📈 Mean Reversion Backtester

A high-performance quantitative research tool for simulating mean reversion trading strategies on historical equity data.

Built with Streamlit for an interactive UI and powered by vectorized backtesting logic for fast, efficient performance evaluation.

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🚀 Features

🎛 Dynamic Parameter Tuning

Adjust strategy inputs in real time:

• Buy Threshold (% below previous close)
• Take Profit (% target)
• Stop Loss (% risk control)
• Transaction Costs

All updates instantly reflect in performance metrics and charts.

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📊 Institutional-Grade Performance Metrics

Automatically calculates:

• Sharpe Ratio – Risk-adjusted return measurement
• Max Drawdown – Peak-to-trough capital decline
• Profit Factor – Gross profits ÷ Gross losses
• Total Return
• Win Rate
• Trade Count

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📈 Interactive Visualizations

• Equity Curve – Portfolio growth over time
• Underwater Chart – Visual drawdown analysis
• Trade signal overlays on price charts

All charts are powered by Plotly for smooth interactivity.

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🧠 Strategy Logic

This application implements a simple daily mean reversion strategy:

📌 Setup

• Monitors the previous day’s closing price.

🟢 Entry Rule

• Buy when the current price drops a defined percentage below the previous close (oversold condition)

🔴 Exit Rules

• Take Profit: Price rises by a defined percentage
• Stop Loss: Price drops to risk threshold
• Optional transaction cost adjustment

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🛠 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/pinxinfang/MeanReversionTester.git
cd MeanReversionTester

2️⃣ Install Dependencies

pip install streamlit pandas yfinance plotly numpy

Or use a virtual environment (recommended):

python -m venv venv
source venv/bin/activate   # macOS/Linux
venv\Scripts\activate      # Windows
pip install -r requirements.txt

3️⃣ Run the App

streamlit run backtester.py

The application will open automatically in your browser.

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⚡ Technical Highlights

• Fully vectorized performance calculations (no slow loops)
• Efficient pandas-based signal generation
• Real-time portfolio equity tracking
• Clean modular architecture for easy strategy expansion

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📂 Project Structure

MeanReversionTester/
│
├── backtester.py        # Main Streamlit application
├── requirements.txt     # Dependencies
└── README.md

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🔮 Future Improvements

• Multi-asset portfolio testing
• Walk-forward optimization
• Position sizing models
• Monte Carlo simulation
• Strategy comparison dashboard

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⚠️ Disclaimer

This project is for educational and research purposes only. It does not constitute financial advice, investment recommendation, or trading guidance.

Trading involves risk. Past performance does not guarantee future results.