A sophisticated, configurable trading strategy engine designed for systematic trading across multiple asset classes with advanced risk management, market analysis, and AI-driven insights.
TradeHunter transforms manual trading strategies into automated, data-driven hunting algorithms that can:
- Hunt systematically across thousands of symbols using configurable YAML strategies
- Analyze market behavior through volatility patterns, volume analysis, and correlation studies
- Execute trades with precise risk management and position sizing
- Adapt dynamically to changing market conditions and news events
- Integrate with AI through Model Context Protocol (MCP) for enhanced decision-making
TradeHunter/
├── src/ # Source code
│ ├── *.cs # Core trading engine
├── config/ # Configuration files
│ ├── strategies/ # Trading strategy definitions
│ └── environments/ # Environment configurations
├── docs/ # Documentation
│ ├── trading-insights/ # Market analysis & insights
│ ├── api/ # API documentation
│ └── architecture/ # System design docs
├── scripts/ # Automation scripts
│ ├── deployment/ # Deploy & setup scripts
│ └── analysis/ # Data analysis scripts
├── tests/ # Test suites
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
├── tools/ # Development tools
│ └── data-analysis/ # Market data analysis tools
└── TradeHunter.sln # Solution file
# Build the project
dotnet build
# List available strategies
dotnet run --project src -- list
# Hunt with demo mode
dotnet run --project src -- hunt --demo
# Start MCP server for AI integration
dotnet run --project src -- mcp --port 3000- Configurable Strategies: Define entry/exit rules in YAML
- Risk Management: Portfolio-level and position-level controls
- Market Analysis: Technical indicators, volume patterns, news sentiment
- Correlation Tracking: SPY, sector, and peer correlation analysis
- Volatility Modeling: Intraday and historical volatility analysis
- Volume Analysis: Relative volume, unusual activity detection
- News Impact: Sentiment analysis and news-driven trading
- Market Regime Detection: Bull/bear market adaptation
- CLI Interface: Complete command-line control
- MCP Server: AI assistant integration
- REST API: Programmatic access
- Real-time Data: Live market data integration
- Trading Insights - Market behavior analysis and strategies
- API Reference - Complete API documentation
- Architecture - System design and components
- Strategy Development - How to create custom strategies
- Risk Management - Portfolio protection techniques
- US Equities: NYSE, NASDAQ stocks
- ETFs: Sector, index, and thematic ETFs
- Options: Equity options (planned)
- Futures: Index futures (planned)
- Forex: Major currency pairs (planned)
- Sub-millisecond strategy evaluation
- Concurrent processing of multiple strategies
- Real-time market data processing
- Scalable to thousands of symbols
- Memory efficient with streaming data architecture
- Position Sizing: Kelly Criterion, fixed fractional, volatility-based
- Stop Losses: ATR-based, technical level, trailing stops
- Portfolio Limits: Maximum exposure, correlation limits
- Drawdown Protection: Dynamic position sizing reduction
- News Risk: Earnings, FDA approvals, regulatory events
See Architecture Documentation for detailed technical information.
TradeHunter implements systematic trading principles:
- Edge Detection: Statistical advantages in market inefficiencies
- Risk Parity: Balanced risk across positions and time
- Regime Awareness: Adaptive strategies for different market conditions
- Behavioral Alpha: Exploiting predictable human biases
- Technology Leverage: Speed and scale advantages
"In trading, the systematic approach wins over the long term. TradeHunter provides the framework to capture consistent alpha through disciplined execution."