🦅 Options Trading Backtesting Framework

A modular and extensible backtesting framework for Options Trading in Python.
Supports advanced strategies like Long Straddle, Long Strangle, Bull Call Spread, Bear Put Spread, and custom user-defined strategies — complete with PnL analytics, ML-based performance prediction, and risk metrics.

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🚀 Features

• ✅ Fetches NSE/BSE data automatically
• ✅ Implements 5 popular option strategies
• ✅ Simulates positional, intraday, and swing modes
• ✅ Calculates key performance metrics: Cumulative PnL, Max Drawdown, Sharpe & Sortino Ratios, Win Rate
• ✅ Machine Learning–based direction prediction
• ✅ Beautiful tabular summaries and graphs

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🧩 Example Usage

from strategies.bull_call_spread import BullCallSpread
from utils.data_fetcher import get_index_data
from utils.metrics import backtest_strategy

data = get_index_data("^NSEI", "2024-01-01", "2024-12-31")
strategy = BullCallSpread(buy_call_premium=120, sell_call_premium=80)

results, metrics = backtest_strategy(
    strategy, data, lot_size=50, premium=100,
    stop_loss_pct=0.1, target_profit_pct=0.2,
    trading_type="positional"
)

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📈 Output Example

📊 Performance Metrics
╒════════════════════╤════════════╕
│ Metric             │ Value      │
╞════════════════════╪════════════╡
│ Final PnL (₹)      │ 12,500.00  │
│ Max Drawdown (₹)   │ 3,200.00   │
│ Sharpe Ratio       │ 1.42       │
│ Sortino Ratio      │ 1.85       │
│ Win Rate (%)       │ 61.23      │
╘════════════════════╧════════════╛

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🤖 Machine Learning Integration

from utils.ml_evaluator import evaluate_with_ml
from tabulate import tabulate

ml_results = evaluate_with_ml(results)
print(tabulate(ml_results, headers="keys", tablefmt="fancy_grid"))

Predicts the next-day direction of profit/loss using models like:

• Random Forest
• SVM
• Logistic Regression
• Neural Network (MLP)
• KNN
• Decision Tree
• Naive Bayes
• Gradient Boosting

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⚙️ Installation

git clone https://github.com/<your-username>/options-backtesting-framework.git
cd options-backtesting-framework
pip install -r requirements.txt

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🧠 Dependencies

pandas
numpy
matplotlib
scikit-learn
tabulate
nsepython
bse

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📜 License

MIT License © Rohit Baskaran

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💡 Author

👤 Rohit Baskaran
💼 CSE Student | Interested in Trading, Stock Market & AI
📧 brohitbaskaran@gmail.com
🌙 GitHub: clashrohit

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🏁 Next Steps (Optional Add-Ons)

• Add visualization dashboards using Plotly
• Integrate live NSE option chain API
• Add Paper-Trading mode
• Connect with Zerodha Kite or Angel One SmartAPI
• Deploy via Streamlit or Flask for web interface