86 lines
3 KiB
Markdown
86 lines
3 KiB
Markdown
# Feature Store
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## Overview
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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.
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## Key Features
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### Feature Management
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- **Feature Registry**: Central catalog of all ML features
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- **Feature Definitions**: Standardized declarations of feature computation logic
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- **Feature Versioning**: Tracks changes to feature definitions over time
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- **Feature Groups**: Logical grouping of related features
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### Serving Capabilities
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- **Online Serving**: Low-latency access for real-time predictions
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- **Offline Serving**: Batch access for model training
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- **Point-in-time Correctness**: Historical feature values for specific timestamps
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- **Feature Vectors**: Grouped feature retrieval for models
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### Data Quality & Monitoring
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- **Statistics Tracking**: Monitors feature distributions and statistics
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- **Drift Detection**: Identifies shifts in feature patterns
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- **Validation Rules**: Enforces constraints on feature values
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- **Alerting**: Notifies of anomalies or quality issues
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### Operational Features
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- **Caching**: Performance optimization for frequently-used features
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- **Backfilling**: Recomputation of historical feature values
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- **Feature Lineage**: Tracks data sources and transformations
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- **Access Controls**: Security controls for feature access
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## Integration Points
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### Upstream Connections
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- Data Processor (for feature computation)
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- Market Data Gateway (for real-time input data)
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- Data Catalog (for feature metadata)
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### Downstream Consumers
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- Signal Engine (for feature consumption)
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- Strategy Orchestrator (for real-time feature access)
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- Backtest Engine (for historical feature access)
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- Model Training Pipeline
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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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- **Online Storage**: Redis for low-latency access
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- **Offline Storage**: Parquet files in object storage
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- **Metadata Store**: Document database for feature registry
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- **API**: RESTful and gRPC interfaces
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### Architecture Pattern
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- Dual-storage architecture (online/offline)
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- Event-driven feature computation
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- Schema-on-read with strong validation
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- Separation of storage from compute
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## Development Guidelines
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### Feature Definition
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- Feature specification format
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- Transformation function requirements
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- Testing requirements for features
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- Documentation standards
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### Performance Considerations
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- Caching strategies
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- Batch vs. streaming computation
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- Storage optimization techniques
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- Query patterns and optimization
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### Quality Controls
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- Feature validation requirements
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- Monitoring configuration
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- Alerting thresholds
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- Remediation procedures
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## Future Enhancements
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- Feature discovery and recommendations
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- Automated feature generation
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- Enhanced visualization of feature relationships
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- Feature importance tracking
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- Integrated A/B testing for features
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- On-demand feature computation
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