This repository contains self-developed algorithmic trading strategies and tools built using Python. The main objective of this project is to explore, design, and implement data-driven trading algorithms for financial markets.
The project includes:
- Market data analysis
- Trading strategy development
- Backtesting frameworks
- Performance evaluation and visualization
- Fetch historical and live market data
- Implement technical indicators and trading signals
- Develop and backtest multiple trading strategies
- Generate performance reports and visualizations
- Modular code structure for easy expansion
- Python 3.11
- Pandas for data manipulation
- NumPy for numerical calculations
- Matplotlib / Seaborn for plotting
- TA-Lib for technical analysis indicators
- Backtrader / Zipline for backtesting (optional)
- yfinance / Alpha Vantage / Binance API for market data
Algorithmic-Trading-Strategy/
│── data/ # Sample equity CSVs
│── notebooks/ # Jupyter notebooks for exploration
│── src/ # Python modules
│ │── backtest.py # Backtesting logic
│ │── strategy.py # Entry-exit rules
│ │── risk.py # Risk management, position sizing
│ │── dashboard.py # Streamlit dashboard
│── results/ # PnL curves, metrics reports, screenshots
│── requirements.txt # Python dependencies
│── README.md # Project overviewgit clone https://github.com/Soham2511/algorithmic-trading-self-project.git
cd algorithmic-trading-self-projectCopy code
pip install -r requirements.txtCopy code
python backtesting/sample_strategy.py- Add your own strategies in the strategies/ folder
- Modify data sources in data/ as per your preference
- Run backtesting scripts to evaluate strategy performance
This is a self-learning project. Contributions are welcome in the form of:
- New strategies
- Optimization techniques
- Enhanced backtesting modules
This project is licensed under the MIT License.
Project by Soham Jagtap