceiling_annualized", "first"), + ) + ) + cadence_by_timestamp = ( + selected.groupby("timestamp", sort=True) + .agg( + eligible=("decision_eligible", "first"), + kind=("decision_schedule_kind", "first"), + every_bars=("decision_every_bars", "first"), + anchor=("decision_anchor", "first"), + ) + ) + active_risk = risk_by_timestamp[ + ~risk_by_timestamp["status"].isin({"flat", "legacy_none"}) + ] + state_counts = ( + selected.groupby("timestamp")["signal_state"] + .value_counts() + .unstack(fill_value=0) + ) + target_turnover = ( + selected.assign(abs_delta=selected["target_delta"].abs()) + .groupby("timestamp")["abs_delta"] + .sum() + * 0.5 + ) + transitions = int( + selected["prior_signal_state"].ne(selected["signal_state"]).sum() + ) + result: dict[str, object] = { + "decision_rows": int(len(selected)), + "timestamps": timestamps, + "decision_eligible_timestamps": int( + cadence_by_timestamp["eligible"].sum() + ), + "decision_eligible_rate": float( + cadence_by_timestamp["eligible"].mean() + ), + "decision_schedule": ( + { + "kind": "calendar-month-end", + } + if str(cadence_by_timestamp["kind"].iloc[0]) + == "calendar-month-end" + else { + "kind": "every-bars", + "bars": int(cadence_by_timestamp["every_bars"].iloc[0]), + "anchor": str(cadence_by_timestamp["anchor"].iloc[0]), + } + ), + "scheduled_hold_timestamps": int( + (~cadence_by_timestamp["eligible"]).sum() + ), + "signal_transitions": transitions, + "state_change_rate": float(transitions / len(selected)), + "entries": int( + selected["signal_event"].isin( + {"enter_long", "enter_short"} + ).sum() + ), + "exits": int( + selected["signal_event"].isin({"exit_long", "exit_short"}).sum() + ), + "reversals": int( + selected["signal_event"].isin( + {"reverse_long_to_short", "reverse_short_to_long"} + ).sum() + ), + "signal_event_counts": events, + "target_action_counts": actions, + "allocation_status_counts": allocation, + "risk_governor_status_counts": { + str(key): int(value) + for key, value in risk_by_timestamp["status"].value_counts().items() + }, + "risk_limited_dates": int( + risk_by_timestamp["status"].eq("volatility_limited").sum() + ), + "risk_limited_rate": float( + risk_by_timestamp["status"].eq("volatility_limited").mean() + ), + "risk_unavailable_dates": int( + risk_by_timestamp["status"] + .isin({"insufficient_history", "invalid_covariance"}) + .sum() + ), + "average_active_risk_scale": ( + float(active_risk["scale"].mean()) + if not active_risk.empty + else 1.0 + ), + "maximum_pre_governor_annualized_volatility": float( + risk_by_timestamp["pre"].max() + ), + "maximum_post_governor_annualized_volatility": float( + risk_by_timestamp["post"].max() + ), + "average_long_intents": ( + float(state_counts[1].mean()) if 1 in state_counts else 0.0 + ), + "average_short_intents": ( + float(state_counts[-1].mean()) if -1 in state_counts else 0.0 + ), + "average_flat_intents": ( + float(state_counts[0].mean()) if 0 in state_counts else 0.0 + ), + "mean_target_one_way_turnover": float(target_turnover.mean()), + "annualized_target_one_way_turnover": float( + target_turnover.mean() * annual_periods + ), + "average_gross_proposed_target": float( + selected.groupby("timestamp")["proposed_target_weight"] + .apply(lambda values: values.abs().sum()) + .mean() + ), + } + numeric = [ + float(value) + for value in result.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) for value in numeric): + raise PortfolioFailure( + "portfolio.non-finite", + "Signal policy metrics contain non-finite values", + ) + return result + + +def attribution_metrics( + ledger: pd.DataFrame, + simulation: Simulation, + index: pd.Index, +) -> dict[str, object]: + selected = ledger[ledger["timestamp"].isin(index)].copy() + if selected.empty: + raise PortfolioFailure( + "portfolio.population", + "Attribution split has no decision rows", + ) + timestamps = int(selected["timestamp"].nunique()) + annual_periods = annualization_periods(index) + + def aggregate(group: pd.DataFrame) -> dict[str, float | int]: + return { + "observations": int(len(group)), + "total_gross_contribution": float( + group["gross_return_contribution"].sum() + ), + "annualized_gross_contribution": float( + group["gross_return_contribution"].sum() + / timestamps + * annual_periods + ), + "total_cost_contribution": float( + group["cost_contribution"].sum() + ), + "total_net_contribution": float( + group["net_return_contribution"].sum() + ), + "annualized_net_contribution": float( + group["net_return_contribution"].sum() + / timestamps + * annual_periods + ), + "total_one_way_turnover_contribution": float( + group["one_way_turnover_contribution"].sum() + ), + "average_absolute_executed_weight": float( + group["executed_weight"].abs().mean() + ), + "mean_variance_contribution_share": float( + group["variance_contribution_share"].mean() + ), + } + + by_asset = { + str(name): aggregate(group) + for name, group in selected.groupby("asset", sort=True) + } + state_names = {-1: "short", 0: "flat", 1: "long"} + by_signal_state = { + state_names[int(name)]: aggregate(group) + for name, group in selected.groupby("signal_state", sort=True) + } + by_regime = { + str(name): aggregate(group) + for name, group in selected.groupby("regime", sort=True) + } + asset_net = pd.Series( + { + asset: values["total_net_contribution"] + for asset, values in by_asset.items() + }, + dtype=float, + ) + absolute_total = float(asset_net.abs().sum()) + contribution_shares = ( + asset_net.abs() / absolute_total + if absolute_total > 1e-12 + else pd.Series(0.0, index=asset_net.index, dtype=float) + ) + asset_risk = pd.Series( + { + asset: values["mean_variance_contribution_share"] + for asset, values in by_asset.items() + }, + dtype=float, + ) + absolute_risk_total = float(asset_risk.abs().sum()) + risk_shares = ( + asset_risk.abs() / absolute_risk_total + if absolute_risk_total > 1e-12 + else pd.Series(0.0, index=asset_risk.index, dtype=float) + ) + + gross_error = 0.0 + cost_error = 0.0 + net_error = 0.0 + traded_error = 0.0 + risk_share_error = 0.0 + risk_dates = 0 + for timestamp, group in selected.groupby("timestamp", sort=True): + portfolio = simulation.daily.loc[timestamp] + gross_error = max( + gross_error, + abs( + float(group["gross_return_contribution"].sum()) + - float(portfolio["gross_return"]) + ), + ) + cost_error = max( + cost_error, + abs( + float(group["cost_contribution"].sum()) + - float(portfolio["cost"]) + ), + ) + net_error = max( + net_error, + abs( + float(group["net_return_contribution"].sum()) + - float(portfolio["net_return"]) + ), + ) + traded_error = max( + traded_error, + abs( + float(group["trade_weight"].abs().sum()) + - float(portfolio["traded_notional"]) + ), + ) + if float(group["portfolio_variance"].iloc[0]) > 1e-18: + risk_dates += 1 + risk_share_error = max( + risk_share_error, + abs(float(group["variance_contribution_share"].sum()) - 1.0), + ) + tolerance = 1e-10 + reconciliation = { + "passed": ( + gross_error <= tolerance + and cost_error <= tolerance + and net_error <= tolerance + and traded_error <= tolerance + and risk_share_error <= tolerance + ), + "maximum_gross_return_error": gross_error, + "maximum_cost_error": cost_error, + "maximum_net_return_error": net_error, + "maximum_traded_notional_error": traded_error, + "maximum_variance_share_error": risk_share_error, + "variance_attributed_dates": risk_dates, + } + return { + "reconciliation": reconciliation, + "concentration": { + "maximum_absolute_net_contribution_share": ( + float(contribution_shares.max()) + if not contribution_shares.empty + else 0.0 + ), + "absolute_net_contribution_hhi": float( + contribution_shares.pow(2).sum() + ), + "maximum_absolute_variance_contribution_share": ( + float(risk_shares.max()) if not risk_shares.empty else 0.0 + ), + "absolute_variance_contribution_hhi": float( + risk_shares.pow(2).sum() + ), + }, + "by_asset": by_asset, + "by_signal_state": by_signal_state, + "by_regime": by_regime, + } + + +def performance_metrics( + returns: pd.Series, + benchmark: pd.Series, + *, + annual_periods: int | None = None, +) -> dict[str, float | int]: + pair = pd.DataFrame( + {"returns": returns, "benchmark": benchmark} + ).dropna() + if len(pair) < 20: + raise PortfolioFailure( + "portfolio.population", + "Portfolio split has fewer than 20 valid observations", + ) + values = pair["returns"].astype(float) + benchmark_values = pair["benchmark"].astype(float) + annual_periods = ( + annualization_periods(values.index) + if annual_periods is None + else annual_periods + ) + mean = float(values.mean()) + std = float(values.std(ddof=0)) + standardized = ( + (values - mean) / std + if std > 1e-12 + else pd.Series(0.0, index=values.index, dtype=float) + ) + return_skewness = ( + float(standardized.pow(3).mean()) + if std > 1e-12 + else 0.0 + ) + return_kurtosis = ( + float(standardized.pow(4).mean()) + if std > 1e-12 + else 3.0 + ) + annual_volatility = std * math.sqrt(annual_periods) + total_growth = float((1.0 + values).prod()) + annual_return = ( + total_growth ** (annual_periods / len(values)) - 1.0 + if total_growth > 0 + else -1.0 + ) + sharpe = mean / std * math.sqrt(annual_periods) if std > 1e-12 else 0.0 + downside = np.minimum(values.to_numpy(dtype=float), 0.0) + downside_deviation = float(np.sqrt(np.mean(np.square(downside)))) + sortino = ( + mean / downside_deviation * math.sqrt(annual_periods) + if downside_deviation > 1e-12 + else 0.0 + ) + equity = (1.0 + values).cumprod() + drawdown = equity / equity.cummax() - 1.0 + maximum_drawdown = float(drawdown.min()) + calmar = ( + annual_return / abs(maximum_drawdown) + if maximum_drawdown < -1e-12 + else 0.0 + ) + tail_count = max(1, math.ceil(len(values) * 0.05)) + expected_shortfall = -float(values.nsmallest(tail_count).mean()) + benchmark_variance = float(benchmark_values.var(ddof=0)) + beta = ( + float(values.cov(benchmark_values, ddof=0)) / benchmark_variance + if benchmark_variance > 1e-12 + else 0.0 + ) + active = values - benchmark_values + tracking_error_daily = float(active.std(ddof=0)) + tracking_error = tracking_error_daily * math.sqrt(annual_periods) + active_annual_return = float(active.mean()) * annual_periods + information_ratio = ( + float(active.mean()) / tracking_error_daily * math.sqrt(annual_periods) + if tracking_error_daily > 1e-12 + else 0.0 + ) + result: dict[str, float | int] = { + "observations": int(len(values)), + "annualization_periods": int(annual_periods), + "total_return": total_growth - 1.0, + "annual_return": annual_return, + "annual_volatility": annual_volatility, + "sharpe": sharpe, + "period_sharpe": mean / std if std > 1e-12 else 0.0, + "return_skewness": return_skewness, + "return_kurtosis": return_kurtosis, + "sortino": sortino, + "maximum_drawdown": maximum_drawdown, + "calmar": calmar, + "expected_shortfall_95": expected_shortfall, + "positive_rate": float((values > 0).mean()), + "benchmark_beta": beta, + "active_annual_return": active_annual_return, + "tracking_error": tracking_error, + "information_ratio": information_ratio, + } + if not all( + math.isfinite(float(value)) + for value in result.values() + ): + raise PortfolioFailure( + "portfolio.non-finite", + "Performance metrics contain non-finite values", + ) + return result + + +def implementation_metrics( + simulation: Simulation, + index: pd.Index, + *, + annual_periods: int | None = None, +) -> dict[str, float]: + daily = simulation.daily.loc[index] + weights = simulation.weights.loc[index] + annual_periods = ( + annualization_periods(daily.index) + if annual_periods is None + else annual_periods + ) + result = { + "mean_one_way_turnover": float(daily["one_way_turnover"].mean()), + "annualized_one_way_turnover": float( + daily["one_way_turnover"].mean() * annual_periods + ), + "mean_traded_notional": float(daily["traded_notional"].mean()), + "total_cost_drag": float(daily["cost"].sum()), + "rebalance_rate": float(daily["rebalanced"].mean()), + "no_trade_rate": float((~daily["rebalanced"]).mean()), + "average_gross_exposure": float(daily["gross_exposure"].mean()), + "maximum_gross_exposure": float(daily["gross_exposure"].max()), + "average_abs_net_exposure": float(daily["net_exposure"].abs().mean()), + "maximum_abs_net_exposure": float(daily["net_exposure"].abs().max()), + "average_max_abs_weight": float(daily["max_abs_weight"].mean()), + "maximum_abs_weight": float(weights.abs().max().max()), + "average_concentration_hhi": float(daily["concentration_hhi"].mean()), + "mean_volume_participation": float(daily["mean_participation"].mean()), + "maximum_volume_participation": float(daily["max_participation"].max()), + } + if not all(math.isfinite(value) for value in result.values()): + raise PortfolioFailure( + "portfolio.non-finite", + "Implementation metrics contain non-finite values", + ) + return result + + +def constraint_audit( + targets: pd.DataFrame, + *, + gross_target: float = GROSS_TARGET, + max_abs_weight: float = MAX_ABS_WEIGHT, + mandate: dict[str, object] | None = None, +) -> dict[str, object]: + resolved = _resolve_mandate(targets.columns, mandate) + gross_target = float(resolved["gross_limit"]) + max_abs_weight = float(resolved["max_abs_weight"]) + asset_caps = pd.Series( + resolved["asset_max_abs_weights"], + index=targets.columns, + dtype=float, + ) + family = str(resolved["family"]) + asset_position_roles = { + str(asset): str(role) + for asset, role in resolved["asset_position_roles"].items() + } + long_gross_limit = float(resolved["long_gross_limit"]) + short_gross_limit = float(resolved["short_gross_limit"]) + tradable = list(resolved["tradable_assets"]) + context = list(resolved["context_assets"]) + active = targets[targets.abs().sum(axis=1) > 1e-12] + if active.empty: + if family == "dollar-neutral": + raise PortfolioFailure( + "portfolio.no-targets", + "No active targets were constructed", + ) + return { + "passed": True, + "mandate_id": resolved["id"], + "family": family, + "tradable_assets": tradable, + "context_assets": context, + "active_dates": 0, + "maximum_gross_error": 0.0, + "maximum_gross_exposure": 0.0, + "maximum_abs_net_target": 0.0, + "maximum_net_rule_error": 0.0, + "maximum_opposite_exposure": 0.0, + "maximum_context_weight": 0.0, + "maximum_tradable_gross": 0.0, + "maximum_abs_target_weight": 0.0, + "maximum_asset_cap_excess": 0.0, + "asset_max_abs_weights": { + asset: float(asset_caps.loc[asset]) + for asset in targets.columns + }, + "asset_position_roles": asset_position_roles, + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + } + gross = active.abs().sum(axis=1) + net = active.sum(axis=1) + if family == "dollar-neutral": + gross_error = ( + float((gross - gross_target).clip(lower=0.0).max()) + if resolved["risk_policy"] is not None + else float((gross - gross_target).abs().max()) + ) + net_rule_error = float(net.abs().max()) + opposite_exposure = 0.0 + elif family == "long-cash": + gross_error = float((gross - gross_target).clip(lower=0.0).max()) + net_rule_error = float((net - gross).abs().max()) + opposite_exposure = float((-active.clip(upper=0.0)).sum(axis=1).max()) + elif family == "short-cash": + gross_error = float((gross - gross_target).clip(lower=0.0).max()) + net_rule_error = float((net + gross).abs().max()) + opposite_exposure = float(active.clip(lower=0.0).sum(axis=1).max()) + elif family == "asset-role": + long_exposure = active.clip(lower=0.0).sum(axis=1) + short_exposure = (-active.clip(upper=0.0)).sum(axis=1) + gross_error = float( + pd.concat( + [ + (long_exposure - long_gross_limit).clip(lower=0.0), + (short_exposure - short_gross_limit).clip(lower=0.0), + (gross - gross_target).clip(lower=0.0), + ], + axis=1, + ).max().max() + ) + forbidden: list[float] = [] + for asset in active.columns: + role = asset_position_roles[str(asset)] + if role == "long-only": + forbidden.append(float((-active[asset].clip(upper=0.0)).max())) + elif role == "short-only": + forbidden.append(float(active[asset].clip(lower=0.0).max())) + elif role == "context-only": + forbidden.append(float(active[asset].abs().max())) + opposite_exposure = max(forbidden, default=0.0) + net_rule_error = 0.0 + else: + raise PortfolioFailure("mandate.family", "Unknown mandate family") + maximum_weight = float(active.abs().max().max()) + cap_excess = active.abs().subtract(asset_caps, axis="columns") + maximum_asset_cap_excess = float( + cap_excess.clip(lower=0.0).max().max() + ) + context_weight = ( + float(active[context].abs().max().max()) + if context + else 0.0 + ) + tradable_gross = float(active[tradable].abs().sum(axis=1).max()) + passed = ( + gross_error <= 1e-8 + and net_rule_error <= 1e-8 + and opposite_exposure <= 1e-8 + and context_weight <= 1e-8 + and maximum_weight <= max_abs_weight + 1e-8 + and maximum_asset_cap_excess <= 1e-8 + ) + return { + "passed": passed, + "mandate_id": resolved["id"], + "family": family, + "tradable_assets": tradable, + "context_assets": context, + "active_dates": int(len(active)), + "maximum_gross_error": gross_error, + "maximum_gross_exposure": float(gross.max()), + "maximum_abs_net_target": float(net.abs().max()), + "maximum_net_rule_error": net_rule_error, + "maximum_opposite_exposure": opposite_exposure, + "maximum_context_weight": context_weight, + "maximum_tradable_gross": tradable_gross, + "maximum_abs_target_weight": maximum_weight, + "maximum_asset_cap_excess": maximum_asset_cap_excess, + "asset_max_abs_weights": { + asset: float(asset_caps.loc[asset]) + for asset in targets.columns + }, + "asset_position_roles": asset_position_roles, + "long_gross_limit": long_gross_limit, + "short_gross_limit": short_gross_limit, + } diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/rl_core.py b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/rl_core.py new file mode 100644 index 0000000..4064d96 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/judge-sources/judges/rl_core.py @@ -0,0 +1,1934 @@ +"""Fixed bounded RL environment, learner, and baseline primitives.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Any, Callable + +import numpy as np +import pandas as pd + +try: + from judges.portfolio_core import ( + RiskCovarianceCache, + Simulation, + build_risk_covariance_cache, + construct_signal_policy, + decision_schedule_mask, + decision_schedule_sessions, + drift_weights, + execution_risk_metrics, + execute_risk_compliant_book, + implementation_metrics, + performance_metrics, + resolve_implementation_policy, + resolve_portfolio_mandate, + ) +except ModuleNotFoundError: # Package-level deterministic primitive tests. + from autoquant.project_templates.ohlcv_portfolio_lab.portfolio_core import ( + RiskCovarianceCache, + Simulation, + build_risk_covariance_cache, + construct_signal_policy, + decision_schedule_mask, + decision_schedule_sessions, + drift_weights, + execution_risk_metrics, + execute_risk_compliant_book, + implementation_metrics, + performance_metrics, + resolve_implementation_policy, + resolve_portfolio_mandate, + ) + + +ACTIONS = ("candidate", "activity", "intraday", "reversal", "balanced") +EXPERTS = ACTIONS[:4] +ACTION_MIXTURES = { + expert: { + candidate: float(candidate == expert) + for candidate in EXPERTS + } + for expert in EXPERTS +} +ACTION_MIXTURES["balanced"] = { + expert: 1.0 / len(EXPERTS) + for expert in EXPERTS +} +SEEDS = (11, 29, 47) +EPISODES = 12 +LEARNING_RATE = 0.02 +DISCOUNT = 0.30 +EPSILON_START = 0.15 +EPSILON_END = 0.01 +RISK_AVERSION = 0.10 +FEATURE_ABS_LIMIT = 20.0 +RIDGE_PENALTY = 1e-3 +CONTEXTUAL_RIDGE_ITERATIONS = 4 +MIN_SPLIT_OBSERVATIONS = 24 +BASE_STATE_COLUMNS = ( + "volume_regime", + "market_return_5", + "market_volatility_20", + "candidate_trailing_reward_10", + "activity_trailing_reward_10", + "intraday_trailing_reward_10", + "reversal_trailing_reward_10", +) +PRETRADE_STATE_COLUMNS = ( + "pretrade_gross_exposure", + "pretrade_net_exposure", + "pretrade_cash_weight", + "pretrade_max_abs_weight", + "pretrade_concentration_hhi", +) +ACTION_DISTANCE_COLUMNS = tuple( + f"{action}_target_distance" + for action in ACTIONS +) +PREVIOUS_ACTION_COLUMNS = tuple( + f"previous_{action}" + for action in ACTIONS +) +POLICY_STATE_COLUMNS = ( + BASE_STATE_COLUMNS + + PRETRADE_STATE_COLUMNS + + ACTION_DISTANCE_COLUMNS + + PREVIOUS_ACTION_COLUMNS +) + + +class PolicyFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +@dataclass(frozen=True) +class Rollout: + simulation: Simulation + actions: pd.Series + rewards: pd.Series + states: pd.DataFrame + + +@dataclass(frozen=True) +class TrainedPolicy: + weights: np.ndarray + history: list[dict[str, object]] + + +@dataclass(frozen=True) +class TrainingRollout: + actions: pd.Series + states: pd.DataFrame + pretrade: np.ndarray + net_returns: pd.Series + benchmark_returns: pd.Series + rewards: np.ndarray + + +def build_action_targets( + factor_panels: dict[str, pd.DataFrame], + closes: pd.DataFrame, + *, + mandate: dict[str, object] | None = None, + prediction_population: dict[str, object] | None = None, +) -> dict[str, pd.DataFrame]: + if set(factor_panels) != set(EXPERTS): + raise PolicyFailure( + "policy.factors", + "Factor panels must exactly match the fixed expert set", + ) + targets: dict[str, pd.DataFrame] = {} + for action, mixture in ACTION_MIXTURES.items(): + combined = sum( + factor_panels[expert] * weight + for expert, weight in mixture.items() + ) + targets[action] = construct_signal_policy( + combined, + closes, + mandate=mandate, + prediction_population=prediction_population, + include_ledger=False, + ).targets + return targets + + +def build_raw_states( + closes: pd.DataFrame, + volumes: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], +) -> pd.DataFrame: + close_returns = closes.pct_change(fill_method=None) + market_return = close_returns.mean(axis=1) + log_volume = np.log(volumes) + market_log_volume = log_volume.mean(axis=1) + volume_regime = ( + market_log_volume + - market_log_volume.expanding(min_periods=20).mean() + ) + state = pd.DataFrame( + { + "volume_regime": volume_regime, + "market_return_5": market_return.rolling(5, min_periods=5).mean(), + "market_volatility_20": market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0), + } + ) + for expert in EXPERTS: + targets = action_targets[expert] + gross = ( + targets + * (closes.shift(-1) / closes - 1.0) + ).sum(axis=1) + state[f"{expert}_trailing_reward_10"] = ( + gross.shift(1).rolling(10, min_periods=10).mean() + ) + state = state.replace([np.inf, -np.inf], np.nan) + return state + + +def state_with_previous_action( + raw_state: pd.Series, + previous_action: str, +) -> dict[str, float]: + if previous_action not in ACTIONS: + raise PolicyFailure("policy.action", "Unknown previous action") + result = { + key: float(raw_state[key]) + for key in BASE_STATE_COLUMNS + } + result.update( + { + f"previous_{action}": float(action == previous_action) + for action in ACTIONS + } + ) + if not all(math.isfinite(value) for value in result.values()): + raise PolicyFailure("policy.state", "Causal state contains non-finite values") + return result + + +def build_policy_state( + raw_state: pd.Series, + previous_action: str, + pretrade_weights: pd.Series, + action_targets: dict[str, pd.Series], +) -> dict[str, float]: + """Build one causal market-and-execution state at the decision close.""" + + if set(action_targets) != set(ACTIONS): + raise PolicyFailure( + "policy.state-actions", + "Policy state action targets differ from the fixed action set", + ) + if ( + pretrade_weights.index.has_duplicates + or any( + not target.index.equals(pretrade_weights.index) + for target in action_targets.values() + ) + ): + raise PolicyFailure( + "policy.state-assets", + "Policy state books do not share one ordered asset universe", + ) + result = state_with_previous_action(raw_state, previous_action) + result.update( + { + "pretrade_gross_exposure": float( + pretrade_weights.abs().sum() + ), + "pretrade_net_exposure": float(pretrade_weights.sum()), + "pretrade_cash_weight": ( + 1.0 - float(pretrade_weights.abs().sum()) + ), + "pretrade_max_abs_weight": float( + pretrade_weights.abs().max() + ), + "pretrade_concentration_hhi": float( + pretrade_weights.pow(2).sum() + ), + } + ) + result.update( + { + f"{action}_target_distance": float( + 0.5 + * ( + action_targets[action] - pretrade_weights + ).abs().sum() + ) + for action in ACTIONS + } + ) + if set(result) != set(POLICY_STATE_COLUMNS): + raise PolicyFailure( + "policy.state-fields", + "Policy state fields differ from the fixed causal contract", + ) + if not all(math.isfinite(value) for value in result.values()): + raise PolicyFailure( + "policy.state", + "Causal policy state contains non-finite values", + ) + return result + + +def _pretrade_weights( + previous_weights: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + *, + first: bool, +) -> pd.Series: + return ( + pd.Series( + 0.0, + index=previous_weights.index, + dtype=float, + ) + if first + else drift_weights(previous_weights, close_returns.loc[timestamp]) + ) + + +def _account_step( + previous_weights: pd.Series, + proposed: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + forward_return: pd.Series, + close: pd.Series, + volume: pd.Series, + *, + first: bool, + ordinary_rebalance_allowed: bool = True, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, + resolved_mandate: dict[str, object] | None = None, + implementation: dict[str, object] | None = None, + pretrade: pd.Series | None = None, +) -> tuple[pd.Series, pd.Series, pd.Series, dict[str, float | bool]]: + implementation = ( + implementation + if implementation is not None + else resolve_implementation_policy(mandate) + ) + decision_policy = dict(implementation["decision_policy"]) + decision_schedule_kind = str(decision_policy["kind"]) + decision_every_bars = decision_policy.get("bars") + decision_anchor = decision_policy.get("anchor") + pretrade = ( + pretrade + if pretrade is not None + else _pretrade_weights( + previous_weights, + close_returns, + timestamp, + first=first, + ) + ) + current, execution_risk = execute_risk_compliant_book( + pretrade, + proposed, + close_returns, + timestamp, + mandate=mandate, + no_trade_one_way=implementation["no_trade_one_way"], + ordinary_rebalance_allowed=ordinary_rebalance_allowed, + risk_covariance_cache=risk_covariance_cache, + _resolved_mandate=resolved_mandate, + ) + rebalanced = bool(execution_risk["rebalanced"]) + trade = current - pretrade + traded_notional = float(trade.abs().sum()) + one_way_turnover = 0.5 * traded_notional + cost = ( + traded_notional + * implementation["base_cost_bps"] + / 10_000.0 + ) + gross_return = float((current * forward_return).sum()) + net_return = gross_return - cost + reward = net_return - RISK_AVERSION * gross_return**2 + if mandate is None: + benchmark_return = float(forward_return.mean()) + else: + construction = mandate["construction"] + benchmark = construction["benchmark"] + weights = benchmark.get("weights") + if ( + not isinstance(weights, dict) + or set(weights) != set(forward_return.index) + ): + raise PolicyFailure( + "mandate.benchmark", + "Portfolio Mandate benchmark weights are invalid", + ) + benchmark_return = float( + ( + pd.Series( + weights, + index=forward_return.index, + dtype=float, + ) + * forward_return + ).sum() + ) + dollar_volume = close * volume + participation = ( + trade.abs() + * implementation["reference_nav"] + / dollar_volume.replace(0.0, np.nan) + ).fillna(0.0) + row: dict[str, object] = { + "gross_return": gross_return, + "net_return": net_return, + "benchmark_return": benchmark_return, + "reward": reward, + "one_way_turnover": one_way_turnover, + "traded_notional": traded_notional, + "cost": cost, + "gross_exposure": float(current.abs().sum()), + "net_exposure": float(current.sum()), + "cash_weight": 1.0 - float(current.abs().sum()), + "max_abs_weight": float(current.abs().max()), + "concentration_hhi": float(current.pow(2).sum()), + "rebalanced": rebalanced, + "decision_eligible": ordinary_rebalance_allowed, + "decision_schedule_kind": decision_schedule_kind, + "decision_every_bars": decision_every_bars, + "decision_anchor": decision_anchor, + "decision_session": str( + decision_schedule_sessions( + pd.Index([timestamp]), + decision_policy, + ).iloc[0] + ), + "execution_reason": str(execution_risk["execution_reason"]), + "execution_risk_status": str(execution_risk["status"]), + "execution_risk_forecast_available": bool( + execution_risk["forecast_available"] + ), + "execution_risk_observations": int( + execution_risk["observations"] + ), + "pretrade_risk_forecast_annualized": float( + execution_risk["pretrade_forecast_annualized"] + ), + "proposed_risk_forecast_pre_annualized": float( + execution_risk["proposed_forecast_pre_annualized"] + ), + "proposed_risk_forecast_post_annualized": float( + execution_risk["proposed_forecast_post_annualized"] + ), + "executed_risk_forecast_annualized": float( + execution_risk["executed_forecast_annualized"] + ), + "execution_risk_ceiling_annualized": float( + execution_risk["annualized_volatility_ceiling"] + ), + "proposed_runtime_risk_scale": float( + execution_risk["proposed_runtime_scale"] + ), + "execution_risk_repair_scale": float( + execution_risk["risk_repair_scale"] + ), + "proposed_one_way_turnover": float( + execution_risk["proposed_one_way"] + ), + "ordinary_rebalance": bool( + execution_risk["ordinary_rebalance"] + ), + "risk_rebalance_override": bool( + execution_risk["risk_rebalance_override"] + ), + "constraint_rebalance_override": bool( + execution_risk["constraint_rebalance_override"] + ), + "constraint_repair_one_way": float( + execution_risk["constraint_repair_one_way"] + ), + "executed_constraint_maximum_error": float( + execution_risk["executed_constraint_maximum_error"] + ), + "max_participation": float(participation.max()), + "mean_participation": float(participation.mean()), + } + numeric = [ + float(value) + for value in row.values() + if isinstance(value, (int, float)) and not isinstance(value, bool) + ] + if not all(math.isfinite(value) for value in numeric): + raise PolicyFailure( + "policy.non-finite", + "Environment accounting produced non-finite evidence", + ) + return current, trade, participation, row + + +def _learning_step( + previous_weights: pd.Series, + proposed: pd.Series, + close_returns: pd.DataFrame, + timestamp: object, + forward_return: pd.Series, + *, + ordinary_rebalance_allowed: bool, + mandate: dict[str, object] | None, + risk_covariance_cache: RiskCovarianceCache | None, + resolved_mandate: dict[str, object], + implementation: dict[str, object], + pretrade: pd.Series, +) -> tuple[pd.Series, float]: + """Advance the exact governed book without constructing unused evidence.""" + + current, _ = execute_risk_compliant_book( + pretrade, + proposed, + close_returns, + timestamp, + mandate=mandate, + no_trade_one_way=implementation["no_trade_one_way"], + ordinary_rebalance_allowed=ordinary_rebalance_allowed, + risk_covariance_cache=risk_covariance_cache, + _resolved_mandate=resolved_mandate, + _state_only=True, + ) + traded_notional = float((current - pretrade).abs().sum()) + cost = ( + traded_notional + * implementation["base_cost_bps"] + / 10_000.0 + ) + gross_return = float((current * forward_return).sum()) + net_return = gross_return - cost + reward = net_return - RISK_AVERSION * gross_return**2 + if not math.isfinite(reward): + raise PolicyFailure( + "policy.non-finite", + "Learning accounting produced a non-finite reward", + ) + return current, reward + + +def _drift_weight_values( + previous: np.ndarray, + realized_returns: np.ndarray, +) -> np.ndarray: + aligned = np.where(np.isnan(realized_returns), 0.0, realized_returns) + gross_return = float((previous * aligned).sum()) + denominator = 1.0 + gross_return + if not math.isfinite(denominator) or denominator <= 1e-9: + raise PolicyFailure( + "portfolio.bankrupt", + "Portfolio drift denominator is non-positive", + ) + drifted = previous * (1.0 + aligned) / denominator + if not np.isfinite(drifted).all(): + raise PolicyFailure( + "portfolio.non-finite", + "Drift produced non-finite weights", + ) + return drifted + + +def _policy_state_values( + raw_state: np.ndarray, + previous_action_number: int, + pretrade: np.ndarray, + action_targets: np.ndarray, +) -> dict[str, float]: + if ( + raw_state.shape != (len(BASE_STATE_COLUMNS),) + or action_targets.shape != (len(ACTIONS), len(pretrade)) + or not 0 <= previous_action_number < len(ACTIONS) + ): + raise PolicyFailure( + "policy.state-values", + "Array policy state inputs differ from the fixed contract", + ) + result = { + column: float(raw_state[position]) + for position, column in enumerate(BASE_STATE_COLUMNS) + } + result.update( + { + f"previous_{action}": float( + position == previous_action_number + ) + for position, action in enumerate(ACTIONS) + } + ) + absolute = np.abs(pretrade) + result.update( + { + "pretrade_gross_exposure": float(absolute.sum()), + "pretrade_net_exposure": float(pretrade.sum()), + "pretrade_cash_weight": 1.0 - float(absolute.sum()), + "pretrade_max_abs_weight": float(absolute.max()), + "pretrade_concentration_hhi": float( + np.square(pretrade).sum() + ), + } + ) + result.update( + { + f"{action}_target_distance": float( + 0.5 + * np.abs( + action_targets[position] - pretrade + ).sum() + ) + for position, action in enumerate(ACTIONS) + } + ) + if set(result) != set(POLICY_STATE_COLUMNS) or not all( + math.isfinite(value) for value in result.values() + ): + raise PolicyFailure( + "policy.state", + "Causal policy state contains invalid values", + ) + return result + + +def _repair_weight_values( + weights: np.ndarray, + resolved: dict[str, object], +) -> np.ndarray: + roles = resolved["asset_position_roles"] + caps = resolved["asset_max_abs_weights"] + assert isinstance(roles, dict) + assert isinstance(caps, dict) + role_values = resolved.get("_constraint_role_values") + cap_values = resolved.get("_constraint_cap_values") + if ( + not isinstance(role_values, tuple) + or len(role_values) != len(weights) + or not isinstance(cap_values, np.ndarray) + or cap_values.shape != weights.shape + ): + role_values = tuple(str(value) for value in roles.values()) + cap_values = np.asarray( + [float(value) for value in caps.values()], + dtype=float, + ) + resolved["_constraint_role_values"] = role_values + resolved["_constraint_cap_values"] = cap_values + repaired = weights.copy() + for position, role in enumerate(role_values): + cap = float(cap_values[position]) + value = float(repaired[position]) + if role == "context-only": + value = 0.0 + elif role == "long-only": + value = max(value, 0.0) + elif role == "short-only": + value = min(value, 0.0) + repaired[position] = min(max(value, -cap), cap) + + family = str(resolved["family"]) + gross_limit = float(resolved["gross_limit"]) + long_limit = float(resolved["long_gross_limit"]) + short_limit = float(resolved["short_gross_limit"]) + if family == "dollar-neutral": + positive = repaired > 0.0 + negative = repaired < 0.0 + long_exposure = float(repaired[positive].sum()) + short_exposure = float(-repaired[negative].sum()) + funded_side = min( + long_exposure, + short_exposure, + long_limit, + short_limit, + gross_limit / 2.0, + ) + if funded_side <= 1e-15: + repaired.fill(0.0) + else: + repaired[positive] *= funded_side / long_exposure + repaired[negative] *= funded_side / short_exposure + else: + for positive, limit in ( + (True, long_limit), + (False, short_limit), + ): + mask = repaired > 0.0 if positive else repaired < 0.0 + exposure = float( + repaired[mask].sum() + if positive + else -repaired[mask].sum() + ) + if exposure > limit + 1e-15: + repaired[mask] *= limit / exposure + gross = float(np.abs(repaired).sum()) + if gross > gross_limit + 1e-15: + repaired *= gross_limit / gross + if not np.isfinite(repaired).all(): + raise PolicyFailure( + "portfolio.non-finite", + "Mandate repair produced non-finite weights", + ) + return repaired + + +def _govern_weight_values( + weights: np.ndarray, + timestamp: object, + resolved: dict[str, object], + risk_covariance_cache: RiskCovarianceCache, +) -> np.ndarray: + if float(np.abs(weights).sum()) <= 1e-12: + return weights.copy() + policy = resolved["risk_policy"] + if policy is None: + return weights.copy() + assert isinstance(policy, dict) + cached = risk_covariance_cache.get(timestamp) + if cached is None: + raise PolicyFailure( + "portfolio.risk-cache", + "Risk covariance cache is missing a learning timestamp", + ) + observations, covariance = cached + if ( + observations < int(policy["minimum_observations"]) + or covariance is None + ): + return np.zeros_like(weights) + if covariance.shape != (len(weights), len(weights)): + raise PolicyFailure( + "portfolio.risk-cache", + "Risk covariance cache shape is invalid", + ) + variance = float(weights @ covariance @ weights) + if not math.isfinite(variance) or variance < -1e-12: + return np.zeros_like(weights) + forecast = math.sqrt( + max(variance, 0.0) * int(policy["annualization_periods"]) + ) + ceiling = float(policy["annualized_volatility_ceiling"]) + scale = ( + min(1.0, ceiling / forecast) + if forecast > 1e-12 + else 1.0 + ) + return weights * scale + + +def _learning_account_values( + pretrade: np.ndarray, + proposed: np.ndarray, + timestamp: object, + forward_return: np.ndarray, + *, + ordinary_rebalance_allowed: bool, + no_trade_one_way: float, + base_cost_bps: float, + resolved_mandate: dict[str, object], + risk_covariance_cache: RiskCovarianceCache, +) -> tuple[np.ndarray, float, float, float]: + if ordinary_rebalance_allowed: + runtime_proposed = _govern_weight_values( + _repair_weight_values(proposed, resolved_mandate), + timestamp, + resolved_mandate, + risk_covariance_cache, + ) + proposed_one_way = 0.5 * float( + np.abs(runtime_proposed - pretrade).sum() + ) + ordinary_rebalance = ( + proposed_one_way + 1e-12 >= no_trade_one_way + ) + ordinary_book = ( + runtime_proposed if ordinary_rebalance else pretrade + ) + else: + ordinary_rebalance = False + ordinary_book = pretrade + constraint_book = _repair_weight_values( + ordinary_book, + resolved_mandate, + ) + current = ( + constraint_book + if ordinary_rebalance + else _govern_weight_values( + constraint_book, + timestamp, + resolved_mandate, + risk_covariance_cache, + ) + ) + traded_notional = float(np.abs(current - pretrade).sum()) + cost = traded_notional * base_cost_bps / 10_000.0 + gross_return = float((current * forward_return).sum()) + net_return = gross_return - cost + reward = net_return - RISK_AVERSION * gross_return**2 + if not np.isfinite(current).all() or not math.isfinite(reward): + raise PolicyFailure( + "policy.non-finite", + "Array learning accounting produced non-finite evidence", + ) + return current, gross_return, net_return, reward + + +def _learning_step_values( + pretrade: np.ndarray, + proposed: np.ndarray, + timestamp: object, + forward_return: np.ndarray, + *, + ordinary_rebalance_allowed: bool, + no_trade_one_way: float, + base_cost_bps: float, + resolved_mandate: dict[str, object], + risk_covariance_cache: RiskCovarianceCache, +) -> tuple[np.ndarray, float]: + current, _, _, reward = _learning_account_values( + pretrade, + proposed, + timestamp, + forward_return, + ordinary_rebalance_allowed=ordinary_rebalance_allowed, + no_trade_one_way=no_trade_one_way, + base_cost_bps=base_cost_bps, + resolved_mandate=resolved_mandate, + risk_covariance_cache=risk_covariance_cache, + ) + return current, reward + + +def _prepare_array_runtime( + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + index: pd.Index, + mandate: dict[str, object] | None, + risk_covariance_cache: RiskCovarianceCache | None, +) -> tuple[ + dict[str, object], + dict[str, object], + RiskCovarianceCache, + list[object], + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, +]: + resolved = resolve_portfolio_mandate(closes.columns, mandate) + resolved["_constraint_role_values"] = tuple( + str(resolved["asset_position_roles"][str(asset)]) + for asset in closes.columns + ) + resolved["_constraint_cap_values"] = np.asarray( + [ + float(resolved["asset_max_abs_weights"][str(asset)]) + for asset in closes.columns + ], + dtype=float, + ) + implementation = resolve_implementation_policy(mandate) + cache = ( + risk_covariance_cache + if risk_covariance_cache is not None + else build_risk_covariance_cache(closes, mandate=mandate) + ) + decision_mask = decision_schedule_mask( + next(iter(action_targets.values())).index, + dict(implementation["decision_policy"]), + ).reindex(index) + if decision_mask.isna().any(): + raise PolicyFailure( + "policy.decision-cadence", + "Array runtime split is outside the complete decision schedule", + ) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + return ( + resolved, + implementation, + cache, + list(index), + raw_states.loc[ + index, + list(BASE_STATE_COLUMNS), + ].to_numpy(dtype=float), + np.stack( + [ + action_targets[action] + .loc[index, closes.columns] + .to_numpy(dtype=float) + for action in ACTIONS + ], + axis=0, + ), + close_returns.loc[ + index, + closes.columns, + ].to_numpy(dtype=float), + ( + forward_returns.loc[index, closes.columns] + .fillna(0.0) + .to_numpy(dtype=float) + ), + decision_mask.to_numpy(dtype=bool), + ) + + +def _training_rollout( + selector: Callable[[dict[str, float]], str], + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + index: pd.Index, + *, + mandate: dict[str, object] | None, + risk_covariance_cache: RiskCovarianceCache | None, +) -> TrainingRollout: + ( + resolved, + implementation, + cache, + timestamps, + raw_values, + target_values, + close_return_values, + forward_return_values, + decision_values, + ) = _prepare_array_runtime( + raw_states, + action_targets, + closes, + index, + mandate, + risk_covariance_cache, + ) + benchmark = ( + None + if mandate is None + else np.asarray( + [ + float( + mandate["construction"]["benchmark"]["weights"][ + str(asset) + ] + ) + for asset in closes.columns + ], + dtype=float, + ) + ) + previous_values = np.zeros(len(closes.columns), dtype=float) + previous_action_number = ACTIONS.index("balanced") + action_numbers: list[int] = [] + state_rows: list[dict[str, float]] = [] + pretrade_rows: list[np.ndarray] = [] + net_returns: list[float] = [] + benchmark_returns: list[float] = [] + rewards: list[float] = [] + for position, timestamp in enumerate(timestamps): + pretrade = ( + np.zeros(len(closes.columns), dtype=float) + if position == 0 + else _drift_weight_values( + previous_values, + close_return_values[position], + ) + ) + state = _policy_state_values( + raw_values[position], + previous_action_number, + pretrade, + target_values[:, position, :], + ) + action_number = ( + ACTIONS.index(selector(state)) + if decision_values[position] + else previous_action_number + ) + current, _, net_return, reward = _learning_account_values( + pretrade, + target_values[action_number, position], + timestamp, + forward_return_values[position], + ordinary_rebalance_allowed=bool( + decision_values[position] + ), + no_trade_one_way=float( + implementation["no_trade_one_way"] + ), + base_cost_bps=float( + implementation["base_cost_bps"] + ), + resolved_mandate=resolved, + risk_covariance_cache=cache, + ) + action_numbers.append(action_number) + state_rows.append(state) + pretrade_rows.append(pretrade) + net_returns.append(net_return) + benchmark_returns.append( + float(forward_return_values[position].mean()) + if benchmark is None + else float( + ( + benchmark * forward_return_values[position] + ).sum() + ) + ) + rewards.append(reward) + previous_values = current + previous_action_number = action_number + return TrainingRollout( + actions=pd.Series( + [ACTIONS[number] for number in action_numbers], + index=index, + name="action", + ), + states=pd.DataFrame( + state_rows, + index=index, + columns=list(POLICY_STATE_COLUMNS), + ), + pretrade=np.asarray(pretrade_rows, dtype=float), + net_returns=pd.Series( + net_returns, + index=index, + name="net_return", + ), + benchmark_returns=pd.Series( + benchmark_returns, + index=index, + name="benchmark_return", + ), + rewards=np.asarray(rewards, dtype=float), + ) + + +def training_policy_net_sharpe( + selector: Callable[[dict[str, float]], str], + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + index: pd.Index, + *, + mandate: dict[str, object] | None, + risk_covariance_cache: RiskCovarianceCache | None, +) -> float: + """Return exact train-only net Sharpe without artifact-only accounting.""" + + rollout = _training_rollout( + selector, + raw_states, + action_targets, + closes, + index, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + ) + return float( + performance_metrics( + rollout.net_returns, + rollout.benchmark_returns, + )["sharpe"] + ) + + +def _training_opportunity_summary( + rollout: TrainingRollout, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + index: pd.Index, + *, + mandate: dict[str, object] | None, + risk_covariance_cache: RiskCovarianceCache | None, +) -> tuple[np.ndarray, float, float]: + ( + resolved, + implementation, + cache, + timestamps, + _, + target_values, + _, + forward_return_values, + decision_values, + ) = _prepare_array_runtime( + rollout.states.loc[:, list(BASE_STATE_COLUMNS)], + action_targets, + closes, + index, + mandate, + risk_covariance_cache, + ) + oracle_hits: list[bool] = [] + regrets: list[float] = [] + reward_rows: list[list[float]] = [] + for position, timestamp in enumerate(timestamps): + selected_number = ACTIONS.index( + str(rollout.actions.iloc[position]) + ) + if decision_values[position]: + action_rewards = [ + _learning_account_values( + rollout.pretrade[position], + target_values[action_number, position], + timestamp, + forward_return_values[position], + ordinary_rebalance_allowed=True, + no_trade_one_way=float( + implementation["no_trade_one_way"] + ), + base_cost_bps=float( + implementation["base_cost_bps"] + ), + resolved_mandate=resolved, + risk_covariance_cache=cache, + )[3] + for action_number in range(len(ACTIONS)) + ] + ranked = sorted( + range(len(ACTIONS)), + key=lambda number: (-action_rewards[number], number), + ) + oracle_number = ranked[0] + selected_reward = action_rewards[selected_number] + oracle_reward = action_rewards[oracle_number] + else: + oracle_number = selected_number + selected_reward = float(rollout.rewards[position]) + oracle_reward = selected_reward + action_rewards = [selected_reward] * len(ACTIONS) + reward_rows.append(action_rewards) + oracle_hits.append(selected_number == oracle_number) + regrets.append(max(0.0, oracle_reward - selected_reward)) + return ( + np.asarray(reward_rows, dtype=float), + float(np.mean(oracle_hits)), + float(np.mean(regrets)), + ) + + +def rollout_policy( + selector: Callable[[dict[str, float]], str], + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + index: pd.Index, + *, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> Rollout: + if len(index) < MIN_SPLIT_OBSERVATIONS: + raise PolicyFailure( + "policy.population", + "Policy rollout split is too short", + ) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + resolved_mandate = resolve_portfolio_mandate( + closes.columns, + mandate, + ) + implementation = resolve_implementation_policy(mandate) + previous_weights = pd.Series(0.0, index=closes.columns, dtype=float) + previous_action = "balanced" + decision_policy = dict(implementation["decision_policy"]) + complete_index = next(iter(action_targets.values())).index + decision_mask = decision_schedule_mask( + complete_index, + decision_policy, + ).reindex(index) + if decision_mask.isna().any(): + raise PolicyFailure( + "policy.decision-cadence", + "Policy split is outside the complete decision schedule", + ) + daily_rows: list[dict[str, object]] = [] + weight_rows: list[pd.Series] = [] + trade_rows: list[pd.Series] = [] + participation_rows: list[pd.Series] = [] + actions: list[str] = [] + state_rows: list[dict[str, float]] = [] + for position, timestamp in enumerate(index): + first = position == 0 + decision_eligible = bool(decision_mask.loc[timestamp]) + pretrade = _pretrade_weights( + previous_weights, + close_returns, + timestamp, + first=first, + ) + state = build_policy_state( + raw_states.loc[timestamp], + previous_action, + pretrade, + { + action: action_targets[action].loc[timestamp] + for action in ACTIONS + }, + ) + action = ( + selector(state) + if decision_eligible + else previous_action + ) + if action not in ACTIONS: + raise PolicyFailure( + "policy.action", + f"Policy selected unknown action: {action}", + ) + current, trade, participation, row = _account_step( + previous_weights, + action_targets[action].loc[timestamp], + close_returns, + timestamp, + forward_returns.loc[timestamp].fillna(0.0), + closes.loc[timestamp], + volumes.loc[timestamp], + first=first, + ordinary_rebalance_allowed=decision_eligible, + mandate=mandate, + risk_covariance_cache=risk_covariance_cache, + resolved_mandate=resolved_mandate, + implementation=implementation, + pretrade=pretrade, + ) + daily_rows.append(row) + weight_rows.append(current) + trade_rows.append(trade) + participation_rows.append(participation) + actions.append(action) + state_rows.append(state) + previous_weights = current + previous_action = action + simulation = Simulation( + daily=pd.DataFrame(daily_rows, index=index), + weights=pd.DataFrame(weight_rows, index=index), + trades=pd.DataFrame(trade_rows, index=index), + participation=pd.DataFrame(participation_rows, index=index), + ) + return Rollout( + simulation=simulation, + actions=pd.Series(actions, index=index, name="action"), + rewards=simulation.daily["reward"].copy(), + states=pd.DataFrame( + state_rows, + index=index, + columns=list(POLICY_STATE_COLUMNS), + ), + ) + + +def one_step_action_opportunities( + rollout: Rollout, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + index: pd.Index, + *, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> list[dict[str, object]]: + """Audit every governed action from the actual policy pretrade book.""" + + if ( + not rollout.actions.index.equals(index) + or not rollout.simulation.daily.index.equals(index) + or not rollout.simulation.weights.index.equals(index) + or not rollout.simulation.trades.index.equals(index) + or set(action_targets) != set(ACTIONS) + ): + raise PolicyFailure( + "policy.opportunity-identity", + "Opportunity audit inputs differ from the selected rollout", + ) + cache = ( + risk_covariance_cache + if risk_covariance_cache is not None + else build_risk_covariance_cache(closes, mandate=mandate) + ) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + resolved_mandate = resolve_portfolio_mandate( + closes.columns, + mandate, + ) + implementation = resolve_implementation_policy(mandate) + zero = pd.Series(0.0, index=closes.columns, dtype=float) + decision_policy = dict(implementation["decision_policy"]) + complete_index = next(iter(action_targets.values())).index + decision_mask = decision_schedule_mask( + complete_index, + decision_policy, + ).reindex(index) + if decision_mask.isna().any(): + raise PolicyFailure( + "policy.decision-cadence", + "Opportunity split is outside the complete decision schedule", + ) + rows: list[dict[str, object]] = [] + for position, timestamp in enumerate(index): + decision_eligible = bool(decision_mask.loc[timestamp]) + selected = str(rollout.actions.loc[timestamp]) + previous_weights = ( + zero + if position == 0 + else rollout.simulation.weights.loc[index[position - 1]] + ) + pretrade = ( + zero.copy() + if position == 0 + else drift_weights(previous_weights, close_returns.loc[timestamp]) + ) + forward_return = forward_returns.loc[timestamp].fillna(0.0) + action_evidence: dict[str, dict[str, object]] = {} + held_step = ( + None + if decision_eligible + else _account_step( + previous_weights, + action_targets[selected].loc[timestamp], + close_returns, + timestamp, + forward_return, + closes.loc[timestamp], + volumes.loc[timestamp], + first=position == 0, + ordinary_rebalance_allowed=False, + mandate=mandate, + risk_covariance_cache=cache, + resolved_mandate=resolved_mandate, + implementation=implementation, + pretrade=pretrade, + ) + ) + for action in ACTIONS: + proposed = action_targets[action].loc[timestamp] + current, trade, _, daily = ( + held_step + if held_step is not None + else _account_step( + previous_weights, + proposed, + close_returns, + timestamp, + forward_return, + closes.loc[timestamp], + volumes.loc[timestamp], + first=position == 0, + ordinary_rebalance_allowed=True, + mandate=mandate, + risk_covariance_cache=cache, + resolved_mandate=resolved_mandate, + implementation=implementation, + pretrade=pretrade, + ) + ) + action_evidence[action] = { + "proposedWeights": { + asset: float(proposed[asset]) + for asset in closes.columns + }, + "executedWeights": { + asset: float(current[asset]) + for asset in closes.columns + }, + "trades": { + asset: float(trade[asset]) + for asset in closes.columns + }, + "grossReturn": float(daily["gross_return"]), + "netReturn": float(daily["net_return"]), + "reward": float(daily["reward"]), + "oneWayTurnover": float(daily["one_way_turnover"]), + "cost": float(daily["cost"]), + "grossExposure": float(daily["gross_exposure"]), + "netExposure": float(daily["net_exposure"]), + "executionRiskStatus": str( + daily["execution_risk_status"] + ), + "executionRiskForecastAvailable": bool( + daily["execution_risk_forecast_available"] + ), + "executionRiskObservations": int( + daily["execution_risk_observations"] + ), + "pretradeRiskForecastAnnualized": float( + daily["pretrade_risk_forecast_annualized"] + ), + "executedRiskForecastAnnualized": float( + daily["executed_risk_forecast_annualized"] + ), + "executionRiskCeilingAnnualized": float( + daily["execution_risk_ceiling_annualized"] + ), + "riskRebalanceOverride": bool( + daily["risk_rebalance_override"] + ), + "constraintRebalanceOverride": bool( + daily["constraint_rebalance_override"] + ), + "constraintRepairOneWay": float( + daily["constraint_repair_one_way"] + ), + "executedConstraintMaximumError": float( + daily["executed_constraint_maximum_error"] + ), + "executionReason": str(daily["execution_reason"]), + } + selected_evidence = action_evidence[selected] + actual_daily = rollout.simulation.daily.loc[timestamp] + if ( + not np.allclose( + np.asarray( + list(selected_evidence["executedWeights"].values()), + dtype=float, + ), + rollout.simulation.weights.loc[timestamp].to_numpy(dtype=float), + rtol=0.0, + atol=1e-12, + ) + or not np.allclose( + np.asarray( + list(selected_evidence["trades"].values()), + dtype=float, + ), + rollout.simulation.trades.loc[timestamp].to_numpy(dtype=float), + rtol=0.0, + atol=1e-12, + ) + or any( + not math.isclose( + float(selected_evidence[artifact_field]), + float(actual_daily[daily_field]), + rel_tol=0.0, + abs_tol=1e-12, + ) + for artifact_field, daily_field in ( + ("grossReturn", "gross_return"), + ("netReturn", "net_return"), + ("reward", "reward"), + ("oneWayTurnover", "one_way_turnover"), + ("cost", "cost"), + ) + ) + ): + raise PolicyFailure( + "policy.opportunity-selected", + "Selected opportunity does not reproduce the actual rollout", + ) + ranked = sorted( + ACTIONS, + key=lambda action: ( + -float(action_evidence[action]["reward"]), + ACTIONS.index(action), + ), + ) + if decision_eligible: + oracle = ranked[0] + selected_rank = ranked.index(selected) + 1 + else: + oracle = selected + selected_rank = 1 + selected_reward = float(selected_evidence["reward"]) + oracle_reward = float(action_evidence[oracle]["reward"]) + regret = oracle_reward - selected_reward + if regret < -1e-12: + raise PolicyFailure( + "policy.opportunity-regret", + "Selected opportunity regret is negative", + ) + candidate_reward = float(action_evidence["candidate"]["reward"]) + balanced_reward = float(action_evidence["balanced"]["reward"]) + rows.append( + { + "timestamp": timestamp, + "decisionEligible": decision_eligible, + "decisionSchedule": { + key: value + for key, value in decision_policy.items() + if key != "source" + }, + "decisionSession": str( + decision_schedule_sessions( + pd.Index([timestamp]), + decision_policy, + ).iloc[0] + ), + "selectedAction": selected, + "oracleAction": oracle, + "selectedRank": selected_rank, + "oracleHit": selected == oracle, + "selectedReward": selected_reward, + "oracleReward": oracle_reward, + "realizedRegret": max(0.0, regret), + "candidateMinusSelectedReward": ( + candidate_reward - selected_reward + ), + "candidateMinusBalancedReward": ( + candidate_reward - balanced_reward + ), + "pretradeWeights": { + asset: float(pretrade[asset]) + for asset in closes.columns + }, + "forwardReturns": { + asset: float(forward_return[asset]) + for asset in closes.columns + }, + "actions": action_evidence, + } + ) + return rows + + +def compact_opportunity_rows( + rows: list[dict[str, Any]], +) -> list[dict[str, Any]]: + """Store one shared executed book when the schedule holds every sleeve.""" + + execution_fields = { + "executedWeights", + "trades", + "grossReturn", + "netReturn", + "reward", + "oneWayTurnover", + "cost", + "grossExposure", + "netExposure", + "executionRiskStatus", + "executionRiskForecastAvailable", + "executionRiskObservations", + "pretradeRiskForecastAnnualized", + "executedRiskForecastAnnualized", + "executionRiskCeilingAnnualized", + "riskRebalanceOverride", + "constraintRebalanceOverride", + "constraintRepairOneWay", + "executedConstraintMaximumError", + "executionReason", + } + compacted: list[dict[str, Any]] = [] + for row in rows: + if row["decisionEligible"]: + compacted.append({**row, "sharedExecution": None}) + continue + selected_evidence = row["actions"][row["selectedAction"]] + compacted.append( + { + **row, + "actions": { + action: { + "proposedWeights": evidence["proposedWeights"] + } + for action, evidence in row["actions"].items() + }, + "sharedExecution": { + field: selected_evidence[field] + for field in execution_fields + }, + } + ) + return compacted + + +def train_q_policy( + encoder: Callable[[dict[str, float]], np.ndarray], + feature_count: int, + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + train_index: pd.Index, + *, + seed: int, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> TrainedPolicy: + if seed not in SEEDS: + raise PolicyFailure("policy.seed", "Seed is outside the fixed set") + rng = np.random.default_rng(seed) + weights = rng.normal(0.0, 1e-5, size=(len(ACTIONS), feature_count)) + close_returns = closes.pct_change(fill_method=None) + forward_returns = closes.shift(-1) / closes - 1.0 + resolved_mandate = resolve_portfolio_mandate( + closes.columns, + mandate, + ) + resolved_mandate["_constraint_role_values"] = tuple( + str( + resolved_mandate["asset_position_roles"][str(asset)] + ) + for asset in closes.columns + ) + resolved_mandate["_constraint_cap_values"] = np.asarray( + [ + float( + resolved_mandate["asset_max_abs_weights"][str(asset)] + ) + for asset in closes.columns + ], + dtype=float, + ) + implementation = resolve_implementation_policy(mandate) + decision_policy = dict(implementation["decision_policy"]) + complete_index = next(iter(action_targets.values())).index + decision_mask = decision_schedule_mask( + complete_index, + decision_policy, + ).reindex(train_index) + if decision_mask.isna().any(): + raise PolicyFailure( + "policy.decision-cadence", + "Training split is outside the complete decision schedule", + ) + cache = ( + risk_covariance_cache + if risk_covariance_cache is not None + else build_risk_covariance_cache(closes, mandate=mandate) + ) + timestamps = list(train_index) + raw_values = raw_states.loc[ + train_index, + list(BASE_STATE_COLUMNS), + ].to_numpy(dtype=float) + action_target_values = np.stack( + [ + action_targets[action] + .loc[train_index, closes.columns] + .to_numpy(dtype=float) + for action in ACTIONS + ], + axis=0, + ) + close_return_values = close_returns.loc[ + train_index, + closes.columns, + ].to_numpy(dtype=float) + forward_return_values = ( + forward_returns.loc[train_index, closes.columns] + .fillna(0.0) + .to_numpy(dtype=float) + ) + decision_values = decision_mask.to_numpy(dtype=bool) + history: list[dict[str, object]] = [] + for episode in range(EPISODES): + fraction = episode / max(1, EPISODES - 1) + epsilon = EPSILON_START + fraction * (EPSILON_END - EPSILON_START) + previous_values = np.zeros(len(closes.columns), dtype=float) + previous_action_number = ACTIONS.index("balanced") + total_reward = 0.0 + action_counts = {action: 0 for action in ACTIONS} + for position, timestamp in enumerate(timestamps): + first = position == 0 + decision_eligible = bool(decision_values[position]) + pretrade_values = ( + np.zeros(len(closes.columns), dtype=float) + if first + else _drift_weight_values( + previous_values, + close_return_values[position], + ) + ) + state = _policy_state_values( + raw_values[position], + previous_action_number, + pretrade_values, + action_target_values[:, position, :], + ) + encoded = encoder(state) + q_values = weights @ encoded + if not decision_eligible: + action_number = previous_action_number + elif rng.random() < epsilon: + action_number = int(rng.integers(0, len(ACTIONS))) + else: + action_number = int(np.argmax(q_values)) + action = ACTIONS[action_number] + current_values, reward = _learning_step_values( + pretrade_values, + action_target_values[action_number, position], + timestamp, + forward_return_values[position], + ordinary_rebalance_allowed=decision_eligible, + no_trade_one_way=float( + implementation["no_trade_one_way"] + ), + base_cost_bps=float( + implementation["base_cost_bps"] + ), + resolved_mandate=resolved_mandate, + risk_covariance_cache=cache, + ) + done = position == len(train_index) - 1 + if done: + target = reward + else: + next_pretrade_values = _drift_weight_values( + current_values, + close_return_values[position + 1], + ) + next_state = _policy_state_values( + raw_values[position + 1], + action_number, + next_pretrade_values, + action_target_values[:, position + 1, :], + ) + next_encoded = encoder(next_state) + next_q_values = weights @ next_encoded + bootstrap = ( + float(np.max(next_q_values)) + if bool(decision_values[position + 1]) + else float( + next_q_values[action_number] + ) + ) + target = reward + DISCOUNT * bootstrap + error = float(np.clip(target - q_values[action_number], -0.10, 0.10)) + weights[action_number] += LEARNING_RATE * error * encoded + if not np.isfinite(weights).all(): + raise PolicyFailure( + "policy.non-finite", + f"Seed {seed} produced non-finite Q weights", + ) + total_reward += reward + action_counts[action] += 1 + previous_values = current_values + previous_action_number = action_number + history.append( + { + "episode": episode + 1, + "epsilon": float(epsilon), + "totalReward": total_reward, + "meanReward": total_reward / len(train_index), + "actionCounts": action_counts, + } + ) + return TrainedPolicy(weights=weights, history=history) + + +def q_selector( + weights: np.ndarray, + encoder: Callable[[dict[str, float]], np.ndarray], +) -> Callable[[dict[str, float]], str]: + def select(state: dict[str, float]) -> str: + return ACTIONS[int(np.argmax(weights @ encoder(state)))] + + return select + + +def fixed_selector(action: str) -> Callable[[dict[str, float]], str]: + if action not in ACTIONS: + raise PolicyFailure("policy.action", "Unknown fixed action") + return lambda _: action + + +def train_contextual_ridge( + raw_states: pd.DataFrame, + action_targets: dict[str, pd.DataFrame], + closes: pd.DataFrame, + volumes: pd.DataFrame, + train_index: pd.Index, + *, + mandate: dict[str, object] | None = None, + risk_covariance_cache: RiskCovarianceCache | None = None, +) -> dict[str, object]: + """Fit a deterministic train-only same-pretrade contextual comparator.""" + + cache = ( + risk_covariance_cache + if risk_covariance_cache is not None + else build_risk_covariance_cache(closes, mandate=mandate) + ) + selector = fixed_selector("balanced") + history: list[dict[str, object]] = [] + model: dict[str, object] | None = None + for iteration in range(CONTEXTUAL_RIDGE_ITERATIONS): + behavior = _training_rollout( + selector, + raw_states, + action_targets, + closes, + train_index, + mandate=mandate, + risk_covariance_cache=cache, + ) + ( + opportunity_rewards, + behavior_oracle_hit_rate, + behavior_mean_regret, + ) = _training_opportunity_summary( + behavior, + action_targets, + closes, + train_index, + mandate=mandate, + risk_covariance_cache=cache, + ) + raw = behavior.states.loc[ + train_index, + list(POLICY_STATE_COLUMNS), + ] + mean = raw.mean() + scale = raw.std(ddof=0).replace(0.0, 1.0) + normalized = (raw - mean) / scale + design = np.column_stack( + [ + np.ones(len(normalized)), + normalized.to_numpy(dtype=float), + ] + ) + gram = ( + design.T @ design + + RIDGE_PENALTY * np.eye(design.shape[1]) + ) + coefficients: list[list[float]] = [] + for action_number, _ in enumerate(ACTIONS): + target = opportunity_rewards[:, action_number] + coefficients.append( + np.linalg.solve(gram, design.T @ target).tolist() + ) + model = { + "method": ( + "iterative-same-pretrade-contextual-ridge-v1" + ), + "labelScope": "train-only", + "anchorAction": "balanced", + "iterations": CONTEXTUAL_RIDGE_ITERATIONS, + "columns": list(POLICY_STATE_COLUMNS), + "mean": mean.to_dict(), + "scale": scale.to_dict(), + "coefficients": coefficients, + } + selector = ridge_selector(model) + improved = _training_rollout( + selector, + raw_states, + action_targets, + closes, + train_index, + mandate=mandate, + risk_covariance_cache=cache, + ) + history.append( + { + "iteration": iteration + 1, + "trainingRows": len(train_index), + "sharedPretradeActionEvaluations": ( + len(train_index) * len(ACTIONS) + ), + "behaviorActionFrequency": { + action: float( + (behavior.actions == action).mean() + ) + for action in ACTIONS + }, + "improvedActionFrequency": { + action: float( + (improved.actions == action).mean() + ) + for action in ACTIONS + }, + "improvedTrainingNetSharpe": float( + performance_metrics( + improved.net_returns, + improved.benchmark_returns, + )["sharpe"] + ), + "behaviorOracleHitRate": behavior_oracle_hit_rate, + "behaviorMeanRealizedRegret": behavior_mean_regret, + } + ) + if model is None: + raise PolicyFailure( + "policy.ridge", + "Contextual ridge did not execute its fixed iterations", + ) + model["history"] = history + return model + + +def ridge_selector(model: dict[str, object]) -> Callable[[dict[str, float]], str]: + columns = list(model["columns"]) + mean = dict(model["mean"]) + scale = dict(model["scale"]) + coefficients = np.asarray(model["coefficients"], dtype=float) + + def select(state: dict[str, float]) -> str: + values = np.asarray( + [ + (state[column] - float(mean[column])) / float(scale[column]) + for column in columns + ], + dtype=float, + ) + encoded = np.concatenate(([1.0], values)) + return ACTIONS[int(np.argmax(coefficients @ encoded))] + + return select + + +def chronological_folds(index: pd.Index) -> dict[str, dict[str, pd.Index]]: + count = len(index) + boundaries = { + "fold-1": (int(count * 0.50), int(count * 0.65), int(count * 0.80)), + "fold-2": (int(count * 0.65), int(count * 0.80), count), + } + folds: dict[str, dict[str, pd.Index]] = {} + for name, (train_end, validation_end, test_end) in boundaries.items(): + split = { + "train": index[:train_end], + "validation": index[train_end:validation_end], + "test": index[validation_end:test_end], + } + if any(len(values) < MIN_SPLIT_OBSERVATIONS for values in split.values()): + raise PolicyFailure( + "policy.population", + f"{name} contains a split shorter than {MIN_SPLIT_OBSERVATIONS}", + ) + folds[name] = split + return folds + + +def rollout_metrics(rollout: Rollout) -> dict[str, object]: + index = rollout.simulation.daily.index + action_frequency = { + action: float((rollout.actions == action).mean()) + for action in ACTIONS + } + result = { + "net": performance_metrics( + rollout.simulation.daily["net_return"], + rollout.simulation.daily["benchmark_return"], + ), + "gross": performance_metrics( + rollout.simulation.daily["gross_return"], + rollout.simulation.daily["benchmark_return"], + ), + "implementation": implementation_metrics( + rollout.simulation, + index, + ), + "execution_risk": execution_risk_metrics( + rollout.simulation, + index, + ), + "cumulative_reward": float(rollout.rewards.sum()), + "mean_reward": float(rollout.rewards.mean()), + "action_frequency": action_frequency, + } + return result diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/program.md b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/program.md new file mode 100644 index 0000000..08efa86 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/program.md @@ -0,0 +1,142 @@ +# Governed RL Factor-Policy Study + +## Question + +Can a deterministic causal state representation help a fixed, bounded +Q-learning policy select factor mixtures across chronological regimes, after +the same target constraints, drift, turnover, and costs used by the portfolio +laboratory? + +## Editable closure + +Edit only `models/**`. Preserve: + +```python +FEATURE_NAMES = (...) + +def encode_state(state: dict[str, float]) -> list[float]: + ... +``` + +The function receives one close-`t` scalar state. It must be pure, +deterministic, finite, bounded, and exactly aligned with `FEATURE_NAMES`. +Available fields are: + +- `volume_regime`; +- `market_return_5`; +- `market_volatility_20`; +- `candidate_trailing_reward_10`; +- `activity_trailing_reward_10`; +- `intraday_trailing_reward_10`; +- `reversal_trailing_reward_10`; +- `pretrade_gross_exposure`, `pretrade_net_exposure`, + `pretrade_cash_weight`, `pretrade_max_abs_weight`, and + `pretrade_concentration_hhi`; +- `candidate_target_distance`, `activity_target_distance`, + `intraday_target_distance`, `reversal_target_distance`, and + `balanced_target_distance`, each measured as one-way distance from the exact + drifted pretrade book to that governed sleeve target; +- `previous_candidate`, `previous_activity`, `previous_intraday`, + `previous_reversal`, and `previous_balanced`. + +Do not read files, environment variables, clocks, randomness, or mutable global +state from the encoder. Test one representation hypothesis at a time. + +## Fixed Judge authority + +The Study declares `factors/**`, `strategies/factor-population.json`, and +`strategies/portfolio-mandate.json` as fixed, content-locked dependencies. +They are copied into the Session worktree for execution but are not editable +in this Study. The Judge independently checks +the `factors.candidate` pandas API, +alignment, determinism, numeric output, and prefix causality before using it. + +The Judge owns: + +- candidate, activity, intraday, reversal, and equal-blend governed signal + sleeves; +- the Factor Population's prediction/context partition and evaluation mode; + it never derives prediction eligibility from position permission; +- fixed percentile entry/exit hysteresis, inverse-volatility conviction, + request-permitted long/cash, short/cash, dollar-neutral, or explicit + per-asset-role target construction, context-only exclusions, locked side + limits, and Portfolio Mandate asset caps; +- the shared trailing 60-row covariance forecast, 20-observation minimum, + Portfolio Mandate annualized volatility ceiling, and scale-down-only + governance on every action sleeve before RL selection and reward; +- the Portfolio Mandate's locked dataset-start or verified XNYS session-start + decision schedule: RL may choose a sleeve only on eligible base bars and + must hold its prior sleeve between them; an off-schedule Q update bootstraps + only through that held action rather than a choice the policy was not + allowed to make; +- drift, the Portfolio Mandate's no-trade threshold, then shared final-book + compliance where risk may override no-trade only through minimum + proportional scale-down; +- off-schedule ordinary rebalancing is forbidden while every-bar risk repair + may still flatten or proportionally scale down an unsafe drifted book; +- full-notional Portfolio Mandate costs and one identical structured + benchmark weight vector across every action sleeve; +- next-bar reward and fixed quadratic risk penalty; +- linear Q-learning with 12 episodes, learning rate 0.02, discount 0.30, + and epsilon 0.15 → 0.01, frozen before Study validation by the fixed + train-only blocked stability audit; +- seeds 11, 29, and 47; +- two expanding chronological train/validation/test folds; +- fixed-factor, training-selected expert, equal blend, and a deterministic + four-iteration contextual-ridge baseline trained only on same-pretrade + action rewards from the fold's train path; +- all metrics, artifacts, and the validation-only promotion score. + +Candidate code cannot improve by changing any evaluation rule above. +Each action's sleeve maintains its own causal intent history; RL chooses a +sleeve but never controls its signal triggers, position permissions, or +position/risk sizing. + +## Evidence discipline + +Inspect every seed and fold, seed dispersion, failures, action frequencies, +turnover/cost/risk, RL-minus-best-baseline, RL-minus-candidate-factor, and +candidate-action-frequency evidence. A positive RL Sharpe does not prove RL +added value. +Inspect action-run length, transition/retention, single-bar churn, Q-margin +ties, and the chosen-versus-runner-up rationale ledger. Q margins are +uncalibrated linear-model scores. Their exact feature decomposition explains +one frozen comparison only; it is not probability, confidence, causal +importance, or a selection metric. Realized outcomes conditioned on the chosen +action are descriptive and endogenous. +Inspect the same-pretrade one-step opportunity ledger separately. It replays +all five governed sleeves from the actual policy pretrade book for the same +next bar, so selected rank/regret and candidate-versus-balanced reward are +locally comparable after identical turnover, cost, no-trade, and risk rules. +The ex-post local best is known only after that bar and never propagates an +alternate path. Validation/test opportunity evidence cannot enter training, +KEEP/REVERT, or a trading decision. The fixed contextual baseline separately +uses the same execution primitive only on train dates to fit action rewards; +its model evidence declares that scope and every fixed iteration. +Inspect the independent full-path attribution versus each fold's +validation-selected mechanical baseline. Diagnose gross selection edge, +incremental cost, net active return, tracking error, information ratio, +relative drawdown, market-regime buckets, policy/baseline action pairs, and +per-asset gross contribution. Gross edge minus incremental cost must reconcile +net active return, and asset contributions must reconcile gross edge. These +conditional tables are descriptive diagnostics, not extra selection +objectives. Test remains visible audit only. +Reconcile final-book risk coverage, pretrade breaches, risk-only overrides, +executed breaches, and execution reasons across every declared policy path; +these are implementation context and cannot select the editable encoder. + +Any failed declared seed/fold fails the complete Run; successful seeds are +never averaged after silently dropping a failed trial. + +Core counts unique encoder sources across the complete fixed-evaluation +Project family. It does not assign this fold/seed aggregate a single-path DSR: +the expanding folds and repeated seeds are dependent evidence, not one longer +return history. Treat the explicit unsupported reason as statistical honesty, +while continuing to inspect baseline advantage, dispersion, and failures. + +Test-fold evidence never enters the objective. Repeatedly editing after reading +it consumes its holdout value; disclose that fact and obtain a new external +holdout before making a production claim. + +This laboratory emits target-weight research only. It has no Broker, +OpenAlice UTA, order, or live-trading authority. diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/study.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/study.json new file mode 100644 index 0000000..610a669 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/inputs/study.json @@ -0,0 +1,62 @@ +{ + "dataset": { + "asset_class": "synthetic-multi-asset", + "id": "synthetic-ohlcv-research-desk-fixture", + "paths": [ + "ohlcv/**" + ], + "time_range": { + "end": "2025-08-11", + "start": "2024-01-02" + }, + "universe": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "version": "v1" + }, + "dependencies": { + "paths": [ + "factors/**", + "strategies/factor-claim.json", + "strategies/factor-population.json", + "strategies/portfolio-mandate.json", + "strategies/research-horizon.json" + ] + }, + "description": "Challenge a locked candidate factor and fixed reference sleeves with a bounded adaptive state representation", + "editable": { + "paths": [ + "models/**" + ] + }, + "id": "ohlcv-rl-factor-policy", + "judge": { + "arguments": [], + "entrypoint": "judges/ohlcv_rl_factor.py", + "kind": "python", + "paths": [ + "judges/ohlcv_rl_factor.py", + "judges/rl_core.py", + "judges/portfolio_core.py" + ], + "timeout_seconds": 120 + }, + "name": "Governed RL Factor Policy", + "objective": { + "direction": "maximize", + "metric": "validation_mean_net_sharpe", + "minimum_improvement": 0.2 + }, + "program": "program.md", + "schema_version": 1, + "subject": { + "kind": "model", + "name": "rl-state-encoder", + "version": "working" + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/judge-output.json b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/judge-output.json new file mode 100644 index 0000000..823ccaa --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/judge-output.json @@ -0,0 +1,6255 @@ +{ + "artifacts": [ + { + "description": "State/action/reward semantics, folds, seeds, baselines, comparisons, warnings, and complete metrics", + "kind": "rl-report", + "path": "rl-report.json" + }, + { + "description": "Exact candidate feature names, configuration, Q weights, and contextual-ridge baseline parameters", + "kind": "policy-models", + "path": "policy-models.json" + }, + { + "description": "Every episode for every declared fold and seed", + "kind": "training-history", + "path": "training-history.json" + }, + { + "description": "Timestamped validation/test actions, rewards, returns, turnover, costs, and executed-book risk compliance", + "kind": "policy-actions", + "path": "policy-actions.csv" + }, + { + "description": "Exact raw state, encoded features, action Q values, runner-up margins, and linear feature contributions", + "kind": "policy-rationales", + "path": "policy-rationales.json" + }, + { + "description": "Same-pretrade one-step governed action books, rewards, local oracle rank/regret, and candidate factor opportunity evidence", + "kind": "policy-opportunities", + "path": "policy-opportunities.json" + }, + { + "description": "Full-path active return, cost, regime, action, and asset attribution versus each fold's validation-selected mechanical baseline", + "kind": "policy-incremental-attribution", + "path": "policy-incremental-attribution.json" + } + ], + "errors": [], + "metrics": { + "baselines": { + "fold-1": { + "best_training_expert": { + "selected": "balanced", + "test": { + "action_frequency": { + "activity": 0.0, + "balanced": 1.0, + "candidate": 0.0, + "intraday": 0.0, + "reversal": 0.0 + }, + "cumulative_reward": 0.19826919458856235, + "execution_risk": { + "active_dates": 60, + "dates": 60, + "executed_breach_dates": 0, + "executed_breach_rate": 0.0, + "execution_reason_counts": { + "rebalance_threshold_met": 60 + }, + "forecast_available_dates": 60, + "forecast_coverage": 1.0, + "forecast_unavailable_dates": 0, + "maximum_ceiling_error": 0.0, + "maximum_executed_forecast_annualized": 0.12399550936178655, + "mean_executed_forecast_annualized": 0.09955823716221386, + "pretrade_breach_dates": 0, + "pretrade_breach_rate": 0.0, + "risk_rebalance_override_dates": 0, + "risk_rebalance_override_rate": 0.0, + "status": "available", + "status_counts": { + "within_ceiling": 60 + } + }, + "gross": { + "active_annual_return": 0.8846994572332069, + "annual_return": 2.167687440537969, + "annual_volatility": 0.1257269080648806, + "annualization_periods": 252, + "benchmark_beta": 0.9636772191809247, + "calmar": 65.2169989742349, + "expected_shortfall_95": 0.011038805186222342, + "information_ratio": 7.110776195562119, + "maximum_drawdown": -0.03323807403947476, + "observations": 60, + "period_sharpe": 0.5829654706344279, + "positive_rate": 0.6833333333333333, + "return_kurtosis": 2.3497541372303945, + "return_skewness": -0.047053938672255255, + "sharpe": 9.254289949418544, + "sortino": 22.84918643515654, + "total_return": 0.3159044710936141, + "tracking_error": 0.124416720889817 + }, + "implementation": { + "annualized_one_way_turnover": 164.33235563990223, + "average_abs_net_exposure": 2.185751579730777e-17, + "average_concentration_hhi": 0.24296099809072055, + "average_gross_exposure": 1.0, + "average_max_abs_weight": 0.29626386188456016, + "maximum_abs_net_exposure": 1.1102230246251565e-16, + "maximum_abs_weight": 0.3, + "maximum_gross_exposure": 1.0000000000000002, + "maximum_volume_participation": 0.024800884163995136, + "mean_one_way_turnover": 0.6521125223805644, + "mean_traded_notional": 1.3042250447611288, + "mean_volume_participation": 0.0025501573128645563, + "no_trade_rate": 0.0, + "rebalance_rate": 1.0, + "total_cost_drag": 0.07825350268566772 + }, + "mean_reward": 0.003304486576476039, + "net": { + "active_annual_return": 0.5560347459534025, + "annual_return": 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10.134608547831329 + }, + "objective": { + "direction": "maximize", + "metric": "validation_mean_net_sharpe", + "minimumImprovement": 0.2 + }, + "project": { + "id": "sample-research-desk", + "name": "Sample Research Desk" + }, + "schemaVersion": 1, + "startedAt": "2026-08-02T16:21:10.371861+00:00", + "status": "succeeded", + "study": { + "hash": "554c8666ae72af7f45196d01a7834ed000d37368068538013f75ef68300eeec5", + "id": "ohlcv-rl-factor-policy", + "name": "Governed RL Factor Policy", + "programHash": "2776778aac6e2f8060a5ff4c8937440ec72fdd31b528a2eb355a89cb9904af4b" + }, + "studyInputHash": "4f84736c6e8080fa7cdc2e2aae4e69ce8bff63c2a560b95bdfa1660a3b936413", + "subject": { + "kind": "model", + "name": "rl-state-encoder", + "sourceHash": "d6dfb8b951e8816a396f346ffc073188d02142ecbaa097815817083dc3a1266d", + "sourcePaths": [ + "models/candidate.py" + ], + "version": "working" + }, + "summary": "Governed factor-mixture policy evaluated across all fixed folds/seeds; validation mean net Sharpe=10.134609" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/sources/models/candidate.py b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/sources/models/candidate.py new file mode 100644 index 0000000..e973776 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/sources/models/candidate.py @@ -0,0 +1,31 @@ +"""Agent-editable baseline state representation for the governed RL lab.""" + +from __future__ import annotations + + +FEATURE_NAMES = ( + "bias", + "previous_activity", + "previous_intraday", + "previous_reversal", + "previous_balanced", +) + + +def encode_state(state: dict[str, float]) -> list[float]: + """Encode one causal state without using regime information. + + This intentionally weak baseline can learn one globally preferred factor + mixture and switching persistence, but cannot adapt to the observable + volume regime or execution context. Research candidates may add causal + market fields, pretrade-book summaries, per-sleeve target distances, and + bounded interactions while preserving this pure row-level API. + """ + + return [ + 1.0, + state["previous_activity"], + state["previous_intraday"], + state["previous_reversal"], + state["previous_balanced"], + ] diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/stderr.txt b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/stderr.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/stdout.txt b/projects/sample-research-desk/runs/run-20260802T162110371861Z-439f0306a9be/stdout.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/artifacts/daily-factor-evidence.csv 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"factors/candidate.py": "1e29d70f72ce3afaf3d018f9d60c77026148423f57b52fb22f55f8127679ba6a" + }, + "studyHash": "97019f1d7c32cf514975e782b29556f9414892651fc9bc41c5509746a2f3cb2e", + "studyInputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/factor_diagnostics.py b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/factor_diagnostics.py new file mode 100644 index 0000000..e675aff --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/factor_diagnostics.py @@ -0,0 +1,535 @@ +"""Fixed, causal diagnostics for the OHLCV Factor Lab.""" + +from __future__ import annotations + +import math +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.intervals import timestamp_label + +HORIZONS = (1, 5, 10) +SPLIT_NAMES = ("train", "validation", "test") +REGIME_NAMES = ("up-calm", "up-stressed", "down-calm", "down-stressed") +STYLE_NAMES = ( + "momentum_20", + "reversal_5", + "realized_volatility_20", + "relative_volume_20", +) + + +def _ranges(length: int) -> dict[str, tuple[int, int]]: + train_end = int(length * 0.60) + validation_end = int(length * 0.80) + return { + "train": (0, train_end), + "validation": (train_end, validation_end), + "test": (validation_end, length), + } + + +def purged_split_masks( + index: pd.DatetimeIndex, + horizons: tuple[int, ...] = HORIZONS, +) -> tuple[ + dict[int, dict[str, pd.Series]], + dict[str, Any], + pd.Series, +]: + """Build dataset-fixed masks whose targets cannot cross split boundaries.""" + + if not index.is_monotonic_increasing or index.has_duplicates: + raise ValueError("Diagnostic index must be unique and chronological") + ranges = _ranges(len(index)) + positions = np.arange(len(index)) + base_labels = pd.Series("unassigned", index=index, dtype="object") + protocol: dict[str, Any] = { + "method": "dataset-fixed-chronological-60-20-20", + "candidateDependent": False, + "targetCrossesBoundary": False, + "horizons": {}, + "splits": {}, + } + for name, (start, stop) in ranges.items(): + if stop <= start: + raise ValueError(f"Chronological split {name} is empty") + base_labels.iloc[start:stop] = name + protocol["splits"][name] = { + "start": timestamp_label(index[start]), + "end": timestamp_label(index[stop - 1]), + "rows": stop - start, + } + + masks: dict[int, dict[str, pd.Series]] = {} + for horizon in horizons: + if not isinstance(horizon, int) or horizon <= 0: + raise ValueError("Forward horizons must be positive integers") + masks[horizon] = {} + horizon_protocol: dict[str, Any] = {} + for name, (start, stop) in ranges.items(): + if stop - start <= horizon: + raise ValueError( + f"Chronological split {name} is too short for horizon {horizon}" + ) + eligible = (positions >= start) & (positions + horizon < stop) + mask = pd.Series(eligible, index=index, dtype=bool) + masks[horizon][name] = mask + signal_positions = positions[eligible] + horizon_protocol[name] = { + "signalStart": timestamp_label(index[signal_positions[0]]), + "signalEnd": timestamp_label(index[signal_positions[-1]]), + "targetEnd": timestamp_label( + index[signal_positions[-1] + horizon] + ), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + protocol["horizons"][str(horizon)] = horizon_protocol + return masks, protocol, base_labels + + +def chronological_fold_masks( + index: pd.DatetimeIndex, + horizon: int = 1, +) -> tuple[dict[str, pd.Series], dict[str, dict[str, Any]]]: + """Split each fixed chronological partition in half and purge each fold.""" + + positions = np.arange(len(index)) + masks: dict[str, pd.Series] = {} + protocol: dict[str, dict[str, Any]] = {} + for split, (start, stop) in _ranges(len(index)).items(): + middle = start + (stop - start) // 2 + for number, (fold_start, fold_stop) in enumerate( + ((start, middle), (middle, stop)), + start=1, + ): + name = f"{split}_{number}" + eligible = ( + (positions >= fold_start) + & (positions + horizon < fold_stop) + ) + if not eligible.any(): + raise ValueError(f"Chronological fold {name} is empty") + masks[name] = pd.Series(eligible, index=index, dtype=bool) + selected = positions[eligible] + protocol[name] = { + "split": split, + "start": timestamp_label(index[fold_start]), + "end": timestamp_label(index[fold_stop - 1]), + "signalEnd": timestamp_label(index[selected[-1]]), + "targetEnd": timestamp_label(index[selected[-1] + horizon]), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + return masks, protocol + + +def forward_return_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + return { + horizon: closes.shift(-horizon) / closes - 1.0 + for horizon in horizons + } + + +def forward_realized_volatility_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Return complete-window, unannualized forward realized volatility. + + At signal close t and horizon h, the target is the square root of the sum + of squared close-to-close log returns from t -> t+1 through t+h-1 -> t+h. + A missing constituent return invalidates the whole target window. + """ + + log_returns = np.log(closes / closes.shift(1)) + output: dict[int, pd.DataFrame] = {} + for horizon in horizons: + future_squared = [ + log_returns.shift(-offset).pow(2) + for offset in range(1, horizon + 1) + ] + squared_sum = sum(future_squared) + complete = sum(item.notna().astype(int) for item in future_squared) + output[horizon] = squared_sum.pow(0.5).where(complete == horizon) + return output + + +def factor_outcome_panels( + closes: pd.DataFrame, + outcome: str, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Materialize one fixed Factor outcome over the requested horizons.""" + + if outcome == "forward-return": + return forward_return_panels(closes, horizons) + if outcome == "forward-realized-volatility": + return forward_realized_volatility_panels(closes, horizons) + raise ValueError(f"Unsupported Factor outcome: {outcome}") + + +def daily_rank_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, + constant_left_value: float | None = None, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["right"].nunique() < 2: + continue + if pair["left"].nunique() < 2: + if constant_left_value is not None: + values[timestamp] = float(constant_left_value) + continue + value = pair["left"].rank(method="average").corr( + pair["right"].rank(method="average") + ) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def daily_pearson_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["left"].nunique() < 2 or pair["right"].nunique() < 2: + continue + value = pair["left"].corr(pair["right"]) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def hac_inference( + values: pd.Series, + *, + maximum_lag: int = 5, +) -> dict[str, float | int | None | str]: + """Return deterministic Newey-West mean inference with Bartlett weights.""" + + clean = values.dropna().astype(float) + count = len(clean) + lag = min(maximum_lag, max(0, count - 1)) + if count < 2: + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + array = clean.to_numpy(dtype=float) + centered = array - float(array.mean()) + long_run_variance = float(np.dot(centered, centered) / count) + for offset in range(1, lag + 1): + covariance = float( + np.dot(centered[offset:], centered[:-offset]) / count + ) + weight = 1.0 - offset / (lag + 1.0) + long_run_variance += 2.0 * weight * covariance + long_run_variance = max(0.0, long_run_variance) + standard_error = math.sqrt(long_run_variance / count) + if standard_error <= 1e-12: + t_statistic: float | None = None + p_value: float | None = None + else: + t_statistic = float(array.mean()) / standard_error + p_value = math.erfc(abs(t_statistic) / math.sqrt(2.0)) + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": standard_error, + "t_statistic": t_statistic, + "normal_approximation_p_value": p_value, + } + + +def descriptive_ic( + values: pd.Series, + *, + minimum_observations: int = 3, +) -> dict[str, float | int | None | dict[str, Any]]: + clean = values.dropna().astype(float) + if len(clean) < minimum_observations: + hac = hac_inference(clean) + hac.update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return { + "mean_ic": None, + "standard_deviation": None, + "icir": None, + "hit_rate": None, + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": False, + "hac": hac, + } + mean = float(clean.mean()) + standard_deviation = float(clean.std(ddof=0)) + return { + "mean_ic": mean, + "standard_deviation": standard_deviation, + "icir": ( + mean / standard_deviation + if standard_deviation > 1e-12 + else None + ), + "hit_rate": float((clean > 0).mean()), + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": True, + "hac": hac_inference(clean), + } + + +def daily_quantile_outcomes( + factors: pd.DataFrame, + returns: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + rows: list[dict[str, Any]] = [] + for timestamp in factors.index.intersection(returns.index): + pair = pd.DataFrame( + { + "factor": factors.loc[timestamp], + "outcome": returns.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets or pair["factor"].nunique() < 3: + continue + ordered = pair.sort_values( + ["factor"], + kind="mergesort", + ) + groups = np.array_split(np.arange(len(ordered)), 3) + low, middle, high = ( + float(ordered.iloc[group]["outcome"].mean()) + for group in groups + ) + rows.append( + { + "timestamp": timestamp, + "low": low, + "middle": middle, + "high": high, + "high_minus_low": high - low, + } + ) + if not rows: + return pd.DataFrame( + columns=("low", "middle", "high", "high_minus_low"), + index=pd.DatetimeIndex([], name="timestamp"), + ) + return pd.DataFrame(rows).set_index("timestamp").sort_index() + + +def quantile_summary( + daily: pd.DataFrame, + *, + minimum_observations: int = 3, +) -> dict[str, Any]: + clean = daily.dropna() + observations = int(len(clean)) + if observations < minimum_observations: + return { + "mean_outcome_by_quantile": { + "low": None, + "middle": None, + "high": None, + }, + "high_minus_low": None, + "monotonicity": None, + "observations": observations, + } + means = { + label: float(clean[label].mean()) + for label in ("low", "middle", "high") + } + ordered = pd.Series([0.0, 1.0, 2.0]) + ranked_means = pd.Series(list(means.values())).rank(method="average") + monotonicity = ordered.corr(ranked_means) + return { + "mean_outcome_by_quantile": means, + "high_minus_low": float(clean["high_minus_low"].mean()), + "monotonicity": ( + float(monotonicity) + if monotonicity is not None and math.isfinite(float(monotonicity)) + else None + ), + "observations": observations, + } + + +def causal_regime_labels(closes: pd.DataFrame) -> pd.Series: + """Label the signal close using only trailing market information.""" + + market_return = closes.pct_change(fill_method=None).mean(axis=1) + trailing_direction = ( + (1.0 + market_return) + .rolling(20, min_periods=20) + .apply(np.prod, raw=True) + - 1.0 + ) + trailing_volatility = market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0) + lagged_threshold = trailing_volatility.shift(1).rolling( + 60, + min_periods=20, + ).median() + labels = pd.Series(pd.NA, index=closes.index, dtype="object") + valid = ( + trailing_direction.notna() + & trailing_volatility.notna() + & lagged_threshold.notna() + ) + for timestamp in closes.index[valid]: + direction = "up" if trailing_direction.loc[timestamp] >= 0 else "down" + volatility = ( + "stressed" + if trailing_volatility.loc[timestamp] + > lagged_threshold.loc[timestamp] + else "calm" + ) + labels.loc[timestamp] = f"{direction}-{volatility}" + return labels + + +def style_proxy_panels( + closes: pd.DataFrame, + volumes: pd.DataFrame, +) -> dict[str, pd.DataFrame]: + daily_returns = closes.pct_change(fill_method=None) + return { + "momentum_20": closes / closes.shift(20) - 1.0, + "reversal_5": -(closes / closes.shift(5) - 1.0), + "realized_volatility_20": daily_returns.rolling( + 20, + min_periods=20, + ).std(ddof=0), + "relative_volume_20": ( + volumes / volumes.rolling(20, min_periods=20).mean() - 1.0 + ), + } + + +def cross_sectional_rank_residual( + candidate: pd.DataFrame, + style: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + """Remove one contemporaneous style exposure from candidate ranks.""" + + index = candidate.index.intersection(style.index) + columns = candidate.columns.intersection(style.columns) + output = pd.DataFrame(np.nan, index=index, columns=columns, dtype=float) + for timestamp in index: + pair = pd.DataFrame( + { + "candidate": candidate.loc[timestamp, columns], + "style": style.loc[timestamp, columns], + } + ).dropna() + if ( + len(pair) < minimum_assets + or pair["candidate"].nunique() < 2 + or pair["style"].nunique() < 2 + ): + continue + candidate_rank = pair["candidate"].rank(method="average", pct=True) + style_rank = pair["style"].rank(method="average", pct=True) + candidate_centered = candidate_rank - float(candidate_rank.mean()) + style_centered = style_rank - float(style_rank.mean()) + denominator = float(np.dot(style_centered, style_centered)) + if denominator <= 1e-15: + continue + beta = float( + np.dot(style_centered, candidate_centered) / denominator + ) + output.loc[timestamp, pair.index] = ( + candidate_centered - beta * style_centered + ) + return output + + +def equal_rank_blend( + candidate: pd.DataFrame, + style: pd.DataFrame, +) -> pd.DataFrame: + """Return a same-timestamp equal blend of cross-sectional percentile ranks.""" + + candidate_rank = candidate.rank(axis=1, method="average", pct=True) + style_rank = style.rank(axis=1, method="average", pct=True) + available = candidate.notna() & style.notna() + return ((candidate_rank + style_rank) / 2.0).where(available) + + +def per_asset_rank_correlation( + factors: pd.DataFrame, + returns: pd.DataFrame, + mask: pd.Series, + *, + minimum_observations: int = 10, +) -> dict[str, dict[str, float | int | None]]: + result: dict[str, dict[str, float | int | None]] = {} + for asset in factors.columns.intersection(returns.columns): + pair = pd.DataFrame( + { + "factor": factors.loc[mask, asset], + "forward_return": returns.loc[mask, asset], + } + ).dropna() + value: float | None = None + if ( + len(pair) >= minimum_observations + and pair["factor"].nunique() >= 2 + and pair["forward_return"].nunique() >= 2 + ): + correlation = pair["factor"].rank(method="average").corr( + pair["forward_return"].rank(method="average") + ) + if correlation is not None and math.isfinite(float(correlation)): + value = float(correlation) + result[str(asset)] = { + "rank_correlation": value, + "observations": int(len(pair)), + } + return result diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/ohlcv_factor.py b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/ohlcv_factor.py new file mode 100644 index 0000000..f4f0311 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/judge-sources/judges/ohlcv_factor.py @@ -0,0 +1,2657 @@ +"""Fixed no-lookahead Judge for the OHLCV Factor Lab reference Project.""" + +from __future__ import annotations + +import importlib +import json +import math +import os +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.factor_runtime import ( + FactorRuntimeError, + build_factor_panel, + evaluate_factor, + factor_contract, + values_to_wide, +) +from autoquant.factor_claims import ( + FACTOR_CLAIM, + FORWARD_RETURN_OUTCOME, + factor_outcome, + factor_outcome_contract, + load_factor_claim, +) +from autoquant.intervals import ( + IntervalContractError, + load_multi_interval_asset, + timestamp_label, +) +from autoquant.prediction_modes import ( + CROSS_SECTIONAL_MODE, + FACTOR_POPULATION, + SINGLE_ASSET_TEMPORAL_MODE, + TEMPORAL_EVALUATION_MODES, + TWO_ASSET_RELATIVE_VALUE_MODE, + PredictionModeError, + load_factor_population, + resolve_prediction_population, +) +from autoquant.horizons import ( + RESEARCH_HORIZON, + load_research_horizon, +) +from judges.factor_diagnostics import ( + HORIZONS, + REGIME_NAMES, + STYLE_NAMES, + causal_regime_labels, + chronological_fold_masks, + cross_sectional_rank_residual, + daily_pearson_correlation, + daily_quantile_outcomes, + daily_rank_correlation, + descriptive_ic, + equal_rank_blend, + factor_outcome_panels, + hac_inference, + per_asset_rank_correlation, + purged_split_masks, + quantile_summary, + style_proxy_panels, +) + + +REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume") +MIN_ASSETS_PER_DATE = 4 +MIN_IC_DATES_PER_SPLIT = 20 +PRIMARY_HORIZON = 1 +TEMPORAL_QUALIFICATION_METHOD = ( + "request-claim-aware-one-style-temporal-neutralization-v1" +) + + +class JudgeFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +def _write_output(value: dict[str, Any]) -> None: + Path(os.environ["AUTOQUANT_RUN_OUTPUT"]).write_text( + json.dumps(value, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _load_contract() -> tuple[dict[str, Any], Path]: + study = json.loads( + Path(os.environ["AUTOQUANT_STUDY_PATH"]).read_text(encoding="utf-8") + ) + data_root = Path(os.environ["AUTOQUANT_DATA_ROOT"]).resolve() + if not data_root.is_dir(): + raise JudgeFailure("dataset.root", "AUTOQUANT_DATA_ROOT is not a directory") + return study, data_root + + +def _load_horizon() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / RESEARCH_HORIZON + try: + return load_research_horizon(path) + except Exception as error: + raise JudgeFailure( + "horizon.contract", + f"Invalid fixed Horizon Mandate: {error}", + ) from error + + +def _load_factor_claim() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_CLAIM + try: + return load_factor_claim(path) + except Exception as error: + raise JudgeFailure( + "factor-claim.contract", + f"Invalid fixed Factor claim: {error}", + ) from error + + +def _load_prediction_universe( + research_universe: list[str], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], list[str], list[str], str, str]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_POPULATION + try: + factor_population = load_factor_population(path) + except Exception as error: + raise JudgeFailure( + "prediction-universe.contract", + f"Invalid fixed prediction-universe authority: {error}", + ) from error + try: + population = resolve_prediction_population( + research_universe, + factor_claim, + factor_population, + ) + except PredictionModeError as error: + raise JudgeFailure(error.code, str(error)) from error + return ( + population.as_metrics(), + list(population.prediction_assets), + list(population.context_assets), + population.authority, + population.evaluation_mode, + ) + + +def _load_asset(data_root: Path, asset: str, start: str, end: str) -> pd.DataFrame: + try: + multi_interval = load_multi_interval_asset( + data_root, + asset, + start=start, + end=end, + ) + except IntervalContractError as error: + raise JudgeFailure(error.code, str(error)) from error + if multi_interval is not None: + if len(multi_interval) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 base observations in the Study range", + ) + return multi_interval + source = (data_root / "ohlcv" / f"{asset}.csv").resolve() + if data_root not in source.parents or not source.is_file(): + raise JudgeFailure("dataset.asset", f"Missing confined OHLCV file for {asset}") + frame = pd.read_csv(source) + if tuple(frame.columns) != REQUIRED_COLUMNS: + raise JudgeFailure( + "dataset.columns", + f"{asset} columns must be exactly {', '.join(REQUIRED_COLUMNS)}", + ) + frame["timestamp"] = pd.to_datetime( + frame["timestamp"], + format="%Y-%m-%d", + errors="raise", + ) + if frame["timestamp"].duplicated().any() or not frame["timestamp"].is_monotonic_increasing: + raise JudgeFailure( + "dataset.time-order", + f"{asset} timestamps must be unique and chronological", + ) + for column in REQUIRED_COLUMNS[1:]: + frame[column] = pd.to_numeric(frame[column], errors="raise") + numeric = frame[list(REQUIRED_COLUMNS[1:])].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise JudgeFailure("dataset.non-finite", f"{asset} contains non-finite OHLCV") + if (frame[["open", "high", "low", "close", "volume"]] <= 0).any().any(): + raise JudgeFailure("dataset.non-positive", f"{asset} contains non-positive OHLCV") + if ( + (frame["high"] < frame[["open", "close"]].max(axis=1)).any() + or (frame["low"] > frame[["open", "close"]].min(axis=1)).any() + ): + raise JudgeFailure("dataset.bar-shape", f"{asset} contains invalid bars") + selected = frame[ + (frame["timestamp"] >= pd.Timestamp(start)) + & (frame["timestamp"] <= pd.Timestamp(end)) + ].copy() + if len(selected) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 observations in the Study range", + ) + return selected.reset_index(drop=True) + + +def _split_metrics(values: pd.Series) -> dict[str, Any]: + if len(values) < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "judge.population", + f"Chronological split has only {len(values)} valid IC dates", + ) + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _component_split_metrics(values: pd.Series) -> dict[str, Any]: + """Disclose sparse component evidence without failing a valid final factor.""" + + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _masked(values: pd.Series, mask: pd.Series) -> pd.Series: + return values.reindex(mask.index[mask]).dropna() + + +def _temporal_correlation_contributions( + left: pd.Series, + right: pd.Series, + mask: pd.Series, + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Return timestamp contributions whose mean is one split correlation.""" + + selected = pd.DataFrame( + { + "left": left.reindex(mask.index[mask]), + "right": right.reindex(mask.index[mask]), + } + ).dropna() + result = pd.Series(index=mask.index, dtype=float) + if len(selected) < 3: + return result + if rank: + selected = selected.rank(method="average", pct=True) + left_centered = selected["left"] - float(selected["left"].mean()) + right_centered = selected["right"] - float(selected["right"].mean()) + denominator = math.sqrt( + float((left_centered**2).mean()) + * float((right_centered**2).mean()) + ) + if denominator <= 1e-12: + if constant_left_value is not None: + result.loc[selected.index] = float(constant_left_value) + return result + result.loc[selected.index] = ( + left_centered * right_centered / denominator + ) + return result + + +def _temporal_daily( + left_panel: pd.DataFrame, + right_panel: pd.DataFrame, + masks: dict[str, pd.Series], + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Evaluate one prediction asset across time without context targets.""" + + left = left_panel.iloc[:, 0] + right = right_panel.iloc[:, 0] + result = pd.Series(index=left_panel.index, dtype=float) + for split in ("train", "validation", "test"): + contribution = _temporal_correlation_contributions( + left, + right, + masks[split], + rank=rank, + constant_left_value=constant_left_value, + ) + result.loc[contribution.dropna().index] = contribution.dropna() + return result + + +def _preflight_temporal_primary_validation( + factor_panel: pd.DataFrame, + outcome_panel: pd.DataFrame, + validation_mask: pd.Series, + *, + evaluation_mode: str, + horizon: int, +) -> None: + """Reject an unavailable fixed objective with an exact research reason.""" + + selected = pd.DataFrame( + { + "factor": factor_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + "outcome": outcome_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + } + ).dropna() + observations = int(len(selected)) + factor_values = int(selected["factor"].nunique()) + target_values = int(selected["outcome"].nunique()) + context = ( + f"evaluationMode={evaluation_mode}, split=validation, " + f"primaryHorizon={horizon}, pairedObservations={observations}, " + f"distinctFactorValues={factor_values}, " + f"distinctTargetValues={target_values}, " + f"minimumObservations={MIN_IC_DATES_PER_SPLIT}" + ) + if observations < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "factor.temporal-primary-observations", + "Primary temporal validation has too few finite factor/target " + f"pairs ({context})", + ) + if factor_values < 2: + raise JudgeFailure( + "factor.temporal-primary-candidate-variation", + "Primary temporal validation candidate has no usable variation " + f"({context})", + ) + if target_values < 2: + raise JudgeFailure( + "factor.temporal-primary-target-variation", + "Primary temporal validation outcome has no usable variation " + f"({context})", + ) + + +def _relative_value_spread_panel( + panel: pd.DataFrame, + prediction_assets: list[str], +) -> pd.DataFrame: + """Reduce one authorized pair to the causal first-minus-second contrast.""" + + if len(prediction_assets) != 2: + raise JudgeFailure( + "prediction-universe.relative-value-pair", + "Relative-value spread construction requires exactly two assets", + ) + left, right = prediction_assets + return pd.DataFrame( + { + f"{left}-minus-{right}": panel[left] - panel[right], + }, + index=panel.index, + ) + + +def _temporal_transform_panels( + candidate: pd.DataFrame, + style: pd.DataFrame, + masks: dict[str, pd.Series], +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Build target-free temporal residual and equal-rank blend panels.""" + + residual = pd.DataFrame(index=candidate.index, columns=candidate.columns) + blend = pd.DataFrame(index=candidate.index, columns=candidate.columns) + column = candidate.columns[0] + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + pair = pd.DataFrame( + { + "candidate": candidate[column].reindex(index), + "style": style[column].reindex(index), + } + ).dropna() + if pair.empty: + continue + ranks = pair.rank(method="average", pct=True) + candidate_centered = ranks["candidate"] - float( + ranks["candidate"].mean() + ) + style_centered = ranks["style"] - float(ranks["style"].mean()) + denominator = float((style_centered**2).sum()) + beta = ( + float((candidate_centered * style_centered).sum()) / denominator + if denominator > 1e-12 + else 0.0 + ) + residual.loc[pair.index, column] = ( + candidate_centered - beta * style_centered + ) + blend.loc[pair.index, column] = ( + ranks["candidate"] + ranks["style"] + ) / 2.0 + return residual.astype(float), blend.astype(float) + + +def _temporal_split_metrics( + values: pd.Series, + *, + horizon: int, + minimum_observations: int = MIN_IC_DATES_PER_SPLIT, +) -> dict[str, Any]: + clean = values.dropna().astype(float) + result = descriptive_ic( + clean, + minimum_observations=minimum_observations, + ) + result["hac"] = hac_inference(clean, maximum_lag=max(1, int(horizon))) + if len(clean) < minimum_observations: + result["hac"].update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return result + + +def _style_summary(values: pd.Series) -> dict[str, float | int | None]: + clean = values.dropna().astype(float) + if len(clean) < 3: + return { + "mean_rank_correlation": None, + "mean_absolute_rank_correlation": None, + "observations": int(len(clean)), + } + return { + "mean_rank_correlation": float(clean.mean()), + "mean_absolute_rank_correlation": float(clean.abs().mean()), + "observations": int(len(clean)), + } + + +def _count_summary(values: pd.Series) -> dict[str, float | int]: + clean = values.astype(int) + return { + "minimum": int(clean.min()), + "median": float(clean.median()), + "maximum": int(clean.max()), + } + + +def _equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + *, + common_available: pd.DataFrame | None = None, +) -> pd.DataFrame: + names = list(panels) + first = panels[names[0]] + if common_available is None: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for panel in panels.values(): + common_available &= panel.notna() + ranks = [ + panel.rank(axis=1, method="average", pct=True) + for panel in panels.values() + ] + return (sum(ranks) / float(len(ranks))).where(common_available) + + +def _temporal_equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + masks: dict[str, pd.Series], + *, + common_available: pd.Series | None = None, +) -> pd.DataFrame: + """Build one target-free equal-rank blend within each fixed split.""" + + names = list(panels) + first = panels[names[0]] + column = first.columns[0] + if common_available is None: + common_available = pd.Series(True, index=first.index) + for panel in panels.values(): + common_available &= panel.iloc[:, 0].notna() + blend = pd.DataFrame(index=first.index, columns=[column], dtype=float) + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + values = pd.DataFrame( + { + name: panels[name].iloc[:, 0].reindex(index) + for name in names + } + ) + ranks = values.rank(method="average", pct=True) + selected = ranks.mean(axis=1).where( + common_available.reindex(index).fillna(False) + ) + blend.loc[selected.index, column] = selected + return blend.astype(float) + + +def _context_distribution(values: pd.Series) -> dict[str, Any]: + clean = values.dropna().astype(float) + if clean.empty: + return { + "observations": 0, + "mean": None, + "standard_deviation": None, + "minimum": None, + "quartile_25": None, + "median": None, + "quartile_75": None, + "maximum": None, + } + return { + "observations": int(len(clean)), + "mean": float(clean.mean()), + "standard_deviation": ( + float(clean.std(ddof=1)) if len(clean) > 1 else 0.0 + ), + "minimum": float(clean.min()), + "quartile_25": float(clean.quantile(0.25)), + "median": float(clean.median()), + "quartile_75": float(clean.quantile(0.75)), + "maximum": float(clean.max()), + } + + +def _timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Diagnose one cross-section-constant causal market-state component.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_daily = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _component_split_metrics( + _masked( + factor_daily[horizon], + split_masks[horizon][split] + & states.eq(state), + ) + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-timestamp-context-v1", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _temporal_timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Condition temporal Factor correlation contributions on fixed states.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_contributions = { + horizon: _temporal_daily( + factor_panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _temporal_split_metrics( + _masked( + factor_contributions[horizon], + split_masks[horizon][split] + & states.eq(state), + ), + horizon=horizon, + minimum_observations=3, + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-temporal-context-v2", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "conditional_measure": ( + "within-split-temporal-rank-correlation-contribution" + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _component_evidence( + declarations: list[dict[str, Any]], + component_panels: dict[str, pd.DataFrame], + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + coverage: dict[str, dict[str, float]], + evaluation_mode: str, + outcome_contract: dict[str, Any], +) -> dict[str, Any]: + """Build target-fixed diagnostics for candidate-declared components.""" + + temporal = evaluation_mode in TEMPORAL_EVALUATION_MODES + metadata_by_name = {item["id"]: item for item in declarations} + all_names = list(component_panels) + names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "cross-sectional-score" + ] + context_names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "timestamp-context" + ] + score_panels = { + name: ( + _relative_value_spread_panel( + component_panels[name], + list(component_panels[name].columns), + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else component_panels[name].iloc[:, :1] + if temporal + else component_panels[name] + ) + for name in names + } + + def target_daily( + panel: pd.DataFrame, + horizon: int, + *, + constant_left_value: float | None = None, + ) -> pd.Series: + if temporal: + return _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=constant_left_value, + ) + return daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=constant_left_value, + ) + + def split_quality( + daily: pd.Series, + horizon: int, + split: str, + ) -> dict[str, Any]: + selected = _masked(daily, split_masks[horizon][split]) + return ( + _temporal_split_metrics(selected, horizon=horizon) + if temporal + else _component_split_metrics(selected) + ) + + def association_daily( + left: pd.DataFrame, + right: pd.DataFrame, + ) -> pd.Series: + if temporal: + return _temporal_daily( + left, + right, + split_masks[PRIMARY_HORIZON], + rank=True, + ) + return daily_rank_correlation( + left, + right, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + + raw_daily = { + name: { + horizon: target_daily(score_panels[name], horizon) + for horizon in HORIZONS + } + for name in names + } + raw_quality = { + name: { + str(horizon): { + split: split_quality( + raw_daily[name][horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + for name in names + } + composite_association_daily = { + name: association_daily(score_panels[name], factor_panel) + for name in names + } + composite_association = { + name: { + split: _style_summary( + _masked( + composite_association_daily[name], + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + } + for name in names + } + + pair_daily: dict[frozenset[str], pd.Series] = {} + pairwise: list[dict[str, Any]] = [] + for left_index, left in enumerate(names): + for right in names[left_index + 1 :]: + daily = association_daily( + score_panels[left], + score_panels[right], + ) + pair_daily[frozenset((left, right))] = daily + pairwise.append( + { + "left": left, + "right": right, + "splits": { + split: _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + }, + } + ) + + nearest_peers: dict[str, str | None] = {} + for name in names: + candidates: list[tuple[str, float]] = [] + for pair, daily in pair_daily.items(): + if name not in pair: + continue + peer = next(item for item in pair if item != name) + summary = _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON]["train"], + ) + ) + absolute = summary["mean_absolute_rank_correlation"] + if absolute is not None: + candidates.append((peer, float(absolute))) + nearest_peers[name] = ( + min(candidates, key=lambda item: (-item[1], item[0]))[0] + if candidates + else None + ) + + residual_quality: dict[str, Any] = {} + for name in names: + peer = nearest_peers[name] + if peer is None: + residual_quality[name] = { + "peer": None, + "selection": ( + "unavailable-single-component" + if len(names) == 1 + else "unavailable-no-finite-train-peer" + ), + "horizon_quality": None, + } + continue + residual = ( + _temporal_transform_panels( + score_panels[name], + score_panels[peer], + split_masks[PRIMARY_HORIZON], + )[0] + if temporal + else cross_sectional_rank_residual( + score_panels[name], + score_panels[peer], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + residual_daily = { + horizon: target_daily( + residual, + horizon, + constant_left_value=0.0, + ) + for horizon in HORIZONS + } + residual_quality[name] = { + "peer": peer, + "selection": ( + "maximum-absolute-train-temporal-rank-association" + if temporal + else "maximum-absolute-mean-train-daily-rank-association" + ), + "horizon_quality": { + str(horizon): { + split: split_quality( + residual_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + }, + } + + common_available: pd.DataFrame | pd.Series | None = None + full_blend_quality: dict[str, Any] | None = None + if names: + first = score_panels[names[0]] + if temporal: + common_available = pd.Series(True, index=first.index) + for name in names: + common_available &= score_panels[name].iloc[:, 0].notna() + full_blend = _temporal_equal_rank_component_blend( + {name: score_panels[name] for name in names}, + split_masks[PRIMARY_HORIZON], + common_available=common_available, + ) + else: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for name in names: + common_available &= score_panels[name].notna() + full_blend = _equal_rank_component_blend( + {name: score_panels[name] for name in names}, + common_available=common_available, + ) + full_blend_daily = { + horizon: target_daily(full_blend, horizon) + for horizon in HORIZONS + } + full_blend_quality = { + str(horizon): { + split: split_quality( + full_blend_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + ablations: dict[str, Any] = {} + for name in names: + remaining = { + candidate: score_panels[candidate] + for candidate in names + if candidate != name + } + if not remaining: + ablations[name] = { + "available": False, + "reason": "single-component", + "horizon_quality": None, + "removal_delta_mean_ic": None, + } + continue + leave_one_out = ( + _temporal_equal_rank_component_blend( + remaining, + split_masks[PRIMARY_HORIZON], + # Score-only common availability is intentionally fixed + # before leave-one-out so every ablation uses one population. + common_available=common_available, + ) + if temporal + else _equal_rank_component_blend( + remaining, + common_available=common_available, + ) + ) + leave_daily = { + horizon: target_daily(leave_one_out, horizon) + for horizon in HORIZONS + } + quality = { + str(horizon): { + split: split_quality( + leave_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + removal_delta: dict[str, float | None] = {} + for split in ("train", "validation", "test"): + primary = str(PRIMARY_HORIZON) + leave_mean = quality[primary][split]["mean_ic"] + full_mean = full_blend_quality[primary][split]["mean_ic"] + removal_delta[split] = ( + float(leave_mean) - float(full_mean) + if leave_mean is not None and full_mean is not None + else None + ) + ablations[name] = { + "available": True, + "reason": None, + "horizon_quality": quality, + "removal_delta_mean_ic": removal_delta, + } + + component_rows: list[dict[str, Any]] = [] + for name in names: + primary = str(PRIMARY_HORIZON) + raw_validation = raw_quality[name][primary]["validation"]["mean_ic"] + residual = residual_quality[name] + residual_validation = ( + residual["horizon_quality"][primary]["validation"]["mean_ic"] + if residual["horizon_quality"] is not None + else None + ) + removal_delta = ( + ablations[name]["removal_delta_mean_ic"]["validation"] + if ablations[name]["available"] + else None + ) + peer = nearest_peers[name] + train_redundancy = None + if peer is not None: + train_redundancy = _style_summary( + _masked( + pair_daily[frozenset((name, peer))], + split_masks[PRIMARY_HORIZON]["train"], + ) + )["mean_absolute_rank_correlation"] + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": raw_quality[name], + "composite_association": composite_association[name], + "nearest_peer": { + "id": peer, + "train_mean_absolute_rank_association": train_redundancy, + }, + "nearest_peer_residual": residual, + "fixed_blend_ablation": ablations[name], + "timestamp_context": None, + "validation_priority_inputs": { + "raw_mean_ic": raw_validation, + "nearest_peer_residual_mean_ic": residual_validation, + "removal_delta_mean_ic": removal_delta, + }, + } + ) + for name in context_names: + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": None, + "composite_association": None, + "nearest_peer": { + "id": None, + "train_mean_absolute_rank_association": None, + }, + "nearest_peer_residual": { + "peer": None, + "selection": "not-applicable-timestamp-context", + "horizon_quality": None, + }, + "fixed_blend_ablation": { + "available": False, + "reason": "not-applicable-timestamp-context", + "horizon_quality": None, + "removal_delta_mean_ic": None, + }, + "timestamp_context": ( + _temporal_timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + if temporal + else _timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + ), + "validation_priority_inputs": { + "raw_mean_ic": None, + "nearest_peer_residual_mean_ic": None, + "removal_delta_mean_ic": None, + }, + } + ) + + raw_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"]["raw_mean_ic"] is not None + ] + strongest_raw = ( + max( + raw_candidates, + key=lambda row: ( + float(row["validation_priority_inputs"]["raw_mean_ic"]), + row["id"], + ), + ) + if raw_candidates + else None + ) + residual_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] is not None + ] + strongest_residual = ( + max( + residual_candidates, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + ), + row["id"], + ), + ) + if residual_candidates + else None + ) + removable = [ + row + for row in component_rows + if row["validation_priority_inputs"]["removal_delta_mean_ic"] + is not None + ] + best_removal = ( + max( + removable, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + ), + row["id"], + ), + ) + if removable + else None + ) + finite_pairs = [ + row + for row in pairwise + if row["splits"]["train"]["mean_absolute_rank_correlation"] + is not None + ] + most_redundant = ( + max( + finite_pairs, + key=lambda row: ( + float( + row["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + or -1.0 + ), + row["left"], + row["right"], + ), + ) + if finite_pairs + else None + ) + return { + "method": "candidate-declared-components-v3", + "declaration": { + "exhaustive_composition_claim": False, + "source_inference": False, + "components": declarations, + }, + "semantics": { + "evaluation_mode": evaluation_mode, + "prediction_target": outcome_contract["targetSemantics"], + "score_measure": ( + "within-split-temporal-rank-correlation-contribution" + if temporal + else "per-date-cross-sectional-rank-ic" + ), + "component_roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "nearest_peer_selection": "train-only-target-free", + "residualization": ( + "within-split-temporal-centered-rank-ols" + if temporal + else "same-timestamp-cross-sectional-centered-rank-ols" + ), + "diagnostic_blend": ( + "equal-weight-within-split-temporal-percentile-ranks-with-" + "common-component-availability" + if temporal + else "equal-weight-cross-sectional-percentile-ranks-with-" + "common-component-availability" + ), + "ablation_target": "fixed-diagnostic-blend-not-candidate-factor", + "timestamp_context": ( + "train-tertile-occupancy-transition-and-conditional-temporal-" + "rank-correlation-contribution" + if temporal + else "train-tertile-occupancy-transition-and-conditional-" + "factor-ic" + ), + "selection_authority": "research-prioritization-only", + "test_role": "visible-audit", + "promotion_authority": "none", + "portfolio_authority": "none", + "rl_action_authority": "none", + "trading_authority": "none", + }, + "trial_disclosure": { + "materialized_components": len(all_names), + "cross_sectional_score_components": len(names), + "timestamp_context_components": len(context_names), + "pairwise_comparisons": len(pairwise), + "component_diagnostics_enter_promotion_score": False, + }, + "components": component_rows, + "pairwise": pairwise, + "fixed_blend": { + "available": full_blend_quality is not None, + "reason": ( + None + if full_blend_quality is not None + else "no-cross-sectional-score-components" + ), + "horizon_quality": full_blend_quality, + }, + "validation_diagnosis": { + "strongest_raw_component": ( + strongest_raw["id"] if strongest_raw is not None else None + ), + "strongest_raw_mean_ic": ( + strongest_raw["validation_priority_inputs"]["raw_mean_ic"] + if strongest_raw is not None + else None + ), + "strongest_residual_component": ( + strongest_residual["id"] + if strongest_residual is not None + else None + ), + "strongest_residual_mean_ic": ( + strongest_residual["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + if strongest_residual is not None + else None + ), + "removal_most_improves_fixed_blend": ( + best_removal["id"] if best_removal is not None else None + ), + "best_removal_delta_mean_ic": ( + best_removal["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + if best_removal is not None + else None + ), + "most_redundant_pair": ( + { + "left": most_redundant["left"], + "right": most_redundant["right"], + "train_mean_absolute_rank_association": ( + most_redundant["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + ), + } + if most_redundant is not None + else None + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + }, + } + + +def _decay_summary( + horizon_metrics: dict[str, dict[str, dict[str, Any]]], +) -> dict[str, dict[str, Any]]: + result: dict[str, dict[str, Any]] = {} + for split in ("train", "validation", "test"): + means = { + horizon: horizon_metrics[horizon][split]["mean_ic"] + for horizon in (str(item) for item in HORIZONS) + } + primary = means[str(PRIMARY_HORIZON)] + ratios: dict[str, float | None] = {} + for horizon in (str(item) for item in HORIZONS): + if horizon == str(PRIMARY_HORIZON): + continue + value = means[horizon] + ratios[ + f"horizon_{horizon}_to_{PRIMARY_HORIZON}" + ] = ( + float(value) / float(primary) + if value is not None + and primary is not None + and abs(float(primary)) > 1e-12 + else None + ) + result[split] = { + "mean_ic_by_horizon": means, + **ratios, + } + return result + + +def _factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Build request-claim-aware style comparison and qualification evidence.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name][ + "observations" + ], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only style overlap is available", + ) + dominant_style = ( + factor_claim["knownStyle"] + if factor_claim["claim"] == "known-style-validation" + else min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + ) + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel = cross_sectional_rank_residual( + factor_panel, + style_panel, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + blend_panel = equal_rank_blend(factor_panel, style_panel) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ) + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + residual_folds = { + name: descriptive_ic( + _masked( + daily["style_neutral_candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + candidate_folds = { + name: descriptive_ic( + _masked( + daily["candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": "request-claim-aware-one-style-rank-neutralization-v2", + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": ( + "request-predeclared-known-style" + if factor_claim["claim"] == "known-style-validation" + else "maximum-absolute-mean-daily-rank-overlap" + ), + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": ( + "same-timestamp-cross-sectional-centered-rank-ols" + ), + "blend": "equal-weight-cross-sectional-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _temporal_factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Qualify one request-authorized asset by association across time.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name]["observations"], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only temporal style overlap is available", + ) + dominant_style = min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel, blend_panel = _temporal_transform_panels( + factor_panel, + style_panel, + split_masks[PRIMARY_HORIZON], + ) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _temporal_split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + def fold_quality(panel: pd.DataFrame) -> dict[str, dict[str, Any]]: + return { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + panel.iloc[:, 0], + outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + constant_left_value=( + 0.0 if panel is residual_panel else None + ), + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + + candidate_folds = fold_quality(factor_panel) + residual_folds = fold_quality(residual_panel) + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": TEMPORAL_QUALIFICATION_METHOD, + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": "maximum-absolute-mean-temporal-rank-overlap", + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": "within-split-temporal-centered-rank-ols", + "blend": "equal-weight-within-split-temporal-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _evaluate() -> tuple[ + dict[str, Any], + dict[str, Any], + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, Any] | None, +]: + global HORIZONS, PRIMARY_HORIZON + study, data_root = _load_contract() + research_horizon = _load_horizon() + factor_claim = _load_factor_claim() + outcome_kind = factor_outcome(factor_claim) + outcome_contract = factor_outcome_contract(factor_claim) + HORIZONS = tuple(research_horizon["diagnosticForwardBars"]) + PRIMARY_HORIZON = int(research_horizon["primaryForwardBars"]) + dataset = study["dataset"] + universe = dataset["universe"] + ( + prediction_population, + prediction_assets, + context_assets, + prediction_authority, + evaluation_mode, + ) = _load_prediction_universe(universe, factor_claim) + if ( + outcome_kind != FORWARD_RETURN_OUTCOME + and evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + ): + raise JudgeFailure( + "factor.outcome-relative-value", + "Forward realized volatility does not define a two-asset " + "relative-value target contrast", + ) + minimum_evaluation_assets = ( + 1 + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + 2 + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else MIN_ASSETS_PER_DATE + ) + ) + time_range = dataset["time_range"] + module = importlib.import_module("factors.candidate") + frames: dict[str, pd.DataFrame] = {} + close_by_asset: dict[str, pd.Series] = {} + volume_by_asset: dict[str, pd.Series] = {} + for asset in universe: + frame = _load_asset( + data_root, + asset, + time_range["start"], + time_range["end"], + ) + frames[asset] = frame + timestamp = pd.DatetimeIndex(frame["timestamp"]) + close = frame["close"].copy() + close.index = timestamp + volume = frame["volume"].copy() + volume.index = timestamp + close_by_asset[asset] = close + volume_by_asset[asset] = volume + + try: + panel = build_factor_panel(frames, universe=universe) + factor_evaluation = evaluate_factor(module, panel) + factor_panel = values_to_wide( + panel, + factor_evaluation.values, + universe=universe, + ) + except FactorRuntimeError as error: + raise JudgeFailure(error.code, str(error)) from error + coverage = { + asset: float( + factor_evaluation.values.loc[panel["asset"] == asset].notna().mean() + ) + for asset in universe + } + source_factor_panel = factor_panel + source_close_panel = pd.DataFrame(close_by_asset).reindex( + source_factor_panel.index + ) + source_volume_panel = pd.DataFrame(volume_by_asset).reindex( + source_factor_panel.index + ) + research_factor_panel = source_factor_panel + research_close_panel = source_close_panel + research_volume_panel = source_volume_panel + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE: + prediction_timeline = pd.DatetimeIndex( + close_by_asset[prediction_assets[0]].index + ) + research_factor_panel = research_factor_panel.reindex( + prediction_timeline + ) + research_close_panel = research_close_panel.reindex( + prediction_timeline + ) + research_volume_panel = research_volume_panel.reindex( + prediction_timeline + ) + factor_panel = research_factor_panel[prediction_assets] + close_panel = research_close_panel[prediction_assets] + timeline = pd.DatetimeIndex(research_factor_panel.index) + split_masks, split_protocol, base_split_labels = purged_split_masks( + timeline, + HORIZONS, + ) + fold_masks, fold_protocol = chronological_fold_masks( + timeline, + PRIMARY_HORIZON, + ) + outcome_panels = factor_outcome_panels( + close_panel, + outcome_kind, + HORIZONS, + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE: + association_factor_panel = _relative_value_spread_panel( + factor_panel, + prediction_assets, + ) + association_outcome_panels = { + horizon: _relative_value_spread_panel( + outcome_panels[horizon], + prediction_assets, + ) + for horizon in HORIZONS + } + else: + association_factor_panel = factor_panel + association_outcome_panels = outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + _preflight_temporal_primary_validation( + association_factor_panel, + association_outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON]["validation"], + evaluation_mode=evaluation_mode, + horizon=PRIMARY_HORIZON, + ) + source_input_counts = source_close_panel.notna().sum(axis=1).astype(int) + source_factor_counts = source_factor_panel.notna().sum(axis=1).astype(int) + input_counts = close_panel.notna().sum(axis=1).astype(int) + factor_counts = factor_panel.notna().sum(axis=1).astype(int) + paired_counts = { + horizon: ( + factor_panel.notna() & outcome_panels[horizon].notna() + ).sum(axis=1).astype(int) + for horizon in HORIZONS + } + source_timeline = pd.DatetimeIndex(source_factor_panel.index) + source_paired_counts = { + horizon: paired_counts[horizon] + .reindex(source_timeline) + .fillna(0) + .astype(int) + for horizon in HORIZONS + } + possible_rows = int(len(source_timeline) * len(universe)) + observed_rows = int(source_input_counts.sum()) + prediction_possible_rows = int(len(timeline) * len(prediction_assets)) + prediction_observed_rows = int(input_counts.sum()) + input_availability = { + "method": "observed-only-no-fill-v1", + "missing_observation": "absent-no-fill", + "timestamps": int(len(source_timeline)), + "observed_rows": observed_rows, + "possible_rows": possible_rows, + "observation_coverage": float( + observed_rows / possible_rows + ), + "complete_timestamps": int( + source_input_counts.eq(len(universe)).sum() + ), + "prediction_universe": { + "authority": prediction_authority, + "assets": prediction_assets, + "context_assets": context_assets, + "observed_rows": prediction_observed_rows, + "possible_rows": prediction_possible_rows, + "observation_coverage": float( + prediction_observed_rows / prediction_possible_rows + ), + "complete_timestamps": int( + input_counts.eq(len(prediction_assets)).sum() + ), + "timeline_timestamps": int(len(timeline)), + }, + "eligible_factor_timestamps": { + str(horizon): int( + paired_counts[horizon] + .ge(minimum_evaluation_assets) + .sum() + ) + for horizon in HORIZONS + }, + "minimum_assets_per_factor_timestamp": minimum_evaluation_assets, + "assets_per_timestamp": { + "input": _count_summary(source_input_counts), + "factor": _count_summary(source_factor_counts), + "primary_pair": _count_summary( + source_paired_counts[PRIMARY_HORIZON] + ), + }, + "by_asset": { + asset: { + "observations": int( + source_close_panel[asset].notna().sum() + ), + "start": timestamp_label( + source_close_panel[asset].dropna().index[0] + ), + "end": timestamp_label( + source_close_panel[asset].dropna().index[-1] + ), + "input_coverage": float( + source_close_panel[asset].notna().mean() + ), + "factor_coverage": coverage[asset], + } + for asset in universe + }, + } + components = factor_evaluation.components + component_declarations = ( + components.declaration() if components is not None else None + ) + component_panels = ( + { + name: values_to_wide( + panel, + components.values[name], + universe=universe, + )[prediction_assets] + for name in components.values.columns + } + if components is not None + else {} + ) + component_coverage = ( + { + name: { + asset: float( + components.values.loc[ + panel["asset"] == asset, + name, + ].notna().mean() + ) + for asset in universe + } + for name in components.values.columns + } + if components is not None + else {} + ) + component_evidence = ( + _component_evidence( + component_declarations, + component_panels, + ( + association_factor_panel + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else factor_panel + ), + ( + association_outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else outcome_panels + ), + split_masks, + component_coverage, + evaluation_mode, + outcome_contract, + ) + if component_declarations is not None + else None + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + daily_ic_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=False, + ) + for horizon in HORIZONS + } + else: + daily_ic_by_horizon = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: daily_pearson_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + horizon_metrics = { + str(horizon): { + split: { + **( + _temporal_split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + "pearson_ic": ( + _temporal_split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + splits = horizon_metrics[str(PRIMARY_HORIZON)] + validation_mean_ic = float(splits["validation"]["mean_ic"]) + + quantile_daily = ( + { + horizon: pd.DataFrame( + columns=["low", "middle", "high", "high_minus_low"], + index=pd.DatetimeIndex([], name="timestamp"), + dtype=float, + ) + for horizon in HORIZONS + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + horizon: daily_quantile_outcomes( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + ) + quantile_analysis = { + str(horizon): { + split: quantile_summary( + quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ) + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + primary_ic = daily_ic_by_horizon[PRIMARY_HORIZON] + chronological_folds = ( + { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + association_factor_panel.iloc[:, 0], + association_outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + name: descriptive_ic(_masked(primary_ic, mask)) + for name, mask in fold_masks.items() + } + ) + regimes = causal_regime_labels(research_close_panel) + regime_stability = { + split: { + regime: descriptive_ic( + _masked( + primary_ic, + split_masks[PRIMARY_HORIZON][split] + & regimes.eq(regime).fillna(False), + ) + ) + for regime in REGIME_NAMES + } + for split in ("train", "validation", "test") + } + prediction_style_panels = { + name: values[prediction_assets] + for name, values in style_proxy_panels( + research_close_panel, + research_volume_panel, + ).items() + } + styles = ( + { + name: _relative_value_spread_panel( + values, + prediction_assets, + ) + for name, values in prediction_style_panels.items() + } + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else prediction_style_panels + ) + style_correlations: dict[str, dict[str, Any]] = { + split: {} + for split in ("train", "validation", "test") + } + for style in STYLE_NAMES: + daily_style = ( + _temporal_daily( + association_factor_panel, + styles[style], + split_masks[PRIMARY_HORIZON], + rank=True, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else daily_rank_correlation( + factor_panel, + styles[style], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + for split in style_correlations: + style_correlations[split][style] = _style_summary( + _masked( + daily_style, + split_masks[PRIMARY_HORIZON][split], + ) + ) + qualification_builder = ( + _temporal_factor_qualification + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _factor_qualification + ) + factor_qualification, qualification_evidence = qualification_builder( + association_factor_panel, + styles, + association_outcome_panels, + split_masks, + fold_masks, + base_split_labels, + style_correlations, + factor_claim, + ) + per_asset_stability = { + split: per_asset_rank_correlation( + factor_panel, + outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON][split], + ) + for split in ("train", "validation", "test") + } + + ranked = ( + association_factor_panel.rank(method="average", pct=True) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else association_factor_panel.rank(axis=1, pct=True) + ) + turnover = float(ranked.diff().abs().mean(axis=1).dropna().mean()) + metrics = { + "validation_mean_ic": validation_mean_ic, + "factor_api": factor_contract(factor_evaluation), + "research_horizon": research_horizon, + "factor_claim": factor_claim, + "factor_outcome": outcome_contract, + "prediction_universe": prediction_population, + "train": splits["train"], + "validation": splits["validation"], + "test": splits["test"], + "horizon_quality": horizon_metrics, + "factor_decay": _decay_summary(horizon_metrics), + "quantile_analysis": quantile_analysis, + "stability": { + "chronological_folds": chronological_folds, + "causal_regimes": regime_stability, + "per_asset": per_asset_stability, + }, + "style_correlations": style_correlations, + "factor_qualification": factor_qualification, + "split_protocol": { + **split_protocol, + "folds": fold_protocol, + }, + "mean_coverage": float(sum(coverage.values()) / len(coverage)), + "input_availability": input_availability, + "mean_rank_turnover": turnover, + "assets": int(len(universe)), + "prediction_assets": int(len(prediction_assets)), + "ic_dates": int(len(primary_ic)), + "research_integrity": { + "selection_split": "validation", + "test_role": "visible-diagnostic", + "test_enters_selection": False, + "external_holdout_rule": ( + "required-after-visible-test-and-candidate-iteration" + ), + }, + } + if component_evidence is not None: + metrics["factor_components"] = component_evidence + if not all( + math.isfinite(float(value)) + for value in ( + validation_mean_ic, + metrics["mean_coverage"], + metrics["mean_rank_turnover"], + ) + ): + raise JudgeFailure("judge.non-finite", "Judge produced non-finite metrics") + report = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "dataset": { + "id": dataset["id"], + "version": dataset["version"], + "universe": universe, + "predictionAssets": prediction_assets, + "contextAssets": context_assets, + "timeRange": time_range, + }, + "researchHorizon": research_horizon, + "semantics": { + "target": outcome_contract["targetSemantics"], + "outcome": outcome_contract, + "measure": ( + ( + "within-split temporal Spearman and Pearson correlation " + "contributions for the single request-authorized asset" + ) + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + "within-split temporal Spearman and Pearson correlation " + "contributions between the first-minus-second factor " + "contrast and first-minus-second forward-return contrast" + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else ( + "per-date cross-sectional Spearman rank IC and Pearson IC " + f"over the fixed {prediction_authority} evaluation universe" + ) + ) + ), + "researchUniverse": ( + "complete Study universe available to candidate features" + ), + "predictionUniverse": ( + "caller-owned factorPolicy.predictionAssets for request-" + "bound decision-signal claims; complete research universe " + "for novel-factor and known-style-validation claims" + ), + "horizons": list(HORIZONS), + "primaryHorizon": PRIMARY_HORIZON, + "split": ( + "dataset-fixed chronological 60/20/20 with horizon-specific " + "boundary purge" + ), + "score": ( + "validation mean rank IC at the fixed primary " + f"{PRIMARY_HORIZON}-bar horizon only" + ), + "inference": ( + ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag equal to each forward horizon and two-sided normal-" + "approximation p-value" + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag 5 and two-sided normal-approximation p-value" + ) + ), + "quantiles": ( + "unavailable-for-temporal-evaluation-v1" + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else "fixed low/middle/high cross-sectional groups" + ), + "regimes": ( + "causal trailing market direction and volatility versus " + "lagged rolling threshold" + ), + "styles": list(STYLE_NAMES), + "qualification": { + "method": factor_qualification["method"], + "claim": factor_claim, + "styleSelection": ( + "request-predeclared" + if factor_claim["claim"] == "known-style-validation" + else "train-only" + ), + "neutralization": factor_qualification["semantics"][ + "neutralization" + ], + "blend": factor_qualification["semantics"]["blend"], + "testRole": "visible audit only", + "tradingAuthority": "none", + }, + "components": ( + { + "method": component_evidence["method"], + "evaluationMode": component_evidence["semantics"][ + "evaluation_mode" + ], + "scoreMeasure": component_evidence["semantics"][ + "score_measure" + ], + "predictionTarget": component_evidence["semantics"][ + "prediction_target" + ], + "declaration": "candidate-explicit-not-source-inferred", + "roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "exhaustiveCompositionClaim": False, + "nearestPeerSelection": "train-only-target-free", + "ablationTarget": ( + "fixed-diagnostic-blend-not-candidate-factor" + ), + "residualization": component_evidence["semantics"][ + "residualization" + ], + "diagnosticBlend": component_evidence["semantics"][ + "diagnostic_blend" + ], + "timestampContext": component_evidence["semantics"][ + "timestamp_context" + ], + "testRole": "visible audit only", + "portfolioAuthority": "none", + "rlActionAuthority": "none", + "tradingAuthority": "none", + } + if component_evidence is not None + else None + ), + "testRole": ( + "visible diagnostic evidence; never enters candidate selection" + ), + }, + "causalityAuditCuts": list(factor_evaluation.causality_cuts), + "componentCausalityAuditCuts": ( + list(factor_evaluation.causality_cuts) + if component_evidence is not None + else [] + ), + "coverageByAsset": coverage, + "inputAvailability": input_availability, + "splitProtocol": split_protocol, + "foldProtocol": fold_protocol, + "metrics": metrics, + } + daily_evidence = pd.DataFrame( + { + "split": base_split_labels, + "regime": regimes.fillna("unavailable"), + }, + index=timeline, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + daily_evidence[f"rank_ic_h{horizon}"] = ( + daily_ic_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence[f"pearson_ic_h{horizon}"] = ( + daily_pearson_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence.index.name = "timestamp" + availability_evidence = pd.DataFrame( + { + "input_assets": source_input_counts, + "factor_assets": source_factor_counts, + **{ + f"paired_assets_h{horizon}": source_paired_counts[horizon] + for horizon in HORIZONS + }, + }, + index=source_timeline, + ) + availability_evidence.index.name = "timestamp" + + quantile_rows: list[dict[str, Any]] = [] + for horizon in HORIZONS: + for split in ("train", "validation", "test"): + selected = quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ).dropna() + for timestamp, row in selected.iterrows(): + quantile_rows.append( + { + "timestamp": timestamp, + "split": split, + "horizon": horizon, + "low": float(row["low"]), + "middle": float(row["middle"]), + "high": float(row["high"]), + "high_minus_low": float(row["high_minus_low"]), + } + ) + quantile_evidence = pd.DataFrame( + quantile_rows, + columns=[ + "timestamp", + "split", + "horizon", + "low", + "middle", + "high", + "high_minus_low", + ], + ) + return ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + ( + { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "evidence": component_evidence, + } + if component_evidence is not None + else None + ), + ) + + +def main() -> None: + try: + ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + component_evidence, + ) = _evaluate() + artifacts = Path(os.environ["AUTOQUANT_ARTIFACTS_DIR"]) + report_path = artifacts / "factor-report.json" + report_path.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + daily_artifact = daily_evidence.copy() + daily_artifact.index = [ + timestamp_label(value) for value in daily_artifact.index + ] + daily_artifact.index.name = "timestamp" + quantile_artifact = quantile_evidence.copy() + quantile_artifact["timestamp"] = quantile_artifact["timestamp"].map( + timestamp_label + ) + qualification_artifact = qualification_evidence.copy() + qualification_artifact.index = [ + timestamp_label(value) for value in qualification_artifact.index + ] + qualification_artifact.index.name = "timestamp" + availability_artifact = availability_evidence.copy() + availability_artifact.index = [ + timestamp_label(value) + for value in availability_artifact.index + ] + availability_artifact.index.name = "timestamp" + daily_artifact.to_csv( + artifacts / "daily-factor-evidence.csv", + float_format="%.17g", + ) + quantile_artifact.to_csv( + artifacts / "factor-quantiles.csv", + index=False, + float_format="%.17g", + ) + qualification_artifact.to_csv( + artifacts / "factor-qualification.csv", + float_format="%.17g", + ) + availability_artifact.to_csv( + artifacts / "factor-availability.csv", + ) + if component_evidence is not None: + (artifacts / "factor-components.json").write_text( + json.dumps(component_evidence, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + output_artifacts = [ + { + "kind": "factor-report", + "path": "factor-report.json", + "description": ( + "Factor semantics, purged split protocol, complete " + "tear sheet, coverage, and causality audit" + ), + }, + { + "kind": "factor-daily", + "path": "daily-factor-evidence.csv", + "description": ( + "Timestamped split, causal regime, and purge-aware " + "request-bound outcome rank and Pearson IC" + ), + }, + { + "kind": "factor-quantiles", + "path": "factor-quantiles.csv", + "description": ( + "Timestamped fixed-tertile outcome levels and " + "high-minus-low outcome spread by split and horizon" + ), + }, + { + "kind": "factor-availability", + "path": "factor-availability.csv", + "description": ( + "Per-timestamp observed input, finite factor, and " + "horizon-paired cross-sectional asset counts" + ), + }, + { + "kind": "factor-qualification", + "path": "factor-qualification.csv", + "description": ( + "Train-selected style, candidate/style/residual/" + "blend daily rank IC, and visible-test audit" + ), + }, + ] + if component_evidence is not None: + output_artifacts.append( + { + "kind": "factor-components", + "path": "factor-components.json", + "description": ( + "Candidate-declared component quality, redundancy, " + "nearest-peer residual, and fixed-blend ablation evidence" + ), + } + ) + _write_output( + { + "schema_version": 1, + "status": "succeeded", + "summary": ( + "Causal purge-aware factor tear sheet completed; " + f"{metrics['factor_outcome']['kind']} validation " + f"{PRIMARY_HORIZON}-bar mean " + "rank IC=" + f"{metrics['validation_mean_ic']:.6f}" + ), + "metrics": metrics, + "artifacts": output_artifacts, + "errors": [], + } + ) + except JudgeFailure as error: + scientific_limit_codes = { + "factor.temporal-primary-observations", + "factor.temporal-primary-candidate-variation", + "factor.temporal-primary-target-variation", + } + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": ( + "scientific-limit" + if error.code in scientific_limit_codes + else "repair-required" + ), + "summary": str(error), + "metrics": {}, + "artifacts": [], + "errors": [{"code": error.code, "message": str(error)}], + } + ) + except Exception as error: # Preserve candidate/Judge diagnostics as evidence. + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": "repair-required", + "summary": f"Factor evaluation raised {type(error).__name__}", + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": "factor.exception", + "message": f"{type(error).__name__}: {error}", + } + ], + } + ) + + +if __name__ == "__main__": + main() diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/program.md b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/program.md new file mode 100644 index 0000000..1605769 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/program.md @@ -0,0 +1,173 @@ +# Mine a causal OHLCV factor + +## Research question + +Can one causal transformation of ordinary OHLCV history produce stable +information about the caller-bound future outcome across the fixed +prediction-eligible universe and both held-out chronological periods? + +## Editable API + +Edit only `factors/candidate.py` and preserve: + +```python +def compute_factor(panel: pandas.DataFrame) -> pandas.Series: + ... +``` + +`panel` is the observed Study universe in long form, with one row per available +`asset`/`timestamp` and base plus available completed higher-interval OHLCV. +The complete research universe remains available for causal cross-asset +features. The fixed Judge, not candidate code, selects target observations: +`decision-signal` evaluates caller-owned `factorPolicy.predictionAssets`, while +`novel-factor` and `known-style-validation` evaluate the complete research +universe. Core freezes that evaluation-only authority in +`strategies/factor-population.json`; this Factor Lab has no Portfolio Mandate. +Inspect `predictionUniverse.evaluationMode` in Factor diagnostics. +A decision signal with exactly one eligible asset uses within-split temporal +Spearman/Pearson evidence for that asset. Exactly two eligible assets use +within-split temporal evidence between the first-minus-second factor contrast +and matching forward-return contrast. A later Portfolio lane must separately +prove a symmetric two-sided dollar-neutral Mandate before monetization. Four +or more eligible assets use the cross-sectional contract. Three require +explicit caller-owned relative-basket contrast weights. No mode may borrow +context-only target observations. +The fixed Factor Claim also binds `outcome`. `forward-return` uses the simple +close-to-close return from signal close `t` to `t+h`. `forward-realized- +volatility` uses the unannualized square root of summed squared close-to-close +log returns over the next `h` observed base bars. The latter supports one +temporal prediction asset or at least four cross-sectional assets, and a +higher score means higher predicted risk—not positive expected return. It has +no Portfolio or RL admission path. Do not invert, monetize, or relabel it. +Aligned inputs are rectangular; V4 daily and V5 observed-bar input is ragged and +does not invent, fill, or globally intersect missing/pre-listing/closed-market +rows. V5 temporal targets, split boundaries, and purge counts follow the one +prediction asset's observed bars, not context-only union timestamps. +For asynchronous V5 context, explicitly use a backward as-of operation and +accept only source rows whose completed timestamp is at or before the target +row. Core never fills an absent context observation or aligns civil dates. +Use ordinary `groupby("asset")` for rolling time-series features and +`groupby("timestamp")` for contemporaneous cross-sectional context. The +returned Series must align exactly with the input index. Missing warm-up values +are allowed. Future timestamps, centered windows, negative shifts, global +full-sample normalization, external data, and mutation of the input are not. + +Missing input is not the same state as an observed false condition or a neutral +score. When the candidate branches on a required regime, filter, denominator, +or other component, preserve `NaN` until that input exists unless the Research +Request explicitly predeclares a different missing-data policy. In particular, +`Series.where(condition, other=0)` treats an unavailable/`NaN` predicate as +false; mask the unavailable predicate separately before returning the factor. +Reconcile final-factor coverage against every required component before +execution and explain any intentional excess coverage in `research.md`. + +A sparse binary event indicator is still evaluated as a temporal association +score; it does not create an event population or estimate a conditional event +return. If the caller instead froze an OHLCV-observable price event, delayed +entry/exit clock, overlap policy, unconditional history, and matched reference, +use `ohlcv-event-study-lab`. Do not disguise that descriptive question as a +Factor merely to obtain a Run. When the fixed primary temporal population has +too few finite pairs or no candidate/target variation, preserve the structured +failed Run as the truthful `scientific-limit`, report that exact bounded +answer, and change the hypothesis or route only as separately declared +work—not the missing values or Judge. Do not repeat the unchanged Run. + +When a hypothesis has meaningful sub-signals, also export +`FACTOR_COMPONENTS` and +`compute_factor_components(panel) -> pandas.DataFrame`. Declare one causal +column per falsifiable source component, including its label, role, claimed +`base`/3h/4h/6h/12h/1d intervals, and hypothesis. Use +`cross-sectional-score` for values meant to rank assets and +`timestamp-context` for one market/regime value shared by every asset at a +timestamp. Context components must be exactly cross-sectionally constant; +Core evaluates their train-tertile occupancy and transitions. Cross-sectional +Runs measure score components with per-date rank IC and context with +conditional final-factor IC. Single-asset and two-asset relative-value Runs +measure score components and context with within-split temporal rank- +correlation contributions. The component table must remain aligned, +deterministic, numeric, immutable, and prefix causal. Do not declare +presentation-only duplicates or imply that Core inferred column use. Temporal +quantile attribution remains unavailable; do not treat that explicit protocol +boundary as positive evidence. + +## Iteration protocol + +Before this protocol begins on a new caller assignment, replace the generic +scaffold candidate with the first predeclared caller-relevant candidate +without executing the scaffold. Start the governed Session only after that +source is fixed; its baseline Run is the first visible audit. Never rewrite +Core's `testGuidanceObservability=not-observable` as a factual claim that test +evidence was unused. + +1. Run `aq orient . --json` and read the current immutable leader's + `researchAgenda`. Treat its ordered moves as validation-only scientific + priorities, not executable actions or permission to inspect test for + selection. +2. Read `strategies/factor-claim.json`, + `strategies/factor-population.json`, the current candidate, and immutable + leader evidence. The request-bound `decision-signal`, `novel-factor`, or + `known-style-validation` claim, outcome, and prediction population are + fixed evidence authority, not editable strategy metadata. +3. State one falsifiable hypothesis about price or volume behavior. +4. Make one coherent code change inside the editable closure. +5. Run the bounded Experiment command supplied by the Session. +6. Inspect validation/test primary-horizon IC, HAC strength, diagnostic + horizon decay, tertile monotonicity/spread, train-selected dominant style, + style-neutral residual + primary-horizon association, equal-blend uplift, declared-component + raw/residual association, pairwise + redundancy, fixed diagnostic-blend leave-one-out delta, residual fold + stability, asset/regime stability, observed input/factor/target-pair + availability, coverage, turnover, errors, verdict, Project-family trial + count, and family-wise adjusted HAC significance. +7. KEEP only when the fixed objective improves; otherwise accept restoration + and form a different hypothesis. + +The fixed objective is validation mean IC only. Test IC is visible diagnostic +evidence and never enters KEEP/REVERT. Changing a candidate after inspecting +test evidence consumes its holdout value; obtain a new external period or +dataset before a production-grade claim. + +Starting a new Session does not create a fresh statistical search. Core counts +unique editable source hashes across every Run with the same fixed Study, +Judge, data, dependencies, and objective. Treat the Bonferroni-HAC result as a +selection-risk diagnostic, not permission to hide failed attempts or override +the immutable verdict. + +Session construction reuses an exact successful current baseline Run. It +executes a new baseline only when Study, program, candidate, Judge, dataset, +dependency, or Harness identity differs. + +The Judge fixes dataset-derived split dates and purges each declared diagnostic +horizon before a boundary. Treat sparse regimes, one weak fold, one dominant +asset, fast decay, or near-perfect overlap with a familiar OHLCV style as +findings to explain—not fields to hide or alternate scores to optimize +opportunistically. + +For a `decision-signal` claim, validation requires statistically supported +positive raw IC and positive raw IC in both fixed chronological folds. The +train-selected style, residual, and blend remain disclosure without creating +a novelty hurdle. For a `novel-factor` claim, the dominant comparison style is chosen on train +overlap only. Validation asks whether raw and style-neutral IC are positive +with fixed HAC t at least 1.96, whether an equal rank blend improves the +selected style, and whether both residual folds remain positive. For a +`known-style-validation` claim, the request fixes the comparison style before +research; validation instead requires at least 0.95 train rank identity, +positive statistically supported raw IC, and positive raw IC in both fixed +chronological folds. This prioritizes the next research lane; it does not +change KEEP/REVERT, replace Project-family selection adjustment, or +automatically admit the source into Portfolio or RL. + +When the fixed outcome is `forward-realized-volatility`, a positive funnel is +terminal risk-model evidence for this in-sample Study. Freeze, report, and seek +a fresh external holdout; never treat the result as an expected-return signal +or open Portfolio/RL work merely because the association is positive. + +Component leave-one-out applies only to the Judge's fixed equal-rank +diagnostic blend. It is not an ablation of arbitrary `compute_factor` code. +Component validation evidence may prioritize the next hypothesis, but it never +changes `validation_mean_ic`, KEEP/REVERT, Portfolio mechanics, or the +governed RL action set. Test component evidence remains visible audit only. + +Do not modify the Study, Judge, program, dataset, or AutoQuant Core to improve a +candidate. Do not treat this synthetic benchmark as a real-market alpha claim. diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/study.json b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/study.json new file mode 100644 index 0000000..98fb40d --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/inputs/study.json @@ -0,0 +1,59 @@ +{ + "dataset": { + "asset_class": "synthetic-multi-asset", + "id": "synthetic-ohlcv-research-desk-fixture", + "paths": [ + "ohlcv/**" + ], + "time_range": { + "end": "2025-08-11", + "start": "2024-01-02" + }, + "universe": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "version": "v1" + }, + "dependencies": { + "paths": [ + "strategies/factor-claim.json", + "strategies/factor-population.json", + "strategies/research-horizon.json" + ] + }, + "description": "Mine causal factor evidence on the shared research snapshot", + "editable": { + "paths": [ + "factors/**" + ] + }, + "id": "ohlcv-factor-quality", + "judge": { + "arguments": [], + "entrypoint": "judges/ohlcv_factor.py", + "kind": "python", + "paths": [ + "judges/ohlcv_factor.py", + "judges/factor_diagnostics.py" + ], + "timeout_seconds": 60 + }, + "name": "OHLCV Factor Quality", + "objective": { + "direction": "maximize", + "metric": "validation_mean_ic", + "minimum_improvement": 0.01 + }, + "program": "program.md", + "schema_version": 1, + "subject": { + "kind": "factor", + "name": "candidate-factor", + "version": "working" + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/judge-output.json b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/judge-output.json new file mode 100644 index 0000000..c1c2fa1 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/judge-output.json @@ -0,0 +1,2644 @@ +{ + "artifacts": [ + { + "description": "Factor semantics, purged split protocol, complete tear sheet, coverage, and causality audit", + "kind": "factor-report", + "path": "factor-report.json" + }, + { + "description": "Timestamped split, causal regime, and purge-aware request-bound outcome rank and Pearson IC", + "kind": "factor-daily", + "path": "daily-factor-evidence.csv" + }, + { + "description": "Timestamped fixed-tertile outcome levels and high-minus-low outcome spread by split and horizon", + "kind": "factor-quantiles", + "path": "factor-quantiles.csv" + }, + { + "description": "Per-timestamp observed input, finite factor, and horizon-paired cross-sectional asset counts", + "kind": "factor-availability", + "path": "factor-availability.csv" + }, + { + "description": "Train-selected style, candidate/style/residual/blend daily rank IC, and visible-test audit", + "kind": "factor-qualification", + "path": "factor-qualification.csv" + }, + { + "description": "Candidate-declared component quality, redundancy, nearest-peer residual, and fixed-blend ablation evidence", + "kind": "factor-components", + "path": "factor-components.json" + } + ], + "errors": [], + "metrics": { + "assets": 6, + "factor_api": { + "assets": 6, + "causality_audit": "whole-panel-timestamp-prefix", + "causality_cuts": [ + "2024-10-22 00:00:00", + "2025-03-18 00:00:00", + "2025-08-08 00:00:00" + ], + "cross_asset_context": "same-or-prior-timestamp-explicit-candidate", + "input": "long-form-observed-universe", + "kind": "panel-v2", + "observation_coverage": 1.0, + "possible_rows": 2520, + "rows": 2520, + "shape": "rectangular", + "timestamps": 420 + }, + "factor_claim": { + "authority": "quantitative-decision-support", + "claim": "novel-factor", + "id": "factor-claim-a002cb5137e5253b", + "kind": "autoquant-factor-claim", + "knownStyle": null, + "schemaVersion": 1, + "selectionAuthority": "validation-only", + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "testRole": 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"programHash": "cf5dfc868822c29b6c8323f698dfaff8bb896519473f07e747b40f82e264ab0e" + }, + "studyInputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c", + "subject": { + "kind": "factor", + "name": "candidate-factor", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourcePaths": [ + "factors/candidate.py" + ], + "version": "working" + }, + "summary": "Causal purge-aware factor tear sheet completed; forward-return validation 1-bar mean rank IC=-0.031325" +} diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/sources/factors/candidate.py b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/sources/factors/candidate.py new file mode 100644 index 0000000..6ee5eeb --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/sources/factors/candidate.py @@ -0,0 +1,128 @@ +"""Agent-editable baseline factor for the OHLCV Portfolio Lab.""" + +from __future__ import annotations + +import pandas as pd + + +AVAILABLE_FEATURE_INTERVALS = [] + +_BASE_COMPONENT = { + "base_momentum_10": { + "label": "10-base-bar momentum", + "role": "cross-sectional-score", + "intervals": ["base"], + "hypothesis": ( + "Recent relative strength persists over the next base bar." + ), + }, +} + +_FEATURE_COMPONENTS = { + "3h": ( + "momentum_3h_4", + 4, + { + "label": "Four completed 3-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["3h"], + "hypothesis": ( + "Short intraday trend persists beyond the latest completed " + "3-hour bar." + ), + }, + ), + "12h": ( + "momentum_12h_2", + 2, + { + "label": "Two completed 12-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["12h"], + "hypothesis": "Half-day trend filters noisy base-bar momentum.", + }, + ), + "1d": ( + "momentum_1d_3", + 3, + { + "label": "Three completed daily bars momentum", + "role": "cross-sectional-score", + "intervals": ["1d"], + "hypothesis": ( + "Multi-day relative strength persists at the next base close." + ), + }, + ), +} + +FACTOR_COMPONENTS = dict(_BASE_COMPONENT) +for _interval in AVAILABLE_FEATURE_INTERVALS: + if _interval in _FEATURE_COMPONENTS: + _name, _periods, _declaration = _FEATURE_COMPONENTS[_interval] + FACTOR_COMPONENTS[_name] = _declaration + + +def _completed_bar_return( + panel: pd.DataFrame, + interval: str, + periods: int, +) -> pd.Series: + close_column = f"close__{interval}" + bar_column = f"bar_close__{interval}" + if close_column not in panel or bar_column not in panel: + return pd.Series(float("nan"), index=panel.index, dtype=float) + completed = panel.loc[ + panel[bar_column].notna(), + ["asset", bar_column, close_column], + ].drop_duplicates(["asset", bar_column], keep="first") + completed["return"] = completed.groupby( + "asset", + sort=False, + )[close_column].pct_change(periods, fill_method=None) + lookup = completed.set_index( + ["asset", bar_column], + )["return"] + keys = pd.MultiIndex.from_frame( + panel.loc[:, ["asset", bar_column]], + ) + return pd.Series( + lookup.reindex(keys).to_numpy(dtype=float), + index=panel.index, + dtype=float, + ) + + +def compute_factor_components(panel: pd.DataFrame) -> pd.DataFrame: + """Declare causal components without changing downstream factor authority.""" + + components = { + "base_momentum_10": panel.groupby( + "asset", + sort=False, + )["close"].pct_change(10, fill_method=None), + } + for interval in AVAILABLE_FEATURE_INTERVALS: + if interval in _FEATURE_COMPONENTS: + name, periods, _declaration = _FEATURE_COMPONENTS[interval] + components[name] = _completed_bar_return( + panel, + interval, + periods, + ) + return pd.DataFrame(components, index=panel.index) + + +def compute_factor(panel: pd.DataFrame) -> pd.Series: + """Return causal relative multi-horizon momentum for construction. + + The fixed Judge owns signal state/hysteresis, conviction/volatility sizing, + target and executed weights, attribution, delay, drift, costs, benchmark, + splits, metrics, and stress tests. Change only this factor while testing + one falsifiable hypothesis at a time. + """ + + components = compute_factor_components(panel) + raw = components.mean(axis=1, skipna=True) + market_center = raw.groupby(panel["timestamp"], sort=False).transform("mean") + return (raw - market_center).rename("relative_multi_horizon_momentum") diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/stderr.txt b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/stderr.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/stdout.txt b/projects/sample-research-desk/runs/run-20260802T162901303630Z-df20b4f0c097/stdout.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/daily-factor-evidence.csv 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a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-availability.csv b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-availability.csv new file mode 100644 index 0000000..5acd221 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/artifacts/factor-availability.csv @@ -0,0 +1,421 @@ +timestamp,input_assets,factor_assets,paired_assets_h1,paired_assets_h5,paired_assets_h10 +2024-01-02,6,0,0,0,0 +2024-01-03,6,0,0,0,0 +2024-01-04,6,0,0,0,0 +2024-01-05,6,0,0,0,0 +2024-01-08,6,0,0,0,0 +2024-01-09,6,0,0,0,0 +2024-01-10,6,0,0,0,0 +2024-01-11,6,0,0,0,0 +2024-01-12,6,0,0,0,0 +2024-01-15,6,0,0,0,0 +2024-01-16,6,6,6,6,6 +2024-01-17,6,6,6,6,6 +2024-01-18,6,6,6,6,6 +2024-01-19,6,6,6,6,6 +2024-01-22,6,6,6,6,6 +2024-01-23,6,6,6,6,6 +2024-01-24,6,6,6,6,6 +2024-01-25,6,6,6,6,6 +2024-01-26,6,6,6,6,6 +2024-01-29,6,6,6,6,6 +2024-01-30,6,6,6,6,6 +2024-01-31,6,6,6,6,6 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"portfolioAuthority": "none", + "predictionTarget": "close-t-to-close-t-plus-h-simple-return", + "residualization": "same-timestamp-cross-sectional-centered-rank-ols", + "rlActionAuthority": "none", + "roles": [ + "cross-sectional-score", + "timestamp-context" + ], + "scoreMeasure": "per-date-cross-sectional-rank-ic", + "testRole": "visible audit only", + "timestampContext": "train-tertile-occupancy-transition-and-conditional-factor-ic", + "tradingAuthority": "none" + }, + "horizons": [ + 1, + 5, + 10 + ], + "inference": "Newey-West/Bartlett HAC mean t-statistic with maximum lag 5 and two-sided normal-approximation p-value", + "measure": "per-date cross-sectional Spearman rank IC and Pearson IC over the fixed factor-claim-complete-research-universe evaluation universe", + "outcome": { + "annualization": "not-applicable", + "downstreamMeaning": "expected-return-research-only", + "explicit": false, + "kind": "forward-return", + "label": "forward close-to-close return", + "portfolioAuthority": "qualification-gated", + "rlAuthority": "portfolio-evidence-gated", + "scoreDirection": "higher-factor-predicts-higher-outcome", + "targetSemantics": "close-t-to-close-t-plus-h-simple-return", + "tradingAuthority": "none" + }, + "predictionUniverse": "caller-owned factorPolicy.predictionAssets for request-bound decision-signal claims; complete research universe for novel-factor and known-style-validation claims", + "primaryHorizon": 1, + "qualification": { + "blend": "equal-weight-cross-sectional-percentile-ranks", + "claim": { + "authority": "quantitative-decision-support", + "claim": "novel-factor", + "id": "factor-claim-a002cb5137e5253b", + "kind": "autoquant-factor-claim", + "knownStyle": null, + "schemaVersion": 1, + "selectionAuthority": "validation-only", + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "testRole": "visible-audit", + "tradingAuthority": "none" + }, + "method": "request-claim-aware-one-style-rank-neutralization-v2", + "neutralization": "same-timestamp-cross-sectional-centered-rank-ols", + "styleSelection": "train-only", + "testRole": "visible audit only", + "tradingAuthority": "none" + }, + "quantiles": "fixed low/middle/high cross-sectional groups", + "regimes": "causal trailing market direction and volatility versus lagged rolling threshold", + "researchUniverse": "complete Study universe available to candidate features", + "score": "validation mean rank IC at the fixed primary 1-bar horizon only", + "split": "dataset-fixed chronological 60/20/20 with horizon-specific boundary purge", + "styles": [ + "momentum_20", + "reversal_5", + "realized_volatility_20", + "relative_volume_20" + ], + "target": "close-t-to-close-t-plus-h-simple-return", + "testRole": "visible diagnostic evidence; never enters candidate selection" + }, + "splitProtocol": { + "candidateDependent": false, + "horizons": { + "1": { + "test": { + "eligibleSignalRows": 83, + "purgedBoundaryRows": 1, + "signalEnd": "2025-08-08", + "signalStart": "2025-04-16", + "targetEnd": "2025-08-11" + }, + "train": { + "eligibleSignalRows": 251, + "purgedBoundaryRows": 1, + "signalEnd": "2024-12-17", + "signalStart": "2024-01-02", + "targetEnd": "2024-12-18" + }, + "validation": { + "eligibleSignalRows": 83, + "purgedBoundaryRows": 1, + "signalEnd": "2025-04-14", + "signalStart": "2024-12-19", + "targetEnd": "2025-04-15" + } + }, + "10": { + "test": { + "eligibleSignalRows": 74, + "purgedBoundaryRows": 10, + "signalEnd": "2025-07-28", + "signalStart": "2025-04-16", + "targetEnd": "2025-08-11" + }, + "train": { + "eligibleSignalRows": 242, + "purgedBoundaryRows": 10, + "signalEnd": "2024-12-04", + "signalStart": "2024-01-02", + "targetEnd": "2024-12-18" + }, + "validation": { + "eligibleSignalRows": 74, + "purgedBoundaryRows": 10, + "signalEnd": "2025-04-01", + "signalStart": "2024-12-19", + "targetEnd": "2025-04-15" + } + }, + "5": { + "test": { + "eligibleSignalRows": 79, + "purgedBoundaryRows": 5, + "signalEnd": "2025-08-04", + "signalStart": "2025-04-16", + "targetEnd": "2025-08-11" + }, + "train": { + "eligibleSignalRows": 247, + "purgedBoundaryRows": 5, + "signalEnd": "2024-12-11", + "signalStart": "2024-01-02", + "targetEnd": "2024-12-18" + }, + "validation": { + "eligibleSignalRows": 79, + "purgedBoundaryRows": 5, + "signalEnd": "2025-04-08", + "signalStart": "2024-12-19", + "targetEnd": "2025-04-15" + } + } + }, + "method": "dataset-fixed-chronological-60-20-20", + "splits": { + "test": { + "end": "2025-08-11", + "rows": 84, + "start": "2025-04-16" + }, + "train": { + "end": "2024-12-18", + "rows": 252, + "start": "2024-01-02" + }, + "validation": { + "end": "2025-04-15", + "rows": 84, + "start": "2024-12-19" + } + }, + "targetCrossesBoundary": false + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dataset-files.json 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a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-claim.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-claim.json new file mode 100644 index 0000000..bca8f07 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-claim.json @@ -0,0 +1,16 @@ +{ + "authority": "quantitative-decision-support", + "claim": "novel-factor", + "id": "factor-claim-a002cb5137e5253b", + "kind": "autoquant-factor-claim", + "knownStyle": null, + "schemaVersion": 1, + "selectionAuthority": "validation-only", + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "testRole": "visible-audit", + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-population.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-population.json new file mode 100644 index 0000000..b19f0db --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/factor-population.json @@ -0,0 +1,43 @@ +{ + "assetPredictionRoles": { + "ALPHA": "prediction", + "BRAVO": "prediction", + "CHARLIE": "prediction", + "DELTA": "prediction", + "ECHO": "prediction", + "FOXTROT": "prediction" + }, + "authority": "factor-claim-complete-research-universe", + "claim": "novel-factor", + "contextAssets": [], + "evaluationAuthority": "factor-evaluation-only", + "evaluationMode": "cross-sectional", + "id": "factor-population-78adf037945d08ec", + "kind": "autoquant-factor-population", + "outcome": "forward-return", + "portfolioAuthority": "none", + "predictionAssets": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "relativeValuePair": null, + "researchUniverse": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "schemaVersion": 1, + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/research-horizon.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/research-horizon.json new file mode 100644 index 0000000..f660e08 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/dependency-sources/strategies/research-horizon.json @@ -0,0 +1,24 @@ +{ + "authority": "quantitative-decision-support", + "diagnosticForwardBars": [ + 1, + 5, + 10 + ], + "id": "horizon-c3a43b664e7f6568", + "kind": "autoquant-research-horizon", + "primaryForwardBars": 1, + "schemaVersion": 1, + "selectionAuthority": { + "diagnostics": "context-only", + "primary": "validation-only" + }, + "source": { + "horizon": "reference decision-bar horizon", + "horizonPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "targetSemantics": "close-t-to-close-t-plus-n-decision-bars", + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/identity.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/identity.json new file mode 100644 index 0000000..5f2ade5 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/identity.json @@ -0,0 +1,41 @@ +{ + "datasetHash": "0d740a8f0f285f69eae626fc1e76e9d60d57b42acd03842708ed3675570e97f7", + "datasetSourceHashes": { + 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"ebf6fa40cbcbcdc18490439865e6a7de1a0322abc8853b528b3745bc5e70618d" + }, + "evaluationRole": "research-selection", + "harness": { + "buildProvenance": "source-checkout", + "commit": "4a14ca5fd4190437b7ecacc83918e66de6237a1a", + "dirty": true, + "id": "autoquant.python-judge", + "python": "3.11.15", + "sourceHash": "a97ebe7fbc35de1c32ee8101caaea4d7ec26af8054ad483cb47abce6b6d2ffc0", + "version": "0.9.31" + }, + "inputHash": "b060cb61a0168729197bfa5caa63bf94fbae498edea258bf766e4b5cfa7f1aef", + "judgeHash": "56705fc131a41e4466b261e1996ed0a840cd4c44b9f36c3f15b447d0914a0a40", + "judgeHashes": { + "judges/factor_diagnostics.py": "74ab1e25aed0e1744e75095f37b3d5e173bcf35a8f3f3d029495ce4e471a3d51", + "judges/ohlcv_factor.py": "867aa996912293af7cd5cfdb2b07ecf3ad3b540fa1f69a702daccccefbff5310" + }, + "programHash": "cf5dfc868822c29b6c8323f698dfaff8bb896519473f07e747b40f82e264ab0e", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourceHashes": { + "factors/candidate.py": "1e29d70f72ce3afaf3d018f9d60c77026148423f57b52fb22f55f8127679ba6a" + }, + "studyHash": "97019f1d7c32cf514975e782b29556f9414892651fc9bc41c5509746a2f3cb2e", + "studyInputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c" +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/factor_diagnostics.py b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/factor_diagnostics.py new file mode 100644 index 0000000..e675aff --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/factor_diagnostics.py @@ -0,0 +1,535 @@ +"""Fixed, causal diagnostics for the OHLCV Factor Lab.""" + +from __future__ import annotations + +import math +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.intervals import timestamp_label + +HORIZONS = (1, 5, 10) +SPLIT_NAMES = ("train", "validation", "test") +REGIME_NAMES = ("up-calm", "up-stressed", "down-calm", "down-stressed") +STYLE_NAMES = ( + "momentum_20", + "reversal_5", + "realized_volatility_20", + "relative_volume_20", +) + + +def _ranges(length: int) -> dict[str, tuple[int, int]]: + train_end = int(length * 0.60) + validation_end = int(length * 0.80) + return { + "train": (0, train_end), + "validation": (train_end, validation_end), + "test": (validation_end, length), + } + + +def purged_split_masks( + index: pd.DatetimeIndex, + horizons: tuple[int, ...] = HORIZONS, +) -> tuple[ + dict[int, dict[str, pd.Series]], + dict[str, Any], + pd.Series, +]: + """Build dataset-fixed masks whose targets cannot cross split boundaries.""" + + if not index.is_monotonic_increasing or index.has_duplicates: + raise ValueError("Diagnostic index must be unique and chronological") + ranges = _ranges(len(index)) + positions = np.arange(len(index)) + base_labels = pd.Series("unassigned", index=index, dtype="object") + protocol: dict[str, Any] = { + "method": "dataset-fixed-chronological-60-20-20", + "candidateDependent": False, + "targetCrossesBoundary": False, + "horizons": {}, + "splits": {}, + } + for name, (start, stop) in ranges.items(): + if stop <= start: + raise ValueError(f"Chronological split {name} is empty") + base_labels.iloc[start:stop] = name + protocol["splits"][name] = { + "start": timestamp_label(index[start]), + "end": timestamp_label(index[stop - 1]), + "rows": stop - start, + } + + masks: dict[int, dict[str, pd.Series]] = {} + for horizon in horizons: + if not isinstance(horizon, int) or horizon <= 0: + raise ValueError("Forward horizons must be positive integers") + masks[horizon] = {} + horizon_protocol: dict[str, Any] = {} + for name, (start, stop) in ranges.items(): + if stop - start <= horizon: + raise ValueError( + f"Chronological split {name} is too short for horizon {horizon}" + ) + eligible = (positions >= start) & (positions + horizon < stop) + mask = pd.Series(eligible, index=index, dtype=bool) + masks[horizon][name] = mask + signal_positions = positions[eligible] + horizon_protocol[name] = { + "signalStart": timestamp_label(index[signal_positions[0]]), + "signalEnd": timestamp_label(index[signal_positions[-1]]), + "targetEnd": timestamp_label( + index[signal_positions[-1] + horizon] + ), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + protocol["horizons"][str(horizon)] = horizon_protocol + return masks, protocol, base_labels + + +def chronological_fold_masks( + index: pd.DatetimeIndex, + horizon: int = 1, +) -> tuple[dict[str, pd.Series], dict[str, dict[str, Any]]]: + """Split each fixed chronological partition in half and purge each fold.""" + + positions = np.arange(len(index)) + masks: dict[str, pd.Series] = {} + protocol: dict[str, dict[str, Any]] = {} + for split, (start, stop) in _ranges(len(index)).items(): + middle = start + (stop - start) // 2 + for number, (fold_start, fold_stop) in enumerate( + ((start, middle), (middle, stop)), + start=1, + ): + name = f"{split}_{number}" + eligible = ( + (positions >= fold_start) + & (positions + horizon < fold_stop) + ) + if not eligible.any(): + raise ValueError(f"Chronological fold {name} is empty") + masks[name] = pd.Series(eligible, index=index, dtype=bool) + selected = positions[eligible] + protocol[name] = { + "split": split, + "start": timestamp_label(index[fold_start]), + "end": timestamp_label(index[fold_stop - 1]), + "signalEnd": timestamp_label(index[selected[-1]]), + "targetEnd": timestamp_label(index[selected[-1] + horizon]), + "eligibleSignalRows": int(eligible.sum()), + "purgedBoundaryRows": horizon, + } + return masks, protocol + + +def forward_return_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + return { + horizon: closes.shift(-horizon) / closes - 1.0 + for horizon in horizons + } + + +def forward_realized_volatility_panels( + closes: pd.DataFrame, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Return complete-window, unannualized forward realized volatility. + + At signal close t and horizon h, the target is the square root of the sum + of squared close-to-close log returns from t -> t+1 through t+h-1 -> t+h. + A missing constituent return invalidates the whole target window. + """ + + log_returns = np.log(closes / closes.shift(1)) + output: dict[int, pd.DataFrame] = {} + for horizon in horizons: + future_squared = [ + log_returns.shift(-offset).pow(2) + for offset in range(1, horizon + 1) + ] + squared_sum = sum(future_squared) + complete = sum(item.notna().astype(int) for item in future_squared) + output[horizon] = squared_sum.pow(0.5).where(complete == horizon) + return output + + +def factor_outcome_panels( + closes: pd.DataFrame, + outcome: str, + horizons: tuple[int, ...] = HORIZONS, +) -> dict[int, pd.DataFrame]: + """Materialize one fixed Factor outcome over the requested horizons.""" + + if outcome == "forward-return": + return forward_return_panels(closes, horizons) + if outcome == "forward-realized-volatility": + return forward_realized_volatility_panels(closes, horizons) + raise ValueError(f"Unsupported Factor outcome: {outcome}") + + +def daily_rank_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, + constant_left_value: float | None = None, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["right"].nunique() < 2: + continue + if pair["left"].nunique() < 2: + if constant_left_value is not None: + values[timestamp] = float(constant_left_value) + continue + value = pair["left"].rank(method="average").corr( + pair["right"].rank(method="average") + ) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def daily_pearson_correlation( + left: pd.DataFrame, + right: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.Series: + values: dict[pd.Timestamp, float] = {} + for timestamp in left.index.intersection(right.index): + pair = pd.DataFrame( + { + "left": left.loc[timestamp], + "right": right.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets: + continue + if pair["left"].nunique() < 2 or pair["right"].nunique() < 2: + continue + value = pair["left"].corr(pair["right"]) + if value is not None and math.isfinite(float(value)): + values[timestamp] = float(value) + return pd.Series(values, dtype=float).sort_index() + + +def hac_inference( + values: pd.Series, + *, + maximum_lag: int = 5, +) -> dict[str, float | int | None | str]: + """Return deterministic Newey-West mean inference with Bartlett weights.""" + + clean = values.dropna().astype(float) + count = len(clean) + lag = min(maximum_lag, max(0, count - 1)) + if count < 2: + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + array = clean.to_numpy(dtype=float) + centered = array - float(array.mean()) + long_run_variance = float(np.dot(centered, centered) / count) + for offset in range(1, lag + 1): + covariance = float( + np.dot(centered[offset:], centered[:-offset]) / count + ) + weight = 1.0 - offset / (lag + 1.0) + long_run_variance += 2.0 * weight * covariance + long_run_variance = max(0.0, long_run_variance) + standard_error = math.sqrt(long_run_variance / count) + if standard_error <= 1e-12: + t_statistic: float | None = None + p_value: float | None = None + else: + t_statistic = float(array.mean()) / standard_error + p_value = math.erfc(abs(t_statistic) / math.sqrt(2.0)) + return { + "method": "newey-west-bartlett", + "maximum_lag": lag, + "standard_error": standard_error, + "t_statistic": t_statistic, + "normal_approximation_p_value": p_value, + } + + +def descriptive_ic( + values: pd.Series, + *, + minimum_observations: int = 3, +) -> dict[str, float | int | None | dict[str, Any]]: + clean = values.dropna().astype(float) + if len(clean) < minimum_observations: + hac = hac_inference(clean) + hac.update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return { + "mean_ic": None, + "standard_deviation": None, + "icir": None, + "hit_rate": None, + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": False, + "hac": hac, + } + mean = float(clean.mean()) + standard_deviation = float(clean.std(ddof=0)) + return { + "mean_ic": mean, + "standard_deviation": standard_deviation, + "icir": ( + mean / standard_deviation + if standard_deviation > 1e-12 + else None + ), + "hit_rate": float((clean > 0).mean()), + "observations": int(len(clean)), + "minimum_observations": minimum_observations, + "sufficient": True, + "hac": hac_inference(clean), + } + + +def daily_quantile_outcomes( + factors: pd.DataFrame, + returns: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + rows: list[dict[str, Any]] = [] + for timestamp in factors.index.intersection(returns.index): + pair = pd.DataFrame( + { + "factor": factors.loc[timestamp], + "outcome": returns.loc[timestamp], + } + ).dropna() + if len(pair) < minimum_assets or pair["factor"].nunique() < 3: + continue + ordered = pair.sort_values( + ["factor"], + kind="mergesort", + ) + groups = np.array_split(np.arange(len(ordered)), 3) + low, middle, high = ( + float(ordered.iloc[group]["outcome"].mean()) + for group in groups + ) + rows.append( + { + "timestamp": timestamp, + "low": low, + "middle": middle, + "high": high, + "high_minus_low": high - low, + } + ) + if not rows: + return pd.DataFrame( + columns=("low", "middle", "high", "high_minus_low"), + index=pd.DatetimeIndex([], name="timestamp"), + ) + return pd.DataFrame(rows).set_index("timestamp").sort_index() + + +def quantile_summary( + daily: pd.DataFrame, + *, + minimum_observations: int = 3, +) -> dict[str, Any]: + clean = daily.dropna() + observations = int(len(clean)) + if observations < minimum_observations: + return { + "mean_outcome_by_quantile": { + "low": None, + "middle": None, + "high": None, + }, + "high_minus_low": None, + "monotonicity": None, + "observations": observations, + } + means = { + label: float(clean[label].mean()) + for label in ("low", "middle", "high") + } + ordered = pd.Series([0.0, 1.0, 2.0]) + ranked_means = pd.Series(list(means.values())).rank(method="average") + monotonicity = ordered.corr(ranked_means) + return { + "mean_outcome_by_quantile": means, + "high_minus_low": float(clean["high_minus_low"].mean()), + "monotonicity": ( + float(monotonicity) + if monotonicity is not None and math.isfinite(float(monotonicity)) + else None + ), + "observations": observations, + } + + +def causal_regime_labels(closes: pd.DataFrame) -> pd.Series: + """Label the signal close using only trailing market information.""" + + market_return = closes.pct_change(fill_method=None).mean(axis=1) + trailing_direction = ( + (1.0 + market_return) + .rolling(20, min_periods=20) + .apply(np.prod, raw=True) + - 1.0 + ) + trailing_volatility = market_return.rolling( + 20, + min_periods=20, + ).std(ddof=0) + lagged_threshold = trailing_volatility.shift(1).rolling( + 60, + min_periods=20, + ).median() + labels = pd.Series(pd.NA, index=closes.index, dtype="object") + valid = ( + trailing_direction.notna() + & trailing_volatility.notna() + & lagged_threshold.notna() + ) + for timestamp in closes.index[valid]: + direction = "up" if trailing_direction.loc[timestamp] >= 0 else "down" + volatility = ( + "stressed" + if trailing_volatility.loc[timestamp] + > lagged_threshold.loc[timestamp] + else "calm" + ) + labels.loc[timestamp] = f"{direction}-{volatility}" + return labels + + +def style_proxy_panels( + closes: pd.DataFrame, + volumes: pd.DataFrame, +) -> dict[str, pd.DataFrame]: + daily_returns = closes.pct_change(fill_method=None) + return { + "momentum_20": closes / closes.shift(20) - 1.0, + "reversal_5": -(closes / closes.shift(5) - 1.0), + "realized_volatility_20": daily_returns.rolling( + 20, + min_periods=20, + ).std(ddof=0), + "relative_volume_20": ( + volumes / volumes.rolling(20, min_periods=20).mean() - 1.0 + ), + } + + +def cross_sectional_rank_residual( + candidate: pd.DataFrame, + style: pd.DataFrame, + *, + minimum_assets: int = 4, +) -> pd.DataFrame: + """Remove one contemporaneous style exposure from candidate ranks.""" + + index = candidate.index.intersection(style.index) + columns = candidate.columns.intersection(style.columns) + output = pd.DataFrame(np.nan, index=index, columns=columns, dtype=float) + for timestamp in index: + pair = pd.DataFrame( + { + "candidate": candidate.loc[timestamp, columns], + "style": style.loc[timestamp, columns], + } + ).dropna() + if ( + len(pair) < minimum_assets + or pair["candidate"].nunique() < 2 + or pair["style"].nunique() < 2 + ): + continue + candidate_rank = pair["candidate"].rank(method="average", pct=True) + style_rank = pair["style"].rank(method="average", pct=True) + candidate_centered = candidate_rank - float(candidate_rank.mean()) + style_centered = style_rank - float(style_rank.mean()) + denominator = float(np.dot(style_centered, style_centered)) + if denominator <= 1e-15: + continue + beta = float( + np.dot(style_centered, candidate_centered) / denominator + ) + output.loc[timestamp, pair.index] = ( + candidate_centered - beta * style_centered + ) + return output + + +def equal_rank_blend( + candidate: pd.DataFrame, + style: pd.DataFrame, +) -> pd.DataFrame: + """Return a same-timestamp equal blend of cross-sectional percentile ranks.""" + + candidate_rank = candidate.rank(axis=1, method="average", pct=True) + style_rank = style.rank(axis=1, method="average", pct=True) + available = candidate.notna() & style.notna() + return ((candidate_rank + style_rank) / 2.0).where(available) + + +def per_asset_rank_correlation( + factors: pd.DataFrame, + returns: pd.DataFrame, + mask: pd.Series, + *, + minimum_observations: int = 10, +) -> dict[str, dict[str, float | int | None]]: + result: dict[str, dict[str, float | int | None]] = {} + for asset in factors.columns.intersection(returns.columns): + pair = pd.DataFrame( + { + "factor": factors.loc[mask, asset], + "forward_return": returns.loc[mask, asset], + } + ).dropna() + value: float | None = None + if ( + len(pair) >= minimum_observations + and pair["factor"].nunique() >= 2 + and pair["forward_return"].nunique() >= 2 + ): + correlation = pair["factor"].rank(method="average").corr( + pair["forward_return"].rank(method="average") + ) + if correlation is not None and math.isfinite(float(correlation)): + value = float(correlation) + result[str(asset)] = { + "rank_correlation": value, + "observations": int(len(pair)), + } + return result diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/ohlcv_factor.py b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/ohlcv_factor.py new file mode 100644 index 0000000..f4f0311 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/judge-sources/judges/ohlcv_factor.py @@ -0,0 +1,2657 @@ +"""Fixed no-lookahead Judge for the OHLCV Factor Lab reference Project.""" + +from __future__ import annotations + +import importlib +import json +import math +import os +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from autoquant.factor_runtime import ( + FactorRuntimeError, + build_factor_panel, + evaluate_factor, + factor_contract, + values_to_wide, +) +from autoquant.factor_claims import ( + FACTOR_CLAIM, + FORWARD_RETURN_OUTCOME, + factor_outcome, + factor_outcome_contract, + load_factor_claim, +) +from autoquant.intervals import ( + IntervalContractError, + load_multi_interval_asset, + timestamp_label, +) +from autoquant.prediction_modes import ( + CROSS_SECTIONAL_MODE, + FACTOR_POPULATION, + SINGLE_ASSET_TEMPORAL_MODE, + TEMPORAL_EVALUATION_MODES, + TWO_ASSET_RELATIVE_VALUE_MODE, + PredictionModeError, + load_factor_population, + resolve_prediction_population, +) +from autoquant.horizons import ( + RESEARCH_HORIZON, + load_research_horizon, +) +from judges.factor_diagnostics import ( + HORIZONS, + REGIME_NAMES, + STYLE_NAMES, + causal_regime_labels, + chronological_fold_masks, + cross_sectional_rank_residual, + daily_pearson_correlation, + daily_quantile_outcomes, + daily_rank_correlation, + descriptive_ic, + equal_rank_blend, + factor_outcome_panels, + hac_inference, + per_asset_rank_correlation, + purged_split_masks, + quantile_summary, + style_proxy_panels, +) + + +REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume") +MIN_ASSETS_PER_DATE = 4 +MIN_IC_DATES_PER_SPLIT = 20 +PRIMARY_HORIZON = 1 +TEMPORAL_QUALIFICATION_METHOD = ( + "request-claim-aware-one-style-temporal-neutralization-v1" +) + + +class JudgeFailure(ValueError): + def __init__(self, code: str, message: str): + self.code = code + super().__init__(message) + + +def _write_output(value: dict[str, Any]) -> None: + Path(os.environ["AUTOQUANT_RUN_OUTPUT"]).write_text( + json.dumps(value, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _load_contract() -> tuple[dict[str, Any], Path]: + study = json.loads( + Path(os.environ["AUTOQUANT_STUDY_PATH"]).read_text(encoding="utf-8") + ) + data_root = Path(os.environ["AUTOQUANT_DATA_ROOT"]).resolve() + if not data_root.is_dir(): + raise JudgeFailure("dataset.root", "AUTOQUANT_DATA_ROOT is not a directory") + return study, data_root + + +def _load_horizon() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / RESEARCH_HORIZON + try: + return load_research_horizon(path) + except Exception as error: + raise JudgeFailure( + "horizon.contract", + f"Invalid fixed Horizon Mandate: {error}", + ) from error + + +def _load_factor_claim() -> dict[str, Any]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_CLAIM + try: + return load_factor_claim(path) + except Exception as error: + raise JudgeFailure( + "factor-claim.contract", + f"Invalid fixed Factor claim: {error}", + ) from error + + +def _load_prediction_universe( + research_universe: list[str], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], list[str], list[str], str, str]: + path = Path(os.environ["AUTOQUANT_PROJECT_ROOT"]) / FACTOR_POPULATION + try: + factor_population = load_factor_population(path) + except Exception as error: + raise JudgeFailure( + "prediction-universe.contract", + f"Invalid fixed prediction-universe authority: {error}", + ) from error + try: + population = resolve_prediction_population( + research_universe, + factor_claim, + factor_population, + ) + except PredictionModeError as error: + raise JudgeFailure(error.code, str(error)) from error + return ( + population.as_metrics(), + list(population.prediction_assets), + list(population.context_assets), + population.authority, + population.evaluation_mode, + ) + + +def _load_asset(data_root: Path, asset: str, start: str, end: str) -> pd.DataFrame: + try: + multi_interval = load_multi_interval_asset( + data_root, + asset, + start=start, + end=end, + ) + except IntervalContractError as error: + raise JudgeFailure(error.code, str(error)) from error + if multi_interval is not None: + if len(multi_interval) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 base observations in the Study range", + ) + return multi_interval + source = (data_root / "ohlcv" / f"{asset}.csv").resolve() + if data_root not in source.parents or not source.is_file(): + raise JudgeFailure("dataset.asset", f"Missing confined OHLCV file for {asset}") + frame = pd.read_csv(source) + if tuple(frame.columns) != REQUIRED_COLUMNS: + raise JudgeFailure( + "dataset.columns", + f"{asset} columns must be exactly {', '.join(REQUIRED_COLUMNS)}", + ) + frame["timestamp"] = pd.to_datetime( + frame["timestamp"], + format="%Y-%m-%d", + errors="raise", + ) + if frame["timestamp"].duplicated().any() or not frame["timestamp"].is_monotonic_increasing: + raise JudgeFailure( + "dataset.time-order", + f"{asset} timestamps must be unique and chronological", + ) + for column in REQUIRED_COLUMNS[1:]: + frame[column] = pd.to_numeric(frame[column], errors="raise") + numeric = frame[list(REQUIRED_COLUMNS[1:])].to_numpy(dtype=float) + if not np.isfinite(numeric).all(): + raise JudgeFailure("dataset.non-finite", f"{asset} contains non-finite OHLCV") + if (frame[["open", "high", "low", "close", "volume"]] <= 0).any().any(): + raise JudgeFailure("dataset.non-positive", f"{asset} contains non-positive OHLCV") + if ( + (frame["high"] < frame[["open", "close"]].max(axis=1)).any() + or (frame["low"] > frame[["open", "close"]].min(axis=1)).any() + ): + raise JudgeFailure("dataset.bar-shape", f"{asset} contains invalid bars") + selected = frame[ + (frame["timestamp"] >= pd.Timestamp(start)) + & (frame["timestamp"] <= pd.Timestamp(end)) + ].copy() + if len(selected) < 120: + raise JudgeFailure( + "dataset.observations", + f"{asset} has fewer than 120 observations in the Study range", + ) + return selected.reset_index(drop=True) + + +def _split_metrics(values: pd.Series) -> dict[str, Any]: + if len(values) < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "judge.population", + f"Chronological split has only {len(values)} valid IC dates", + ) + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _component_split_metrics(values: pd.Series) -> dict[str, Any]: + """Disclose sparse component evidence without failing a valid final factor.""" + + return descriptive_ic( + values, + minimum_observations=MIN_IC_DATES_PER_SPLIT, + ) + + +def _masked(values: pd.Series, mask: pd.Series) -> pd.Series: + return values.reindex(mask.index[mask]).dropna() + + +def _temporal_correlation_contributions( + left: pd.Series, + right: pd.Series, + mask: pd.Series, + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Return timestamp contributions whose mean is one split correlation.""" + + selected = pd.DataFrame( + { + "left": left.reindex(mask.index[mask]), + "right": right.reindex(mask.index[mask]), + } + ).dropna() + result = pd.Series(index=mask.index, dtype=float) + if len(selected) < 3: + return result + if rank: + selected = selected.rank(method="average", pct=True) + left_centered = selected["left"] - float(selected["left"].mean()) + right_centered = selected["right"] - float(selected["right"].mean()) + denominator = math.sqrt( + float((left_centered**2).mean()) + * float((right_centered**2).mean()) + ) + if denominator <= 1e-12: + if constant_left_value is not None: + result.loc[selected.index] = float(constant_left_value) + return result + result.loc[selected.index] = ( + left_centered * right_centered / denominator + ) + return result + + +def _temporal_daily( + left_panel: pd.DataFrame, + right_panel: pd.DataFrame, + masks: dict[str, pd.Series], + *, + rank: bool, + constant_left_value: float | None = None, +) -> pd.Series: + """Evaluate one prediction asset across time without context targets.""" + + left = left_panel.iloc[:, 0] + right = right_panel.iloc[:, 0] + result = pd.Series(index=left_panel.index, dtype=float) + for split in ("train", "validation", "test"): + contribution = _temporal_correlation_contributions( + left, + right, + masks[split], + rank=rank, + constant_left_value=constant_left_value, + ) + result.loc[contribution.dropna().index] = contribution.dropna() + return result + + +def _preflight_temporal_primary_validation( + factor_panel: pd.DataFrame, + outcome_panel: pd.DataFrame, + validation_mask: pd.Series, + *, + evaluation_mode: str, + horizon: int, +) -> None: + """Reject an unavailable fixed objective with an exact research reason.""" + + selected = pd.DataFrame( + { + "factor": factor_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + "outcome": outcome_panel.iloc[:, 0].reindex( + validation_mask.index[validation_mask] + ), + } + ).dropna() + observations = int(len(selected)) + factor_values = int(selected["factor"].nunique()) + target_values = int(selected["outcome"].nunique()) + context = ( + f"evaluationMode={evaluation_mode}, split=validation, " + f"primaryHorizon={horizon}, pairedObservations={observations}, " + f"distinctFactorValues={factor_values}, " + f"distinctTargetValues={target_values}, " + f"minimumObservations={MIN_IC_DATES_PER_SPLIT}" + ) + if observations < MIN_IC_DATES_PER_SPLIT: + raise JudgeFailure( + "factor.temporal-primary-observations", + "Primary temporal validation has too few finite factor/target " + f"pairs ({context})", + ) + if factor_values < 2: + raise JudgeFailure( + "factor.temporal-primary-candidate-variation", + "Primary temporal validation candidate has no usable variation " + f"({context})", + ) + if target_values < 2: + raise JudgeFailure( + "factor.temporal-primary-target-variation", + "Primary temporal validation outcome has no usable variation " + f"({context})", + ) + + +def _relative_value_spread_panel( + panel: pd.DataFrame, + prediction_assets: list[str], +) -> pd.DataFrame: + """Reduce one authorized pair to the causal first-minus-second contrast.""" + + if len(prediction_assets) != 2: + raise JudgeFailure( + "prediction-universe.relative-value-pair", + "Relative-value spread construction requires exactly two assets", + ) + left, right = prediction_assets + return pd.DataFrame( + { + f"{left}-minus-{right}": panel[left] - panel[right], + }, + index=panel.index, + ) + + +def _temporal_transform_panels( + candidate: pd.DataFrame, + style: pd.DataFrame, + masks: dict[str, pd.Series], +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Build target-free temporal residual and equal-rank blend panels.""" + + residual = pd.DataFrame(index=candidate.index, columns=candidate.columns) + blend = pd.DataFrame(index=candidate.index, columns=candidate.columns) + column = candidate.columns[0] + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + pair = pd.DataFrame( + { + "candidate": candidate[column].reindex(index), + "style": style[column].reindex(index), + } + ).dropna() + if pair.empty: + continue + ranks = pair.rank(method="average", pct=True) + candidate_centered = ranks["candidate"] - float( + ranks["candidate"].mean() + ) + style_centered = ranks["style"] - float(ranks["style"].mean()) + denominator = float((style_centered**2).sum()) + beta = ( + float((candidate_centered * style_centered).sum()) / denominator + if denominator > 1e-12 + else 0.0 + ) + residual.loc[pair.index, column] = ( + candidate_centered - beta * style_centered + ) + blend.loc[pair.index, column] = ( + ranks["candidate"] + ranks["style"] + ) / 2.0 + return residual.astype(float), blend.astype(float) + + +def _temporal_split_metrics( + values: pd.Series, + *, + horizon: int, + minimum_observations: int = MIN_IC_DATES_PER_SPLIT, +) -> dict[str, Any]: + clean = values.dropna().astype(float) + result = descriptive_ic( + clean, + minimum_observations=minimum_observations, + ) + result["hac"] = hac_inference(clean, maximum_lag=max(1, int(horizon))) + if len(clean) < minimum_observations: + result["hac"].update( + { + "standard_error": None, + "t_statistic": None, + "normal_approximation_p_value": None, + } + ) + return result + + +def _style_summary(values: pd.Series) -> dict[str, float | int | None]: + clean = values.dropna().astype(float) + if len(clean) < 3: + return { + "mean_rank_correlation": None, + "mean_absolute_rank_correlation": None, + "observations": int(len(clean)), + } + return { + "mean_rank_correlation": float(clean.mean()), + "mean_absolute_rank_correlation": float(clean.abs().mean()), + "observations": int(len(clean)), + } + + +def _count_summary(values: pd.Series) -> dict[str, float | int]: + clean = values.astype(int) + return { + "minimum": int(clean.min()), + "median": float(clean.median()), + "maximum": int(clean.max()), + } + + +def _equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + *, + common_available: pd.DataFrame | None = None, +) -> pd.DataFrame: + names = list(panels) + first = panels[names[0]] + if common_available is None: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for panel in panels.values(): + common_available &= panel.notna() + ranks = [ + panel.rank(axis=1, method="average", pct=True) + for panel in panels.values() + ] + return (sum(ranks) / float(len(ranks))).where(common_available) + + +def _temporal_equal_rank_component_blend( + panels: dict[str, pd.DataFrame], + masks: dict[str, pd.Series], + *, + common_available: pd.Series | None = None, +) -> pd.DataFrame: + """Build one target-free equal-rank blend within each fixed split.""" + + names = list(panels) + first = panels[names[0]] + column = first.columns[0] + if common_available is None: + common_available = pd.Series(True, index=first.index) + for panel in panels.values(): + common_available &= panel.iloc[:, 0].notna() + blend = pd.DataFrame(index=first.index, columns=[column], dtype=float) + for split in ("train", "validation", "test"): + index = masks[split].index[masks[split]] + values = pd.DataFrame( + { + name: panels[name].iloc[:, 0].reindex(index) + for name in names + } + ) + ranks = values.rank(method="average", pct=True) + selected = ranks.mean(axis=1).where( + common_available.reindex(index).fillna(False) + ) + blend.loc[selected.index, column] = selected + return blend.astype(float) + + +def _context_distribution(values: pd.Series) -> dict[str, Any]: + clean = values.dropna().astype(float) + if clean.empty: + return { + "observations": 0, + "mean": None, + "standard_deviation": None, + "minimum": None, + "quartile_25": None, + "median": None, + "quartile_75": None, + "maximum": None, + } + return { + "observations": int(len(clean)), + "mean": float(clean.mean()), + "standard_deviation": ( + float(clean.std(ddof=1)) if len(clean) > 1 else 0.0 + ), + "minimum": float(clean.min()), + "quartile_25": float(clean.quantile(0.25)), + "median": float(clean.median()), + "quartile_75": float(clean.quantile(0.75)), + "maximum": float(clean.max()), + } + + +def _timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Diagnose one cross-section-constant causal market-state component.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_daily = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _component_split_metrics( + _masked( + factor_daily[horizon], + split_masks[horizon][split] + & states.eq(state), + ) + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-timestamp-context-v1", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _temporal_timestamp_context_evidence( + panel: pd.DataFrame, + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], +) -> dict[str, Any]: + """Condition temporal Factor correlation contributions on fixed states.""" + + values = panel.bfill(axis=1).iloc[:, 0].astype(float) + train = _masked(values, split_masks[PRIMARY_HORIZON]["train"]) + if train.empty: + raise JudgeFailure( + "factor.component-context-train", + "Timestamp-context component has no finite training observation", + ) + lower = float(train.quantile(1.0 / 3.0)) + upper = float(train.quantile(2.0 / 3.0)) + states = pd.Series("middle", index=values.index, dtype="object") + states.loc[values <= lower] = "low" + states.loc[values > upper] = "high" + states.loc[values.isna()] = "unavailable" + factor_contributions = { + horizon: _temporal_daily( + factor_panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + split_evidence: dict[str, Any] = {} + for split in ("train", "validation", "test"): + primary_mask = split_masks[PRIMARY_HORIZON][split] + selected_values = values.reindex( + primary_mask.index[primary_mask] + ) + selected_states = states.reindex( + primary_mask.index[primary_mask] + ) + available_states = selected_states[ + selected_states.ne("unavailable") + ] + observations = int(len(available_states)) + occupancy = { + state: { + "observations": int(available_states.eq(state).sum()), + "rate": ( + float(available_states.eq(state).mean()) + if observations + else None + ), + } + for state in ("low", "middle", "high") + } + transition_observations = max(observations - 1, 0) + transitions = ( + int( + available_states.ne( + available_states.shift(1) + ).iloc[1:].sum() + ) + if transition_observations + else 0 + ) + split_evidence[split] = { + "distribution": _context_distribution(selected_values), + "state_occupancy": occupancy, + "transitions": { + "observations": transition_observations, + "changes": transitions, + "rate": ( + float(transitions / transition_observations) + if transition_observations + else None + ), + }, + "conditional_factor_horizon_quality": { + str(horizon): { + state: _temporal_split_metrics( + _masked( + factor_contributions[horizon], + split_masks[horizon][split] + & states.eq(state), + ), + horizon=horizon, + minimum_observations=3, + ) + for state in ("low", "middle", "high") + } + for horizon in HORIZONS + }, + } + return { + "method": "train-tertile-temporal-context-v2", + "state_selection": { + "split": "train", + "target_enters_thresholds": False, + "lower": lower, + "upper": upper, + "labels": ["low", "middle", "high"], + }, + "splits": split_evidence, + "conditional_measure": ( + "within-split-temporal-rank-correlation-contribution" + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + "trading_authority": "none", + } + + +def _component_evidence( + declarations: list[dict[str, Any]], + component_panels: dict[str, pd.DataFrame], + factor_panel: pd.DataFrame, + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + coverage: dict[str, dict[str, float]], + evaluation_mode: str, + outcome_contract: dict[str, Any], +) -> dict[str, Any]: + """Build target-fixed diagnostics for candidate-declared components.""" + + temporal = evaluation_mode in TEMPORAL_EVALUATION_MODES + metadata_by_name = {item["id"]: item for item in declarations} + all_names = list(component_panels) + names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "cross-sectional-score" + ] + context_names = [ + name + for name in all_names + if metadata_by_name[name]["role"] == "timestamp-context" + ] + score_panels = { + name: ( + _relative_value_spread_panel( + component_panels[name], + list(component_panels[name].columns), + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else component_panels[name].iloc[:, :1] + if temporal + else component_panels[name] + ) + for name in names + } + + def target_daily( + panel: pd.DataFrame, + horizon: int, + *, + constant_left_value: float | None = None, + ) -> pd.Series: + if temporal: + return _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=constant_left_value, + ) + return daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=constant_left_value, + ) + + def split_quality( + daily: pd.Series, + horizon: int, + split: str, + ) -> dict[str, Any]: + selected = _masked(daily, split_masks[horizon][split]) + return ( + _temporal_split_metrics(selected, horizon=horizon) + if temporal + else _component_split_metrics(selected) + ) + + def association_daily( + left: pd.DataFrame, + right: pd.DataFrame, + ) -> pd.Series: + if temporal: + return _temporal_daily( + left, + right, + split_masks[PRIMARY_HORIZON], + rank=True, + ) + return daily_rank_correlation( + left, + right, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + + raw_daily = { + name: { + horizon: target_daily(score_panels[name], horizon) + for horizon in HORIZONS + } + for name in names + } + raw_quality = { + name: { + str(horizon): { + split: split_quality( + raw_daily[name][horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + for name in names + } + composite_association_daily = { + name: association_daily(score_panels[name], factor_panel) + for name in names + } + composite_association = { + name: { + split: _style_summary( + _masked( + composite_association_daily[name], + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + } + for name in names + } + + pair_daily: dict[frozenset[str], pd.Series] = {} + pairwise: list[dict[str, Any]] = [] + for left_index, left in enumerate(names): + for right in names[left_index + 1 :]: + daily = association_daily( + score_panels[left], + score_panels[right], + ) + pair_daily[frozenset((left, right))] = daily + pairwise.append( + { + "left": left, + "right": right, + "splits": { + split: _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON][split], + ) + ) + for split in ("train", "validation", "test") + }, + } + ) + + nearest_peers: dict[str, str | None] = {} + for name in names: + candidates: list[tuple[str, float]] = [] + for pair, daily in pair_daily.items(): + if name not in pair: + continue + peer = next(item for item in pair if item != name) + summary = _style_summary( + _masked( + daily, + split_masks[PRIMARY_HORIZON]["train"], + ) + ) + absolute = summary["mean_absolute_rank_correlation"] + if absolute is not None: + candidates.append((peer, float(absolute))) + nearest_peers[name] = ( + min(candidates, key=lambda item: (-item[1], item[0]))[0] + if candidates + else None + ) + + residual_quality: dict[str, Any] = {} + for name in names: + peer = nearest_peers[name] + if peer is None: + residual_quality[name] = { + "peer": None, + "selection": ( + "unavailable-single-component" + if len(names) == 1 + else "unavailable-no-finite-train-peer" + ), + "horizon_quality": None, + } + continue + residual = ( + _temporal_transform_panels( + score_panels[name], + score_panels[peer], + split_masks[PRIMARY_HORIZON], + )[0] + if temporal + else cross_sectional_rank_residual( + score_panels[name], + score_panels[peer], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + residual_daily = { + horizon: target_daily( + residual, + horizon, + constant_left_value=0.0, + ) + for horizon in HORIZONS + } + residual_quality[name] = { + "peer": peer, + "selection": ( + "maximum-absolute-train-temporal-rank-association" + if temporal + else "maximum-absolute-mean-train-daily-rank-association" + ), + "horizon_quality": { + str(horizon): { + split: split_quality( + residual_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + }, + } + + common_available: pd.DataFrame | pd.Series | None = None + full_blend_quality: dict[str, Any] | None = None + if names: + first = score_panels[names[0]] + if temporal: + common_available = pd.Series(True, index=first.index) + for name in names: + common_available &= score_panels[name].iloc[:, 0].notna() + full_blend = _temporal_equal_rank_component_blend( + {name: score_panels[name] for name in names}, + split_masks[PRIMARY_HORIZON], + common_available=common_available, + ) + else: + common_available = pd.DataFrame( + True, + index=first.index, + columns=first.columns, + ) + for name in names: + common_available &= score_panels[name].notna() + full_blend = _equal_rank_component_blend( + {name: score_panels[name] for name in names}, + common_available=common_available, + ) + full_blend_daily = { + horizon: target_daily(full_blend, horizon) + for horizon in HORIZONS + } + full_blend_quality = { + str(horizon): { + split: split_quality( + full_blend_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + ablations: dict[str, Any] = {} + for name in names: + remaining = { + candidate: score_panels[candidate] + for candidate in names + if candidate != name + } + if not remaining: + ablations[name] = { + "available": False, + "reason": "single-component", + "horizon_quality": None, + "removal_delta_mean_ic": None, + } + continue + leave_one_out = ( + _temporal_equal_rank_component_blend( + remaining, + split_masks[PRIMARY_HORIZON], + # Score-only common availability is intentionally fixed + # before leave-one-out so every ablation uses one population. + common_available=common_available, + ) + if temporal + else _equal_rank_component_blend( + remaining, + common_available=common_available, + ) + ) + leave_daily = { + horizon: target_daily(leave_one_out, horizon) + for horizon in HORIZONS + } + quality = { + str(horizon): { + split: split_quality( + leave_daily[horizon], + horizon, + split, + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + removal_delta: dict[str, float | None] = {} + for split in ("train", "validation", "test"): + primary = str(PRIMARY_HORIZON) + leave_mean = quality[primary][split]["mean_ic"] + full_mean = full_blend_quality[primary][split]["mean_ic"] + removal_delta[split] = ( + float(leave_mean) - float(full_mean) + if leave_mean is not None and full_mean is not None + else None + ) + ablations[name] = { + "available": True, + "reason": None, + "horizon_quality": quality, + "removal_delta_mean_ic": removal_delta, + } + + component_rows: list[dict[str, Any]] = [] + for name in names: + primary = str(PRIMARY_HORIZON) + raw_validation = raw_quality[name][primary]["validation"]["mean_ic"] + residual = residual_quality[name] + residual_validation = ( + residual["horizon_quality"][primary]["validation"]["mean_ic"] + if residual["horizon_quality"] is not None + else None + ) + removal_delta = ( + ablations[name]["removal_delta_mean_ic"]["validation"] + if ablations[name]["available"] + else None + ) + peer = nearest_peers[name] + train_redundancy = None + if peer is not None: + train_redundancy = _style_summary( + _masked( + pair_daily[frozenset((name, peer))], + split_masks[PRIMARY_HORIZON]["train"], + ) + )["mean_absolute_rank_correlation"] + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": raw_quality[name], + "composite_association": composite_association[name], + "nearest_peer": { + "id": peer, + "train_mean_absolute_rank_association": train_redundancy, + }, + "nearest_peer_residual": residual, + "fixed_blend_ablation": ablations[name], + "timestamp_context": None, + "validation_priority_inputs": { + "raw_mean_ic": raw_validation, + "nearest_peer_residual_mean_ic": residual_validation, + "removal_delta_mean_ic": removal_delta, + }, + } + ) + for name in context_names: + component_rows.append( + { + **metadata_by_name[name], + "coverage_by_asset": coverage[name], + "mean_coverage": float( + sum(coverage[name].values()) / len(coverage[name]) + ), + "raw_horizon_quality": None, + "composite_association": None, + "nearest_peer": { + "id": None, + "train_mean_absolute_rank_association": None, + }, + "nearest_peer_residual": { + "peer": None, + "selection": "not-applicable-timestamp-context", + "horizon_quality": None, + }, + "fixed_blend_ablation": { + "available": False, + "reason": "not-applicable-timestamp-context", + "horizon_quality": None, + "removal_delta_mean_ic": None, + }, + "timestamp_context": ( + _temporal_timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + if temporal + else _timestamp_context_evidence( + component_panels[name], + factor_panel, + outcome_panels, + split_masks, + ) + ), + "validation_priority_inputs": { + "raw_mean_ic": None, + "nearest_peer_residual_mean_ic": None, + "removal_delta_mean_ic": None, + }, + } + ) + + raw_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"]["raw_mean_ic"] is not None + ] + strongest_raw = ( + max( + raw_candidates, + key=lambda row: ( + float(row["validation_priority_inputs"]["raw_mean_ic"]), + row["id"], + ), + ) + if raw_candidates + else None + ) + residual_candidates = [ + row + for row in component_rows + if row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] is not None + ] + strongest_residual = ( + max( + residual_candidates, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + ), + row["id"], + ), + ) + if residual_candidates + else None + ) + removable = [ + row + for row in component_rows + if row["validation_priority_inputs"]["removal_delta_mean_ic"] + is not None + ] + best_removal = ( + max( + removable, + key=lambda row: ( + float( + row["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + ), + row["id"], + ), + ) + if removable + else None + ) + finite_pairs = [ + row + for row in pairwise + if row["splits"]["train"]["mean_absolute_rank_correlation"] + is not None + ] + most_redundant = ( + max( + finite_pairs, + key=lambda row: ( + float( + row["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + or -1.0 + ), + row["left"], + row["right"], + ), + ) + if finite_pairs + else None + ) + return { + "method": "candidate-declared-components-v3", + "declaration": { + "exhaustive_composition_claim": False, + "source_inference": False, + "components": declarations, + }, + "semantics": { + "evaluation_mode": evaluation_mode, + "prediction_target": outcome_contract["targetSemantics"], + "score_measure": ( + "within-split-temporal-rank-correlation-contribution" + if temporal + else "per-date-cross-sectional-rank-ic" + ), + "component_roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "nearest_peer_selection": "train-only-target-free", + "residualization": ( + "within-split-temporal-centered-rank-ols" + if temporal + else "same-timestamp-cross-sectional-centered-rank-ols" + ), + "diagnostic_blend": ( + "equal-weight-within-split-temporal-percentile-ranks-with-" + "common-component-availability" + if temporal + else "equal-weight-cross-sectional-percentile-ranks-with-" + "common-component-availability" + ), + "ablation_target": "fixed-diagnostic-blend-not-candidate-factor", + "timestamp_context": ( + "train-tertile-occupancy-transition-and-conditional-temporal-" + "rank-correlation-contribution" + if temporal + else "train-tertile-occupancy-transition-and-conditional-" + "factor-ic" + ), + "selection_authority": "research-prioritization-only", + "test_role": "visible-audit", + "promotion_authority": "none", + "portfolio_authority": "none", + "rl_action_authority": "none", + "trading_authority": "none", + }, + "trial_disclosure": { + "materialized_components": len(all_names), + "cross_sectional_score_components": len(names), + "timestamp_context_components": len(context_names), + "pairwise_comparisons": len(pairwise), + "component_diagnostics_enter_promotion_score": False, + }, + "components": component_rows, + "pairwise": pairwise, + "fixed_blend": { + "available": full_blend_quality is not None, + "reason": ( + None + if full_blend_quality is not None + else "no-cross-sectional-score-components" + ), + "horizon_quality": full_blend_quality, + }, + "validation_diagnosis": { + "strongest_raw_component": ( + strongest_raw["id"] if strongest_raw is not None else None + ), + "strongest_raw_mean_ic": ( + strongest_raw["validation_priority_inputs"]["raw_mean_ic"] + if strongest_raw is not None + else None + ), + "strongest_residual_component": ( + strongest_residual["id"] + if strongest_residual is not None + else None + ), + "strongest_residual_mean_ic": ( + strongest_residual["validation_priority_inputs"][ + "nearest_peer_residual_mean_ic" + ] + if strongest_residual is not None + else None + ), + "removal_most_improves_fixed_blend": ( + best_removal["id"] if best_removal is not None else None + ), + "best_removal_delta_mean_ic": ( + best_removal["validation_priority_inputs"][ + "removal_delta_mean_ic" + ] + if best_removal is not None + else None + ), + "most_redundant_pair": ( + { + "left": most_redundant["left"], + "right": most_redundant["right"], + "train_mean_absolute_rank_association": ( + most_redundant["splits"]["train"][ + "mean_absolute_rank_correlation" + ] + ), + } + if most_redundant is not None + else None + ), + "authority": "research-prioritization-only", + "test_enters_diagnosis": False, + }, + } + + +def _decay_summary( + horizon_metrics: dict[str, dict[str, dict[str, Any]]], +) -> dict[str, dict[str, Any]]: + result: dict[str, dict[str, Any]] = {} + for split in ("train", "validation", "test"): + means = { + horizon: horizon_metrics[horizon][split]["mean_ic"] + for horizon in (str(item) for item in HORIZONS) + } + primary = means[str(PRIMARY_HORIZON)] + ratios: dict[str, float | None] = {} + for horizon in (str(item) for item in HORIZONS): + if horizon == str(PRIMARY_HORIZON): + continue + value = means[horizon] + ratios[ + f"horizon_{horizon}_to_{PRIMARY_HORIZON}" + ] = ( + float(value) / float(primary) + if value is not None + and primary is not None + and abs(float(primary)) > 1e-12 + else None + ) + result[split] = { + "mean_ic_by_horizon": means, + **ratios, + } + return result + + +def _factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Build request-claim-aware style comparison and qualification evidence.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name][ + "observations" + ], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only style overlap is available", + ) + dominant_style = ( + factor_claim["knownStyle"] + if factor_claim["claim"] == "known-style-validation" + else min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + ) + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel = cross_sectional_rank_residual( + factor_panel, + style_panel, + minimum_assets=MIN_ASSETS_PER_DATE, + ) + blend_panel = equal_rank_blend(factor_panel, style_panel) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: daily_rank_correlation( + panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ) + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + residual_folds = { + name: descriptive_ic( + _masked( + daily["style_neutral_candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + candidate_folds = { + name: descriptive_ic( + _masked( + daily["candidate"][PRIMARY_HORIZON], + mask, + ) + ) + for name, mask in fold_masks.items() + } + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": "request-claim-aware-one-style-rank-neutralization-v2", + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": ( + "request-predeclared-known-style" + if factor_claim["claim"] == "known-style-validation" + else "maximum-absolute-mean-daily-rank-overlap" + ), + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": ( + "same-timestamp-cross-sectional-centered-rank-ols" + ), + "blend": "equal-weight-cross-sectional-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _temporal_factor_qualification( + factor_panel: pd.DataFrame, + styles: dict[str, pd.DataFrame], + outcome_panels: dict[int, pd.DataFrame], + split_masks: dict[int, dict[str, pd.Series]], + fold_masks: dict[str, pd.Series], + split_labels: pd.Series, + style_correlations: dict[str, dict[str, Any]], + factor_claim: dict[str, Any], +) -> tuple[dict[str, Any], pd.DataFrame]: + """Qualify one request-authorized asset by association across time.""" + + candidates = { + name: { + "mean_rank_correlation": style_correlations["train"][name][ + "mean_rank_correlation" + ], + "mean_absolute_rank_correlation": style_correlations["train"][ + name + ]["mean_absolute_rank_correlation"], + "observations": style_correlations["train"][name]["observations"], + } + for name in STYLE_NAMES + } + finite = [ + (name, value["mean_rank_correlation"]) + for name, value in candidates.items() + if value["mean_rank_correlation"] is not None + ] + if not finite: + raise JudgeFailure( + "factor.qualification-style", + "No finite train-only temporal style overlap is available", + ) + dominant_style = min( + finite, + key=lambda item: (-abs(float(item[1])), item[0]), + )[0] + style_panel = styles[dominant_style].reindex_like(factor_panel) + residual_panel, blend_panel = _temporal_transform_panels( + factor_panel, + style_panel, + split_masks[PRIMARY_HORIZON], + ) + panels = { + "candidate": factor_panel, + "dominant_style": style_panel, + "style_neutral_candidate": residual_panel, + "equal_rank_blend": blend_panel, + } + daily = { + signal: { + horizon: _temporal_daily( + panel, + outcome_panels[horizon], + split_masks[horizon], + rank=True, + constant_left_value=( + 0.0 + if signal == "style_neutral_candidate" + else None + ), + ) + for horizon in HORIZONS + } + for signal, panel in panels.items() + } + horizon_quality = { + str(horizon): { + split: { + signal: _temporal_split_metrics( + _masked( + daily[signal][horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + for signal in panels + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + def fold_quality(panel: pd.DataFrame) -> dict[str, dict[str, Any]]: + return { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + panel.iloc[:, 0], + outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + constant_left_value=( + 0.0 if panel is residual_panel else None + ), + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + + candidate_folds = fold_quality(factor_panel) + residual_folds = fold_quality(residual_panel) + evidence = pd.DataFrame( + { + "split": split_labels, + "dominant_style": dominant_style, + }, + index=factor_panel.index, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + for signal in panels: + evidence[f"{signal}_rank_ic_h{horizon}"] = ( + daily[signal][horizon] + .reindex(factor_panel.index) + .where(eligible) + ) + evidence.index.name = "timestamp" + return { + "method": TEMPORAL_QUALIFICATION_METHOD, + "claim": factor_claim, + "selection": { + "split": "train", + "criterion": "maximum-absolute-mean-temporal-rank-overlap", + "dominant_style": dominant_style, + "candidates": candidates, + "validation_enters_selection": False, + "test_enters_selection": False, + }, + "semantics": { + "neutralization": "within-split-temporal-centered-rank-ols", + "blend": "equal-weight-within-split-temporal-percentile-ranks", + "target_enters_neutralization": False, + "selection_authority": "research-context-only", + "trading_authority": "none", + }, + "horizon_quality": horizon_quality, + "stability": { + "candidate_chronological_folds": candidate_folds, + "style_neutral_chronological_folds": residual_folds, + }, + }, evidence + + +def _evaluate() -> tuple[ + dict[str, Any], + dict[str, Any], + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, Any] | None, +]: + global HORIZONS, PRIMARY_HORIZON + study, data_root = _load_contract() + research_horizon = _load_horizon() + factor_claim = _load_factor_claim() + outcome_kind = factor_outcome(factor_claim) + outcome_contract = factor_outcome_contract(factor_claim) + HORIZONS = tuple(research_horizon["diagnosticForwardBars"]) + PRIMARY_HORIZON = int(research_horizon["primaryForwardBars"]) + dataset = study["dataset"] + universe = dataset["universe"] + ( + prediction_population, + prediction_assets, + context_assets, + prediction_authority, + evaluation_mode, + ) = _load_prediction_universe(universe, factor_claim) + if ( + outcome_kind != FORWARD_RETURN_OUTCOME + and evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + ): + raise JudgeFailure( + "factor.outcome-relative-value", + "Forward realized volatility does not define a two-asset " + "relative-value target contrast", + ) + minimum_evaluation_assets = ( + 1 + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + 2 + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else MIN_ASSETS_PER_DATE + ) + ) + time_range = dataset["time_range"] + module = importlib.import_module("factors.candidate") + frames: dict[str, pd.DataFrame] = {} + close_by_asset: dict[str, pd.Series] = {} + volume_by_asset: dict[str, pd.Series] = {} + for asset in universe: + frame = _load_asset( + data_root, + asset, + time_range["start"], + time_range["end"], + ) + frames[asset] = frame + timestamp = pd.DatetimeIndex(frame["timestamp"]) + close = frame["close"].copy() + close.index = timestamp + volume = frame["volume"].copy() + volume.index = timestamp + close_by_asset[asset] = close + volume_by_asset[asset] = volume + + try: + panel = build_factor_panel(frames, universe=universe) + factor_evaluation = evaluate_factor(module, panel) + factor_panel = values_to_wide( + panel, + factor_evaluation.values, + universe=universe, + ) + except FactorRuntimeError as error: + raise JudgeFailure(error.code, str(error)) from error + coverage = { + asset: float( + factor_evaluation.values.loc[panel["asset"] == asset].notna().mean() + ) + for asset in universe + } + source_factor_panel = factor_panel + source_close_panel = pd.DataFrame(close_by_asset).reindex( + source_factor_panel.index + ) + source_volume_panel = pd.DataFrame(volume_by_asset).reindex( + source_factor_panel.index + ) + research_factor_panel = source_factor_panel + research_close_panel = source_close_panel + research_volume_panel = source_volume_panel + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE: + prediction_timeline = pd.DatetimeIndex( + close_by_asset[prediction_assets[0]].index + ) + research_factor_panel = research_factor_panel.reindex( + prediction_timeline + ) + research_close_panel = research_close_panel.reindex( + prediction_timeline + ) + research_volume_panel = research_volume_panel.reindex( + prediction_timeline + ) + factor_panel = research_factor_panel[prediction_assets] + close_panel = research_close_panel[prediction_assets] + timeline = pd.DatetimeIndex(research_factor_panel.index) + split_masks, split_protocol, base_split_labels = purged_split_masks( + timeline, + HORIZONS, + ) + fold_masks, fold_protocol = chronological_fold_masks( + timeline, + PRIMARY_HORIZON, + ) + outcome_panels = factor_outcome_panels( + close_panel, + outcome_kind, + HORIZONS, + ) + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE: + association_factor_panel = _relative_value_spread_panel( + factor_panel, + prediction_assets, + ) + association_outcome_panels = { + horizon: _relative_value_spread_panel( + outcome_panels[horizon], + prediction_assets, + ) + for horizon in HORIZONS + } + else: + association_factor_panel = factor_panel + association_outcome_panels = outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + _preflight_temporal_primary_validation( + association_factor_panel, + association_outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON]["validation"], + evaluation_mode=evaluation_mode, + horizon=PRIMARY_HORIZON, + ) + source_input_counts = source_close_panel.notna().sum(axis=1).astype(int) + source_factor_counts = source_factor_panel.notna().sum(axis=1).astype(int) + input_counts = close_panel.notna().sum(axis=1).astype(int) + factor_counts = factor_panel.notna().sum(axis=1).astype(int) + paired_counts = { + horizon: ( + factor_panel.notna() & outcome_panels[horizon].notna() + ).sum(axis=1).astype(int) + for horizon in HORIZONS + } + source_timeline = pd.DatetimeIndex(source_factor_panel.index) + source_paired_counts = { + horizon: paired_counts[horizon] + .reindex(source_timeline) + .fillna(0) + .astype(int) + for horizon in HORIZONS + } + possible_rows = int(len(source_timeline) * len(universe)) + observed_rows = int(source_input_counts.sum()) + prediction_possible_rows = int(len(timeline) * len(prediction_assets)) + prediction_observed_rows = int(input_counts.sum()) + input_availability = { + "method": "observed-only-no-fill-v1", + "missing_observation": "absent-no-fill", + "timestamps": int(len(source_timeline)), + "observed_rows": observed_rows, + "possible_rows": possible_rows, + "observation_coverage": float( + observed_rows / possible_rows + ), + "complete_timestamps": int( + source_input_counts.eq(len(universe)).sum() + ), + "prediction_universe": { + "authority": prediction_authority, + "assets": prediction_assets, + "context_assets": context_assets, + "observed_rows": prediction_observed_rows, + "possible_rows": prediction_possible_rows, + "observation_coverage": float( + prediction_observed_rows / prediction_possible_rows + ), + "complete_timestamps": int( + input_counts.eq(len(prediction_assets)).sum() + ), + "timeline_timestamps": int(len(timeline)), + }, + "eligible_factor_timestamps": { + str(horizon): int( + paired_counts[horizon] + .ge(minimum_evaluation_assets) + .sum() + ) + for horizon in HORIZONS + }, + "minimum_assets_per_factor_timestamp": minimum_evaluation_assets, + "assets_per_timestamp": { + "input": _count_summary(source_input_counts), + "factor": _count_summary(source_factor_counts), + "primary_pair": _count_summary( + source_paired_counts[PRIMARY_HORIZON] + ), + }, + "by_asset": { + asset: { + "observations": int( + source_close_panel[asset].notna().sum() + ), + "start": timestamp_label( + source_close_panel[asset].dropna().index[0] + ), + "end": timestamp_label( + source_close_panel[asset].dropna().index[-1] + ), + "input_coverage": float( + source_close_panel[asset].notna().mean() + ), + "factor_coverage": coverage[asset], + } + for asset in universe + }, + } + components = factor_evaluation.components + component_declarations = ( + components.declaration() if components is not None else None + ) + component_panels = ( + { + name: values_to_wide( + panel, + components.values[name], + universe=universe, + )[prediction_assets] + for name in components.values.columns + } + if components is not None + else {} + ) + component_coverage = ( + { + name: { + asset: float( + components.values.loc[ + panel["asset"] == asset, + name, + ].notna().mean() + ) + for asset in universe + } + for name in components.values.columns + } + if components is not None + else {} + ) + component_evidence = ( + _component_evidence( + component_declarations, + component_panels, + ( + association_factor_panel + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else factor_panel + ), + ( + association_outcome_panels + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else outcome_panels + ), + split_masks, + component_coverage, + evaluation_mode, + outcome_contract, + ) + if component_declarations is not None + else None + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES: + daily_ic_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=True, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: _temporal_daily( + association_factor_panel, + association_outcome_panels[horizon], + split_masks[horizon], + rank=False, + ) + for horizon in HORIZONS + } + else: + daily_ic_by_horizon = { + horizon: daily_rank_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + daily_pearson_by_horizon = { + horizon: daily_pearson_correlation( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + horizon_metrics = { + str(horizon): { + split: { + **( + _temporal_split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_ic_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + "pearson_ic": ( + _temporal_split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ), + horizon=horizon, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _split_metrics( + _masked( + daily_pearson_by_horizon[horizon], + split_masks[horizon][split], + ) + ) + ), + } + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + splits = horizon_metrics[str(PRIMARY_HORIZON)] + validation_mean_ic = float(splits["validation"]["mean_ic"]) + + quantile_daily = ( + { + horizon: pd.DataFrame( + columns=["low", "middle", "high", "high_minus_low"], + index=pd.DatetimeIndex([], name="timestamp"), + dtype=float, + ) + for horizon in HORIZONS + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + horizon: daily_quantile_outcomes( + factor_panel, + outcome_panels[horizon], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + for horizon in HORIZONS + } + ) + quantile_analysis = { + str(horizon): { + split: quantile_summary( + quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ) + ) + for split in ("train", "validation", "test") + } + for horizon in HORIZONS + } + + primary_ic = daily_ic_by_horizon[PRIMARY_HORIZON] + chronological_folds = ( + { + name: _temporal_split_metrics( + _temporal_correlation_contributions( + association_factor_panel.iloc[:, 0], + association_outcome_panels[PRIMARY_HORIZON].iloc[:, 0], + mask, + rank=True, + ), + horizon=PRIMARY_HORIZON, + ) + for name, mask in fold_masks.items() + } + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else { + name: descriptive_ic(_masked(primary_ic, mask)) + for name, mask in fold_masks.items() + } + ) + regimes = causal_regime_labels(research_close_panel) + regime_stability = { + split: { + regime: descriptive_ic( + _masked( + primary_ic, + split_masks[PRIMARY_HORIZON][split] + & regimes.eq(regime).fillna(False), + ) + ) + for regime in REGIME_NAMES + } + for split in ("train", "validation", "test") + } + prediction_style_panels = { + name: values[prediction_assets] + for name, values in style_proxy_panels( + research_close_panel, + research_volume_panel, + ).items() + } + styles = ( + { + name: _relative_value_spread_panel( + values, + prediction_assets, + ) + for name, values in prediction_style_panels.items() + } + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else prediction_style_panels + ) + style_correlations: dict[str, dict[str, Any]] = { + split: {} + for split in ("train", "validation", "test") + } + for style in STYLE_NAMES: + daily_style = ( + _temporal_daily( + association_factor_panel, + styles[style], + split_masks[PRIMARY_HORIZON], + rank=True, + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else daily_rank_correlation( + factor_panel, + styles[style], + minimum_assets=MIN_ASSETS_PER_DATE, + ) + ) + for split in style_correlations: + style_correlations[split][style] = _style_summary( + _masked( + daily_style, + split_masks[PRIMARY_HORIZON][split], + ) + ) + qualification_builder = ( + _temporal_factor_qualification + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else _factor_qualification + ) + factor_qualification, qualification_evidence = qualification_builder( + association_factor_panel, + styles, + association_outcome_panels, + split_masks, + fold_masks, + base_split_labels, + style_correlations, + factor_claim, + ) + per_asset_stability = { + split: per_asset_rank_correlation( + factor_panel, + outcome_panels[PRIMARY_HORIZON], + split_masks[PRIMARY_HORIZON][split], + ) + for split in ("train", "validation", "test") + } + + ranked = ( + association_factor_panel.rank(method="average", pct=True) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else association_factor_panel.rank(axis=1, pct=True) + ) + turnover = float(ranked.diff().abs().mean(axis=1).dropna().mean()) + metrics = { + "validation_mean_ic": validation_mean_ic, + "factor_api": factor_contract(factor_evaluation), + "research_horizon": research_horizon, + "factor_claim": factor_claim, + "factor_outcome": outcome_contract, + "prediction_universe": prediction_population, + "train": splits["train"], + "validation": splits["validation"], + "test": splits["test"], + "horizon_quality": horizon_metrics, + "factor_decay": _decay_summary(horizon_metrics), + "quantile_analysis": quantile_analysis, + "stability": { + "chronological_folds": chronological_folds, + "causal_regimes": regime_stability, + "per_asset": per_asset_stability, + }, + "style_correlations": style_correlations, + "factor_qualification": factor_qualification, + "split_protocol": { + **split_protocol, + "folds": fold_protocol, + }, + "mean_coverage": float(sum(coverage.values()) / len(coverage)), + "input_availability": input_availability, + "mean_rank_turnover": turnover, + "assets": int(len(universe)), + "prediction_assets": int(len(prediction_assets)), + "ic_dates": int(len(primary_ic)), + "research_integrity": { + "selection_split": "validation", + "test_role": "visible-diagnostic", + "test_enters_selection": False, + "external_holdout_rule": ( + "required-after-visible-test-and-candidate-iteration" + ), + }, + } + if component_evidence is not None: + metrics["factor_components"] = component_evidence + if not all( + math.isfinite(float(value)) + for value in ( + validation_mean_ic, + metrics["mean_coverage"], + metrics["mean_rank_turnover"], + ) + ): + raise JudgeFailure("judge.non-finite", "Judge produced non-finite metrics") + report = { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "dataset": { + "id": dataset["id"], + "version": dataset["version"], + "universe": universe, + "predictionAssets": prediction_assets, + "contextAssets": context_assets, + "timeRange": time_range, + }, + "researchHorizon": research_horizon, + "semantics": { + "target": outcome_contract["targetSemantics"], + "outcome": outcome_contract, + "measure": ( + ( + "within-split temporal Spearman and Pearson correlation " + "contributions for the single request-authorized asset" + ) + if evaluation_mode == SINGLE_ASSET_TEMPORAL_MODE + else ( + "within-split temporal Spearman and Pearson correlation " + "contributions between the first-minus-second factor " + "contrast and first-minus-second forward-return contrast" + if evaluation_mode == TWO_ASSET_RELATIVE_VALUE_MODE + else ( + "per-date cross-sectional Spearman rank IC and Pearson IC " + f"over the fixed {prediction_authority} evaluation universe" + ) + ) + ), + "researchUniverse": ( + "complete Study universe available to candidate features" + ), + "predictionUniverse": ( + "caller-owned factorPolicy.predictionAssets for request-" + "bound decision-signal claims; complete research universe " + "for novel-factor and known-style-validation claims" + ), + "horizons": list(HORIZONS), + "primaryHorizon": PRIMARY_HORIZON, + "split": ( + "dataset-fixed chronological 60/20/20 with horizon-specific " + "boundary purge" + ), + "score": ( + "validation mean rank IC at the fixed primary " + f"{PRIMARY_HORIZON}-bar horizon only" + ), + "inference": ( + ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag equal to each forward horizon and two-sided normal-" + "approximation p-value" + ) + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else ( + "Newey-West/Bartlett HAC mean t-statistic with maximum " + "lag 5 and two-sided normal-approximation p-value" + ) + ), + "quantiles": ( + "unavailable-for-temporal-evaluation-v1" + if evaluation_mode in TEMPORAL_EVALUATION_MODES + else "fixed low/middle/high cross-sectional groups" + ), + "regimes": ( + "causal trailing market direction and volatility versus " + "lagged rolling threshold" + ), + "styles": list(STYLE_NAMES), + "qualification": { + "method": factor_qualification["method"], + "claim": factor_claim, + "styleSelection": ( + "request-predeclared" + if factor_claim["claim"] == "known-style-validation" + else "train-only" + ), + "neutralization": factor_qualification["semantics"][ + "neutralization" + ], + "blend": factor_qualification["semantics"]["blend"], + "testRole": "visible audit only", + "tradingAuthority": "none", + }, + "components": ( + { + "method": component_evidence["method"], + "evaluationMode": component_evidence["semantics"][ + "evaluation_mode" + ], + "scoreMeasure": component_evidence["semantics"][ + "score_measure" + ], + "predictionTarget": component_evidence["semantics"][ + "prediction_target" + ], + "declaration": "candidate-explicit-not-source-inferred", + "roles": [ + "cross-sectional-score", + "timestamp-context", + ], + "exhaustiveCompositionClaim": False, + "nearestPeerSelection": "train-only-target-free", + "ablationTarget": ( + "fixed-diagnostic-blend-not-candidate-factor" + ), + "residualization": component_evidence["semantics"][ + "residualization" + ], + "diagnosticBlend": component_evidence["semantics"][ + "diagnostic_blend" + ], + "timestampContext": component_evidence["semantics"][ + "timestamp_context" + ], + "testRole": "visible audit only", + "portfolioAuthority": "none", + "rlActionAuthority": "none", + "tradingAuthority": "none", + } + if component_evidence is not None + else None + ), + "testRole": ( + "visible diagnostic evidence; never enters candidate selection" + ), + }, + "causalityAuditCuts": list(factor_evaluation.causality_cuts), + "componentCausalityAuditCuts": ( + list(factor_evaluation.causality_cuts) + if component_evidence is not None + else [] + ), + "coverageByAsset": coverage, + "inputAvailability": input_availability, + "splitProtocol": split_protocol, + "foldProtocol": fold_protocol, + "metrics": metrics, + } + daily_evidence = pd.DataFrame( + { + "split": base_split_labels, + "regime": regimes.fillna("unavailable"), + }, + index=timeline, + ) + for horizon in HORIZONS: + eligible = ( + split_masks[horizon]["train"] + | split_masks[horizon]["validation"] + | split_masks[horizon]["test"] + ) + daily_evidence[f"rank_ic_h{horizon}"] = ( + daily_ic_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence[f"pearson_ic_h{horizon}"] = ( + daily_pearson_by_horizon[horizon].reindex(timeline).where(eligible) + ) + daily_evidence.index.name = "timestamp" + availability_evidence = pd.DataFrame( + { + "input_assets": source_input_counts, + "factor_assets": source_factor_counts, + **{ + f"paired_assets_h{horizon}": source_paired_counts[horizon] + for horizon in HORIZONS + }, + }, + index=source_timeline, + ) + availability_evidence.index.name = "timestamp" + + quantile_rows: list[dict[str, Any]] = [] + for horizon in HORIZONS: + for split in ("train", "validation", "test"): + selected = quantile_daily[horizon].reindex( + split_masks[horizon][split].index[ + split_masks[horizon][split] + ] + ).dropna() + for timestamp, row in selected.iterrows(): + quantile_rows.append( + { + "timestamp": timestamp, + "split": split, + "horizon": horizon, + "low": float(row["low"]), + "middle": float(row["middle"]), + "high": float(row["high"]), + "high_minus_low": float(row["high_minus_low"]), + } + ) + quantile_evidence = pd.DataFrame( + quantile_rows, + columns=[ + "timestamp", + "split", + "horizon", + "low", + "middle", + "high", + "high_minus_low", + ], + ) + return ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + ( + { + "schemaVersion": 1, + "inputHash": os.environ["AUTOQUANT_INPUT_HASH"], + "evidence": component_evidence, + } + if component_evidence is not None + else None + ), + ) + + +def main() -> None: + try: + ( + metrics, + report, + daily_evidence, + quantile_evidence, + qualification_evidence, + availability_evidence, + component_evidence, + ) = _evaluate() + artifacts = Path(os.environ["AUTOQUANT_ARTIFACTS_DIR"]) + report_path = artifacts / "factor-report.json" + report_path.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + daily_artifact = daily_evidence.copy() + daily_artifact.index = [ + timestamp_label(value) for value in daily_artifact.index + ] + daily_artifact.index.name = "timestamp" + quantile_artifact = quantile_evidence.copy() + quantile_artifact["timestamp"] = quantile_artifact["timestamp"].map( + timestamp_label + ) + qualification_artifact = qualification_evidence.copy() + qualification_artifact.index = [ + timestamp_label(value) for value in qualification_artifact.index + ] + qualification_artifact.index.name = "timestamp" + availability_artifact = availability_evidence.copy() + availability_artifact.index = [ + timestamp_label(value) + for value in availability_artifact.index + ] + availability_artifact.index.name = "timestamp" + daily_artifact.to_csv( + artifacts / "daily-factor-evidence.csv", + float_format="%.17g", + ) + quantile_artifact.to_csv( + artifacts / "factor-quantiles.csv", + index=False, + float_format="%.17g", + ) + qualification_artifact.to_csv( + artifacts / "factor-qualification.csv", + float_format="%.17g", + ) + availability_artifact.to_csv( + artifacts / "factor-availability.csv", + ) + if component_evidence is not None: + (artifacts / "factor-components.json").write_text( + json.dumps(component_evidence, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + output_artifacts = [ + { + "kind": "factor-report", + "path": "factor-report.json", + "description": ( + "Factor semantics, purged split protocol, complete " + "tear sheet, coverage, and causality audit" + ), + }, + { + "kind": "factor-daily", + "path": "daily-factor-evidence.csv", + "description": ( + "Timestamped split, causal regime, and purge-aware " + "request-bound outcome rank and Pearson IC" + ), + }, + { + "kind": "factor-quantiles", + "path": "factor-quantiles.csv", + "description": ( + "Timestamped fixed-tertile outcome levels and " + "high-minus-low outcome spread by split and horizon" + ), + }, + { + "kind": "factor-availability", + "path": "factor-availability.csv", + "description": ( + "Per-timestamp observed input, finite factor, and " + "horizon-paired cross-sectional asset counts" + ), + }, + { + "kind": "factor-qualification", + "path": "factor-qualification.csv", + "description": ( + "Train-selected style, candidate/style/residual/" + "blend daily rank IC, and visible-test audit" + ), + }, + ] + if component_evidence is not None: + output_artifacts.append( + { + "kind": "factor-components", + "path": "factor-components.json", + "description": ( + "Candidate-declared component quality, redundancy, " + "nearest-peer residual, and fixed-blend ablation evidence" + ), + } + ) + _write_output( + { + "schema_version": 1, + "status": "succeeded", + "summary": ( + "Causal purge-aware factor tear sheet completed; " + f"{metrics['factor_outcome']['kind']} validation " + f"{PRIMARY_HORIZON}-bar mean " + "rank IC=" + f"{metrics['validation_mean_ic']:.6f}" + ), + "metrics": metrics, + "artifacts": output_artifacts, + "errors": [], + } + ) + except JudgeFailure as error: + scientific_limit_codes = { + "factor.temporal-primary-observations", + "factor.temporal-primary-candidate-variation", + "factor.temporal-primary-target-variation", + } + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": ( + "scientific-limit" + if error.code in scientific_limit_codes + else "repair-required" + ), + "summary": str(error), + "metrics": {}, + "artifacts": [], + "errors": [{"code": error.code, "message": str(error)}], + } + ) + except Exception as error: # Preserve candidate/Judge diagnostics as evidence. + _write_output( + { + "schema_version": 1, + "status": "failed", + "failure_disposition": "repair-required", + "summary": f"Factor evaluation raised {type(error).__name__}", + "metrics": {}, + "artifacts": [], + "errors": [ + { + "code": "factor.exception", + "message": f"{type(error).__name__}: {error}", + } + ], + } + ) + + +if __name__ == "__main__": + main() diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/program.md b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/program.md new file mode 100644 index 0000000..1605769 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/program.md @@ -0,0 +1,173 @@ +# Mine a causal OHLCV factor + +## Research question + +Can one causal transformation of ordinary OHLCV history produce stable +information about the caller-bound future outcome across the fixed +prediction-eligible universe and both held-out chronological periods? + +## Editable API + +Edit only `factors/candidate.py` and preserve: + +```python +def compute_factor(panel: pandas.DataFrame) -> pandas.Series: + ... +``` + +`panel` is the observed Study universe in long form, with one row per available +`asset`/`timestamp` and base plus available completed higher-interval OHLCV. +The complete research universe remains available for causal cross-asset +features. The fixed Judge, not candidate code, selects target observations: +`decision-signal` evaluates caller-owned `factorPolicy.predictionAssets`, while +`novel-factor` and `known-style-validation` evaluate the complete research +universe. Core freezes that evaluation-only authority in +`strategies/factor-population.json`; this Factor Lab has no Portfolio Mandate. +Inspect `predictionUniverse.evaluationMode` in Factor diagnostics. +A decision signal with exactly one eligible asset uses within-split temporal +Spearman/Pearson evidence for that asset. Exactly two eligible assets use +within-split temporal evidence between the first-minus-second factor contrast +and matching forward-return contrast. A later Portfolio lane must separately +prove a symmetric two-sided dollar-neutral Mandate before monetization. Four +or more eligible assets use the cross-sectional contract. Three require +explicit caller-owned relative-basket contrast weights. No mode may borrow +context-only target observations. +The fixed Factor Claim also binds `outcome`. `forward-return` uses the simple +close-to-close return from signal close `t` to `t+h`. `forward-realized- +volatility` uses the unannualized square root of summed squared close-to-close +log returns over the next `h` observed base bars. The latter supports one +temporal prediction asset or at least four cross-sectional assets, and a +higher score means higher predicted risk—not positive expected return. It has +no Portfolio or RL admission path. Do not invert, monetize, or relabel it. +Aligned inputs are rectangular; V4 daily and V5 observed-bar input is ragged and +does not invent, fill, or globally intersect missing/pre-listing/closed-market +rows. V5 temporal targets, split boundaries, and purge counts follow the one +prediction asset's observed bars, not context-only union timestamps. +For asynchronous V5 context, explicitly use a backward as-of operation and +accept only source rows whose completed timestamp is at or before the target +row. Core never fills an absent context observation or aligns civil dates. +Use ordinary `groupby("asset")` for rolling time-series features and +`groupby("timestamp")` for contemporaneous cross-sectional context. The +returned Series must align exactly with the input index. Missing warm-up values +are allowed. Future timestamps, centered windows, negative shifts, global +full-sample normalization, external data, and mutation of the input are not. + +Missing input is not the same state as an observed false condition or a neutral +score. When the candidate branches on a required regime, filter, denominator, +or other component, preserve `NaN` until that input exists unless the Research +Request explicitly predeclares a different missing-data policy. In particular, +`Series.where(condition, other=0)` treats an unavailable/`NaN` predicate as +false; mask the unavailable predicate separately before returning the factor. +Reconcile final-factor coverage against every required component before +execution and explain any intentional excess coverage in `research.md`. + +A sparse binary event indicator is still evaluated as a temporal association +score; it does not create an event population or estimate a conditional event +return. If the caller instead froze an OHLCV-observable price event, delayed +entry/exit clock, overlap policy, unconditional history, and matched reference, +use `ohlcv-event-study-lab`. Do not disguise that descriptive question as a +Factor merely to obtain a Run. When the fixed primary temporal population has +too few finite pairs or no candidate/target variation, preserve the structured +failed Run as the truthful `scientific-limit`, report that exact bounded +answer, and change the hypothesis or route only as separately declared +work—not the missing values or Judge. Do not repeat the unchanged Run. + +When a hypothesis has meaningful sub-signals, also export +`FACTOR_COMPONENTS` and +`compute_factor_components(panel) -> pandas.DataFrame`. Declare one causal +column per falsifiable source component, including its label, role, claimed +`base`/3h/4h/6h/12h/1d intervals, and hypothesis. Use +`cross-sectional-score` for values meant to rank assets and +`timestamp-context` for one market/regime value shared by every asset at a +timestamp. Context components must be exactly cross-sectionally constant; +Core evaluates their train-tertile occupancy and transitions. Cross-sectional +Runs measure score components with per-date rank IC and context with +conditional final-factor IC. Single-asset and two-asset relative-value Runs +measure score components and context with within-split temporal rank- +correlation contributions. The component table must remain aligned, +deterministic, numeric, immutable, and prefix causal. Do not declare +presentation-only duplicates or imply that Core inferred column use. Temporal +quantile attribution remains unavailable; do not treat that explicit protocol +boundary as positive evidence. + +## Iteration protocol + +Before this protocol begins on a new caller assignment, replace the generic +scaffold candidate with the first predeclared caller-relevant candidate +without executing the scaffold. Start the governed Session only after that +source is fixed; its baseline Run is the first visible audit. Never rewrite +Core's `testGuidanceObservability=not-observable` as a factual claim that test +evidence was unused. + +1. Run `aq orient . --json` and read the current immutable leader's + `researchAgenda`. Treat its ordered moves as validation-only scientific + priorities, not executable actions or permission to inspect test for + selection. +2. Read `strategies/factor-claim.json`, + `strategies/factor-population.json`, the current candidate, and immutable + leader evidence. The request-bound `decision-signal`, `novel-factor`, or + `known-style-validation` claim, outcome, and prediction population are + fixed evidence authority, not editable strategy metadata. +3. State one falsifiable hypothesis about price or volume behavior. +4. Make one coherent code change inside the editable closure. +5. Run the bounded Experiment command supplied by the Session. +6. Inspect validation/test primary-horizon IC, HAC strength, diagnostic + horizon decay, tertile monotonicity/spread, train-selected dominant style, + style-neutral residual + primary-horizon association, equal-blend uplift, declared-component + raw/residual association, pairwise + redundancy, fixed diagnostic-blend leave-one-out delta, residual fold + stability, asset/regime stability, observed input/factor/target-pair + availability, coverage, turnover, errors, verdict, Project-family trial + count, and family-wise adjusted HAC significance. +7. KEEP only when the fixed objective improves; otherwise accept restoration + and form a different hypothesis. + +The fixed objective is validation mean IC only. Test IC is visible diagnostic +evidence and never enters KEEP/REVERT. Changing a candidate after inspecting +test evidence consumes its holdout value; obtain a new external period or +dataset before a production-grade claim. + +Starting a new Session does not create a fresh statistical search. Core counts +unique editable source hashes across every Run with the same fixed Study, +Judge, data, dependencies, and objective. Treat the Bonferroni-HAC result as a +selection-risk diagnostic, not permission to hide failed attempts or override +the immutable verdict. + +Session construction reuses an exact successful current baseline Run. It +executes a new baseline only when Study, program, candidate, Judge, dataset, +dependency, or Harness identity differs. + +The Judge fixes dataset-derived split dates and purges each declared diagnostic +horizon before a boundary. Treat sparse regimes, one weak fold, one dominant +asset, fast decay, or near-perfect overlap with a familiar OHLCV style as +findings to explain—not fields to hide or alternate scores to optimize +opportunistically. + +For a `decision-signal` claim, validation requires statistically supported +positive raw IC and positive raw IC in both fixed chronological folds. The +train-selected style, residual, and blend remain disclosure without creating +a novelty hurdle. For a `novel-factor` claim, the dominant comparison style is chosen on train +overlap only. Validation asks whether raw and style-neutral IC are positive +with fixed HAC t at least 1.96, whether an equal rank blend improves the +selected style, and whether both residual folds remain positive. For a +`known-style-validation` claim, the request fixes the comparison style before +research; validation instead requires at least 0.95 train rank identity, +positive statistically supported raw IC, and positive raw IC in both fixed +chronological folds. This prioritizes the next research lane; it does not +change KEEP/REVERT, replace Project-family selection adjustment, or +automatically admit the source into Portfolio or RL. + +When the fixed outcome is `forward-realized-volatility`, a positive funnel is +terminal risk-model evidence for this in-sample Study. Freeze, report, and seek +a fresh external holdout; never treat the result as an expected-return signal +or open Portfolio/RL work merely because the association is positive. + +Component leave-one-out applies only to the Judge's fixed equal-rank +diagnostic blend. It is not an ablation of arbitrary `compute_factor` code. +Component validation evidence may prioritize the next hypothesis, but it never +changes `validation_mean_ic`, KEEP/REVERT, Portfolio mechanics, or the +governed RL action set. Test component evidence remains visible audit only. + +Do not modify the Study, Judge, program, dataset, or AutoQuant Core to improve a +candidate. Do not treat this synthetic benchmark as a real-market alpha claim. diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/study.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/study.json new file mode 100644 index 0000000..98fb40d --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/inputs/study.json @@ -0,0 +1,59 @@ +{ + "dataset": { + "asset_class": "synthetic-multi-asset", + "id": "synthetic-ohlcv-research-desk-fixture", + "paths": [ + "ohlcv/**" + ], + "time_range": { + "end": "2025-08-11", + "start": "2024-01-02" + }, + "universe": [ + "ALPHA", + "BRAVO", + "CHARLIE", + "DELTA", + "ECHO", + "FOXTROT" + ], + "version": "v1" + }, + "dependencies": { + "paths": [ + "strategies/factor-claim.json", + "strategies/factor-population.json", + "strategies/research-horizon.json" + ] + }, + "description": "Mine causal factor evidence on the shared research snapshot", + "editable": { + "paths": [ + "factors/**" + ] + }, + "id": "ohlcv-factor-quality", + "judge": { + "arguments": [], + "entrypoint": "judges/ohlcv_factor.py", + "kind": "python", + "paths": [ + "judges/ohlcv_factor.py", + "judges/factor_diagnostics.py" + ], + "timeout_seconds": 60 + }, + "name": "OHLCV Factor Quality", + "objective": { + "direction": "maximize", + "metric": "validation_mean_ic", + "minimum_improvement": 0.01 + }, + "program": "program.md", + "schema_version": 1, + "subject": { + "kind": "factor", + "name": "candidate-factor", + "version": "working" + } +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/judge-output.json b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/judge-output.json new file mode 100644 index 0000000..c1c2fa1 --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/judge-output.json @@ -0,0 +1,2644 @@ +{ + "artifacts": [ + { + "description": "Factor semantics, purged split protocol, complete tear sheet, coverage, and causality audit", + "kind": "factor-report", + "path": "factor-report.json" + }, + { + "description": "Timestamped split, causal regime, and purge-aware request-bound outcome rank and Pearson IC", + "kind": "factor-daily", + "path": "daily-factor-evidence.csv" + }, + { + "description": "Timestamped fixed-tertile outcome levels and high-minus-low outcome spread by split and horizon", + "kind": "factor-quantiles", + "path": "factor-quantiles.csv" + }, + { + "description": "Per-timestamp observed input, finite factor, and horizon-paired cross-sectional asset counts", + "kind": "factor-availability", + "path": "factor-availability.csv" + }, + { + "description": "Train-selected style, candidate/style/residual/blend daily rank IC, and visible-test audit", + "kind": "factor-qualification", + "path": "factor-qualification.csv" + }, + { + "description": "Candidate-declared component quality, redundancy, nearest-peer residual, and fixed-blend ablation evidence", + "kind": "factor-components", + "path": "factor-components.json" + } + ], + "errors": [], + "metrics": { + "assets": 6, + "factor_api": { + "assets": 6, + "causality_audit": "whole-panel-timestamp-prefix", + "causality_cuts": [ + "2024-10-22 00:00:00", + "2025-03-18 00:00:00", + "2025-08-08 00:00:00" + ], + "cross_asset_context": "same-or-prior-timestamp-explicit-candidate", + "input": "long-form-observed-universe", + "kind": "panel-v2", + "observation_coverage": 1.0, + "possible_rows": 2520, + "rows": 2520, + "shape": "rectangular", + "timestamps": 420 + }, + "factor_claim": { + "authority": "quantitative-decision-support", + "claim": "novel-factor", + "id": "factor-claim-a002cb5137e5253b", + "kind": "autoquant-factor-claim", + "knownStyle": null, + "schemaVersion": 1, + "selectionAuthority": "validation-only", + "source": { + "factorPolicy": "reference-default", + "kind": "template-default", + "requestHash": null + }, + "testRole": 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"programHash": "cf5dfc868822c29b6c8323f698dfaff8bb896519473f07e747b40f82e264ab0e" + }, + "studyInputHash": "0ac049ad4a62e6e30c0d9bf085b8b139cefe8b2559634d660c13976ff642463c", + "subject": { + "kind": "factor", + "name": "candidate-factor", + "sourceHash": "72e7ef57b95bfec64189290ee76943e33bd6c64c34c627f5abdc39d6c54058bc", + "sourcePaths": [ + "factors/candidate.py" + ], + "version": "working" + }, + "summary": "Causal purge-aware factor tear sheet completed; forward-return validation 1-bar mean rank IC=-0.031325" +} diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/sources/factors/candidate.py b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/sources/factors/candidate.py new file mode 100644 index 0000000..6ee5eeb --- /dev/null +++ b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/sources/factors/candidate.py @@ -0,0 +1,128 @@ +"""Agent-editable baseline factor for the OHLCV Portfolio Lab.""" + +from __future__ import annotations + +import pandas as pd + + +AVAILABLE_FEATURE_INTERVALS = [] + +_BASE_COMPONENT = { + "base_momentum_10": { + "label": "10-base-bar momentum", + "role": "cross-sectional-score", + "intervals": ["base"], + "hypothesis": ( + "Recent relative strength persists over the next base bar." + ), + }, +} + +_FEATURE_COMPONENTS = { + "3h": ( + "momentum_3h_4", + 4, + { + "label": "Four completed 3-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["3h"], + "hypothesis": ( + "Short intraday trend persists beyond the latest completed " + "3-hour bar." + ), + }, + ), + "12h": ( + "momentum_12h_2", + 2, + { + "label": "Two completed 12-hour bars momentum", + "role": "cross-sectional-score", + "intervals": ["12h"], + "hypothesis": "Half-day trend filters noisy base-bar momentum.", + }, + ), + "1d": ( + "momentum_1d_3", + 3, + { + "label": "Three completed daily bars momentum", + "role": "cross-sectional-score", + "intervals": ["1d"], + "hypothesis": ( + "Multi-day relative strength persists at the next base close." + ), + }, + ), +} + +FACTOR_COMPONENTS = dict(_BASE_COMPONENT) +for _interval in AVAILABLE_FEATURE_INTERVALS: + if _interval in _FEATURE_COMPONENTS: + _name, _periods, _declaration = _FEATURE_COMPONENTS[_interval] + FACTOR_COMPONENTS[_name] = _declaration + + +def _completed_bar_return( + panel: pd.DataFrame, + interval: str, + periods: int, +) -> pd.Series: + close_column = f"close__{interval}" + bar_column = f"bar_close__{interval}" + if close_column not in panel or bar_column not in panel: + return pd.Series(float("nan"), index=panel.index, dtype=float) + completed = panel.loc[ + panel[bar_column].notna(), + ["asset", bar_column, close_column], + ].drop_duplicates(["asset", bar_column], keep="first") + completed["return"] = completed.groupby( + "asset", + sort=False, + )[close_column].pct_change(periods, fill_method=None) + lookup = completed.set_index( + ["asset", bar_column], + )["return"] + keys = pd.MultiIndex.from_frame( + panel.loc[:, ["asset", bar_column]], + ) + return pd.Series( + lookup.reindex(keys).to_numpy(dtype=float), + index=panel.index, + dtype=float, + ) + + +def compute_factor_components(panel: pd.DataFrame) -> pd.DataFrame: + """Declare causal components without changing downstream factor authority.""" + + components = { + "base_momentum_10": panel.groupby( + "asset", + sort=False, + )["close"].pct_change(10, fill_method=None), + } + for interval in AVAILABLE_FEATURE_INTERVALS: + if interval in _FEATURE_COMPONENTS: + name, periods, _declaration = _FEATURE_COMPONENTS[interval] + components[name] = _completed_bar_return( + panel, + interval, + periods, + ) + return pd.DataFrame(components, index=panel.index) + + +def compute_factor(panel: pd.DataFrame) -> pd.Series: + """Return causal relative multi-horizon momentum for construction. + + The fixed Judge owns signal state/hysteresis, conviction/volatility sizing, + target and executed weights, attribution, delay, drift, costs, benchmark, + splits, metrics, and stress tests. Change only this factor while testing + one falsifiable hypothesis at a time. + """ + + components = compute_factor_components(panel) + raw = components.mean(axis=1, skipna=True) + market_center = raw.groupby(panel["timestamp"], sort=False).transform("mean") + return (raw - market_center).rename("relative_multi_horizon_momentum") diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/stderr.txt b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/stderr.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/stdout.txt b/projects/sample-research-desk/runs/run-20260802T180952308716Z-01a1b0a4a0b1/stdout.txt new file mode 100644 index 0000000..e69de29 diff --git a/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/assessment.json b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/assessment.json new file mode 100644 index 0000000..33e1d7e --- /dev/null +++ b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/assessment.json @@ -0,0 +1,32 @@ +{ + "authority": "research-validation-only", + "claimHash": "7ce1e8333db1e27c065ff71d58608a8783cd95091c664a89e2c5a412b58bc003", + "claimId": "research-claim-152fbe9c75ff3514", + "evidenceRefs": [ + { + "id": "run-20260802T180952308716Z-01a1b0a4a0b1", + "kind": "run", + "sha256": "40ab82a60e847eed5ddbd248f6a1700b7bec3d230596541e30df3cffcefc7f81" + }, + { + "id": "factor-diagnostics:run-20260802T180952308716Z-01a1b0a4a0b1", + "kind": "explorer", + "sha256": "3d6ed6d8e8af137f853f7f17174685fd3a2d94c1630594e8fca2e479df1e156f" + } + ], + "gates": { + "holdout": "missing", + "primary": "contradict", + "selectionPassed": null + }, + "id": "verification-assessment-b22fffaf4e5dab59", + "kind": "autoquant-verification-assessment", + "limitations": [ + "holdout-missing", + "required-selection-evidence-missing" + ], + "primaryImprovement": -0.031325301204819286, + "schemaVersion": 1, + "tradingAuthority": "none", + "verdict": "inconclusive" +} diff --git a/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/claim.json b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/claim.json new file mode 100644 index 0000000..0d4694c --- /dev/null +++ b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/claim.json @@ -0,0 +1,17 @@ +{ + "authority": "research-validation-only", + "direction": "maximize", + "id": "research-claim-152fbe9c75ff3514", + "kind": "autoquant-research-claim", + "metric": "validation_mean_ic", + "minimumEffect": 0.01, + "requirements": { + "baselineRequired": true, + "holdoutRequired": true, + "minimumSampleSize": 30, + "selectionRequired": true + }, + "schemaVersion": 1, + "statement": "该外部指标在样本外具有稳定的正向 Rank IC。", + "tradingAuthority": "none" +} diff --git a/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/manifest.json b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/manifest.json new file mode 100644 index 0000000..4340f17 --- /dev/null +++ b/projects/sample-research-desk/verifications/verification-assessment-b22fffaf4e5dab59/manifest.json @@ -0,0 +1,7 @@ +{ + "assessmentHash": "9c898e5215ab345acbec6601d7dc324e241481b719018a9c5cc12a79872cdc09", + "claimHash": "5ea80233f89a66c947a33698aa946bd4ec31f10418af7814418c484ab287e1c8", + "completed": true, + "id": "verification-assessment-b22fffaf4e5dab59", + "schemaVersion": 1 +} diff --git a/scripts/check_doc_links.py b/scripts/check_doc_links.py index b1609cb..a60bf6b 100644 --- a/scripts/check_doc_links.py +++ b/scripts/check_doc_links.py @@ -12,10 +12,13 @@ ROOT = Path(__file__).resolve().parents[1] IGNORED_PARTS = { ".git", + ".next", ".pytest_cache", ".venv", "__pycache__", "data", + "node_modules", + "out", } LINK_PATTERN = re.compile( r"\[\[" @@ -84,7 +87,7 @@ def main() -> int: print(failure.render(), file=sys.stderr) return 1 - print(f"✓ {checked} documentation double-links resolve") + print(f"OK: {checked} documentation double-links resolve") return 0 diff --git a/studio-web/README.md b/studio-web/README.md new file mode 100644 index 0000000..9ac64fa --- /dev/null +++ b/studio-web/README.md @@ -0,0 +1,38 @@ +# AutoQuant Studio Web + +This is the repository-owned Next.js Evidence Console for factor, portfolio, +and governed-RL research. It uses the existing read-only Core snapshot and +contains no plugin, broker, account, order, or live-trading integration. + +The replay K-line uses `lightweight-charts@5.2.0` for candlesticks, volume, +factor-signal panes, and event markers. Its Apache-2.0 distribution and visible +TradingView attribution remain intact. + +## Run locally + +In the repository root: + +```powershell +uv run aq studio serve . --no-open +``` + +In `studio-web/`: + +```powershell +npm install +npm run dev +``` + +The web app reads `http://127.0.0.1:8765/api/v1/snapshot` by default. To use a +different loopback port, set `AUTOQUANT_STUDIO_CORE_URL` to an unauthenticated +loopback HTTP origin such as `http://127.0.0.1:8877`. + +When Core is unavailable, the app does not silently present fixtures as +verified evidence. Use the visible “使用演示数据” action to enter deterministic +demo mode. + +## Boundary + +Private hosts and plugins live outside this repository. Public frontend code +accepts only the normalized Studio snapshot and does not forward credentials, +authenticated headers, proprietary payloads, or tool invocations. diff --git a/studio-web/app/api/studio/event-intake/route.js b/studio-web/app/api/studio/event-intake/route.js new file mode 100644 index 0000000..3adfead --- /dev/null +++ b/studio-web/app/api/studio/event-intake/route.js @@ -0,0 +1,76 @@ +import { execFile } from "node:child_process"; +import { mkdtemp, rm, writeFile } from "node:fs/promises"; +import os from "node:os"; +import path from "node:path"; +import { promisify } from "node:util"; +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; + +export const dynamic = "force-dynamic"; +export const runtime = "nodejs"; + +const execFileAsync = promisify(execFile); +const MAX_BYTES = 64 * 1024 * 1024; +const PROJECT_ID = /^[a-z0-9][a-z0-9-]{0,63}$/; +const ADAPTERS = new Set(["a-share-announcement", "crypto-event", "financial-news"]); +let activeProjectId = null; + +async function coreSnapshot() { + const response = await fetch(resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL), { + cache: "no-store", + headers: { accept: "application/json" }, + redirect: "error", + signal: AbortSignal.timeout(5000), + }); + if (!response.ok) throw new Error(`Core returned HTTP ${response.status}`); + return validateCoreSnapshot(await response.json()); +} + +export async function POST(request) { + let staging = null; + let acquired = false; + try { + const origin = request.headers.get("origin"); + if (origin && new URL(origin).origin !== new URL(request.url).origin) { + return NextResponse.json({ ok: false, error: { code: "studio.event-intake.origin", message: "Cross-origin event intake is not allowed." } }, { status: 403 }); + } + const contentLength = Number(request.headers.get("content-length")); + if (!Number.isSafeInteger(contentLength) || contentLength < 1 || contentLength > MAX_BYTES) { + throw new Error("Event intake requires a valid Content-Length within 64 MiB"); + } + if (!request.headers.get("content-type")?.toLowerCase().startsWith("multipart/form-data;")) { + throw new Error("Event intake requires multipart/form-data"); + } + const form = await request.formData(); + const projectId = String(form.get("projectId") || ""); + const file = form.get("package"); + if (!PROJECT_ID.test(projectId)) throw new Error("A valid existing projectId is required"); + if (!file || typeof file.arrayBuffer !== "function" || file.size < 1 || file.size > MAX_BYTES) throw new Error("A JSON event package up to 64 MiB is required"); + const packageText = await file.text(); + const value = JSON.parse(packageText); + if (value?.kind !== "autoquant-event-package" || !ADAPTERS.has(value?.adapterKind)) throw new Error("Unsupported event package contract"); + const snapshot = await coreSnapshot(); + const project = snapshot.projects.find((item) => item.id === projectId && item.valid); + if (!project || !path.isAbsolute(project.rootDir || "")) throw new Error("The selected verified Core Project is unavailable"); + if (activeProjectId) return NextResponse.json({ ok: false, error: { code: "studio.event-intake.busy", message: `${activeProjectId} is already importing events.` } }, { status: 409 }); + + activeProjectId = projectId; + acquired = true; + staging = await mkdtemp(path.join(os.tmpdir(), "autoquant-studio-events-")); + const packagePath = path.join(staging, "event-package.json"); + await writeFile(packagePath, packageText, { encoding: "utf8", flag: "wx" }); + const { stdout } = await execFileAsync( + "uv", + ["run", "aq", "event", "intake", project.rootDir, "--package", packagePath, "--json"], + { cwd: path.resolve(process.cwd(), ".."), windowsHide: true, timeout: 120_000, maxBuffer: 16 * 1024 * 1024 }, + ); + const result = JSON.parse(stdout); + if (result?.ok !== true || result.command !== "event.intake" || typeof result.data?.snapshotHash !== "string") throw new Error("Core did not return a verified event snapshot"); + return NextResponse.json({ ok: true, eventSnapshot: result.data }, { headers: { "Cache-Control": "no-store" } }); + } catch (error) { + return NextResponse.json({ ok: false, error: { code: "studio.event-intake.failed", message: (error instanceof Error ? error.message : "Event intake failed").slice(0, 4000) } }, { status: 400, headers: { "Cache-Control": "no-store" } }); + } finally { + if (acquired) activeProjectId = null; + if (staging) await rm(staging, { recursive: true, force: true }); + } +} diff --git a/studio-web/app/api/studio/intake/route.js b/studio-web/app/api/studio/intake/route.js new file mode 100644 index 0000000..b2d0081 --- /dev/null +++ b/studio-web/app/api/studio/intake/route.js @@ -0,0 +1,125 @@ +import { execFile } from "node:child_process"; +import { access, mkdir, mkdtemp, rm, writeFile } from "node:fs/promises"; +import os from "node:os"; +import path from "node:path"; +import { promisify } from "node:util"; +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; +import { + INTAKE_LIMITS, + selectIntakeTarget, + summarizeIntakeResult, + validateIntakeDocuments, +} from "@/lib/core-intake"; + +export const dynamic = "force-dynamic"; +export const runtime = "nodejs"; + +const execFileAsync = promisify(execFile); +let activeProjectId = null; + +function upload(value, label) { + if (!value || typeof value.arrayBuffer !== "function" || typeof value.size !== "number") { + throw new Error(`${label} file is required`); + } + return value; +} + +async function coreSnapshot() { + const response = await fetch(resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL), { + cache: "no-store", + headers: { accept: "application/json" }, + redirect: "error", + signal: AbortSignal.timeout(5000), + }); + if (!response.ok) throw new Error(`Core returned HTTP ${response.status}`); + return validateCoreSnapshot(await response.json()); +} + +export async function POST(request) { + let staging = null; + let acquired = false; + try { + const origin = request.headers.get("origin"); + if (origin && new URL(origin).origin !== new URL(request.url).origin) { + return NextResponse.json({ ok: false, error: { code: "studio.intake.origin", message: "Cross-origin data intake is not allowed." } }, { status: 403 }); + } + const contentLength = Number(request.headers.get("content-length")); + if (!Number.isSafeInteger(contentLength) || contentLength < 1 || contentLength > INTAKE_LIMITS.totalBytes) { + throw new Error("Upload requires a valid Content-Length within the 256 MB intake limit"); + } + if (!request.headers.get("content-type")?.toLowerCase().startsWith("multipart/form-data;")) { + throw new Error("Data intake requires multipart/form-data"); + } + + const form = await request.formData(); + const target = selectIntakeTarget({ + projectId: form.get("projectId"), + template: form.get("template"), + name: form.get("name") || undefined, + }, await coreSnapshot()); + const requestFile = upload(form.get("request"), "Research Request"); + const packageFile = upload(form.get("dataset"), "Dataset package"); + const sourceFiles = form.getAll("source").map((file) => upload(file, "Data source")); + const sourcePaths = form.getAll("sourcePath").map(String); + + if (requestFile.size > INTAKE_LIMITS.jsonBytes || packageFile.size > INTAKE_LIMITS.jsonBytes) { + throw new Error("Request and dataset-package JSON files must each be at most 2 MB"); + } + if (!sourceFiles.length || sourceFiles.length > INTAKE_LIMITS.sourceFiles || sourceFiles.length !== sourcePaths.length) { + throw new Error("Upload 1 to 128 data files with matching relative paths"); + } + if (sourceFiles.some((file) => file.size > INTAKE_LIMITS.sourceBytes)) { + throw new Error("Each data file must be at most 64 MB"); + } + const totalBytes = requestFile.size + packageFile.size + sourceFiles.reduce((sum, file) => sum + file.size, 0); + if (totalBytes > INTAKE_LIMITS.totalBytes) throw new Error("Upload exceeds the 256 MB intake limit"); + + const requestText = await requestFile.text(); + const packageText = await packageFile.text(); + validateIntakeDocuments(requestText, packageText, sourcePaths); + try { + await access(path.join(target.projectsDir, target.projectId)); + throw new Error("The target Project already exists"); + } catch (error) { + if (error?.code !== "ENOENT") throw error; + } + if (activeProjectId) { + return NextResponse.json({ ok: false, error: { code: "studio.intake.busy", message: `${activeProjectId} is already being imported.` } }, { status: 409 }); + } + + activeProjectId = target.projectId; + acquired = true; + staging = await mkdtemp(path.join(os.tmpdir(), "autoquant-studio-intake-")); + const requestPath = path.join(staging, "request.json"); + const packagePath = path.join(staging, "dataset-package.json"); + await writeFile(requestPath, requestText, { encoding: "utf8", flag: "wx" }); + await writeFile(packagePath, packageText, { encoding: "utf8", flag: "wx" }); + for (let index = 0; index < sourceFiles.length; index += 1) { + const targetPath = path.resolve(staging, ...sourcePaths[index].split("/")); + if (path.relative(staging, targetPath).startsWith("..")) throw new Error("Data file escaped the intake boundary"); + await mkdir(path.dirname(targetPath), { recursive: true }); + await writeFile(targetPath, Buffer.from(await sourceFiles[index].arrayBuffer()), { flag: "wx" }); + } + + // ponytail: intake is serialized locally; replace with ComputeJob scheduling when concurrent imports are required. + const args = ["run", "aq", "project", "intake", target.workspaceRoot, target.projectId, "--request", requestPath, "--dataset", packagePath, "--template", target.template]; + if (target.name) args.push("--name", target.name); + args.push("--json"); + const { stdout } = await execFileAsync("uv", args, { + cwd: path.resolve(process.cwd(), ".."), + windowsHide: true, + timeout: 180_000, + maxBuffer: 16 * 1024 * 1024, + }); + return NextResponse.json({ ok: true, intake: summarizeIntakeResult(JSON.parse(stdout)) }, { headers: { "Cache-Control": "no-store" } }); + } catch (error) { + return NextResponse.json({ + ok: false, + error: { code: "studio.intake.failed", message: (error instanceof Error ? error.message : "Data intake failed").slice(0, 4000) }, + }, { status: 400, headers: { "Cache-Control": "no-store" } }); + } finally { + if (acquired) activeProjectId = null; + if (staging) await rm(staging, { recursive: true, force: true }); + } +} diff --git a/studio-web/app/api/studio/operator/route.js b/studio-web/app/api/studio/operator/route.js new file mode 100644 index 0000000..05d0e49 --- /dev/null +++ b/studio-web/app/api/studio/operator/route.js @@ -0,0 +1,66 @@ +import { execFile } from "node:child_process"; +import { mkdtemp, rm, writeFile } from "node:fs/promises"; +import { tmpdir } from "node:os"; +import { join } from "node:path"; +import { promisify } from "node:util"; +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; +import { validateOperatorRequest } from "@/lib/research-console"; + +export const dynamic = "force-dynamic"; +export const runtime = "nodejs"; + +const execFileAsync = promisify(execFile); +const MAX_BODY_BYTES = 1024 * 1024; + +function response(payload, status = 200) { + return NextResponse.json(payload, { status, headers: { "Cache-Control": "no-store" } }); +} + +export async function POST(request) { + let temporary = null; + try { + const origin = request.headers.get("origin"); + if (origin && new URL(origin).origin !== new URL(request.url).origin) { + return response({ ok: false, error: { code: "studio.operator.origin", message: "Cross-origin Operator requests are not allowed." } }, 403); + } + const declaredLength = Number(request.headers.get("content-length")); + if (!Number.isSafeInteger(declaredLength) || declaredLength < 1 || declaredLength > MAX_BODY_BYTES) { + return response({ ok: false, error: { code: "studio.operator.length", message: "Operator request requires a valid Content-Length within 1 MiB." } }, 413); + } + const raw = await request.text(); + if (Buffer.byteLength(raw, "utf8") > MAX_BODY_BYTES) throw new Error("Operator request exceeds 1 MiB"); + + const snapshotResponse = await fetch(resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL), { + cache: "no-store", + headers: { accept: "application/json" }, + redirect: "error", + signal: AbortSignal.timeout(15_000), + }); + if (!snapshotResponse.ok) throw new Error(`Core returned HTTP ${snapshotResponse.status}`); + const snapshot = validateCoreSnapshot(await snapshotResponse.json()); + const target = validateOperatorRequest(JSON.parse(raw), snapshot); + + temporary = await mkdtemp(join(tmpdir(), "autoquant-operator-")); + const requestFile = join(temporary, "request.json"); + await writeFile(requestFile, `${JSON.stringify(target.request, null, 2)}\n`, { encoding: "utf8", flag: "wx" }); + const { stdout } = await execFileAsync( + "uv", + ["run", "aq", "operator", "invoke", target.project.rootDir, "--request", requestFile, "--json"], + { cwd: target.project.rootDir, windowsHide: true, timeout: 120_000, maxBuffer: 16 * 1024 * 1024 }, + ); + const payload = JSON.parse(stdout); + return response(payload, payload.ok === false ? 400 : 200); + } catch (error) { + if (typeof error?.stdout === "string" && error.stdout.trim()) { + try { + return response(JSON.parse(error.stdout), 400); + } catch { + // Fall through to the bounded error envelope. + } + } + return response({ ok: false, error: { code: "studio.operator.failed", message: error instanceof Error ? error.message : "Operator request failed." } }, 400); + } finally { + if (temporary) await rm(temporary, { recursive: true, force: true }); + } +} diff --git a/studio-web/app/api/studio/run/route.js b/studio-web/app/api/studio/run/route.js new file mode 100644 index 0000000..4372421 --- /dev/null +++ b/studio-web/app/api/studio/run/route.js @@ -0,0 +1,56 @@ +import { execFile } from "node:child_process"; +import { promisify } from "node:util"; +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; +import { selectRunTarget, summarizeRunResult } from "@/lib/core-run"; + +export const dynamic = "force-dynamic"; +export const runtime = "nodejs"; + +const execFileAsync = promisify(execFile); +let activeStudyId = null; + +export async function POST(request) { + try { + const origin = request.headers.get("origin"); + if (origin && new URL(origin).origin !== new URL(request.url).origin) { + return NextResponse.json({ ok: false, error: { code: "studio.run.origin", message: "Cross-origin research execution is not allowed." } }, { status: 403 }); + } + + const response = await fetch(resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL), { + cache: "no-store", + headers: { accept: "application/json" }, + redirect: "error", + signal: AbortSignal.timeout(5000), + }); + if (!response.ok) throw new Error(`Core returned HTTP ${response.status}`); + const target = selectRunTarget(await request.json(), validateCoreSnapshot(await response.json())); + if (activeStudyId) { + return NextResponse.json( + { ok: false, error: { code: "studio.run.busy", message: `${activeStudyId} is already running.` } }, + { status: 409 }, + ); + } + + activeStudyId = target.studyId; + try { + // ponytail: one local execution lock; provider plugins can replace it when concurrent dispatch is real. + const { stdout } = await execFileAsync( + "uv", + ["run", "aq", "job", "execute", target.project.rootDir, "--study", target.studyId, "--executor", "cpu", "--json"], + { cwd: target.project.rootDir, windowsHide: true, timeout: 120_000, maxBuffer: 16 * 1024 * 1024 }, + ); + return NextResponse.json( + { ok: true, run: summarizeRunResult(JSON.parse(stdout)) }, + { headers: { "Cache-Control": "no-store" } }, + ); + } finally { + activeStudyId = null; + } + } catch (error) { + return NextResponse.json( + { ok: false, error: { code: "studio.run.failed", message: error instanceof Error ? error.message : "Research execution failed." } }, + { status: 400, headers: { "Cache-Control": "no-store" } }, + ); + } +} diff --git a/studio-web/app/api/studio/snapshot/route.js b/studio-web/app/api/studio/snapshot/route.js new file mode 100644 index 0000000..630e983 --- /dev/null +++ b/studio-web/app/api/studio/snapshot/route.js @@ -0,0 +1,32 @@ +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; + +export const dynamic = "force-dynamic"; + +export async function GET() { + try { + const url = resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL); + const response = await fetch(url, { + cache: "no-store", + headers: { accept: "application/json" }, + redirect: "error", + signal: AbortSignal.timeout(15_000), + }); + if (!response.ok) throw new Error(`Core returned HTTP ${response.status}`); + const snapshot = validateCoreSnapshot(await response.json()); + return NextResponse.json(snapshot, { + headers: { "Cache-Control": "no-store" }, + }); + } catch { + return NextResponse.json( + { + ok: false, + error: { + code: "studio.core.unavailable", + message: "The local read-only AutoQuant Core snapshot is unavailable.", + }, + }, + { status: 502, headers: { "Cache-Control": "no-store" } }, + ); + } +} diff --git a/studio-web/app/api/studio/verify-factor/route.js b/studio-web/app/api/studio/verify-factor/route.js new file mode 100644 index 0000000..efccce6 --- /dev/null +++ b/studio-web/app/api/studio/verify-factor/route.js @@ -0,0 +1,56 @@ +import { execFile } from "node:child_process"; +import path from "node:path"; +import { promisify } from "node:util"; +import { NextResponse } from "next/server"; +import { resolveCoreSnapshotUrl, validateCoreSnapshot } from "@/lib/core-snapshot"; + +export const dynamic = "force-dynamic"; +export const runtime = "nodejs"; + +const execFileAsync = promisify(execFile); +const SAFE_ID = /^[a-z0-9][a-z0-9._-]{0,127}$/; +let activeRunId = null; + +async function coreSnapshot() { + const response = await fetch(resolveCoreSnapshotUrl(process.env.AUTOQUANT_STUDIO_CORE_URL), { + cache: "no-store", headers: { accept: "application/json" }, redirect: "error", signal: AbortSignal.timeout(5000), + }); + if (!response.ok) throw new Error(`Core returned HTTP ${response.status}`); + return validateCoreSnapshot(await response.json()); +} + +export async function POST(request) { + try { + const origin = request.headers.get("origin"); + if (origin && new URL(origin).origin !== new URL(request.url).origin) return NextResponse.json({ ok: false, error: { code: "studio.verify.origin", message: "Cross-origin verification is not allowed." } }, { status: 403 }); + const body = await request.json(); + const statement = typeof body?.statement === "string" ? body.statement.trim() : ""; + const minimumEffect = Number(body?.minimumEffect); + const minimumSampleSize = Number(body?.minimumSampleSize); + if (!SAFE_ID.test(body?.projectId || "") || !SAFE_ID.test(body?.runId || "")) throw new Error("Valid projectId and runId are required"); + if (!statement || statement.length > 500) throw new Error("Claim statement must contain 1 to 500 characters"); + if (!Number.isFinite(minimumEffect) || minimumEffect < 0) throw new Error("minimumEffect must be non-negative"); + if (!Number.isInteger(minimumSampleSize) || minimumSampleSize < 1 || minimumSampleSize > 1_000_000) throw new Error("minimumSampleSize must be an integer from 1 to 1,000,000"); + if (typeof body.requireHoldout !== "boolean" || typeof body.requireSelection !== "boolean") throw new Error("Claim evidence requirements must be explicit booleans"); + const snapshot = await coreSnapshot(); + const project = snapshot.projects.find((item) => item.id === body.projectId && item.valid); + const run = project?.runs?.find((item) => item.id === body.runId && item.primaryMetric === "validation_mean_ic"); + if (!project || !run || !path.isAbsolute(project.rootDir || "")) throw new Error("The selected verified Factor Run is unavailable"); + if (activeRunId) return NextResponse.json({ ok: false, error: { code: "studio.verify.busy", message: `${activeRunId} is already being assessed.` } }, { status: 409 }); + activeRunId = body.runId; + try { + const args = ["run", "aq", "verify", "factor", project.rootDir, "--run", body.runId, "--statement", statement, "--minimum-effect", String(minimumEffect), "--minimum-sample-size", String(minimumSampleSize)]; + if (body.requireHoldout) args.push("--require-holdout"); + if (body.requireSelection) args.push("--require-selection"); + args.push("--json"); + const { stdout } = await execFileAsync("uv", args, { cwd: path.resolve(process.cwd(), ".."), windowsHide: true, timeout: 120_000, maxBuffer: 16 * 1024 * 1024 }); + const result = JSON.parse(stdout); + if (result?.ok !== true || result.command !== "verify.factor" || typeof result.data?.assessment?.id !== "string") throw new Error("Core did not return a verified assessment"); + return NextResponse.json({ ok: true, verification: result.data }, { headers: { "Cache-Control": "no-store" } }); + } finally { + activeRunId = null; + } + } catch (error) { + return NextResponse.json({ ok: false, error: { code: "studio.verify.failed", message: (error instanceof Error ? error.message : "Verification failed").slice(0, 4000) } }, { status: 400, headers: { "Cache-Control": "no-store" } }); + } +} diff --git a/studio-web/app/audit/page.jsx b/studio-web/app/audit/page.jsx new file mode 100644 index 0000000..e608a34 --- /dev/null +++ b/studio-web/app/audit/page.jsx @@ -0,0 +1,103 @@ +"use client"; + +import { auditChain, factor } from "@/lib/data"; +import { useStudio } from "@/components/studio-context"; +import { EmptyState, PageHeading, Panel, StatusChip } from "@/components/ui"; +import { factorVerificationFrom } from "@/lib/verification"; + +const checks = [ + ["因子定义已锁定", "fdef:92ae4170"], + ["ResearchFrame 可寻址", factor.frameId], + ["数据集校验和一致", "ds:0bf7c9d2"], + ["回放包保留可见性原因", factor.bundleId], + ["运行环境镜像可获得", "image-86f1"], + ["随机种子与参数已保存", "seed: 240801"], +]; + +export default function AuditPage() { + const { source, demoEnabled } = useStudio(); + + if (source.mode === "connected" && !demoEnabled) { + const snapshot = source.snapshot; + const project = snapshot.projects[0]; + const coreDiagnostics = [...snapshot.diagnostics, ...(project?.diagnostics || [])]; + const explorer = project?.factorExplorer; + const verification = factorVerificationFrom(project, snapshot.diagnostics); + const reproduce = (project?.commands || []).find((item) => item.id === "run.factor" && item.effect === "read-only"); + return ( + <> + +
+ +
+ {[ + ["Harness", `AQ ${snapshot.harness.version}`, snapshot.harness.commit.slice(0, 12)], + ["StudioSnapshot", `schema v${snapshot.schemaVersion}`, snapshot.generatedAt], + ["Project", project?.id || "no-project", project?.valid ? "verified" : "diagnostics"], + ...(explorer?.run ? [["ExperimentRun", explorer.run.id, explorer.run.inputHash?.slice(0, 12) || "no input hash"], ["Dataset", explorer.dataset?.id || "unknown", explorer.dataset?.hash?.slice(0, 12) || "no dataset hash"]] : []), + ...((project?.studies || []).map((study) => ["Study", study.id, study.datasetHash?.slice(0, 12) || "no dataset hash"])), + ].map((node, index) =>
{String(index + 1).padStart(2, "0")}
{node[0]}{node[1]} · {node[2]}
{project?.valid ? "verified" : "可寻址"}
)} +
+ +
+ + {coreDiagnostics.length ?
{coreDiagnostics.map((item, index) =>
{item.code}

{item.message}

{item.category}
)}
: } + + +
+
Deterministic verdict

{verification.verdict.detail}

{verification.verdict.id}
+
Selection adjustment

{verification.selection.available ? verification.selection.method : "missing — 不声明已校正显著性"}

{verification.selection.passes === true ? "passes" : verification.selection.passes === false ? "fails" : "inconclusive"}
+
External holdout

{verification.holdout.available ? verification.holdout.state : "missing — 当前 test 不能冒充新 holdout"}

{verification.holdout.state}
+
Trading authority

研究与审计专用

{verification.authority.tradingAuthority || "missing"}
+
+ + {reproduce ?
Research CLI · read-only{reproduce.argv.join(" ")}只读取并验证不可变研究证据；tradingAuthority=none。
: } +
+
+ + ); + } + + return ( + <> + +
+ +
+ {auditChain.map((node, index) => ( +
+ {String(index + 1).padStart(2, "0")} +
{node.kind}{node.id} · {node.version} · {node.hash}
+ {node.state} +
+ ))} +
+ + +
+ +
+ {checks.map(([label, value]) => ( +
+
{label}{value}
+ +
+ ))} +
+ + +
+ Research CLI + aq research reproduce EXP-240801-17 --frame {factor.frameId} + 指令只重建研究环境与产物，不连接账户或交易服务。 +
+ +
+
+ + ); +} diff --git a/studio-web/app/data/page.jsx b/studio-web/app/data/page.jsx new file mode 100644 index 0000000..8181fa3 --- /dev/null +++ b/studio-web/app/data/page.jsx @@ -0,0 +1,103 @@ +"use client"; + +import { adapters } from "@/lib/data"; +import { DataIntake } from "@/components/data-intake"; +import { useStudio } from "@/components/studio-context"; +import { DataTable, PageHeading, Panel, StatusChip } from "@/components/ui"; + +const mappings = [ + ["event_time", "源事件实际发生时间", "不可由发布时间替代"], + ["published_at", "来源首次发布时间", "保存来源时区"], + ["observed_at", "适配器首次观察时间", "用于测量采集延迟"], + ["available_at", "研究系统可使用时间", "point-in-time 过滤主键"], + ["revised_at", "当前版本修订时间", "原版本仍需可寻址"], +]; + +const eventAdapters = [ + ["a-share-announcement", "A 股公告"], + ["crypto-event", "加密事件"], + ["financial-news", "财经新闻"], +]; + +export default function DataPage() { + const { source, demoEnabled } = useStudio(); + + if (source.mode === "connected" && !demoEnabled) { + const snapshot = source.snapshot; + const project = snapshot.projects[0]; + return ( + <> + +
+ + + StudyDataset资产类时间范围校验和 + {(project?.studies || []).map((study) => {study.name}{study.id}{study.dataset ? `${study.dataset.id}@${study.dataset.version}` : "未绑定"}{study.dataset?.asset_class || "未声明"}{study.dataset?.time_range ? `${study.dataset.time_range.start} → ${study.dataset.time_range.end}` : "未声明"}{study.datasetHash?.slice(0, 12) || "未声明"})} + + + +
{eventAdapters.map(([kind, name]) => { + const packages = (project?.eventSnapshots || []).filter((item) => item.adapterKind === kind); + const latest = packages.at(-1); + return
{name}

{latest ? `${latest.id}@${latest.version} · ${latest.eventCount} events · ${latest.availableStart} → ${latest.availableEnd}` : "当前 Core snapshot 未声明覆盖率、延迟、许可或修订状态。"}

{latest ? "已验证" : "未接入"}
; + })}
+ + + 字段含义约束{mappings.map((row) => {row[0]}{row[1]}{row[2]})} + +
+ + ); + } + + return ( + <> + +
+ + + 适配器状态历史覆盖最近观察延迟许可边界 + + {adapters.map((adapter) => ( + + {adapter.name} + + {adapter.coverage} + {adapter.lastSeen} + {adapter.latency} + {adapter.rights} + + ))} + + + + +
+ + + 字段含义约束 + {mappings.map((row) => {row[0]}{row[1]}{row[2]})} + + +
+ +
+
DatasetSnapshotcn-event-snapshot@2026.08.01
+
Schemaevent-contract/v4
+
覆盖率91.7% 可用于当前测试
+
缺失2 个异常日期低于 60%
+
修订17 条事件保留多版本
+
校验和ds:0bf7c9d2
+
+ +
许可不是脚注： 受限正文不会被复制进研究产物；产物只保留可分发元数据、引用和用户本地连接标识。
+
+
+
+ + ); +} diff --git a/studio-web/app/events/page.jsx b/studio-web/app/events/page.jsx new file mode 100644 index 0000000..fd7dca3 --- /dev/null +++ b/studio-web/app/events/page.jsx @@ -0,0 +1,16 @@ +import { EventWorkbench } from "@/components/event-workbench"; +import { ButtonLink, PageHeading } from "@/components/ui"; + +export default function EventsPage() { + return ( + <> + 返回回放圈选} + /> + + + ); +} diff --git a/studio-web/app/factors/[factorId]/page.jsx b/studio-web/app/factors/[factorId]/page.jsx new file mode 100644 index 0000000..9157281 --- /dev/null +++ b/studio-web/app/factors/[factorId]/page.jsx @@ -0,0 +1,122 @@ +"use client"; + +import { PerformanceChart } from "@/components/charts"; +import { ResearchSubject } from "@/components/research-subject"; +import { useStudio } from "@/components/studio-context"; +import { ButtonLink, EmptyState, Metric, ObjectLink, PageHeading, Panel, StatusChip } from "@/components/ui"; +import { factor, metrics } from "@/lib/data"; + +export default function FactorPassport() { + const { source, subject, demoEnabled, factor: activeFactor } = useStudio(); + + if (source.mode === "connected" && !demoEnabled) { + const project = source.snapshot?.projects?.[0]; + const study = project?.studies?.find((item) => item.subjectKind === "factor") || project?.studies?.[0]; + const explorer = project?.factorExplorer; + return ( + <> + 进入标的驱动回放} + /> + +
+ +
+ {(subject?.diagnostics || []).map((detail) => ( +
+
{detail}{subject.label}
+ {subject.unresolved.length ? "待补语义" : "必检"} +
+ ))} +
+ + + {explorer ? ( +
+
Run{explorer.run?.id}
+
Dataset hash{study?.datasetHash}
+
+ ) : ( + + )} + +
+ + ); + } + + return ( + <> + + 打开实验室 + 进入关键区间 + + } + /> + +
+
因子 ID / 版本{factor.id} · {factor.version}
+
当前研究帧{factor.frameId}
+
最近实验EXP-240801-17 · 成功
+
可信状态2 项需要复核
+
+ +
+ {metrics.map((metric) => )} +
+ +
+
+ + + + +
+
定义zscore(event_surprise × diffusion_strength)
+
频率日频 · 事件后 1 个交易日生效
+
标的池A 股全市场 · ST/停牌过滤
+
可见性available_at <= ResearchFrame.as_of
+
处理中立化行业 + 对数市值
+
作者证据Cohort A/B compare@RB-6F19A2
+
+ +
+ +
+ +
+
时间一致性未来事件排除
通过
+
数据覆盖91.7% 样本
部分
+
许可状态新闻正文受限
受限
+
修订风险1 条附件修订
已修订
+
+ + + + + + + + + + + +
+
v1.7加入修订敏感性约束
当前
+
v1.6扩大事件后窗口
2026-07-18
+
v1.5加入新闻适配器
2026-06-29
+
+ +
+
+ + ); +} diff --git a/studio-web/app/factors/page.jsx b/studio-web/app/factors/page.jsx new file mode 100644 index 0000000..646eadb --- /dev/null +++ b/studio-web/app/factors/page.jsx @@ -0,0 +1,5 @@ +import { FactorManagement } from "@/components/factor-management"; + +export default function FactorsPage() { + return ; +} diff --git a/studio-web/app/globals.css b/studio-web/app/globals.css new file mode 100644 index 0000000..c683a4e --- /dev/null +++ b/studio-web/app/globals.css @@ -0,0 +1,2232 @@ +:root { + /* Primitive tokens */ + --aq-ink-950: #0b0d11; + --aq-ink-925: #121419; + --aq-ink-900: #101217; + --aq-ink-850: #171a20; + --aq-ink-800: #1b1e25; + --aq-ink-active: #20232b; + --aq-ink-trust: #15171d; + --aq-ink-chart: #0f1116; + --aq-ink-timeline: #101217; + --aq-ink-event: #49351e; + --aq-line-700: #2b2e36; + --aq-line-soft: #23262d; + --aq-paper-100: #f3f5f7; + --aq-paper-200: #d7dbe0; + --aq-paper-300: #adb3bd; + --aq-cyan-400: #42c7d8; + --aq-cyan-300: #72d6e2; + --aq-cyan-200: #91e4ef; + --aq-amber-400: #f1b35c; + --aq-mint-400: #6bcb9a; + --aq-coral-400: #ec7c73; + --aq-slate-400: #728198; + --aq-black-a35: rgb(0 0 0 / 35%); + --aq-canvas-a94: rgb(11 17 24 / 94%); + --aq-cyan-a18: rgb(82 199 217 / 18%); + --aq-cyan-a05: rgb(82 199 217 / 5%); + --aq-cyan-a04: rgb(82 199 217 / 4%); + --aq-amber-a20: rgb(241 179 92 / 20%); + --aq-amber-a09: rgb(241 179 92 / 9%); + --aq-amber-a07: rgb(241 179 92 / 7%); + --aq-amber-a50: rgb(241 179 92 / 50%); + --aq-mint-a45: rgb(107 203 154 / 45%); + --aq-mint-a10: rgb(107 203 154 / 10%); + --aq-coral-a55: rgb(236 124 115 / 55%); + --aq-coral-a10: rgb(236 124 115 / 10%); + --aq-slate-a55: rgb(114 129 152 / 55%); + --aq-slate-a10: rgb(114 129 152 / 10%); + --aq-slate-a07: rgb(114 129 152 / 7%); + --aq-slate-a02: rgb(114 129 152 / 2%); + + /* Semantic tokens */ + --ink-950: var(--aq-ink-950); + --ink-925: var(--aq-ink-925); + --ink-900: var(--aq-ink-900); + --ink-850: var(--aq-ink-850); + --ink-800: var(--aq-ink-800); + --line-700: var(--aq-line-700); + --line-soft: var(--aq-line-soft); + --paper-100: var(--aq-paper-100); + --paper-200: var(--aq-paper-200); + --paper-300: var(--aq-paper-300); + --signal-cyan: var(--aq-cyan-400); + --event-amber: var(--aq-amber-400); + --success-mint: var(--aq-mint-400); + --danger-coral: var(--aq-coral-400); + --muted-slate: var(--aq-slate-400); + --focus: var(--aq-cyan-200); + + /* Component tokens */ + --panel-background: var(--ink-925); + --control-background: var(--ink-800); + --control-border: var(--line-700); + --control-radius: 3px; + --motion-fast: 120ms; + --nav-width: 178px; + --tab-height: 44px; + --context-height: 34px; + --bar-height: calc(var(--tab-height) + var(--context-height)); + --radius: var(--control-radius); + --ease-out: cubic-bezier(0.23, 1, 0.32, 1); + color-scheme: dark; +} + +* { + box-sizing: border-box; +} + +html { + background: var(--ink-950); + min-width: 320px; +} + +body { + margin: 0; + min-height: 100dvh; + background: var(--ink-950); + color: var(--paper-100); + font-family: "Segoe UI Variable Text", "Segoe UI", system-ui, sans-serif; + font-size: 13px; + line-height: 1.5; + text-rendering: optimizeLegibility; +} + +button, +input, +select { + font: inherit; +} + +button, +a, +select, +input { + touch-action: manipulation; + -webkit-tap-highlight-color: var(--aq-cyan-a18); +} + +button { + border: 0; +} + +a { + color: inherit; + text-decoration: none; +} + +:focus-visible { + outline: 2px solid var(--focus); + outline-offset: 2px; +} + +.mono, +time, +.metric strong, +td.numeric, +.timeline-tick, +.field-value { + font-family: "Cascadia Mono", "SFMono-Regular", Consolas, monospace; + font-variant-numeric: tabular-nums; +} + +.skip-link { + position: fixed; + left: 12px; + top: 8px; + z-index: 100; + transform: translateY(-150%); + background: var(--signal-cyan); + color: var(--ink-950); + padding: 8px 12px; + font-weight: 700; + transition: transform 120ms var(--ease-out); +} + +.skip-link:focus-visible { + transform: translateY(0); +} + +.app-shell { + min-height: 100dvh; +} + +.nav-rail { + position: fixed; + inset: 0 auto 0 0; + z-index: 30; + width: var(--nav-width); + display: flex; + flex-direction: column; + background: var(--ink-900); + border-right: 1px solid var(--line-700); + overscroll-behavior: contain; +} + +.brand-block { + height: var(--tab-height); + display: flex; + align-items: center; + gap: 10px; + padding: 0 12px; + border-bottom: 1px solid var(--line-700); +} + +.brand-mark { + width: 24px; + height: 24px; + display: grid; + place-items: center; + border: 1px solid var(--signal-cyan); + color: var(--signal-cyan); + font: 700 11px/1 "Cascadia Mono", monospace; + letter-spacing: -0.04em; +} + +.brand-block div { + display: flex; + flex-direction: column; + line-height: 1.1; +} + +.brand-block strong { + font-size: 14px; + letter-spacing: -0.01em; +} + +.brand-block div span { + margin-top: 3px; + color: var(--paper-300); + font-size: 11px; + letter-spacing: 0.09em; + text-transform: uppercase; +} + +.nav-list { + display: grid; + gap: 1px; + padding: 10px 8px; +} + +.nav-section { + padding: 13px 8px 5px; + color: #626a78; + font: 600 9px/1 "Cascadia Mono", monospace; + letter-spacing: 0.1em; + text-transform: uppercase; +} + +.nav-link { + min-height: 32px; + display: grid; + grid-template-columns: 30px 1fr; + align-items: center; + gap: 5px; + padding: 0 6px; + border-left: 2px solid transparent; + color: var(--paper-300); + transition: background-color 120ms ease, color 120ms ease, border-color 120ms ease; +} + +.nav-link:hover { + background: var(--ink-800); + color: var(--paper-100); +} + +.nav-link[aria-current="page"] { + border-left-color: var(--signal-cyan); + background: var(--aq-ink-active); + color: var(--paper-100); +} + +.nav-code { + color: var(--muted-slate); + font: 600 10px/1 "Cascadia Mono", monospace; + letter-spacing: 0.04em; +} + +.nav-link[aria-current="page"] .nav-code { + color: var(--signal-cyan); +} + +.nav-footer { + margin-top: auto; + display: grid; + gap: 6px; + padding: 14px 16px 18px; + border-top: 1px solid var(--line-700); + color: var(--muted-slate); + font-size: 11px; +} + +.state-line { + color: var(--paper-300); +} + +.state-line b { + margin-right: 5px; + color: var(--event-amber); + font-family: "Cascadia Mono", monospace; + font-size: 10px; +} + +.workspace { + min-width: 0; + margin-left: var(--nav-width); +} + +.workspace-header { + position: sticky; + top: 0; + z-index: 20; + height: var(--bar-height); + background: var(--ink-950); +} + +.workspace-tabs { + height: var(--tab-height); + display: flex; + align-items: stretch; + gap: 4px; + overflow-x: auto; + padding: 7px 10px 0; + border-bottom: 1px solid var(--line-700); + scrollbar-width: none; +} + +.workspace-tabs::-webkit-scrollbar { + display: none; +} + +.workspace-tabs a { + min-width: max-content; + display: inline-flex; + align-items: center; + padding: 0 9px; + border-radius: 3px 3px 0 0; + color: var(--paper-300); + font-size: 12px; +} + +.workspace-tabs a:hover { + color: var(--paper-100); + background: var(--ink-850); +} + +.workspace-tabs a[aria-current="page"] { + background: var(--ink-800); + color: var(--paper-100); + font-weight: 650; +} + +.workspace-bar { + height: var(--context-height); + display: flex; + align-items: center; + justify-content: space-between; + gap: 16px; + padding: 0 12px; + background: var(--ink-900); + border-bottom: 1px solid var(--line-700); +} + +.context-path, +.bar-status { + display: flex; + align-items: center; + gap: 9px; + min-width: 0; +} + +.context-path > span:first-child, +.bar-status { + color: var(--paper-300); + font-size: 12px; +} + +.context-path a { + overflow: hidden; + color: var(--paper-100); + font-weight: 600; + text-overflow: ellipsis; + white-space: nowrap; +} + +.context-path .mono { + color: var(--signal-cyan); + font-size: 11px; +} + +.bar-status time { + color: var(--paper-100); + font-size: 11px; +} + +.status-dot { + width: 7px; + height: 7px; + border-radius: 50%; + background: var(--muted-slate); +} + +.status-dot.known { + background: var(--success-mint); +} + +.mobile-menu { + display: none; +} + +.main-content { + min-width: 0; + padding: 10px; +} + +.source-gate { + min-height: calc(100dvh - var(--bar-height) - 48px); + display: grid; + align-content: center; + justify-items: start; + max-width: 720px; + margin: 0 auto; +} + +.source-gate p:not(.eyebrow) { + max-width: 62ch; + color: var(--paper-300); +} + +.source-banner { + display: flex; + align-items: center; + gap: 10px; + margin: -10px 0 18px; + padding: 8px 10px; + border: 1px solid var(--aq-amber-a50); + background: var(--aq-amber-a07); + color: var(--paper-300); + font-size: 11px; +} + +.source-banner strong { + color: var(--event-amber); + font: 700 10px/1 "Cascadia Mono", monospace; +} + +.source-switch { + width: fit-content; + border: 0; + border-bottom: 1px solid var(--line-700); + background: transparent; + color: var(--paper-300); + padding: 2px 0; + cursor: pointer; + font-size: 10px; + text-align: left; +} + +.source-switch:hover { + color: var(--signal-cyan); +} + +.page-heading { + display: flex; + align-items: flex-end; + justify-content: space-between; + gap: 28px; + min-height: 58px; + margin-bottom: 10px; + padding: 8px 12px; + border: 1px solid var(--line-700); + background: var(--ink-900); +} + +.eyebrow { + margin: 0 0 3px; + color: var(--signal-cyan); + font: 600 10px/1.2 "Cascadia Mono", monospace; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +h1, +h2, +h3, +p { + margin-top: 0; +} + +h1 { + margin-bottom: 2px; + font-size: clamp(17px, 1.6vw, 21px); + line-height: 1.18; + letter-spacing: -0.025em; + scroll-margin-top: calc(var(--bar-height) + 16px); + text-wrap: balance; +} + +h2 { + margin-bottom: 0; + font-size: 14px; + line-height: 1.3; + letter-spacing: -0.01em; +} + +h3 { + margin-bottom: 6px; + font-size: 13px; +} + +.page-description { + max-width: 72ch; + margin-bottom: 0; + color: var(--paper-300); + font-size: 11px; +} + +.page-actions, +.button-row { + display: flex; + flex-wrap: wrap; + align-items: center; + gap: 8px; +} + +.button, +.button-secondary, +.button-quiet, +.aq-button, +.segmented button, +.transport button, +.cohort-action { + min-height: 34px; + display: inline-flex; + align-items: center; + justify-content: center; + border: 1px solid transparent; + border-radius: var(--radius); + padding: 0 12px; + cursor: pointer; + white-space: nowrap; + transition: transform 120ms var(--ease-out), background-color 120ms ease, border-color 120ms ease, color 120ms ease; +} + +.button, +.aq-button-primary { + background: var(--signal-cyan); + color: var(--ink-950); + font-weight: 700; +} + +.button-secondary, +.aq-button-secondary { + background: var(--ink-800); + border-color: var(--line-700); + color: var(--paper-100); +} + +.button-quiet, +.aq-button-quiet { + min-height: 32px; + background: transparent; + border-color: var(--line-700); + color: var(--paper-300); +} + +.button:hover, +.aq-button-primary:hover { + background: var(--aq-cyan-300); +} + +.button-secondary:hover, +.button-quiet:hover, +.aq-button-secondary:hover, +.aq-button-quiet:hover { + border-color: var(--muted-slate); + color: var(--paper-100); +} + +.button:active, +.button-secondary:active, +.button-quiet:active, +.aq-button:active, +.transport button:active, +.cohort-action:active { + transform: scale(0.97); +} + +button:disabled, +.button[aria-disabled="true"] { + cursor: not-allowed; + opacity: 0.45; + transform: none; +} + +.panel { + min-width: 0; + min-height: 0; + overflow: hidden; + background: var(--panel-background); + border: 1px solid var(--line-700); + border-radius: 3px; +} + +.panel-header { + min-height: 38px; + display: flex; + align-items: center; + justify-content: space-between; + gap: 10px; + padding: 6px 10px; + border-bottom: 1px solid var(--line-700); +} + +.panel-header p { + margin: 1px 0 0; + color: var(--muted-slate); + font-size: 11px; +} + +.panel-body { + min-width: 0; + padding: 10px; +} + +.panel-header h2::before { + content: ""; + width: 8px; + height: 8px; + display: inline-block; + margin-right: 7px; + border: 1px solid var(--event-amber); + border-radius: 50%; + vertical-align: 1px; +} + +.status-chip { + display: inline-flex; + align-items: center; + min-height: 22px; + padding: 2px 7px; + border: 1px solid var(--line-700); + border-radius: var(--radius); + color: var(--paper-300); + font: 600 10px/1.2 "Cascadia Mono", monospace; + letter-spacing: 0.02em; + text-transform: none; +} + +.status-chip.known { + border-color: var(--aq-mint-a45); + background: var(--aq-mint-a10); + color: var(--success-mint); +} + +.status-chip.partial, +.status-chip.restricted { + border-color: var(--aq-slate-a55); + background: var(--aq-slate-a10); + color: var(--paper-300); +} + +.status-chip.delayed, +.status-chip.revised { + border-color: var(--aq-amber-a50); + background: var(--aq-amber-a09); + color: var(--event-amber); +} + +.status-chip.missing { + border-color: var(--aq-coral-a55); + background: var(--aq-coral-a10); + color: var(--danger-coral); +} + +.metric-row { + display: grid; + grid-template-columns: repeat(5, minmax(0, 1fr)); + border: 1px solid var(--line-700); + border-radius: var(--radius); + background: var(--ink-925); +} + +.metric { + min-width: 0; + display: grid; + gap: 2px; + padding: 13px 14px; + border-right: 1px solid var(--line-700); +} + +.metric:last-child { + border-right: 0; +} + +.metric > span { + color: var(--paper-300); + font-size: 11px; +} + +.metric strong { + font-size: 19px; + line-height: 1.3; +} + +.metric small { + color: var(--muted-slate); + font-size: 10px; +} + +.metric.positive small { + color: var(--success-mint); +} + +.metric.warning small { + color: var(--event-amber); +} + +.grid-2, +.dashboard-grid, +.passport-grid, +.result-grid, +.audit-grid, +.lab-grid { + display: grid; + gap: 10px; +} + +.grid-2, +.result-grid, +.audit-grid, +.lab-grid { + grid-template-columns: minmax(0, 1.4fr) minmax(280px, 0.8fr); +} + +.dashboard-grid { + grid-template-columns: minmax(0, 1.55fr) minmax(320px, 0.75fr); +} + +.passport-grid { + grid-template-columns: repeat(12, minmax(0, 1fr)); +} + +.passport-main { + grid-column: span 8; +} + +.passport-side { + grid-column: span 4; +} + +.stack { + display: grid; + gap: 10px; +} + +.dense-list { + display: grid; +} + +.dense-row { + display: grid; + grid-template-columns: minmax(0, 1fr) auto; + align-items: center; + gap: 18px; + min-height: 49px; + padding: 9px 0; + border-bottom: 1px solid var(--line-soft); +} + +.dense-row:last-child { + border-bottom: 0; +} + +.dense-row strong { + display: block; + font-size: 12px; +} + +.dense-row p, +.dense-row small { + margin: 2px 0 0; + color: var(--muted-slate); + font-size: 11px; +} + +.dense-row > :last-child { + text-align: right; +} + +.object-link { + min-height: 44px; + display: flex; + align-items: center; + justify-content: space-between; + gap: 12px; + padding: 7px 0; + border-bottom: 1px solid var(--line-soft); + color: var(--paper-300); +} + +.object-link:hover { + color: var(--signal-cyan); +} + +.object-link:last-child { + border-bottom: 0; +} + +.object-link b { + color: var(--paper-100); + font-size: 10px; +} + +.trust-strip { + display: grid; + grid-template-columns: repeat(4, minmax(0, 1fr)); + margin-bottom: 14px; + border: 1px solid var(--line-700); + background: var(--aq-ink-trust); +} + +.trust-item { + min-width: 0; + padding: 10px 12px; + border-right: 1px solid var(--line-700); +} + +.trust-item:last-child { + border-right: 0; +} + +.trust-item span { + display: block; + color: var(--muted-slate); + font-size: 10px; +} + +.trust-item strong { + display: block; + overflow: hidden; + margin-top: 2px; + font-size: 12px; + text-overflow: ellipsis; + white-space: nowrap; +} + +.trust-item.warning strong { + color: var(--event-amber); +} + +.table-wrap { + overflow-x: auto; +} + +table { + width: 100%; + border-collapse: collapse; + font-size: 12px; +} + +th, +td { + padding: 10px 9px; + border-bottom: 1px solid var(--line-soft); + text-align: left; + vertical-align: top; +} + +th { + position: sticky; + top: 0; + background: var(--ink-925); + color: var(--muted-slate); + font-size: 10px; + font-weight: 600; + letter-spacing: 0.04em; + text-transform: uppercase; +} + +tbody tr:last-child td { + border-bottom: 0; +} + +tbody tr:hover td { + background: var(--aq-cyan-a04); +} + +td.numeric { + text-align: right; +} + +.field-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 12px 18px; +} + +.field-label { + display: block; + margin-bottom: 3px; + color: var(--muted-slate); + font-size: 10px; + letter-spacing: 0.03em; + text-transform: uppercase; +} + +.field-value { + display: block; + color: var(--paper-200); + font-size: 11px; + overflow-wrap: anywhere; +} + +.notice { + padding: 10px 12px; + border-left: 2px solid var(--event-amber); + background: var(--aq-amber-a07); + color: var(--paper-300); + font-size: 12px; +} + +.notice strong { + color: var(--event-amber); +} + +.empty-state { + min-height: 160px; + display: grid; + align-content: center; + justify-items: start; + gap: 8px; + color: var(--paper-300); +} + +.empty-state strong { + color: var(--paper-100); + font-size: 14px; +} + +.chart-shell { + position: relative; + min-height: 260px; + overflow: hidden; + border: 1px solid var(--line-soft); + background-color: var(--aq-ink-chart); + background-image: + linear-gradient(var(--line-soft) 1px, transparent 1px), + linear-gradient(90deg, var(--line-soft) 1px, transparent 1px); + background-size: 100% 52px, 9% 100%; +} + +.chart-shell svg { + width: 100%; + height: 260px; + display: block; +} + +.chart-shell.lightweight { + min-height: 360px; + background-image: none; +} + +.chart-shell.lightweight.compact { + min-height: 260px; +} + +.lightweight-chart { + width: 100%; + min-height: 360px; +} + +.compact .lightweight-chart { + min-height: 260px; +} + +.chart-axis-label { + position: absolute; + z-index: 2; + padding: 3px 5px; + background: var(--ink-900); + color: var(--paper-300); + font: 10px/1 "Cascadia Mono", monospace; +} + +.chart-axis-label.top { + top: 8px; + left: 9px; +} + +.chart-axis-label.bottom { + right: 9px; + bottom: 8px; +} + +.timeline { + position: relative; + display: grid; + grid-template-columns: repeat(8, minmax(70px, 1fr)); + min-width: 680px; + min-height: 142px; + overflow: hidden; + border: 1px solid var(--line-soft); + background: var(--aq-ink-timeline); +} + +.timeline-cell { + position: relative; + min-height: 142px; + border-right: 1px solid var(--line-soft); +} + +.timeline-cell:last-child { + border-right: 0; +} + +.timeline-tick { + position: absolute; + bottom: 7px; + left: 7px; + color: var(--muted-slate); + font-size: 9px; +} + +.event-marker { + position: absolute; + z-index: 3; + left: 50%; + top: var(--track); + width: 12px; + height: 12px; + transform: translate(-50%, -50%) rotate(45deg); + border: 1px solid var(--event-amber); + background: var(--aq-ink-event); + cursor: pointer; +} + +.event-marker.selected { + background: var(--event-amber); + box-shadow: 0 0 0 3px var(--aq-amber-a20); +} + +.event-marker.cohort-a { + border-color: var(--signal-cyan); +} + +.event-marker.cohort-b { + border-color: var(--success-mint); +} + +.future-curtain { + position: absolute; + z-index: 1; + inset: 0 0 0 var(--cursor); + background: repeating-linear-gradient(135deg, var(--aq-slate-a07) 0 6px, var(--aq-slate-a02) 6px 12px); + border-left: 1px dashed var(--signal-cyan); + pointer-events: none; +} + +.future-curtain::before { + content: "T 后不可见"; + position: absolute; + top: 7px; + left: 7px; + color: var(--signal-cyan); + font: 9px/1 "Cascadia Mono", monospace; +} + +.transport { + display: flex; + flex-wrap: wrap; + align-items: center; + gap: 7px; +} + +.transport button { + min-width: 58px; + background: var(--ink-800); + border: 1px solid var(--line-700); + color: var(--paper-100); +} + +.transport time { + min-width: 128px; + color: var(--signal-cyan); + font-size: 11px; +} + +.replay-layout { + display: grid; + grid-template-columns: minmax(0, 1fr) minmax(300px, 344px); + gap: 10px; +} + +.connected-replay-grid { + display: grid; + grid-template-columns: minmax(0, 1.7fr) minmax(320px, 0.75fr); + gap: 10px; +} + +.connected-replay-canvas .panel-body { + min-height: 510px; +} + +.replay-empty-canvas { + position: relative; + min-height: 488px; + display: grid; + place-items: center; + overflow: hidden; + border: 1px solid var(--line-soft); + background: var(--aq-ink-chart); +} + +.empty-chart-grid { + position: absolute; + inset: 0; + opacity: 0.42; + background-image: + linear-gradient(var(--line-soft) 1px, transparent 1px), + linear-gradient(90deg, var(--line-soft) 1px, transparent 1px); + background-size: 100% 52px, 8.333% 100%; +} + +.replay-empty-canvas .empty-state { + position: relative; + z-index: 1; + max-width: 450px; + justify-items: center; + padding: 24px; + text-align: center; +} + +.connected-replay-side { + align-content: start; +} + +.connected-replay-side .field-grid { + grid-template-columns: 1fr; +} + +.connected-replay-route { + grid-column: 1 / -1; +} + +.connected-replay-route .trust-strip { + margin-bottom: 0; +} + +html[data-graybox="true"] { + --aq-ink-950: #161616; + --aq-ink-925: #242424; + --aq-ink-900: #1d1d1d; + --aq-ink-850: #292929; + --aq-ink-800: #303030; + --aq-ink-active: #383838; + --aq-ink-trust: #282828; + --aq-ink-chart: #202020; + --aq-ink-timeline: #242424; + --aq-line-700: #5c5c5c; + --aq-line-soft: #454545; + --aq-paper-100: #dedede; + --aq-paper-200: #c4c4c4; + --aq-paper-300: #a5a5a5; + --aq-cyan-400: #bcbcbc; + --aq-amber-400: #a9a9a9; + --aq-mint-400: #b4b4b4; + --aq-coral-400: #9b9b9b; + --aq-slate-400: #8a8a8a; +} + +html[data-graybox="true"] *, +html[data-graybox="true"] *::before, +html[data-graybox="true"] *::after { + box-shadow: none !important; + background-image: none !important; + backdrop-filter: none !important; +} + +html[data-graybox="true"] canvas, +html[data-graybox="true"] svg { + opacity: 0 !important; +} + +.evidence-inspector { + position: sticky; + top: calc(var(--bar-height) + 14px); + align-self: start; +} + +.event-title { + margin-bottom: 4px; + font-size: 15px; +} + +.event-summary { + color: var(--paper-300); + font-size: 12px; + line-height: 1.7; +} + +.cohort-tray { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 10px; +} + +.cohort-column { + min-height: 98px; + padding: 10px; + border: 1px solid var(--line-soft); + background: var(--ink-925); +} + +.cohort-column h3 { + color: var(--paper-300); + font-size: 11px; +} + +.cohort-member { + display: flex; + align-items: center; + justify-content: space-between; + gap: 8px; + padding: 5px 0; + color: var(--paper-200); + font-size: 11px; +} + +.cohort-member button { + background: transparent; + color: var(--muted-slate); + cursor: pointer; +} + +.cohort-action { + background: transparent; + border-color: var(--line-700); + color: var(--paper-300); +} + +.cohort-action.active-a { + border-color: var(--signal-cyan); + color: var(--signal-cyan); +} + +.cohort-action.active-b { + border-color: var(--success-mint); + color: var(--success-mint); +} + +.comparison-grid { + display: grid; + grid-template-columns: minmax(0, 1fr) 120px minmax(0, 1fr); + gap: 12px; + align-items: stretch; +} + +.comparison-side { + padding: 14px; + border: 1px solid var(--line-soft); +} + +.comparison-delta { + display: grid; + place-items: center; + align-content: center; + border-top: 1px solid var(--line-700); + border-bottom: 1px solid var(--line-700); + color: var(--paper-300); + text-align: center; +} + +.comparison-delta strong { + color: var(--signal-cyan); + font: 700 18px/1.3 "Cascadia Mono", monospace; +} + +.tag-list { + display: flex; + flex-wrap: wrap; + gap: 5px; +} + +.tag { + padding: 3px 6px; + border: 1px solid var(--line-soft); + color: var(--paper-300); + font-size: 10px; +} + +.form-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 14px; +} + +.form-field { + display: grid; + gap: 6px; +} + +.form-field label, +fieldset legend { + color: var(--paper-300); + font-size: 11px; +} + +select, +input[type="number"], +input[type="text"] { + min-height: 40px; + width: 100%; + border: 1px solid var(--line-700); + border-radius: var(--radius); + background: var(--ink-800); + color: var(--paper-100); + padding: 0 10px; +} + +fieldset { + margin: 0; + border: 1px solid var(--line-soft); + padding: 10px 12px 12px; +} + +.check-grid { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 8px 12px; + margin-top: 7px; +} + +.check-grid label { + min-height: 32px; + display: flex; + align-items: center; + gap: 7px; + color: var(--paper-200); + font-size: 12px; +} + +input[type="checkbox"] { + accent-color: var(--signal-cyan); +} + +.run-summary { + display: grid; + gap: 9px; +} + +.run-summary .field { + display: flex; + justify-content: space-between; + gap: 18px; + padding-bottom: 7px; + border-bottom: 1px solid var(--line-soft); +} + +.run-summary .field span { + color: var(--muted-slate); + font-size: 11px; +} + +.run-summary .field strong { + text-align: right; + font-size: 11px; +} + +.job-progress { + width: 84px; + color: var(--paper-300); + font: 10px/1.2 "Cascadia Mono", monospace; +} + +.audit-chain { + position: relative; + display: grid; + gap: 0; +} + +.audit-node { + position: relative; + display: grid; + grid-template-columns: 28px minmax(0, 1fr) auto; + gap: 10px; + min-height: 64px; + align-items: center; + border-bottom: 1px solid var(--line-soft); +} + +.audit-node:last-child { + border-bottom: 0; +} + +.audit-index { + color: var(--signal-cyan); + font: 600 10px/1 "Cascadia Mono", monospace; +} + +.audit-node strong, +.audit-node span { + display: block; +} + +.audit-node span { + color: var(--muted-slate); + font-size: 10px; +} + +.provenance-card { + display: grid; + gap: 10px; + padding: 12px; + border-left: 2px solid var(--signal-cyan); + background: var(--aq-cyan-a05); +} + +@media (max-width: 1180px) { + .bar-status > span:nth-of-type(2) { + display: none; + } + + .dashboard-grid, + .grid-2, + .result-grid, + .audit-grid, + .lab-grid { + grid-template-columns: minmax(0, 1fr); + } + + .evidence-inspector { + position: static; + } + + .metric-row { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .metric:nth-child(3) { + border-right: 0; + } + + .metric:nth-child(n + 4) { + border-top: 1px solid var(--line-700); + } +} + +@media (max-width: 1040px) { + .replay-layout, + .connected-replay-grid { + grid-template-columns: minmax(0, 1fr); + } + + .evidence-inspector { + position: static; + } +} + +@media (max-width: 900px) { + :root { + --nav-width: 68px; + } + + .brand-block { + justify-content: center; + padding: 0; + } + + .brand-block > div, + .nav-link > span:last-child, + .nav-section, + .nav-footer { + display: none; + } + + .nav-link { + grid-template-columns: 1fr; + justify-items: center; + padding: 0; + } + + .nav-code { + font-size: 11px; + } + + .passport-main, + .passport-side { + grid-column: span 12; + } + + .trust-strip { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .trust-item:nth-child(2) { + border-right: 0; + } + + .trust-item:nth-child(n + 3) { + border-top: 1px solid var(--line-700); + } +} + +@media (max-width: 700px) { + :root { + --nav-width: 0px; + } + +.nav-rail { + width: min(286px, 86vw); + transform: translateX(-102%); + transition: transform 160ms var(--ease-out); + } + + .nav-rail.is-open { + transform: translateX(0); + box-shadow: 16px 0 40px var(--aq-black-a35); + } + + .nav-rail.is-open .brand-block { + justify-content: flex-start; + padding: 0 16px; + } + + .nav-rail.is-open .brand-block > div, + .nav-rail.is-open .nav-link > span:last-child, + .nav-rail.is-open .nav-section, + .nav-rail.is-open .nav-footer { + display: flex; + } + + .nav-rail.is-open .nav-link { + grid-template-columns: 30px 1fr; + justify-items: start; + padding: 0 8px; + } + + .workspace-bar { + padding: 0 12px; + } + + .workspace-tabs { + padding-left: 8px; + } + + .mobile-menu { + min-height: 36px; + display: inline-flex; + align-items: center; + border: 1px solid var(--line-700); + border-radius: var(--radius); + background: var(--ink-800); + color: var(--paper-100); + padding: 0 10px; + } + + .context-path > span:first-child, + .context-path .mono, + .bar-status > span { + display: none; + } + + .bar-status time { + font-size: 9px; + } + + .main-content { + padding: 16px 12px 28px; + } + + .page-heading { + align-items: flex-start; + flex-direction: column; + gap: 14px; + } + + .page-actions { + width: 100%; + } + + .metric-row { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .metric:nth-child(2n) { + border-right: 0; + } + + .metric:nth-child(3) { + border-right: 1px solid var(--line-700); + } + + .metric:nth-child(n + 3) { + border-top: 1px solid var(--line-700); + } + + .metric:last-child { + grid-column: span 2; + border-right: 0; + } + + .trust-strip, + .cohort-tray, + .field-grid, + .form-grid, + .check-grid, + .comparison-grid { + grid-template-columns: minmax(0, 1fr); + } + + .trust-item { + border-right: 0; + border-bottom: 1px solid var(--line-700); + } + + .trust-item:last-child { + border-bottom: 0; + } + + .comparison-delta { + min-height: 72px; + border-right: 1px solid var(--line-700); + border-left: 1px solid var(--line-700); + } + + .panel-body { + padding: 12px; + } + + th, + td { + padding: 9px 7px; + } +} + +/* ===== ResearchLedger ===== */ + +.ledger-layout { + display: grid; + grid-template-columns: minmax(320px, 0.65fr) minmax(0, 1.35fr); + gap: 10px; + align-items: start; +} + +.ledger-sidebar { + position: sticky; + top: calc(var(--bar-height) + 14px); + display: grid; + gap: 10px; + align-content: start; +} + +.ledger-main { + min-width: 0; + display: grid; + gap: 10px; + align-content: start; +} + +.ledger-nav { + display: grid; + gap: 0; +} + +.ledger-stage { + display: grid; + grid-template-columns: 20px 1fr; + gap: 10px; + min-height: 58px; + align-items: start; + padding: 10px 4px 10px 8px; + border-left: 2px solid transparent; + border-bottom: 1px solid var(--line-soft); + background: transparent; + color: inherit; + cursor: pointer; + text-align: left; + transition: background-color 120ms ease, border-color 120ms ease; +} + +.ledger-stage:last-child { + border-bottom: 0; +} + +.ledger-stage:hover { + background: var(--aq-cyan-a04); +} + +.ledger-stage.ledger-stage-active { + border-left-color: var(--signal-cyan); + background: var(--aq-cyan-a05); +} + +.ledger-stage.ledger-stage-missing { + opacity: 0.48; + cursor: default; +} + +.ledger-stage-indicator { + display: grid; + place-items: center; + padding-top: 2px; +} + +.ledger-stage-body { + min-width: 0; +} + +.ledger-stage-header { + display: flex; + align-items: center; + justify-content: space-between; + gap: 8px; +} + +.ledger-stage-body strong { + font-size: 12px; +} + +.ledger-stage-objects { + display: flex; + flex-wrap: wrap; + gap: 4px; + margin-top: 6px; +} + +.ledger-object { + display: inline-flex; + align-items: center; + gap: 5px; + padding: 2px 6px; + border: 1px solid var(--line-soft); + border-radius: var(--radius); + font-size: 10px; +} + +.ledger-object-kind { + color: var(--muted-slate); + font-size: 9px; + letter-spacing: 0.03em; + text-transform: uppercase; +} + +.ledger-object .mono { + font-size: 10px; +} + +.ledger-object-version { + color: var(--signal-cyan); + font-size: 9px; + font-weight: 600; +} + +.ledger-blockers { + margin: 6px 0 0; + padding-left: 16px; + color: var(--event-amber); + font-size: 11px; + line-height: 1.6; +} + +.ledger-stage-dot { + display: block; + width: 8px; + height: 8px; + border-radius: 50%; + background: var(--muted-slate); + flex-shrink: 0; +} + +.ledger-stage-dot-available { + background: var(--success-mint); +} + +.ledger-stage-dot-partial { + background: var(--event-amber); +} + +.ledger-stage-dot-empty, +.ledger-stage-dot-unavailable { + background: var(--muted-slate); + opacity: 0.55; +} + +.receipt-card { + background: var(--ink-925); + border: 1px solid var(--line-700); +} + +.stage-dots { + display: flex; + gap: 4px; + align-items: center; +} + +.stage-dot { + display: block; + width: 7px; + height: 7px; + border-radius: 2px; + flex-shrink: 0; +} + +.stage-dot-available { + background: var(--success-mint); +} + +.stage-dot-partial { + background: var(--event-amber); +} + +.stage-dot-empty, +.stage-dot-unavailable { + background: var(--muted-slate); + opacity: 0.45; +} + +@media (max-width: 1040px) { + .ledger-layout { + grid-template-columns: minmax(0, 1fr); + } + + .ledger-sidebar { + position: static; + } +} + +@media (prefers-reduced-motion: reduce) { + *, + *::before, + *::after { + scroll-behavior: auto !important; + transition-duration: 0.01ms !important; + animation-duration: 0.01ms !important; + animation-iteration-count: 1 !important; + } + + .ledger-stage, + .research-stage-rail button { + transition: none; + } + + .button:active, + .button-secondary:active, + .button-quiet:active, + .transport button:active, + .cohort-action:active { + transform: none; + } +} + +/* ===== Agent Research Console ===== */ + +.research-session-list, +.receipt-list, +.research-inspector-content, +.research-stage-canvas { + display: grid; + gap: 10px; +} + +.research-session-card { + display: grid; + grid-template-columns: minmax(0, 1fr) auto auto; + gap: 14px; + align-items: center; + min-height: 60px; + padding: 10px 12px; + border: 1px solid var(--line-soft); + color: inherit; + text-decoration: none; +} + +.research-session-card:hover, +.research-session-card:focus-visible { + border-color: var(--signal-cyan); + background: var(--aq-cyan-a04); +} + +.research-session-card div:first-child { + display: grid; + gap: 3px; + min-width: 0; +} + +.research-session-card small { + color: var(--muted-slate); + overflow: hidden; + text-overflow: ellipsis; +} + +.research-console { + display: grid; + grid-template-columns: minmax(0, 1fr); + gap: 4px; + align-items: start; + min-width: 0; +} + +.research-conversation, +.research-canvas, +.research-inspector { + display: grid; + gap: 10px; + min-width: 0; + align-content: start; +} + +.research-conversation, +.research-inspector { + position: static; + max-height: none; + overflow: visible; +} + +.research-inspector { + display: none; +} + +.structured-actions, +.button-row { + display: flex; + flex-wrap: wrap; + gap: 8px; +} + +.research-stage-rail { + display: grid; + border: 1px solid var(--line-700); + background: var(--ink-900); +} + +.research-stage-rail button { + display: grid; + grid-template-columns: 26px minmax(0, 1fr); + gap: 9px; + align-items: center; + min-height: 54px; + padding: 8px 10px; + border: 0; + border-bottom: 1px solid var(--line-soft); + background: transparent; + color: inherit; + text-align: left; + cursor: pointer; + transition: background-color 120ms ease, box-shadow 120ms ease; +} + +.research-stage-rail button:last-child { + border-bottom: 0; +} + +.research-stage-rail button.is-active { + background: var(--aq-cyan-a05); + box-shadow: inset 2px 0 0 var(--signal-cyan); +} + +.research-stage-rail button:disabled { + cursor: default; + opacity: 0.5; +} + +.research-stage-rail button > span:last-child { + display: grid; + gap: 2px; +} + +.research-stage-rail small { + color: var(--muted-slate); + font-size: 10px; +} + +.research-stage-index { + display: grid; + place-items: center; + width: 22px; + height: 22px; + border: 1px solid var(--muted-slate); + border-radius: 50%; + color: var(--muted-slate); + font: 600 10px/1 var(--font-mono); +} + +.research-stage-index.state-available { + border-color: var(--success-mint); + color: var(--success-mint); +} + +.research-stage-index.state-partial { + border-color: var(--event-amber); + color: var(--event-amber); +} + +.stage-summary, +.object-list { + display: flex; + flex-wrap: wrap; + gap: 7px; + align-items: center; +} + +.research-object-ref { + display: inline-flex; + align-items: center; + gap: 5px; + max-width: 100%; + padding: 3px 6px; + border: 1px solid var(--line-soft); + border-radius: var(--radius); + font-size: 10px; +} + +.research-object-ref span { + color: var(--muted-slate); +} + +.research-object-ref b { + overflow: hidden; + text-overflow: ellipsis; +} + +.research-object-ref em { + color: var(--signal-cyan); + font-style: normal; +} + +.research-blockers { + margin: 12px 0 0; + padding-left: 20px; + color: var(--event-amber); +} + +.next-actions, +.notice, +.receipt-warning { + color: var(--muted-slate); + font-size: 11px; +} + +.operation-receipt { + display: grid; + gap: 7px; + padding: 10px; + border: 1px solid var(--line-700); + background: var(--ink-925); +} + +.operation-receipt header { + display: flex; + justify-content: space-between; + gap: 8px; + align-items: flex-start; +} + +.operation-receipt header div { + display: grid; + gap: 2px; + min-width: 0; +} + +.operation-receipt small, +.operation-receipt time { + color: var(--muted-slate); + font-size: 10px; + overflow-wrap: anywhere; +} + +.receipt-error { + margin: 0; + color: var(--danger-coral); + font-size: 11px; +} + +.campaign-budget { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 8px; + padding: 12px; + border: 1px solid var(--line-700); + background: var(--ink-900); +} + +.campaign-budget > div { + display: grid; + gap: 5px; +} + +.campaign-budget span { + display: flex; + justify-content: space-between; + gap: 5px; + font-size: 10px; +} + +.campaign-budget em { + color: var(--muted-slate); + font-style: normal; +} + +.campaign-budget progress { + width: 100%; + height: 5px; + accent-color: var(--signal-cyan); +} + +.campaign-budget p { + grid-column: 1 / -1; + margin: 0; + color: var(--muted-slate); + font-size: 10px; +} + +.evidence-review { + min-width: 0; + padding: 10px; + border: 1px solid var(--line-700); + background: var(--ink-900); +} + +.evidence-review [role="tablist"] { + flex-wrap: wrap; +} + +.evidence-review [role="tabpanel"] { + padding-top: 12px; +} + +.evidence-json, +.semantic-diff pre, +.research-inspector-content pre { + max-width: 100%; + max-height: 320px; + overflow: auto; + padding: 9px; + border: 1px solid var(--line-soft); + background: var(--ink-950); + color: var(--pale-slate); + font: 10px/1.55 var(--font-mono); + white-space: pre-wrap; + overflow-wrap: anywhere; +} + +.semantic-diff details, +.technical-details, +.research-task-tray { + border-top: 1px solid var(--line-soft); + padding: 8px 0; +} + +.semantic-diff summary, +.technical-details summary, +.research-task-tray summary { + cursor: pointer; + font-weight: 600; +} + +.research-task-tray { + position: sticky; + bottom: 0; + z-index: 3; + padding: 10px 12px; + border: 1px solid var(--line-700); + background: color-mix(in srgb, var(--ink-900) 96%, transparent); +} + +.research-task-tray p { + color: var(--muted-slate); + font-size: 11px; +} + +.sr-live { + position: absolute; + width: 1px; + height: 1px; + overflow: hidden; + clip: rect(0 0 0 0); + clip-path: inset(50%); +} + +.review-only-banner { + grid-column: 1 / -1; + padding: 8px 10px; + border: 1px solid var(--event-amber); + color: var(--event-amber); + font-size: 11px; +} + +.research-inspector-trigger { + display: none; +} + +.research-review-only-inspector { + display: grid; + min-width: 0; +} + +.research-console :where(button, a, input, textarea, select, summary):focus-visible { + outline: 2px solid var(--signal-cyan); + outline-offset: 2px; +} + +@media (min-width: 1024px) { + .research-console { + grid-template-columns: minmax(230px, 300px) minmax(560px, 1fr); + } + + .research-conversation { + position: sticky; + top: calc(var(--bar-height) + 12px); + max-height: calc(100vh - var(--bar-height) - 24px); + overflow: auto; + } + + .research-inspector-trigger { + display: inline-flex; + position: fixed; + right: 18px; + bottom: 18px; + z-index: 20; + } + + .research-review-only-inspector { + display: none; + } + + .campaign-budget { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } +} + +@media (min-width: 1440px) { + .research-console { + grid-template-columns: minmax(340px, 380px) minmax(560px, 1fr) minmax(320px, 380px); + } + + .research-inspector { + display: grid; + position: sticky; + top: calc(var(--bar-height) + 12px); + max-height: calc(100vh - var(--bar-height) - 24px); + overflow: auto; + } + + .research-inspector-trigger { + display: none; + } +} + +@media (max-width: 560px) { + .research-session-card { + grid-template-columns: minmax(0, 1fr) auto; + } + + .research-session-card .stage-dots { + display: none; + } + + .campaign-budget { + grid-template-columns: minmax(0, 1fr); + } +} diff --git a/studio-web/app/icon.svg b/studio-web/app/icon.svg new file mode 100644 index 0000000..0b462a4 --- /dev/null +++ b/studio-web/app/icon.svg @@ -0,0 +1,5 @@ + + + + AQ + diff --git a/studio-web/app/jobs/page.jsx b/studio-web/app/jobs/page.jsx new file mode 100644 index 0000000..0d16a0e --- /dev/null +++ b/studio-web/app/jobs/page.jsx @@ -0,0 +1,5 @@ +import { JobsWorkbench } from "@/components/jobs-workbench"; + +export default function JobsPage() { + return ; +} diff --git a/studio-web/app/lab/page.jsx b/studio-web/app/lab/page.jsx new file mode 100644 index 0000000..a52d147 --- /dev/null +++ b/studio-web/app/lab/page.jsx @@ -0,0 +1,5 @@ +import { FactorLab } from "@/components/factor-lab"; + +export default function LabPage() { + return ; +} diff --git a/studio-web/app/layout.jsx b/studio-web/app/layout.jsx new file mode 100644 index 0000000..6a0d48b --- /dev/null +++ b/studio-web/app/layout.jsx @@ -0,0 +1,32 @@ +import "@mantine/core/styles.css"; +import "./globals.css"; +import { ColorSchemeScript, MantineProvider, mantineHtmlProps } from "@mantine/core"; +import { StudioProvider } from "@/components/studio-context"; +import { StudioShell } from "@/components/studio-shell"; +import { autoQuantTheme } from "@/lib/theme"; + +export const metadata = { + title: "AutoQuant Studio", + description: "Point-in-time factor research evidence workbench", +}; + +export const viewport = { + themeColor: "#0b1118", +}; + +export default function RootLayout({ children }) { + return ( + + + + + + + + {children} + + + + + ); +} diff --git a/studio-web/app/page.jsx b/studio-web/app/page.jsx new file mode 100644 index 0000000..5615f2a --- /dev/null +++ b/studio-web/app/page.jsx @@ -0,0 +1,176 @@ +"use client"; + +import Link from "next/link"; +import { EvidenceChart } from "@/components/charts"; +import { useStudio } from "@/components/studio-context"; +import { ResearchSubject } from "@/components/research-subject"; +import { Button, ButtonLink, Metric, ObjectLink, PageHeading, Panel, StatusChip } from "@/components/ui"; +import { adapters, factor, jobs, metrics } from "@/lib/data"; + +function studyHref(study) { + if (study.id.includes("portfolio")) return "/portfolio"; + if (study.id.includes("rl")) return "/rl"; + return `/factors/${study.id}`; +} + +export default function ResearchHome() { + const { source, subject, demoEnabled, enableDemo } = useStudio(); + + if (source.mode === "connected" && !demoEnabled) { + const snapshot = source.snapshot; + const project = snapshot.projects[0]; + const counts = project?.counts || {}; + const coreMetrics = [ + ["Studies", counts.studies ?? 0], + ["Runs", counts.runs ?? 0], + ["Sessions", counts.sessions ?? 0], + ["Reports", counts.reports ?? 0], + ["Diagnostics", (snapshot.diagnostics.length + (project?.diagnostics?.length || 0))], + ]; + return ( + <> + 查看演示工作台} + /> +
+
HarnessAQ {snapshot.harness.version}@{snapshot.harness.commit.slice(0, 8)}
+
Snapshotschema v{snapshot.schemaVersion}
+
Generated{snapshot.generatedAt.replace("T", " ").replace("Z", " UTC")}
+
Verification{snapshot.valid ? "全部类别有效" : "存在 Core diagnostics"}
+
+
+ {coreMetrics.map(([label, value]) => )} +
+
+
+ +
+ {snapshot.projects.map((item) => ( +
+
{item.name}

{item.description || item.id}

+ {item.valid ? "verified" : "diagnostics"} +
+ ))} +
+ +
+ + {project ? ( +
+ {project.studies.map((study) => ( + +
{study.name}{study.primaryMetric}
+ {study.subjectKind} + + ))} +
+ ) :

Workspace 中没有 Project。

} + + + {(project?.diagnostics?.length || snapshot.diagnostics.length) ? ( +
+ {[...snapshot.diagnostics, ...(project?.diagnostics || [])].slice(0, 6).map((item, index) => ( +
+
{item.code}

{item.message}

+ {item.category} +
+ ))} +
+ ) :

没有诊断项。

} + +
+
+ + ); + } + + return ( + <> + 继续时序回放} + /> + +
+
研究帧{factor.frameId}
+
回放 Bundle{factor.bundleId}
+
数据覆盖91.7% · 3 类适配器
+
已知限制新闻正文 12.4% 受限
+
+ +
+ {metrics.map((metric) => )} +
+ +
+
+ 打开回放} + > + + + +
+
+
{factor.name}

候选因子已版本化，等待结果复核

+ {factor.version} 可测试 +
+
+
事件 cohort A / B

正向确认 2 条，对照事件 2 条

+ 继续比较 +
+
+
实验 EXP-240801-17

成本后收益和修订敏感性已生成

+ 查看结果 +
+
+
组合与治理式 RL

沿用同一因子、数据版本、冻结样本和审计链

+ 继续研究 +
+
+ +
+ +
+ + + + + + + + + + +
+ {adapters.map((adapter) => ( +
+
{adapter.name}最后观测 {adapter.lastSeen}
+ +
+ ))} +
+ + + +
+ {jobs.slice(0, 3).map((job) => ( +
+
{job.kind}{job.id}
+ +
+ ))} +
+ +
+
+ + ); +} diff --git a/studio-web/app/portfolio/page.jsx b/studio-web/app/portfolio/page.jsx new file mode 100644 index 0000000..9744bda --- /dev/null +++ b/studio-web/app/portfolio/page.jsx @@ -0,0 +1,5 @@ +import { ResearchLane } from "@/components/research-lane"; + +export default function PortfolioPage() { + return ; +} diff --git a/studio-web/app/replay/page.jsx b/studio-web/app/replay/page.jsx new file mode 100644 index 0000000..9de2fe4 --- /dev/null +++ b/studio-web/app/replay/page.jsx @@ -0,0 +1,15 @@ +import { PageHeading } from "@/components/ui"; +import { ReplayWorkbench } from "@/components/replay-workbench"; + +export default function ReplayPage() { + return ( + <> + + + + ); +} diff --git a/studio-web/app/research/[sessionId]/page.jsx b/studio-web/app/research/[sessionId]/page.jsx new file mode 100644 index 0000000..bf0daaa --- /dev/null +++ b/studio-web/app/research/[sessionId]/page.jsx @@ -0,0 +1,28 @@ +"use client"; + +import { use } from "react"; +import { ResearchConsole } from "@/components/research-console"; +import { useStudio } from "@/components/studio-context"; +import { Button, ButtonLink, EmptyState, PageHeading } from "@/components/ui"; +import { selectResearchSession } from "@/lib/research-console"; + +export default function ResearchSessionPage({ params }) { + const { sessionId } = use(params); + const { source, demoEnabled, returnToCore } = useStudio(); + if (demoEnabled) return <>返回台账} />; + if (source.mode !== "connected") return <>返回台账} />; + + let selected; + try { + selected = selectResearchSession(source.snapshot, sessionId); + } catch (error) { + return <>返回台账} />; + } + const { project, bundle, projection } = selected; + return ( + <> + 返回台账} /> + + + ); +} diff --git a/studio-web/app/research/page.jsx b/studio-web/app/research/page.jsx new file mode 100644 index 0000000..dc4be06 --- /dev/null +++ b/studio-web/app/research/page.jsx @@ -0,0 +1,48 @@ +"use client"; + +import Link from "next/link"; +import { useStudio } from "@/components/studio-context"; +import { Button, ButtonLink, EmptyState, PageHeading, Panel, StatusChip } from "@/components/ui"; +import { RESEARCH_STAGE_ORDER, researchSessions } from "@/lib/research-console"; + +function SessionCard({ item }) { + const { project, bundle, projection } = item; + const session = bundle.session; + const stages = projection.ledger?.stages || []; + return ( + +
{session.studyId || session.id}{project.id} · {session.id}
+
+ {RESEARCH_STAGE_ORDER.map((id) => { + const stage = stages.find((value) => value.id === id); + return ; + })} +
+ {projection.state} + + ); +} + +export default function ResearchPage() { + const { source, demoEnabled, returnToCore } = useStudio(); + if (demoEnabled) { + return <>; + } + if (source.mode !== "connected") return <>; + + const items = researchSessions(source.snapshot); + return ( + <> + 审计与复现} /> +
+
Sessions{items.length}
+
Verified ledgers{items.filter((item) => item.projection.state === "available").length}
+
Partial / invalid{items.filter((item) => item.projection.state !== "available").length}
+
SourceCONNECTED CORE
+
+ + {items.length ?
{items.map((item) => )}
: } + + + ); +} diff --git a/studio-web/app/results/page.jsx b/studio-web/app/results/page.jsx new file mode 100644 index 0000000..24a727e --- /dev/null +++ b/studio-web/app/results/page.jsx @@ -0,0 +1,197 @@ +"use client"; + +import { FactorIcChart, PerformanceChart } from "@/components/charts"; +import { useStudio } from "@/components/studio-context"; +import { diagnostics, factor, metrics } from "@/lib/data"; +import { ButtonLink, DataTable, EmptyState, Metric, PageHeading, Panel, StatusChip } from "@/components/ui"; +import { ClaimVerificationForm } from "@/components/claim-verification-form"; +import { factorVerificationFrom } from "@/lib/verification"; + +const buckets = [ + ["D1", "-7.8%", "-0.62", "19.4%"], + ["D2", "-3.1%", "-0.24", "20.7%"], + ["D3", "-0.7%", "-0.05", "22.1%"], + ["D4", "+2.4%", "+0.18", "25.8%"], + ["D5", "+10.8%", "+0.73", "29.2%"], +]; + +export default function ResultsPage() { + const { source, demoEnabled } = useStudio(); + + if (source.mode === "connected" && !demoEnabled) { + const snapshot = source.snapshot; + const project = snapshot.projects[0]; + const coreDiagnostics = [...snapshot.diagnostics, ...(project?.diagnostics || [])]; + const explorer = project?.factorExplorer; + const run = explorer?.run; + const summary = explorer?.summary; + const verification = factorVerificationFrom(project, snapshot.diagnostics); + const publishedVerification = project?.verificationAssessments?.at(-1) || null; + const verifiedRuns = (project?.runs || []).filter((item) => item.studyId === run?.studyId).length; + const quantiles = (explorer?.quantileSummary || []).filter((item) => item.role === "selection"); + const percent = (value) => Number.isFinite(value) ? `${(value * 100).toFixed(2)}%` : "—"; + const decimal = (value) => Number.isFinite(value) ? value.toFixed(4) : "—"; + + return ( + <> + 复制为新实验} + /> + +
+ + + + + +
+ +
+
+ + {explorer?.icPath ? : } + + + {quantiles.length ? ( + + HorizonLowMiddleHighH-LMono + {quantiles.map((row) => H{row.horizon}{percent(row.low)}{percent(row.middle)}{percent(row.high)}{percent(row.highMinusLow)}{decimal(row.monotonicity)})} + + ) : } + +
+ +
+ + {publishedVerification ? ( + +
+ {publishedVerification.assessment.verdict} + {publishedVerification.claim.statement} + {publishedVerification.assessment.id} + {publishedVerification.assessment.limitations.length ? publishedVerification.assessment.limitations.join(" · ") : "全部声明门禁通过"} +
+ + ) : null} + +
+
+
{verification.verdict.label}

{verification.verdict.detail}

+ {verification.verdict.id} +
+
+
Factor qualification

{verification.qualification.available ? verification.qualification.stage : "资格证据缺失"}

+ {verification.qualification.available ? "available" : "inconclusive"} +
+
+
Selection adjustment

{verification.selection.available ? `${verification.selection.method} · ${verification.selection.uniqueTrials ?? "—"} unique trials` : "当前 Core snapshot 未提供选择调整证据"}

+ {verification.selection.passes === true ? "passes" : verification.selection.passes === false ? "fails" : "missing"} +
+
+
Frozen holdout

{verification.holdout.available ? `${verification.holdout.state}${verification.holdout.assessment ? ` · ${verification.holdout.assessment}` : ""}` : "未绑定独立外部 holdout"}

+ {verification.holdout.state} +
+
+
Trading authority

研究证据不连接账户、订单或交易执行。

+ {verification.authority.tradingAuthority || "missing"} +
+
+ + + {verification.robustness.available ? ( +
+
最弱 candidate fold

{verification.robustness.weakestFoldId || "—"}

0 ? "known" : "missing"}>{decimal(verification.robustness.weakestFold)}
+
最弱 style-neutral fold

{verification.robustness.weakestNeutralFoldId || "—"}

0 ? "known" : "missing"}>{decimal(verification.robustness.weakestNeutralFold)}
+
Test isolation

test 不进入 selection 或 qualification diagnosis

{verification.selection.testEntersSelection === false ? "isolated" : "unknown"}
+
+ ) : } + + + {coreDiagnostics.length ? ( +
+ {coreDiagnostics.map((item, index) => ( +
+
{item.code}

{item.message}

+ {item.category} +
+ ))} +
+ ) : ( + + )} + + +
+ {run ? "Core verified" : "ExperimentRun 缺失"} + ExperimentRun + {run?.id || project?.id || "no-project"} + {run?.studyId || "no-study"} · {run?.status || "unavailable"} + input:{run?.inputHash || "unavailable"} + source:{run?.sourceHash || "unavailable"} + AQ {snapshot.harness.version}@{snapshot.harness.commit.slice(0, 8)} · {verifiedRuns} factor runs + 检查 Core 诊断 +
+ + {explorer?.warning ?
选择边界： {explorer.warning}
: null} +
+
+ + ); + } + + return ( + <> + 复制为新实验} + /> + +
+ {metrics.map((metric) => )} +
+ +
+
+ + + + + + 分组年化收益平均暴露年化换手 + {buckets.map((row) => {row.map((cell, index) => 0 ? "numeric mono" : "mono"}>{cell})})} + + +
+ +
+ +
+ {diagnostics.map((item) => ( +
+
{item.label}

{item.detail}

+ +
+ ))} +
+ + +
+ ExperimentRun + EXP-240801-17 + {factor.id} {factor.version} + {factor.frameId} + {factor.dataset} + exp:2cc7c1a0 · engine-0.12.4 + 打开审计链 +
+ +
+
+ + ); +} diff --git a/studio-web/app/rl/page.jsx b/studio-web/app/rl/page.jsx new file mode 100644 index 0000000..624abbf --- /dev/null +++ b/studio-web/app/rl/page.jsx @@ -0,0 +1,5 @@ +import { ResearchLane } from "@/components/research-lane"; + +export default function RlPage() { + return ; +} diff --git a/studio-web/app/strategies/page.jsx b/studio-web/app/strategies/page.jsx new file mode 100644 index 0000000..b9d5864 --- /dev/null +++ b/studio-web/app/strategies/page.jsx @@ -0,0 +1,5 @@ +import { StrategyManagement } from "@/components/strategy-management"; + +export default function StrategiesPage() { + return ; +} diff --git a/studio-web/components/charts.jsx b/studio-web/components/charts.jsx new file mode 100644 index 0000000..1018f44 --- /dev/null +++ b/studio-web/components/charts.jsx @@ -0,0 +1,242 @@ +"use client"; + +import { useEffect, useMemo, useRef } from "react"; +import { + CandlestickSeries, + ColorType, + createChart, + createSeriesMarkers, + HistogramSeries, + LineSeries, +} from "lightweight-charts"; +import { candles, factorSignal } from "@/lib/data"; +import { EmptyState } from "@/components/ui"; + +const START_TIME = Date.parse("2024-02-23T09:30:00+08:00") / 1000; +const EMPTY_EVENTS = []; +const timeFormatter = new Intl.DateTimeFormat("zh-CN", { + timeZone: "Asia/Shanghai", + hour: "2-digit", + minute: "2-digit", + hour12: false, +}); + +function formatChartTime(time) { + return timeFormatter.format(new Date(Number(time) * 1000)); +} + +function linePath(values, width, height, padding = 18) { + const min = Math.min(...values); + const max = Math.max(...values); + return values + .map((value, index) => { + const x = padding + (index / (values.length - 1)) * (width - padding * 2); + const y = height - padding - ((value - min) / Math.max(1, max - min)) * (height - padding * 2); + return `${index === 0 ? "M" : "L"}${x.toFixed(1)},${y.toFixed(1)}`; + }) + .join(" "); +} + +function chartData(cursorRatio) { + const visibleCount = Math.max(2, Math.round(2 + cursorRatio * (candles.length - 2))); + const times = candles.map((_, index) => START_TIME + index * 300); + return { + times, + visibleCount, + candles: candles.slice(0, visibleCount).map(([open, high, low, close], index) => ({ + time: times[index], + open, + high, + low, + close, + })), + volume: candles.slice(0, visibleCount).map(([open, high, low, close], index) => ({ + time: times[index], + value: 46 + ((high - low) * 7) + ((index * 17) % 31), + color: close >= open ? "rgba(82, 199, 217, 0.48)" : "rgba(114, 129, 152, 0.45)", + })), + signal: factorSignal.slice(0, visibleCount).map((value, index) => ({ time: times[index], value })), + }; +} + +function nearestTime(timestamp, times, visibleCount) { + const available = times.slice(0, visibleCount); + return available.reduce((best, time) => ( + Math.abs(time - timestamp) < Math.abs(best - timestamp) ? time : best + ), available[0]); +} + +export function EvidenceChart({ cursorRatio = 0.55, compact = false, events = EMPTY_EVENTS }) { + const containerRef = useRef(null); + const data = useMemo(() => chartData(cursorRatio), [cursorRatio]); + + useEffect(() => { + const container = containerRef.current; + if (!container) return undefined; + + const height = compact ? 260 : 360; + const chart = createChart(container, { + width: container.clientWidth, + height, + layout: { + background: { type: ColorType.Solid, color: "#0d151d" }, + textColor: "#8795a8", + attributionLogo: true, + panes: { + separatorColor: "#273544", + separatorHoverColor: "#52c7d9", + enableResize: true, + }, + }, + grid: { + vertLines: { color: "#1b2732" }, + horzLines: { color: "#1b2732" }, + }, + crosshair: { + vertLine: { color: "#52c7d9", labelBackgroundColor: "#16333f" }, + horzLine: { color: "#526172", labelBackgroundColor: "#273544" }, + }, + rightPriceScale: { borderColor: "#273544" }, + localization: { timeFormatter: formatChartTime }, + timeScale: { + borderColor: "#273544", + timeVisible: true, + secondsVisible: false, + tickMarkFormatter: formatChartTime, + }, + handleScale: compact ? false : undefined, + handleScroll: compact ? false : undefined, + }); + + const candleSeries = chart.addSeries(CandlestickSeries, { + upColor: "#5d7f8d", + downColor: "#4a596c", + wickUpColor: "#afbdcb", + wickDownColor: "#728198", + borderUpColor: "#afbdcb", + borderDownColor: "#728198", + }); + const volumeSeries = chart.addSeries(HistogramSeries, { + priceFormat: { type: "volume" }, + priceLineVisible: false, + lastValueVisible: false, + }, 1); + const signalSeries = chart.addSeries(LineSeries, { + color: "#f1b35c", + lineWidth: 2, + priceLineVisible: false, + lastValueVisible: true, + title: "Factor signal", + }, 2); + + candleSeries.setData(data.candles); + volumeSeries.setData(data.volume); + signalSeries.setData(data.signal); + chart.panes()[0]?.setStretchFactor(5); + chart.panes()[1]?.setStretchFactor(1.35); + chart.panes()[2]?.setStretchFactor(1.65); + + const eventMarkers = events.map((event, index) => ({ + time: nearestTime(Date.parse(event.availableAt) / 1000, data.times, data.visibleCount), + position: "aboveBar", + color: event.evidence === "missing" ? "#d87575" : "#f1b35c", + shape: index % 2 ? "circle" : "arrowDown", + text: event.adapter === "A股公告" ? "公告" : event.adapter === "财经新闻" ? "新闻" : "链上", + })); + createSeriesMarkers(candleSeries, [ + ...eventMarkers, + { + time: data.candles.at(-1).time, + position: "belowBar", + color: "#52c7d9", + shape: "circle", + text: "VISIBLE T", + }, + ].sort((a, b) => a.time - b.time)); + + chart.timeScale().fitContent(); + const resize = new ResizeObserver(([entry]) => { + chart.applyOptions({ width: Math.floor(entry.contentRect.width) }); + }); + resize.observe(container); + + return () => { + resize.disconnect(); + chart.remove(); + }; + }, [compact, data, events]); + + return ( +
+
+ K 线 / 成交量 / 因子信号 +
+ ); +} + +export function FactorIcChart({ path }) { + const containerRef = useRef(null); + const points = useMemo( + () => (path?.points || []) + .filter((point) => Number.isFinite(point.rankIcH1)) + .map((point) => ({ time: point.timestamp, value: point.rankIcH1 })), + [path], + ); + + useEffect(() => { + const container = containerRef.current; + if (!container || !points.length) return undefined; + const chart = createChart(container, { + width: container.clientWidth, + height: 300, + layout: { background: { type: ColorType.Solid, color: "#0d151d" }, textColor: "#8795a8", attributionLogo: true }, + grid: { vertLines: { color: "#1b2732" }, horzLines: { color: "#1b2732" } }, + rightPriceScale: { borderColor: "#273544" }, + timeScale: { borderColor: "#273544" }, + }); + const series = chart.addSeries(LineSeries, { color: "#52c7d9", lineWidth: 2, title: "Rank IC · H1" }); + series.setData(points); + series.createPriceLine({ price: 0, color: "#526172", lineWidth: 1, lineStyle: 2, axisLabelVisible: false }); + chart.timeScale().fitContent(); + const resize = new ResizeObserver(([entry]) => chart.applyOptions({ width: Math.floor(entry.contentRect.width) })); + resize.observe(container); + return () => { + resize.disconnect(); + chart.remove(); + }; + }, [points]); + + if (!points.length) return ; + return ( +
+
+ Rank IC · H1 +
+ ); +} + +export function PerformanceChart() { + const strategy = [0, 3, 2, 7, 10, 9, 15, 18, 17, 23, 27, 31, 29, 36, 39, 43, 47, 45, 51, 56, 59, 64, 68, 72]; + const benchmark = [0, 1, 0, 2, 4, 3, 6, 8, 7, 9, 12, 11, 13, 16, 15, 19, 21, 20, 24, 27, 26, 29, 31, 32]; + return ( +
+ 累计收益，% + 2021-01 至 2025-12 + + 因子组合与基准累计收益 + 示例因子组合累计收益约 72%，同期基准约 32%。 + + + + 因子组合 + 中证全指 + + +
+ ); +} diff --git a/studio-web/components/claim-verification-form.jsx b/studio-web/components/claim-verification-form.jsx new file mode 100644 index 0000000..866f9f9 --- /dev/null +++ b/studio-web/components/claim-verification-form.jsx @@ -0,0 +1,63 @@ +"use client"; + +import { useState } from "react"; +import { useStudio } from "@/components/studio-context"; +import { Button, EmptyState, FormField, Panel, StatusChip } from "@/components/ui"; + +export function ClaimVerificationForm({ projectId, runId }) { + const { retryCore } = useStudio(); + const [state, setState] = useState({ status: "idle", message: "", verdict: null }); + + async function submit(event) { + event.preventDefault(); + const values = new FormData(event.currentTarget); + setState({ status: "running", message: "Core 正在生成不可变裁决…", verdict: null }); + try { + const response = await fetch("/api/studio/verify-factor", { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify({ + projectId, + runId, + statement: values.get("statement"), + minimumEffect: Number(values.get("minimumEffect")), + minimumSampleSize: Number(values.get("minimumSampleSize")), + requireHoldout: values.get("requireHoldout") === "on", + requireSelection: values.get("requireSelection") === "on", + }), + }); + const payload = await response.json(); + if (!response.ok || !payload.ok) throw new Error(payload.error?.message || "裁决失败"); + const assessment = payload.verification.assessment; + setState({ status: "succeeded", message: assessment.id, verdict: assessment.verdict }); + retryCore(); + } catch (error) { + setState({ status: "failed", message: error instanceof Error ? error.message : "裁决失败", verdict: null }); + } + } + + return ( + + {runId ? ( +
+ + + +
+ + +
+
+ + +
+
+ + {state.verdict ? {state.verdict} : null} + {state.message ? {state.message} : null} +
+
+ ) : } + + ); +} diff --git a/studio-web/components/data-intake.jsx b/studio-web/components/data-intake.jsx new file mode 100644 index 0000000..1e73975 --- /dev/null +++ b/studio-web/components/data-intake.jsx @@ -0,0 +1,114 @@ +"use client"; + +import { useState } from "react"; +import { useStudio } from "@/components/studio-context"; +import { Button, FormField, Panel } from "@/components/ui"; + +export function DataIntake() { + const { source, retryCore } = useStudio(); + const [state, setState] = useState({ status: "idle", message: "" }); + const [eventState, setEventState] = useState({ status: "idle", message: "" }); + + async function submit(event) { + event.preventDefault(); + const form = event.currentTarget; + const sourceFiles = Array.from(form.elements.sources.files || []); + const body = new FormData(form); + body.delete("sources"); + sourceFiles.forEach((file) => { + body.append("source", file); + body.append("sourcePath", file.webkitRelativePath || file.name); + }); + setState({ status: "running", message: "Core 正在校验并固化数据…" }); + try { + const response = await fetch("/api/studio/intake", { method: "POST", body }); + const payload = await response.json(); + if (!response.ok || !payload.ok) throw new Error(payload.error?.message || "数据导入失败"); + setState({ status: "succeeded", message: `${payload.intake.projectId} · ${payload.intake.dataset}` }); + form.reset(); + retryCore(); + } catch (error) { + setState({ status: "failed", message: error instanceof Error ? error.message : "数据导入失败" }); + } + } + + async function submitEvents(event) { + event.preventDefault(); + const form = event.currentTarget; + setEventState({ status: "running", message: "Core 正在校验事件时钟与内容哈希…" }); + try { + const response = await fetch("/api/studio/event-intake", { method: "POST", body: new FormData(form) }); + const payload = await response.json(); + if (!response.ok || !payload.ok) throw new Error(payload.error?.message || "事件导入失败"); + setEventState({ status: "succeeded", message: `${payload.eventSnapshot.id}@${payload.eventSnapshot.version} · ${payload.eventSnapshot.eventCount} events` }); + form.reset(); + retryCore(); + } catch (error) { + setEventState({ status: "failed", message: error instanceof Error ? error.message : "事件导入失败" }); + } + } + + return ( + <> + +
+
+ + + + + + + + + + + + + + + + + + +
+
+ 导入要求： package 必须声明来源、许可/terms、市场时钟、价格复权、资产清单；覆盖率、缺失和时间范围由 Core 从文件实测。文件只在本机临时目录校验，导入完成即清理，不接收数据商凭证。 +
+
+ + {state.message ? {state.message} : null} +
+
+ + +
+
+ + + + + + +
+
固定契约： 每条事件必须保留 event_time、published_at、observed_at、available_at、source、license 与内容；Core 拒绝倒置时钟、重复 ID 和篡改。
+
+ + {eventState.message ? {eventState.message} : null} +
+
+ + + ); +} diff --git a/studio-web/components/event-workbench.jsx b/studio-web/components/event-workbench.jsx new file mode 100644 index 0000000..71e215d --- /dev/null +++ b/studio-web/components/event-workbench.jsx @@ -0,0 +1,102 @@ +"use client"; + +import { useState } from "react"; +import { useStudio } from "@/components/studio-context"; +import { ResearchSubject } from "@/components/research-subject"; +import { Button, ButtonLink, DataTable, EmptyState, Panel, StatusChip } from "@/components/ui"; +import { compareCohorts, formatPercent, formatTime } from "@/lib/research"; + +export function EventWorkbench() { + const { events, cohortA, cohortB, assignToCohort, removeFromCohort, setSelectedEventId, source, subject, demoEnabled } = useStudio(); + const comparison = compareCohorts(events, cohortA, cohortB); + const [draftSaved, setDraftSaved] = useState(false); + + if (source.mode === "connected" && !demoEnabled) { + return ( +
+ + + + + +
+

事件组 A

+
反应差—A - B
+

事件组 B

+
+
+ +
+ ); + } + + return ( +
+ + + 事件适配器可用时间状态价格反应事件组 + + {events.map((event) => ( + + + + {event.entity} + + {event.adapter} + {formatTime(event.availableAt)} + + {formatPercent(event.reaction)} + +
+ + + {(cohortA.includes(event.id) || cohortB.includes(event.id)) ? : null} +
+ + + ))} + + + + + setDraftSaved(true)}>{draftSaved ? "候选因子已保存" : "生成候选因子"} : null} + > + {comparison.ready ? ( + <> +
+
+

事件组 A · 正向确认

+
+
样本{comparison.left.count} 条
+
平均反应{formatPercent(comparison.left.meanReaction)}
+
+
{comparison.left.tags.map((tag) => {tag})}
+
+
反应差{formatPercent(comparison.reactionSpread)}A - B
+
+

事件组 B · 对照样本

+
+
样本{comparison.right.count} 条
+
平均反应{formatPercent(comparison.right.meanReaction)}
+
+
{comparison.right.tags.map((tag) => {tag})}
+
+
+ {draftSaved ? ( +
+ 候选因子草案 CF-240801-06 已保存 + 成员、筛选条件、事件窗和 ReplayBundle RB-6F19A2 已锁定。 + 进入因子实验室 +
+ ) : null} + + ) : ( + + )} + +
+ ); +} diff --git a/studio-web/components/factor-lab.jsx b/studio-web/components/factor-lab.jsx new file mode 100644 index 0000000..6d3e761 --- /dev/null +++ b/studio-web/components/factor-lab.jsx @@ -0,0 +1,167 @@ +"use client"; + +import { useState } from "react"; +import { factor } from "@/lib/data"; +import { useStudio } from "@/components/studio-context"; +import { ResearchSubject } from "@/components/research-subject"; +import { RunStudyButton } from "@/components/run-study-button"; +import { Button, ButtonLink, EmptyState, FormField, PageHeading, Panel, StatusChip } from "@/components/ui"; + +const initialConfig = { + universe: "沪深全市场，逐日可交易标的", + horizon: "20 个交易日", + lag: "1", + winsorize: true, + neutralize: true, + costs: true, + revisionGuard: true, +}; + +export function FactorLab() { + const { source, subject, demoEnabled, factor: activeFactor } = useStudio(); + const [config, setConfig] = useState(initialConfig); + const [runId, setRunId] = useState(""); + + const update = (key, value) => { + setConfig((current) => ({ ...current, [key]: value })); + setRunId(""); + }; + + function submit(event) { + event.preventDefault(); + setRunId("EXP-240801-18"); + } + + if (source.mode === "connected" && !demoEnabled) { + const project = source.snapshot.projects[0]; + const study = project?.studies?.find((item) => item.subjectKind === "factor") || project?.studies?.[0]; + const explorer = project?.factorExplorer; + return ( + <> + 查看结果状态} + /> + +
+ +
+
Study{study?.id || "未声明"}
+
Factor{activeFactor.name}
+
Dataset{activeFactor.dataset}
+
Objective{study?.primaryMetric || "未声明"} · {study?.direction || "未声明"}
+
ResearchFrame{activeFactor.frameId}
+
执行边界离线研究，无交易权限
+
+
创建并执行研究 Run
+ + + {explorer?.run ? ( +
+
Run{explorer.run.id}
+
Status{explorer.run.status}
+
Objective{explorer.run.objective?.metric}
+
Validation Rank IC{explorer.summary?.validation?.meanRankIc?.toFixed(6)}
+
Input hash{explorer.run.inputHash}
+
执行边界离线研究，无交易权限
+
+ ) : ( + + )} + +
+ + ); + } + + return ( + <> + 查看最近结果} + /> + +
+
FactorDefinition{factor.id} {factor.version}
+
ResearchFrame{factor.frameId}
+
Dataset{factor.dataset}
+
执行模式研究沙盒，不连接实盘
+
+ +
+ +
+ + + + + + + + update("lag", event.target.value)} /> + + + + +
+ +
+ 研究护栏 +
+ + + + +
+
+ +
+ + +
+ + +
+ +
+
标的池{config.universe}
+
持有期{config.horizon}
+
可见性延迟T + {config.lag}
+
point-in-timeavailable_at 强制约束
+
预计资源GPU A10 1x · 约 19 分钟
+
预算上限¥10.00
+
+ + + {runId ? ( + +
+ 等待研究资源 + {runId} + 已锁定当前配置、数据集版本与 ResearchFrame。此动作不会发送订单或连接交易账户。 + 查看研究任务 +
+ + ) : ( +
研究范围： 这里仅创建离线因子测试，不包含实盘模拟、账户或订单。
+ )} +
+
+ + ); +} diff --git a/studio-web/components/factor-management.jsx b/studio-web/components/factor-management.jsx new file mode 100644 index 0000000..f9f2639 --- /dev/null +++ b/studio-web/components/factor-management.jsx @@ -0,0 +1,355 @@ +"use client"; + +import { useStudio } from "@/components/studio-context"; +import { + ButtonLink, + DataTable, + EmptyState, + Metric, + ObjectLink, + PageHeading, + Panel, + StatusChip, +} from "@/components/ui"; +import { projectFactorManagement } from "@/lib/factor-management"; + +const PHASE_LABELS = { + "not-started": "未开始", + "baseline-ready": "基线已就绪", + researching: "研究中", + reported: "已报告", + stale: "输入过期", + "scientific-limit": "科研边界", + "repair-required": "需要修补", + unspecified: "等待 Core 阶段", + unannounced: "未声明研究 Program", +}; + +const PHASE_DETAILS = { + "not-started": "尚无 Factor Run，治理面等待第一次不可变 Run。", + "baseline-ready": "存在不可变 baseline Run，等待首次 Report。", + researching: "当前 Session 正在生成更多候选，活跃进度与不可变证据并存。", + reported: "当前不可变 Report 已冻结，可继续下一轮治理。", + stale: "Study 输入已变更，旧 Run/Report 尚未重生成。", + "scientific-limit": "固定 Study 已给出科研边界，无需继续尝试。", + "repair-required": "最近 Run 在失败处置上需要修复或重新审视。", +}; + +const GATE_DETAILS = { + passed: "因子证据已经通过，可以进入组合研究。", + "waiting-current-evidence": "当前因子定义还没有对应的有效证据。", + "waiting-current-report": "验证已经完成，仍需冻结成研究结论。", + "blocked-upstream-evidence": "因子的样本外预测证据不足，先回到假设或定义继续研究。", + "blocked-selection-adjusted-evidence": "选择校正尚未通过，不能进入组合研究。", + "blocked-scientific-limit": "当前方法已经触及科学边界，继续搜索没有依据。", + "blocked-failed-evidence": "最近验证失败，先修复证据问题。", +}; + +const GATE_LABELS = { + passed: "可以进入组合研究", + "waiting-current-evidence": "等待当前因子证据", + "waiting-current-report": "等待冻结研究结论", + "blocked-upstream-evidence": "样本外证据不足", + "blocked-selection-adjusted-evidence": "选择校正未通过", + "blocked-scientific-limit": "已触及科学边界", + "blocked-failed-evidence": "最近验证失败", +}; + +const PROGRAM_STAGE_LABELS = { + "factor-evidence-required": "先完成因子证据", + "portfolio-evidence-required": "再完成组合证据", + "optional-rl-in-progress": "正在进行可选 RL 挑战", + "required-research-complete": "必需研究已经完成", +}; + +const ACTION_LABELS = { + "session.start": "开始下一轮因子研究", + "session.complete": "完成本轮因子研究", + "run.execute": "生成新的验证证据", + "run.show": "查看最新证据", + "report.publish": "冻结研究结论", + "report.show": "查看研究结论", + "study.inspect": "检查因子定义", +}; + +function formatNumber(value, digits = 4) { + return Number.isFinite(value) ? value.toFixed(digits) : "—"; +} + +function formatPercent(value, digits = 1) { + return Number.isFinite(value) ? `${(value * 100).toFixed(digits)}%` : "—"; +} + +function formatDate(value) { + if (!value) return "—"; + const parsed = new Date(value); + return Number.isNaN(parsed.valueOf()) ? value : parsed.toISOString().slice(0, 10); +} + +function trimHash(hash, length = 12) { + if (!hash) return "unavailable"; + return hash.length > length ? `${hash.slice(0, length)}…` : hash; +} + +function datasetLabel(dataset) { + return dataset ? `${dataset.id || "未声明"}@${dataset.version || "未声明"}` : "未声明"; +} + +export function FactorManagement() { + const { source, demoEnabled, factor } = useStudio(); + const connected = source.mode === "connected" && !demoEnabled; + const project = connected ? source.snapshot?.projects?.[0] : null; + const view = project ? projectFactorManagement(project) : null; + + if (!connected) { + return ( + + ); + } + + if (!project || !view) { + return ( + <> + 进入因子实验室} + /> + + + ); + } + + const phase = view.status.phase; + const phaseLabel = PHASE_LABELS[phase] || phase; + const phaseDetail = PHASE_DETAILS[phase] || "等待 Core 给出明确的 Factor 阶段投影。"; + const study = view.study; + const dataset = study?.dataset; + const gates = view.gates.factorToPortfolio; + const gateLabel = "因子证据 → 组合研究"; + const sessionLabel = view.progress.laneLatestSession + ? `${view.progress.laneLatestSession.id} · ${view.progress.laneLatestSession.status}` + : "未声明"; + + return ( + <> + + 因子实验室 + 测试结果 + 因子护照 + + } + /> + +
+
因子版本{study?.id || view.factorDefinition.studyId || "未声明"}
+
数据版本{datasetLabel(dataset)}
+
研究计划{view.programAvailable ? "已连接" : "未声明"}
+
当前进度{phaseLabel}
+
+ +
+ + + + + +
+ +
+
+ + {study ? ( +
+
因子版本{study.id}
+
研究类型{view.factorDefinition.subjectKind}
+
研究假设{view.factorDefinition.description || "尚未写明"}
+
判定指标{study.objective ? `${study.objective.metric || "—"} · ${study.objective.direction || "—"}` : "尚未写明"}
+
最低改进门槛{Number.isFinite(study.objective?.minimumImprovement) ? study.objective.minimumImprovement : "尚未写明"}
+
数据版本{datasetLabel(dataset)}
+
历史区间{study.datasetTimeRange ? `${study.datasetTimeRange.start || "—"} → ${study.datasetTimeRange.end || "—"}` : "尚未写明"}
+
资产类型{dataset?.assetClass || "未声明"}
+
研究标的{dataset?.universe?.length || 0} · {dataset?.universe?.slice(0, 6).join(", ") || "未声明"}
+
+ 技术细节 +
+
Study inputHash{trimHash(study.inputHash)}
+
Source hash{trimHash(study.sourceHash)}
+
Dependency hash{trimHash(study.dependencyHash)}
+
Dataset hash{trimHash(study.datasetHash)}
+
可编辑路径{view.dependencies.editableCount}
+
依赖路径{view.dependencies.dependencyCount}
+
+
+
+ ) : ( + + )} + + + +
+
+
+ 整条研究链的进度 +

{PROGRAM_STAGE_LABELS[view.program?.stage] || "等待系统给出研究顺序"}

+ {view.program?.stage || view.program?.method ? ( +
技术细节

{view.program?.stage || "未声明"} · {view.program?.method || "尚未声明验证方法"}

+ ) : null} +
+ {PROGRAM_STAGE_LABELS[view.program?.stage] || "等待研究计划"} +
+
+
+ {gateLabel} +

{GATE_DETAILS[gates?.status] || "等待系统给出明确的验证状态。"}

+ {gates?.explanation ? ( +
技术细节

{gates.explanation}

+ ) : null} +
+ {GATE_LABELS[gates?.status] || "等待验证"} +
+
+
+ 建议下一步 +

{ACTION_LABELS[view.recommendedAction?.id] || "当前没有待执行动作"}

+ {view.recommendedAction?.description ? ( +
技术操作

{view.recommendedAction.description}

+ ) : null} +
+ {view.recommendedAction?.id ? "可执行" : "无待办"} +
+
+ {view.warnings.length ? ( +
研究约束与技术告警

{view.warnings.join(" · ")}

+ ) : null} +
+ {view.recommendedAction?.id ? 在因子实验室中执行 : null} + 查看结果 +
+ +
+ +
+ + {view.evidence.runId ? ( +
+ {view.status.currentRun ? "current" : "历史"} + {view.evidence.runId} + validation mean IC {formatNumber(view.evidence.validationMeanIc, 6)} · ICIR {formatNumber(view.evidence.validationIcir)} · HAC t {formatNumber(view.evidence.validationHacT, 3)} + coverage {formatPercent(view.evidence.meanCoverage)} · turnover {formatNumber(view.evidence.meanRankTurnover, 3)} + input:{trimHash(study?.inputHash)} · source:{trimHash(study?.sourceHash)} + {view.status.currentReportId ? ( + Report: {view.status.currentReportId} + ) : ( + 当前没有冻结当前 Run 的不可变 Report + )} +
+ ) : ( + + )} + + + +
+
+
当前 Session

{sessionLabel}

+ + {view.progress.laneLatestSessionActive ? "活跃" : "无活跃 Session"} + +
+
+
实验历史 / 活跃 Campaign

实验是不可变历史；Campaign 只统计当前活跃 Session 的进度。

+ + {view.progress.experimentCount} exp · {view.progress.activeCampaigns} active camp + +
+
+
已关闭 Session

其他 lane 的活动被过滤；factor lane 仅保留与 Study 对齐的 Session。

+ {view.progress.completedSessions} closed +
+
+ {view.immutableReports.length ? ( + + + 不可变 ReportStudyLeader Run发布 + + + {view.immutableReports.slice(0, 6).map((report) => ( + + {report.id || "—"} + {report.studyId || "—"} + {report.leaderRunId || "—"} + {formatDate(report.publishedAt)} + + ))} + + + ) : ( +

研究 Program 暂未声明任何 Report。

+ )} + + + +
+
+
+ 预测资产 / 上下文资产 +

{view.cohorts?.available ? `${view.cohorts.predictionAssets.length} pred · ${view.cohorts.contextAssets.length} ctx · mode ${view.cohorts.evaluationMode || "—"}` : "Cohort 待 Core 投影"}

+
+ {view.cohorts?.available ? "available" : "no-cohort"} +
+
+
Qualification 阶段

{view.evidence.qualificationStage || "未声明"} · {view.evidence.qualificationExplanation || "待 Core 给出资格裁决"}

+ {view.evidence.qualificationAvailable ? "qualification available" : "no-qualification"} +
+
+
+ Validation 最弱 fold +

candidate {view.evidence.weakestCandidateFoldId || "—"} = {formatNumber(view.evidence.weakestCandidateFoldIc)} · style-neutral {view.evidence.weakestStyleNeutralFoldId || "—"} = {formatNumber(view.evidence.weakestStyleNeutralFoldIc)}

+
+ chronological +
+
+ {view.evidence.warning ? ( +
选择边界： {view.evidence.warning}
+ ) : null} + +
+
+ + + + + + + + + ); +} diff --git a/studio-web/components/jobs-workbench.jsx b/studio-web/components/jobs-workbench.jsx new file mode 100644 index 0000000..0bb4767 --- /dev/null +++ b/studio-web/components/jobs-workbench.jsx @@ -0,0 +1,104 @@ +"use client"; + +import { useState } from "react"; +import { jobs as initialJobs } from "@/lib/data"; +import { ResearchSubject } from "@/components/research-subject"; +import { useStudio } from "@/components/studio-context"; +import { Button, DataTable, EmptyState, PageHeading, Panel, StatusChip } from "@/components/ui"; + +export function JobsWorkbench() { + const { source, subject, demoEnabled } = useStudio(); + const [jobs, setJobs] = useState(initialJobs); + const [selectedId, setSelectedId] = useState(initialJobs[0].id); + const selected = jobs.find((job) => job.id === selectedId) || jobs[0]; + + function retry(id) { + setJobs((current) => current.map((job) => job.id === id ? { ...job, state: "排队", elapsed: "-", output: "等待资源" } : job)); + setSelectedId(id); + } + + if (source.mode === "connected" && !demoEnabled) { + const project = source.snapshot?.projects?.[0]; + const executions = project?.computeJobs || []; + const executors = project?.computeExecutors || []; + return ( + <> + + +
+ + {executions.length ? ( +
+ {executions.map((item) => ( +
+
{item.id}{item.executor.kind} · {item.study.id} · Run {item.runRef?.id || "无"} · attempt {item.retry.attempt}
+ {item.status} +
+ ))} +
+ ) : } + + +
+ {executors.map((executor) => ( +
{executor.kind.toUpperCase()}{executor.provider} · {executor.reason || "本地可用"}
{executor.available ? "available" : "unavailable"}
+ ))} +
+

边界： 这里展示研究任务证据，不暴露 provider 凭据、私有插件协议，也不拥有交易权限。

+ +
+ + ); + } + + return ( + <> + +
+ + + 任务类型状态资源耗时产物成本 + + {jobs.map((job) => ( + + + {job.kind} + + {job.resource} + {job.elapsed} + {job.output} + {job.cost} + + ))} + + + + + +
+
任务类型{selected.kind}
+
当前状态
+
资源请求{selected.resource}
+
运行环境image-86f1 · Python 3.13
+
绑定实验EXP-240801-17
+
产物{selected.output}
+
+ {selected.state === "失败" ? ( +
+ +
+ ) : null} +

边界： 研究任务只计算与保存研究产物，不拥有账户、订单或交易所权限。

+ +
+ + ); +} diff --git a/studio-web/components/replay-workbench.jsx b/studio-web/components/replay-workbench.jsx new file mode 100644 index 0000000..1edb801 --- /dev/null +++ b/studio-web/components/replay-workbench.jsx @@ -0,0 +1,238 @@ +"use client"; + +import { useEffect, useMemo } from "react"; +import { EvidenceChart } from "@/components/charts"; +import { ResearchSubject } from "@/components/research-subject"; +import { useStudio } from "@/components/studio-context"; +import { Button, ButtonLink, EmptyState, Panel, StatusChip } from "@/components/ui"; +import { formatPercent, formatTime, hiddenEventSummary, visibleEvents } from "@/lib/research"; + +const trackByAdapter = { + "A股公告": "25%", + "财经新闻": "51%", + "加密事件": "76%", +}; + +function markerCell(event, steps) { + const target = Date.parse(event.availableAt); + const index = steps.findIndex((step) => Date.parse(step) >= target); + return index < 0 ? steps.length - 1 : index; +} + +function CohortTray({ events, cohortA, cohortB, removeFromCohort }) { + const renderColumn = (label, ids) => ( +
+

事件组 {label} · {ids.length} 条

+ {ids.length ? ids.map((id) => { + const event = events.find((item) => item.id === id); + if (!event) return null; + return ( +
+ {event.title} + +
+ ); + }) : 尚未添加事件} +
+ ); + + return
{renderColumn("A", cohortA)}{renderColumn("B", cohortB)}
; +} + +export function ReplayWorkbench() { + const { + events, + replaySteps, + asOf, + asOfIndex, + selectedEvent, + selectedEventId, + cohortA, + cohortB, + setAsOfIndex, + setSelectedEventId, + stepBackward, + stepForward, + assignToCohort, + removeFromCohort, + source, + subject, + demoEnabled, + } = useStudio(); + + const visible = useMemo(() => visibleEvents(events, asOf), [events, asOf]); + const hidden = useMemo(() => hiddenEventSummary(events, asOf), [events, asOf]); + const selectedVisible = visible.some((event) => event.id === selectedEventId); + const inspectorEvent = selectedVisible ? selectedEvent : visible.at(-1) || null; + const cursorRatio = asOfIndex / (replaySteps.length - 1); + + useEffect(() => { + const handleKey = (event) => { + if (event.key === "[") stepBackward(); + if (event.key === "]") stepForward(); + }; + window.addEventListener("keydown", handleKey); + return () => window.removeEventListener("keydown", handleKey); + }, [stepBackward, stepForward]); + + if (source.mode === "connected" && !demoEnabled) { + return ( +
+ +
+ + + + +
+
研究模板{subject?.label || "Core 未声明"}
+
基础频率{subject?.interval || "Core 未声明"}
+
适配器{subject?.adapters?.length || 0} 类
+
未解析语义{subject?.unresolved?.length || 0} 项
+
+ +
+ ); + } + + return ( +
+
+
+
可见时点 T{formatTime(asOf)} CST
+
已见证据{visible.length} / {events.length} 条
+
市场快照5m · CN + Crypto
+
暂不可见{hidden.total} 条 · 内容未泄露
+
+ + + + + +
+ } + > + +
+
+ {replaySteps.map((step, index) => ( +
+ {formatTime(step).split(" ").at(-1)} + {visible.filter((event) => markerCell(event, replaySteps) === index).map((event) => { + const cohortClass = cohortA.includes(event.id) ? "cohort-a" : cohortB.includes(event.id) ? "cohort-b" : ""; + return ( +
+ ))} +
+
+
+
+ {visible.map((event) => ( + + ))} +
+ + + 比较两组} + > + + +
+ + +
+ ); +} diff --git a/studio-web/components/research-console.jsx b/studio-web/components/research-console.jsx new file mode 100644 index 0000000..2239b89 --- /dev/null +++ b/studio-web/components/research-console.jsx @@ -0,0 +1,917 @@ +"use client"; + +import { useEffect, useMemo, useRef, useState } from "react"; +import { Drawer, NumberInput, Select, Tabs, Text, Textarea, TextInput } from "@mantine/core"; +import { Button, DataTable, EmptyState, Panel, StatusChip } from "@/components/ui"; +import { + READ_ONLY_INTENTS, + RESEARCH_STAGE_ORDER, + buildArtifactReviewDraft, + buildExperimentDefinitionDraft, + buildFactorDefinitionDraft, + buildOperatorRequest, + buildReproductionRequestDraft, + latestCampaignBudget, + parseJsonObject, + receiptFromEnvelope, +} from "@/lib/research-console"; + +const REVIEW_MUTATION_INTENTS = [ + "definition.factor.create", + "definition.experiment.create", + "artifact.decide", + "reproduction.start", +]; + +const STATE_LABEL = { available: "Available", partial: "Partial", empty: "Empty", unavailable: "Unavailable" }; +const INTENT_LABEL = { + "research.inspect": "Inspect ledger", + "research.explain": "Explain blockers", + "research.compare": "Compare exact versions", + "research.reproduction-readiness": "Check reproduction readiness", + "definition.factor.create": "FactorDefinition editor", + "definition.experiment.create": "ExperimentDefinition editor", + "artifact.decide": "Artifact review decision", + "reproduction.start": "Request reproduction", + "campaign.stop": "Stop running Campaign", +}; + +function tone(state) { + if (["available", "completed", "exact-match", "within-tolerance"].includes(state)) return "known"; + if (["unavailable", "empty", "failed", "drift", "stale"].includes(state)) return "missing"; + return "partial"; +} + +function ObjectRef({ value }) { + return ( + + {value.kind} + {value.id} + {value.version == null ? null : v{value.version}} + + ); +} + +export function OperationReceiptCard({ receipt }) { + return ( +
+
+
{INTENT_LABEL[receipt.intent] || receipt.intent}{receipt.requestId}
+ {receipt.status} +
+ {receipt.failedGates?.length ?

Failed gates: {receipt.failedGates.join(", ")}

: null} + {receipt.errors?.map((error, index) =>

{error.code}: {error.message}

)} + {receipt.artifacts?.length ?
{receipt.artifacts.map((item, index) => )}
: null} + {receipt.nextValidActions?.length ? Next: {receipt.nextValidActions.join(" · ")} : null} + +
+ ); +} + +function StageRail({ stages, activeId, onSelect }) { + const stageMap = new Map(stages.map((stage) => [stage.id, stage])); + return ( + + ); +} + +function BudgetBar({ budget }) { + if (!budget) return ; + const metrics = [ + ["Candidates", "candidates", budget.maxCandidates], + ["Wall", "wallSeconds", budget.maxWallSeconds], + ["CPU", "cpuSeconds", budget.maxCpuSeconds], + ["GPU", "gpuSeconds", budget.maxGpuSeconds], + ]; + return ( +
+ {metrics.map(([label, key, maximum]) => { + const used = budget.used?.[key]; + const known = Number.isFinite(used) && Number.isFinite(maximum) && maximum >= 0; + const percent = known && maximum > 0 ? Math.min(100, (used / maximum) * 100) : 0; + return ( +
+ {label}{known ? `${used} / ${maximum}` : "unavailable"} + +
+ ); + })} +

Cost: {budget.used?.cost?.known ? `${budget.used.cost.amount} ${budget.used.cost.currency || ""}` : "unknown"} · Executor: {budget.executorPolicy?.default || "unavailable"} · Holdout: {budget.holdoutPolicy?.sealed === true ? "sealed" : "unavailable"}

+
+ ); +} + +function CandidateRunTable({ experiments }) { + if (!experiments.length) return ; + return ( + + CandidateVersionStageExecutorSpendFailed gateBest evidenceStop / next + {experiments.map((item) => ( + + {item.id} + {item.definitionRef?.version == null ? "unavailable" : `v${item.definitionRef.version}`} + {item.verdict || item.status || "unavailable"} + {item.executor?.kind || "unavailable"} + {item.budgetSpent?.cost?.known ? `${item.budgetSpent.cost.amount} ${item.budgetSpent.cost.currency || ""}` : "unavailable"} + {item.failedGates?.join(", ") || "—"} + {item.hypothesis || item.summary || "unavailable"} + {item.stopReason || item.nextValidActions?.join(", ") || "—"} + + ))} + + ); +} + +function SemanticDiff({ receipts, pendingReceipt }) { + const diff = [...(pendingReceipt ? [pendingReceipt] : receipts)].reverse().flatMap((receipt) => receipt.evidence || []).find((item) => item?.kind === "autoquant-semantic-definition-diff"); + if (!diff) { + const detail = pendingReceipt ? "Current pending receipt has no semantic diff evidence from Core." : "Compare two exact versions to receive a Core-authored diff."; + return ; + } + return ( +
+

{diff.definition?.id} · v{diff.fromVersion} → v{diff.toVersion}

+ {(diff.changes || []).map((change) =>
{change.field}{diff.affectedEvidence?.includes(change.field) ? " · evidence invalidated" : ""}
{JSON.stringify({ before: change.before, after: change.after }, null, 2)}
)} +
+ ); +} + +function EvidenceReview({ stage, bundle, receipts }) { + const budget = latestCampaignBudget(bundle); + return ( + + + {[["outcome", "Outcome"], ["replay", "Replay"], ["cohorts", "Cohorts"], ["robustness", "Robustness"], ["costs", "Costs"], ["provenance", "Provenance"]].map(([value, label]) => {label})} + + + + 
{JSON.stringify(bundle.decisionMatrix || { state: "unavailable" }, null, 2)}
 + + +
{stage.objects?.map((item, index) => )}

{receipts.length} immutable Operator receipt(s).

 + + ); +} + +function StageCanvas({ stage, bundle, receipts }) { + if (!stage) return ; + return ( +
+ +
{STATE_LABEL[stage.state] || stage.state}
{stage.objects?.map((item, index) => )}
+ {stage.blockers?.length ?
    {stage.blockers.map((item) =>
  • {item}
    • )}
: null} + {stage.nextValidActions?.length ?

Next valid actions: {stage.nextValidActions.join(" · ")}

: null} + + {stage.id === "campaign" ? <> : null} + {stage.id === "evidence" ? : null} + {stage.id === "approval" || stage.id === "reproduction" ?
{receipts.length ? receipts.map((receipt) => ) : }
: null} +
+ ); +} + +function exactCompareRefs(stages) { + const refs = stages.flatMap((stage) => stage.objects || []).filter((item) => item.version != null && ["factor-definition", "strategy-definition", "experiment-definition"].includes(item.kind)); + for (let index = 0; index < refs.length; index += 1) { + const match = refs.slice(index + 1).find((item) => item.kind === refs[index].kind && item.id === refs[index].id); + if (match) return [refs[index], match]; + } + return []; +} + +export function FactorDefinitionEditor({ disabled, busy, onReview }) { + const [id, setId] = useState(""); + const [version, setVersion] = useState(1); + const [parentVersion, setParentVersion] = useState(""); + const [createdAt, setCreatedAt] = useState(new Date().toISOString()); + const [hypothesis, setHypothesis] = useState(""); + const [calcKind, setCalcKind] = useState(null); + const [calcIdentity, setCalcIdentity] = useState(""); + const [calcSourceHash, setCalcSourceHash] = useState(""); + const [parametersJson, setParametersJson] = useState("{}"); + const [outputDirection, setOutputDirection] = useState(null); + const [outputUnit, setOutputUnit] = useState(""); + const [dataDependenciesJson, setDataDependenciesJson] = useState("[]"); + const [missingDataPolicy, setMissingDataPolicy] = useState(""); + const [cohortJson, setCohortJson] = useState("{}"); + const [expectedHorizon, setExpectedHorizon] = useState(""); + const [requiredTests, setRequiredTests] = useState(""); + const [failureGates, setFailureGates] = useState(""); + const [error, setError] = useState(null); + + const isDisabled = disabled || busy; + + function handleSubmit() { + setError(null); + try { + if (!id.trim()) throw new Error("id is required"); + if (!Number.isInteger(version) || version < 1) throw new Error("version must be a positive integer"); + + const parentVersionParsed = parentVersion.trim() === "" ? null : Number(parentVersion); + if (parentVersionParsed !== null && (!Number.isInteger(parentVersionParsed) || parentVersionParsed < 1)) { + throw new Error("parentVersion must be blank/null or a positive integer"); + } + if (parentVersionParsed === null && version !== 1) { + throw new Error("parentVersion must be provided for versions greater than 1"); + } + if (!createdAt.trim()) throw new Error("createdAt is required"); + const parsedDate = new Date(createdAt); + if (isNaN(parsedDate.getTime()) || parsedDate.toISOString() !== createdAt) { + throw new Error("createdAt is not a valid ISO timestamp"); + } + + const parameters = parseJsonObject(parametersJson); + const dataDependencies = JSON.parse(dataDependenciesJson); + if (!Array.isArray(dataDependencies)) throw new Error("dataDependencies must be a JSON array"); + const cohort = parseJsonObject(cohortJson); + + const requiredTestsArray = requiredTests + .split("\n") + .map((line) => line.trim()) + .filter((line) => line.length > 0); + + const failureGatesArray = failureGates + .split("\n") + .map((line) => line.trim()) + .filter((line) => line.length > 0); + + const definition = buildFactorDefinitionDraft({ + identity: { + id: id.trim(), + version, + createdAt, + parentVersion: parentVersionParsed, + }, + editable: { + hypothesis: hypothesis.trim(), + calculation: { + kind: calcKind, + identity: calcIdentity.trim(), + sourceHash: calcSourceHash.trim(), + }, + parameters, + output: { + direction: outputDirection, + unit: outputUnit.trim(), + }, + dataDependencies, + missingDataPolicy: missingDataPolicy.trim(), + cohort, + expectedHorizon: expectedHorizon.trim(), + requiredTests: requiredTestsArray, + failureGates: failureGatesArray, + }, + }); + + onReview(definition); + } catch (err) { + setError(err instanceof Error ? err.message : "Invalid input"); + } + } + + return ( +
+ +
+ setId(e.currentTarget.value)} required disabled={isDisabled} /> + v !== "" && setVersion(v)} min={1} required disabled={isDisabled} /> + setParentVersion(e.currentTarget.value)} placeholder="Leave blank for v1" disabled={isDisabled} /> + setCreatedAt(e.currentTarget.value)} required disabled={isDisabled} /> + + setHypothesis(e.currentTarget.value)} disabled={isDisabled} /> + +
+ calculation +