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