# 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