A comprehensive trading intelligence platform for Solana tokens, featuring real-time DEX analysis, ML-powered predictions, smart money tracking, and advanced risk assessment.
- Real-time Price Monitoring - Sub-second updates with WebSocket connections
- Technical Indicators - RSI, MACD, Bollinger Bands, Volume analysis, and custom DEX indicators
- Machine Learning - LSTM price prediction, pump detection, anomaly detection, pattern recognition
- Risk Assessment - Rug pull detection, liquidity analysis, holder distribution monitoring
- Backtesting Framework - Historical strategy testing with walk-forward analysis
- Alert System - Multi-channel notifications (email, Discord, Telegram, push)
- Backend: Python 3.11, FastAPI, WebSockets
- ML/Data: TensorFlow, scikit-learn, pandas, numpy
- Database: PostgreSQL, Redis
- Monitoring: Prometheus, Grafana
- Deployment: Docker, Docker Compose
- Docker and Docker Compose
- Python 3.11+
- Jupiter and DexScreener API access
- Solana RPC endpoint (optional, defaults to public)
- Clone the repository:
git clone https://github.com/yourusername/cryptocompass.git
cd cryptocompass- Set up environment variables:
cp .env.example .env
# Edit .env with your API keys and configuration- Start the services:
docker-compose up -d- Access the dashboard:
- Web UI: http://localhost
- API: http://localhost:8000
- Grafana: http://localhost:3000 (admin/admin)
- Install dependencies:
pip install -r requirements.txt- Run tests:
pytest tests/- Start development server:
uvicorn src.api.main:app --reloadimport requests
response = requests.get("http://localhost:8000/api/tokens/{token_address}")
token_data = response.json()const ws = new WebSocket('ws://localhost:8080/ws');
ws.send(JSON.stringify({
type: 'subscribe',
token: 'token_address_here'
}));
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
console.log('Update:', data);
};backtest_params = {
"token_address": "...",
"strategy": "momentum",
"start_date": "2024-01-01",
"end_date": "2024-12-31",
"initial_capital": 10000
}
response = requests.post("http://localhost:8000/api/backtest", json=backtest_params)
results = response.json()Key configuration options in src/config.py:
- API Settings: Rate limits, timeouts, endpoints
- Indicators: Periods, thresholds, parameters
- Risk Management: Position sizing, stop-loss levels
- ML Models: Training frequency, confidence thresholds
- Alerts: Delivery channels, sensitivity
├── src/
│ ├── api/ # FastAPI application
│ ├── core/ # Technical indicators
│ ├── data/ # Data fetching and aggregation
│ ├── ml/ # Machine learning models
│ ├── backtesting/ # Backtesting engine
│ ├── monitoring/ # Feedback and monitoring
│ └── ui/ # Web dashboard
├── models/ # Trained ML models
├── tests/ # Test suite
└── docker-compose.yml # Container orchestration
The system tracks:
- Prediction accuracy
- Signal success rate
- API latency
- System resource usage
- Model confidence scores
Access metrics at:
- Prometheus: http://localhost:9090
- Grafana dashboards: http://localhost:3000
- Never commit API keys or secrets
- Use environment variables for sensitive data
- Enable HTTPS in production
- Implement rate limiting
- Regular security audits
- Monitor for anomalous activity
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Submit a pull request
MIT License - See LICENSE file for details
- Documentation: docs/
- Issues: GitHub Issues
- Discord: Join our community
This tool is for educational and research purposes only. Cryptocurrency trading involves substantial risk of loss. Always do your own research and never invest more than you can afford to lose.