stock-bot/docs/data-services/feature-store/README.md

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