Mean Reversion Trading Strategy Bot Development

Jose Acosta

Completed work

Game Developer

Matplotlib

pandas

Python

Investment & Asset Management

Mean Reversion Trading Strategy 📈

A sophisticated implementation of a mean-reversion trading bot using real market data and the Ornstein-Uhlenbeck process for optimal entry/exit points.

🌐 Try it Online

No installation required! You can run this project directly in your browser:

📊 Interactive Demo: Click the Binder badge above to launch a full Python environment

🚀 Quick Start: Use the Colab badge for a guided notebook experience

⚡ Live Demo: View sample results (GitHub Pages)

🚀 Features

Advanced OU Parameter Estimation

Maximum Likelihood estimation

Robust statistical analysis

Adaptive parameter updates

Smart Signal Generation

Buy/Sell signals based on mean deviations

Statistical confidence thresholds

Dynamic position sizing

Comprehensive Backtesting

Portfolio performance tracking

Risk metrics calculation

Transaction cost modeling

Rich Visualizations

Price and signal plots

Performance analytics

Real-time monitoring

🏗️ Project Structure

mean_reversion/
├── core/
│   ├── estimator.py     # OU parameter estimation
│   ├── strategy.py      # Trading signal generation
│   ├── backtester.py    # Backtesting engine
│   └── visualizer.py    # Plotting and analytics
├── main.py              # Main execution script
├── mean_reversion_demo.ipynb  # Interactive notebook
├── requirements.txt     # Project dependencies
├── runtime.txt          # Python version for Binder
└── README.md           # Documentation

🔧 Installation

Option 1: Run Online (Recommended for quick testing)

Click the Binder badge above

Wait for the environment to load (~2-3 minutes)

Open mean_reversion_demo.ipynb

Run all cells!

Option 2: Local Installation

Clone the repository:

git clone https://github.com/josetraderx/mean_reversion_OU.git
cd mean_reversion_OU

Install requirements:

pip install -r requirements.txt

📊 Usage

Interactive Notebook (Online)

The mean_reversion_demo.ipynb provides a step-by-step walkthrough with:

Real data fetching

Parameter estimation visualization

Live backtesting results

Interactive plots

Command Line (Local)

Run the main script:

python main.py

The script will prompt you for:

Stock ticker symbol (e.g., AAPL)

Start date (YYYY-MM-DD)

End date (YYYY-MM-DD)

📈 Example Output

===== Mean Reversion Strategy Backtest Summary =====
Symbol: AAPL
Date Range: 2020-01-01 to 2023-01-01
Total Trading Days: 756
Initial Capital: 10000
Final Portfolio Value: 11230.45
Total Return: 12.30%

Estimated Parameters:
  mu: 150.10    # Mean level
  theta: 0.18   # Mean reversion speed
  sigma: 4.32   # Volatility