Nifty Options Trading AI

This project implements an AI-driven trading strategy for Nifty options. It uses historical Nifty index data to backtest a simple options selling strategy based on market volatility. The strategy evaluates daily price movements and executes trades accordingly, providing performance metrics and visualization of cumulative profits.

Strategy Overview

The strategy involves:

• Selling At-the-Money (ATM) +1% Call and Put Options when the market movement is less than 1%.
• Targeting a net profit of ₹100 per trade on both Call and Put positions.

Key Features

• Automated Backtesting: Analyze historical Nifty data with a rule-based trading strategy.
• Performance Metrics: Gain insights into total trades, win rate, and final profit/loss.
• Visualization: Visualize the cumulative P/L over time with matplotlib.

Trade Data

• Total Trades: 248
• Winning Trades: 151
• Win Rate: 60.89%
• Final P/L: ₹10,800.00

How It Works

1. Load historical Nifty data.
2. Calculate daily market movement.
3. Execute trades based on volatility.
4. Aggregate and visualize performance.
5. Generate performance metrics.

Usage

python backtest_strategy.py

Requirements

Install dependencies using:

pip install -r requirements.txt

Future Improvements

• Introduce AI-driven predictions for market movements.
• Automate live trading with broker APIs.

Contributing

Feel free to fork the repository and submit pull requests for improvements!

License

This project is licensed under the MIT License.