This project implements an algorithmic trading strategy using the Relative Strength Index (RSI) to identify potential buy and sell opportunities in financial markets. The strategy analyzes price trends, detects overbought/oversold conditions, and executes trades accordingly.
- Calculates RSI for selected assets
- Implements an automated trading strategy
- Visualizes price trends and RSI indicators
- Backtesting capabilities to evaluate performance
- Customizable parameters for different market conditions
# Clone the repository
git clone https://github.com/Retrospek/AlgorithmicTrading.git
cd AlgorithmicTrading
# Install dependencies
pip install -r requirements.txt
# Run the strategy
python rsi_strategy.py- RSI Calculation: Measures momentum by comparing the magnitude of recent gains to recent losses.
- Overbought Condition: RSI > 70 → Potential sell signal.
- Oversold Condition: RSI < 30 → Potential buy signal.
- Trade Execution: Uses a rule-based system to enter/exit trades based on RSI thresholds.
AlgorithmicTrading/
│── rsi_strategy.py # Main script for RSI-based trading
│── backtesting.py # Code to evaluate strategy performance
│── data/ # Historical market data (optional)
│── utils.py # Helper functions for calculations
│── requirements.txt # Dependencies
│── README.md # Project documentation
│── notebooks/ # Jupyter notebooks for analysis
│── images/ # RSI charts, backtest results, etc.
- Implement stop-loss and take-profit mechanisms
- Integrate with live trading APIs (Alpaca, Binance, etc.)
- Add machine learning enhancements for adaptive RSI tuning
- Explore multi-indicator strategies (e.g., RSI + MACD)
This project is licensed under the MIT License. Feel free to use and modify it as needed.
🚀 Developed by Arjun Mahableshwarkar. Contributions are welcome!