This repository contains a Python project to test the momentum strategy on F&O listed stocks in India. The strategy involves going long on the top 5 performers based on 52-week rolling returns and setting a stop loss at 2 times the Average True Range (ATR) on the entry price. The portfolio will be rebalanced on a weekly basis.

• Introduction
• Project Overview
• Setup and Usage
• Data Collection
• Momentum Strategy
• Backtest and Analysis
• Disclaimer
• Contribution
• License

The Momentum Strategy is a popular trading strategy that aims to capitalize on the continuation of trends in stock prices. This project tests the effectiveness of the momentum strategy on F&O listed stocks in India over a period of 3 years.

• data_collection.py: This module contains functions to fetch OHLC (Open, High, Low, Close) data of F&O listed stocks in India from a data source.

• momentum_strategy.py: The core of the project, where the momentum strategy is implemented. It includes a custom function to identify the top 5 performers based on 52-week rolling returns, buying the top performers, and setting stop-loss levels using 2 times ATR.

• backtesting.py: This script performs the backtest on the 3-year historical data and generates portfolio returns.

• pyfolio_analysis.py: The analysis script that uses the Pyfolio library to analyze the backtest results, providing performance metrics, risk analysis, and visualizations.

1. Install the required Python packages by running the following command: pip install -r requirements.txt

2. Replace the data collection code in data_collection.py with your preferred data source or API to fetch OHLC data for F&O listed stocks in India.

3. Run the backtesting.py script to perform the backtest on the 3-year historical data and generate portfolio returns.

4. The pyfolio_analysis.py script will analyze the backtest results using Pyfolio and produce a comprehensive performance analysis.

The data_collection.py module is responsible for fetching OHLC data for F&O listed stocks in India. Please replace the data collection code with your data source API or preferred method to collect historical stock data.

The momentum strategy is implemented in the momentum_strategy.py module. It identifies the top 5 performers based on 52-week rolling returns, goes long on these top performers, and sets stop-loss levels at 2 times the ATR on the entry price.

The backtesting.py script performs the backtest on 3 years of historical data and generates portfolio returns. The pyfolio_analysis.py script analyzes the backtest results using Pyfolio, providing insights into the strategy's performance and risk characteristics.

Please note that backtesting results do not guarantee future performance. This project is for educational and research purposes only. Always exercise caution and perform thorough testing before implementing any trading strategy in a live environment.

Contributions to the project are welcome! If you find any issues or have suggestions for improvement, feel free to create a pull request or raise an issue in the repository.

This project is licensed under the MIT License. See the LICENSE file for details.

Happy Backtesting and Trading!