work on market-data-gateway

docs/intelligence-services/signal-engine/README.md

@@ -0,0 +1,87 @@
+# 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