# Feature Store

## Overview
The Feature Store service provides a centralized repository for managing, serving, and monitoring machine learning features within the stock-bot platform. It bridges the gap between data engineering and machine learning, ensuring consistent feature computation and reliable feature access for both training and inference.

## Key Features

### Feature Management
- **Feature Registry**: Central catalog of all ML features
- **Feature Definitions**: Standardized declarations of feature computation logic
- **Feature Versioning**: Tracks changes to feature definitions over time
- **Feature Groups**: Logical grouping of related features

### Serving Capabilities
- **Online Serving**: Low-latency access for real-time predictions
- **Offline Serving**: Batch access for model training
- **Point-in-time Correctness**: Historical feature values for specific timestamps
- **Feature Vectors**: Grouped feature retrieval for models

### Data Quality & Monitoring
- **Statistics Tracking**: Monitors feature distributions and statistics
- **Drift Detection**: Identifies shifts in feature patterns
- **Validation Rules**: Enforces constraints on feature values
- **Alerting**: Notifies of anomalies or quality issues

### Operational Features
- **Caching**: Performance optimization for frequently-used features
- **Backfilling**: Recomputation of historical feature values
- **Feature Lineage**: Tracks data sources and transformations
- **Access Controls**: Security controls for feature access

## Integration Points

### Upstream Connections
- Data Processor (for feature computation)
- Market Data Gateway (for real-time input data)
- Data Catalog (for feature metadata)

### Downstream Consumers
- Signal Engine (for feature consumption)
- Strategy Orchestrator (for real-time feature access)
- Backtest Engine (for historical feature access)
- Model Training Pipeline

## Technical Implementation

### Technology Stack
- **Runtime**: Node.js with TypeScript
- **Online Storage**: Redis for low-latency access
- **Offline Storage**: Parquet files in object storage
- **Metadata Store**: Document database for feature registry
- **API**: RESTful and gRPC interfaces

### Architecture Pattern
- Dual-storage architecture (online/offline)
- Event-driven feature computation
- Schema-on-read with strong validation
- Separation of storage from compute

## Development Guidelines

### Feature Definition
- Feature specification format
- Transformation function requirements
- Testing requirements for features
- Documentation standards

### Performance Considerations
- Caching strategies
- Batch vs. streaming computation
- Storage optimization techniques
- Query patterns and optimization

### Quality Controls
- Feature validation requirements
- Monitoring configuration
- Alerting thresholds
- Remediation procedures

## Future Enhancements
- Feature discovery and recommendations
- Automated feature generation
- Enhanced visualization of feature relationships
- Feature importance tracking
- Integrated A/B testing for features
- On-demand feature computation