RetroTrade is a modular, extensible algorithmic trading framework designed for research and experimentation with reinforcement learning (RL), deep learning, and sentiment analysis models. The project supports multiple cryptocurrencies, risk management strategies, and real-time monitoring, making it suitable for both academic and practical trading system development.
Note: This project is not fully complete. Some models are provided as pre-trained and may require further development or fine-tuning.
- Modular architecture for easy extension and customization
- Support for RL agents (PPO, GRU, GARCH, etc.)
- Sentiment analysis integration
- Risk management modules
- Real-time monitoring and Telegram notifications
- Data processing and symbol discovery
- Pre-trained models included (PPO, GRU, Sentiment, Risk Management)
retrotrade/ # Core trading system modules
Agents/ # RL and ML agent implementations
Models/ # Model definitions and utilities
risk_management/ # Risk management models and scripts
sentiment_analysis/ # Sentiment analysis models and scripts
data/ # Raw and processed market data
PPO_Trader/ # PPO agent implementation and configs
GRU_Model/ # GRU agent implementation and models
See docs/Installation.md for detailed setup instructions.
See docs/Usage.md for usage examples and module-specific instructions.
See docs/PretrainedModels.md for information about included pre-trained models.
Full documentation is available in the docs/ folder.
- Python 3.8+
- See
requirements.txtand module-specific requirements files for dependencies.
This project is provided under the MIT License.
- Name: Shereef Ibrahim (xRetro)
- Email: shereef225692@ogr.duzce.edu.tr
- GitHub: https://github.com/xRetr00/xRetroTrade
Disclaimer: This project is for research and educational purposes only. Use at your own risk. Trading cryptocurrencies involves significant risk of loss.