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Market Data Gateway
Overview
The Market Data Gateway (MDG) service serves as the central hub for real-time market data processing and distribution within the stock-bot platform. It acts as the intermediary between external market data providers and internal platform services, ensuring consistent, normalized, and reliable market data delivery.
Key Features
Real-time Data Processing
- WebSocket Streaming: Provides low-latency data streams for market updates
- Multi-source Aggregation: Integrates data from multiple providers (Alpaca, Yahoo Finance, etc.)
- Normalized Data Model: Transforms varied provider formats into a unified platform data model
- Subscription Management: Allows services to subscribe to specific data streams
Data Quality Management
- Validation & Sanitization: Ensures data integrity through validation rules
- Anomaly Detection: Identifies unusual price movements or data issues
- Gap Filling: Interpolation strategies for missing data points
- Data Reconciliation: Cross-validates data from multiple sources
Performance Optimization
- Caching Layer: In-memory cache for frequently accessed data
- Rate Limiting: Protects against API quota exhaustion
- Connection Pooling: Efficiently manages provider connections
- Compression: Minimizes data transfer size for bandwidth efficiency
Operational Resilience
- Automatic Reconnection: Handles provider disconnections gracefully
- Circuit Breaking: Prevents cascade failures during outages
- Failover Mechanisms: Switches to alternative data sources when primary sources fail
- Health Monitoring: Self-reports service health metrics
Integration Points
Upstream Connections
- Alpaca Markets API (primary data source)
- Yahoo Finance API (secondary data source)
- Potential future integrations with IEX, Polygon, etc.
Downstream Consumers
- Strategy Orchestrator
- Risk Guardian
- Trading Dashboard
- Data Persistence Layer
Technical Implementation
Technology Stack
- Runtime: Node.js with TypeScript
- Messaging: WebSockets for real-time streaming
- Caching: Redis for shared cache
- Metrics: Prometheus metrics for monitoring
- Configuration: Environment-based with runtime updates
Architecture Pattern
- Event-driven microservice with publisher-subscriber model
- Horizontally scalable to handle increased data volumes
- Stateless design with external state management
Development Guidelines
Error Handling
- Detailed error classification and handling strategy
- Graceful degradation during partial outages
- Comprehensive error logging with context
Testing Strategy
- Unit tests for data transformation logic
- Integration tests with mock data providers
- Performance tests for throughput capacity
- Chaos testing for resilience verification
Observability
- Detailed logs for troubleshooting
- Performance metrics for optimization
- Health checks for system monitoring
- Tracing for request flow analysis
Future Enhancements
- Support for options and derivatives data
- Real-time news and sentiment integration
- Machine learning-based data quality improvements
- Enhanced historical data query capabilities