A full-featured web application for backtesting moving average crossover trading strategies. Built with Python and Flask, this project demonstrates how to implement trading strategies using control flow concepts from Chapter 4 of Practical Python for Effective Algorithmic Trading.
- Interactive Web Interface: Modern, responsive design with Bootstrap 5
- Strategy Customization: Configure all aspects of your trading strategy
- Real-time Visualization: Interactive charts with Plotly
- Comprehensive Analysis: Detailed performance metrics and trade history
- Educational Tool: Perfect for learning algorithmic trading concepts
Try the application live on Replit: Stock Strategy Backtester
- Python 3.7+
- Flask 2.3.3+
- NumPy 1.25.2+
- pandas 2.1.0+
- Plotly 5.16.1+
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Clone the repository
git clone https://github.com/thekuldeepsingh/trading-strategy-backtester.git cd trading-strategy-backtester -
Create and activate a virtual environment (optional but recommended)
python -m venv venv # Windows venv\Scripts\activate # macOS/Linux source venv/bin/activate
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Install dependencies
pip install -r requirements.txt
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Run the application
python app.py
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Open your browser Navigate to
http://localhost:5000
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Configure Strategy Parameters
- Set the number of days to simulate
- Adjust price volatility and starting price
- Choose between Simple and Exponential Moving Averages
- Set short-term and long-term MA periods
- Configure initial capital and stop-loss percentage
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Run the Backtest
- Click "Run Backtest" to execute the simulation
- View the results in the interactive dashboard
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Analyze Performance
- Check key metrics like total return, win rate, and drawdown
- Examine the trade history for detailed analysis
- Visualize price movements and portfolio value over time
The backtester is built on several core concepts from Chapter 4 of Practical Python for Effective Algorithmic Trading:
- Conditional Statements: Using if/elif/else structures to make trading decisions
- Loops: Using while loops to simulate day-by-day trading
- Logical Operators: Combining conditions to trigger buy/sell signals
- Nested Control Structures: Creating complex decision trees for trading logic
The web application extends these concepts with:
- Flask Backend: Provides API endpoints and serves web pages
- Plotly Visualizations: Creates interactive trading charts
- Bootstrap Frontend: Delivers a responsive, modern user interface
Join The Quantitative Elite community to:
- Share your implementations and improvements
- Get help with customization and advanced strategies
- Connect with other algorithmic traders
- Learn advanced Python trading techniques
Subscribe to our YouTube Channel for video tutorials and live coding sessions.
The backtester can be extended in several ways:
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Add New Strategy Types
- Implement RSI, MACD, or Bollinger Bands strategies
- Create custom indicator combinations
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Connect to Real Market Data
- Integrate with Yahoo Finance, Alpha Vantage, or other data providers
- Test strategies on historical market data
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Implement Parameter Optimization
- Add grid search functionality to find optimal parameters
- Include walk-forward testing to prevent overfitting
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Enhance Risk Management
- Add trailing stops
- Implement position sizing based on volatility
- Add portfolio-level risk controls
For more advanced techniques and a deeper understanding of algorithmic trading with Python, refer to Practical Python for Effective Algorithmic Trading.
This project is licensed under the MIT License - see the LICENSE file for details.
- Based on concepts from Practical Python for Effective Algorithmic Trading
- Trading strategy inspired by classic technical analysis techniques
- Web application designed to make backtesting accessible to all traders
Happy trading! 📈