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

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+# 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