Quant Market Movement Prediction & Signal Generation (Long‑Term Strategy)

This repository contains a Python-based quantitative research project built as part of an inter-hostel competition. The problem statement was provided by Beyond IRR (see Kriti2026_quant.pdf). The project focuses on predicting market movement and generating long-term Buy/Sell signals.

The work explores three approaches:

1. Pure Quantitative (rule/indicator-based) approach
2. Machine Learning (ML) approach
3. Integrated approach combining Quant + ML

The repository is organized so that file names clearly indicate the approach used, and all evaluation outputs/metrics are stored in the performance_stats/ folder with descriptive names.

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Project Objective

• Analyze index/market data
• Build long-term trading signals (Buy/Sell)
• Evaluate strategies using relevant performance metrics (returns, drawdowns, risk-adjusted measures, etc.)
• Compare outcomes across:
  • Quant-only logic
  • ML-only model
  • Integrated Quant+ML pipeline

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Repository Structure

Core approaches

• quantitative_approach.py

  • Implements a traditional quantitative strategy, typically based on engineered indicators, rules, trend logic, or statistical signals.
  • Produces Buy/Sell signals and/or positions for long-term holding periods.

• ML.py

  • Implements the machine learning approach.
  • Includes feature engineering and model training/inference logic to predict market direction/movement and translate predictions into signals.

• integrated_approach.py

  • Implements the hybrid approach, integrating quantitative features/signals with ML predictions (or using quant logic as filters/confirmations).
  • Designed to test whether combining both improves robustness and performance.

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Data / Inputs

• indexes.csv

  • Primary dataset used for the analysis (index/market values and related fields).
  • Used across quant, ML, and integrated workflows.

• Kriti2026_quant.pdf

  • Official problem statement shared by Beyond IRR for the competition.

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Evaluation & Results

• performance_stats/

  • Contains all performance evaluation metrics and outputs, stored with meaningful filenames.
  • This folder is the primary place to look for backtest summaries, metric tables, and evaluation artifacts.

• PERFORMANCE_AND_ML_REPORT.md

  • Consolidated write-up/report describing performance results and ML observations.

• plot_performance.py

  • Utility for generating plots/visualizations of performance and/or equity curves from computed results.

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Additional Notes

• ML_README.md
  • ML-specific notes and documentation (features, models, pipeline details, etc.)

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How to Run (Typical Workflow)

Exact commands may vary depending on how each script is implemented, but a common workflow is:

1. Run Quant strategy

   • python quantitative_approach.py

2. Run ML strategy

   • python ML.py

3. Run Integrated strategy

   • python integrated_approach.py

4. Generate plots

   • python plot_performance.py

After running, check:

• performance_stats/ for saved metrics/results
• PERFORMANCE_AND_ML_REPORT.md for summarized findings

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Expected Outputs

Depending on the approach, the scripts typically generate:

• Predicted market movement labels/scores (ML / integrated)
• Buy/Sell signals or position series
• Backtest performance metrics (saved under performance_stats/)
• Visualizations (via plot_performance.py)

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Disclaimer

This project is for educational/research purposes (inter-hostel competition). It does not constitute financial advice. Trading and investing involve risk, and past performance does not guarantee future results.