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https://github.com/the-swarm-corporation/backtesteragent
An enterprise-grade AI-powered backtesting framework built on the Swarms framework for automated trading strategy validation and optimization.
https://github.com/the-swarm-corporation/backtesteragent
ai backtesting data finance finance-agents jpmorgan yahoofinance
Last synced: 25 days ago
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An enterprise-grade AI-powered backtesting framework built on the Swarms framework for automated trading strategy validation and optimization.
- Host: GitHub
- URL: https://github.com/the-swarm-corporation/backtesteragent
- Owner: The-Swarm-Corporation
- License: mit
- Created: 2024-11-19T00:00:28.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-19T00:19:37.000Z (about 1 month ago)
- Last Synced: 2024-11-19T00:45:27.792Z (about 1 month ago)
- Topics: ai, backtesting, data, finance, finance-agents, jpmorgan, yahoofinance
- Language: Python
- Homepage: https://swarms.ai/
- Size: 0 Bytes
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# BackTesterAgent 🚀
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://github.com/kyegomez/swarms)
[](https://github.com/psf/black)
[](https://pepy.tech/project/backtesteragent)
An enterprise-grade AI-powered backtesting framework built on the Swarms framework for automated trading strategy validation and optimization.
## 🌟 Features
- **Advanced Technical Analysis**: Comprehensive suite of technical indicators (SMA, RSI, MACD)
- **Real-Time Data Integration**: Seamless integration with Yahoo Finance for live market data
- **AI-Powered Decision Making**: Leveraging GPT-4 through the Swarms framework
- **Robust Portfolio Management**: Sophisticated position tracking and trade execution
- **Enterprise-Grade Logging**: Detailed logging with Loguru for production environments
- **Type-Safe Implementation**: Comprehensive type hints and dataclass usage
- **Performance Analytics**: In-depth metrics including Sharpe ratio and maximum drawdown
- **Interactive Visualizations**: Real-time trading activity and portfolio performance charts
## 🛠️ Installation
```bash
pip3 install -U backtester-agent
```
## 📋 Requirements
- Python 3.8+
- backtester package: `pip3 install -U backtester-agent`
- API Key for OpenAI
## 🚀 Quick Start
```python
from backtester_agent.main import run_backtest
run_backtest(cash=500.0, symbol="AAPL", start_date="2024-11-16", end_date="2024-11-18", trade_size=10)
```
## 📊 Example Output
```plaintext
2024-01-18 10:30:15 | INFO | Starting backtest for AAPL
2024-01-18 10:30:16 | INFO | Processing 252 trading days
2024-01-18 10:30:45 | SUCCESS | Backtest completed
Backtest Results:
Initial Portfolio Value: $100,000.00
Final Portfolio Value: $125,432.10
Total Return: 25.43%
Sharpe Ratio: 1.85
Maximum Drawdown: -8.32%
Total Trades: 45
```
## 🔧 Configuration
Configure the agent through environment variables or a config file:
```env
OPENAI_API_KEY=your_api_key_here
WORKSPACE_DIR="agent_workspace"
SWARMS_API_KEY=your_swarms_api_key_here # Get from swarms.ai dashboard
```
## 🏗️ Architecture
```mermaid
graph TD
A[FinancialData] -->|Price Data| B[BackTester]
B -->|Market State| C[FinancialAgent]
C -->|Decisions| D[Portfolio]
D -->|Execution| B
E[Technical Indicators] -->|Analysis| C
```
## 📈 Performance Metrics
The BackTesterAgent provides comprehensive performance analytics:
- Total Return
- Sharpe Ratio
- Maximum Drawdown
- Trade Count
- Win/Loss Ratio
- Risk-Adjusted Return
## 🔍 Logging and Monitoring
Detailed logging is implemented using Loguru:
```python
logger.add(
"backtester_{time}.log",
rotation="500 MB",
retention="10 days",
level="INFO"
)
```
## 🔒 Security
- Environment variable management for sensitive data
- Secure API key handling
- Rate limiting for API calls
- Error handling and validation
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [Swarms Framework](https://github.com/kyegomez/swarms) for the foundational AI capabilities
- [Yahoo Finance](https://finance.yahoo.com/) for market data
- All contributors and maintainers
## 📞 Support
- Documentation: [Link to Docs]
- Issues: [GitHub Issues](https://github.com/yourusername/backtesteragent/issues)
- Email: [email protected]
- Discord: [Join our community](https://discord.gg/backtesteragent)
## 🗺️ Roadmap
- [ ] Advanced strategy optimization
- [ ] Multi-asset portfolio support
- [ ] Machine learning integration
- [ ] Real-time trading capabilities
- [ ] Enhanced risk management features
## 📊 Benchmarks
Performance benchmarks against standard trading strategies:
| Strategy | Return | Sharpe Ratio | Max Drawdown |
|----------|---------|--------------|--------------|
| Buy & Hold | 15.2% | 0.95 | -12.3% |
| BackTesterAgent | 25.4% | 1.85 | -8.3% |
| Market Index | 12.1% | 0.82 | -15.7% |
---
Built with ❤️ by [Swarms](https://swarms.ai)