Description

This project simulates a rolling window strategy optimization and deployment for crypto futures trading. It uses historical Binance Futures data to:

• Collect and prepare 15-minute candlestick data for specified symbols
• Perform a rolling 7-day window genetic algorithm (GA) optimization to find the best Stop Loss (SL) and Take Profit (TP) parameters daily
• Backtest the optimized parameters on the following day’s data to evaluate performance
• Repeat the process day-by-day to simulate a live trading deployment workflow

The strategy optimization focuses on maximizing a custom weighted fitness score incorporating return, Sharpe ratio, Sortino ratio, Calmar ratio, win rate, profit factor, and drawdown penalties.

This approach helps model how a real-world trader or automated system might continuously adapt strategy parameters over time, accounting for market changes and avoiding overfitting by always testing on unseen future data.

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Features

• Rolling 7-day window GA optimization of SL and TP parameters
• Backtesting using backtesting.py library on Binance Futures historical data
• Uses DEAP genetic algorithm framework for strategy parameter search
• Multiprocessing support to speed up GA evaluations
• Detailed daily performance reporting with average returns and statistics

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Use Cases

• Understand rolling window strategy optimization concepts in quantitative trading
• Experiment with genetic algorithm parameter tuning for trading strategies
• Simulate strategy deployment workflows with live-like evaluation

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Technologies

• Python 3
• Binance API
• DEAP (Genetic Algorithms)
• backtesting.py (Backtesting Framework)
• pandas, numpy for data manipulation
• tqdm for progress bars

abhinav00345@gmail.com