ML-AutoTrader

Advanced Machine Learning for Automated Trading

ML-AutoTrader is a sophisticated research platform that leverages cutting-edge machine learning techniques for predictive modeling and automated trading in financial markets. This project implements a comprehensive suite of prediction models and trading strategies, combining traditional technical analysis with advanced machine learning algorithms.

Key Features

• Data Acquisition: Robust integration with yfinance for efficient retrieval of historical stock data.
• Advanced Data Processing: Comprehensive feature engineering pipeline, including technical indicators and derived financial metrics.
• Multi-Model Approach: Implementation of state-of-the-art machine learning models:
  • Random Forest for ensemble-based predictions
  • Long Short-Term Memory (LSTM) networks for sequence modeling
  • Support Vector Machines (SVM) for high-dimensional space mapping
  • XGBoost for gradient boosting
• Traditional Strategies: Incorporation of time-tested technical indicator-based trading strategies.
• Backtesting Engine: Rigorous historical performance evaluation of various strategies.
• Risk Management Module: Implementation of essential risk control measures adhering to modern portfolio theory.
• Visualization Suite: Generation of insightful visualizations for model performance and backtesting results.

Project Architecture

ML-AutoTrader/
│
├── data/
│   ├── raw/                 # Raw data storage
│   └── processed/           # Processed and feature-engineered data
│
├── models/                  # Serialized model storage
│
├── notebooks/               # Jupyter notebooks for exploration and analysis
│
├── src/
│   ├── data/                # Data processing and feature engineering
│   ├── models/              # Model implementations
│   ├── strategies/          # Trading strategy implementations
│   ├── backtesting/         # Backtesting engine
│   ├── risk_management/     # Risk management utilities
│   └── visualization/       # Data and result visualization tools
│
├── tests/                   # Unit and integration tests
│
├── .gitignore               # Git ignore file
├── LICENSE                  # Project license
├── README.md                # Project documentation
├── requirements.txt         # Project dependencies
└── setup.py                 # Package and distribution management

Installation

Ensure you have Python 3.7+ installed on your system.

1. Clone the repository:

   git clone https://github.com/528314503/ML-AutoTrader.git
   cd ML-AutoTrader
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Data Collection:

   python src/data/data_collection.py
   

2. Data Processing:

   python src/data/data_processing.py
   

3. Feature Engineering:

   python src/features/feature_engineering.py
   

4. Model Training:

   python src/models/train_model.py
   

5. Backtesting:

   python src/backtesting/backtest.py
   

6. Visualization:

   python src/visualization/visualize.py
   

Contributing

We welcome contributions to the ML-AutoTrader project. Please refer to our Contribution Guidelines for more information on how to get started.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Disclaimer

ML-AutoTrader is designed for educational and research purposes only. It does not constitute financial advice, and the authors assume no responsibility for trading decisions made based on this tool. Users should exercise caution and seek professional advice before engaging in real-world trading activities.

Acknowledgments

• yfinance for providing access to Yahoo Finance data
• The open-source community for their invaluable contributions to the machine learning and financial analysis ecosystems

---

For more information, please contact the project maintainers.