3.1 KiB
3.1 KiB
Signal Engine
Overview
The Signal Engine service generates, processes, and manages trading signals within the stock-bot platform. It transforms raw market data and feature inputs into actionable trading signals that inform strategy execution decisions, serving as the analytical brain of the trading system.
Key Features
Signal Generation
- Technical Indicators: Comprehensive library of technical analysis indicators
- Statistical Models: Mean-reversion, momentum, and other statistical signals
- Pattern Recognition: Identification of chart patterns and formations
- Custom Signal Definition: Framework for creating proprietary signals
Signal Processing
- Filtering: Noise reduction and signal cleaning
- Aggregation: Combining multiple signals into composite indicators
- Normalization: Standardizing signals across different instruments
- Ranking: Relative strength measurement across instruments
Quality Management
- Signal Strength Metrics: Quantitative assessment of signal reliability
- Historical Performance: Tracking of signal predictive power
- Decay Modeling: Time-based degradation of signal relevance
- Correlation Analysis: Identifying redundant or correlated signals
Operational Features
- Real-time Processing: Low-latency signal generation
- Batch Processing: Overnight/weekend comprehensive signal computation
- Signal Repository: Historical storage of generated signals
- Signal Subscription: Event-based notification of new signals
Integration Points
Upstream Connections
- Market Data Gateway (for price and volume data)
- Feature Store (for derived trading features)
- Alternative Data Services (for sentiment, news factors)
- Data Processor (for preprocessed data)
Downstream Consumers
- Strategy Orchestrator (for signal consumption)
- Backtest Engine (for signal effectiveness analysis)
- Trading Dashboard (for signal visualization)
- Risk Guardian (for risk factor identification)
Technical Implementation
Technology Stack
- Runtime: Node.js with TypeScript
- Calculation Engine: Optimized numerical libraries
- Storage: Time-series database for signal storage
- Messaging: Event-driven notification system
- Parallel Processing: Multi-threaded computation for intensive signals
Architecture Pattern
- Pipeline architecture for signal flow
- Pluggable signal component design
- Separation of data preparation from signal generation
- Event sourcing for signal versioning
Development Guidelines
Signal Development
- Signal specification format
- Performance optimization techniques
- Testing requirements and methodology
- Documentation standards
Quality Controls
- Validation methodology
- Backtesting requirements
- Correlation thresholds
- Signal deprecation process
Operational Considerations
- Computation scheduling
- Resource utilization guidelines
- Monitoring requirements
- Failover procedures
Future Enhancements
- Machine learning-based signal generation
- Adaptive signal weighting
- Real-time signal quality feedback
- Advanced signal visualization
- Cross-asset class signals
- Alternative data integration