Algorithmic Trading & Backtesting System in Python
Python-based algorithmic trading and backtesting system with technical indicators, risk management, and performance analytics.
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I can customize the trading strategy, integrate additional technical indicators, modify risk management rules, add support for new financial instruments, optimi...
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This project is a Python-based algorithmic trading and backtesting system developed to analyze historical financial market data and evaluate rule-based trading strategies. It provides a modular framework for processing market data, generating trading signals, simulating trades, and measuring strategy performance.
The application supports technical indicator calculations, historical data analysis, configurable strategy parameters, and comprehensive performance reporting. It includes risk management features such as stop-loss, take-profit, and position sizing to simulate realistic trading conditions.
The project is designed with a modular architecture to make strategy development, testing, and future enhancements straightforward. It demonstrates practical software engineering practices, data analysis, and financial application development using Python.
Technologies used include Python, Pandas, NumPy, MetaTrader 5 data integration, Matplotlib, and Git for version control.
Future Enhancements
• Live trading support using broker APIs
• Machine learning based signal generation
• Portfolio optimization
• Multi-asset support
• Interactive dashboard
• Performance comparison across multiple strategies
Known Issues
Currently supports offline historical backtesting only. Live trading integration and multi-broker connectivity are planned for future releases.
Installation
1. Install Python 3.10 or later.
2. Clone or download the project source code.
3. Open a terminal in the project directory.
4. Install all required dependencies using:
pip install -r requirements.txt
5. Configure the input market data or sample CSV files.
6. Run the main application using:
python main.py
7. The application will process historical data, execute the trading strategy, and generate performance reports.
Usage
Launch the application after completing the installation. Load the provided historical market data or your own dataset. Configure strategy parameters such as stop loss, take profit, and position sizing if required. Execute the backtest to generate trading signals, performance statistics, equity curves, and trade reports. Review the generated results to evaluate strategy performance.
System Requirements
Operating System: Windows 10/11, Linux, or macOS
Python 3.10+
RAM: Minimum 8 GB
Storage: 500 MB free space
Required Libraries: Pandas, NumPy, Matplotlib
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