work on market-data-gateway

docs/data-services/README.md

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+# Data Services
+
+Data services manage data storage, processing, and discovery across the trading platform, providing structured access to market data, features, and metadata.
+
+## Services
+
+### Data Catalog
+- **Purpose**: Data asset management and discovery
+- **Key Functions**:
+  - Data asset discovery and search capabilities
+  - Metadata management and governance
+  - Data lineage tracking
+  - Schema registry and versioning
+  - Data quality monitoring
+
+### Data Processor
+- **Purpose**: Data transformation and processing pipelines
+- **Key Functions**:
+  - ETL/ELT pipeline orchestration
+  - Data cleaning and normalization
+  - Batch and stream processing
+  - Data validation and quality checks
+
+### Feature Store
+- **Purpose**: ML feature management and serving
+- **Key Functions**:
+  - Online and offline feature storage
+  - Feature computation and serving
+  - Feature statistics and monitoring
+  - Feature lineage and versioning
+  - Real-time feature retrieval for ML models
+
+### Market Data Gateway
+- **Purpose**: Market data storage and historical access
+- **Key Functions**:
+  - Historical market data storage
+  - Data archival and retention policies
+  - Query optimization for time-series data
+  - Data backup and recovery
+
+## Architecture
+
+Data services create a unified data layer that enables efficient data discovery, processing, and consumption across the platform. They ensure data quality, consistency, and accessibility for both operational and analytical workloads.

docs/data-services/data-catalog/README.md

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+# Data Catalog
+
+## Overview
+The Data Catalog service provides a centralized system for data asset discovery, management, and governance within the stock-bot platform. It serves as the single source of truth for all data assets, their metadata, and relationships, enabling efficient data discovery and utilization across the platform.
+
+## Key Features
+
+### Data Asset Management
+- **Asset Registration**: Automated and manual registration of data assets
+- **Metadata Management**: Comprehensive metadata for all data assets
+- **Versioning**: Tracks changes to data assets over time
+- **Schema Registry**: Central repository of data schemas and formats
+
+### Data Discovery
+- **Search Capabilities**: Advanced search across all data assets
+- **Categorization**: Hierarchical categorization of data assets
+- **Tagging**: Flexible tagging system for improved findability
+- **Popularity Tracking**: Identifies most-used data assets
+
+### Data Governance
+- **Access Control**: Fine-grained access control for data assets
+- **Lineage Tracking**: Visualizes data origins and transformations
+- **Quality Metrics**: Monitors and reports on data quality
+- **Compliance Tracking**: Ensures regulatory compliance for sensitive data
+
+### Integration Framework
+- **API-first Design**: Comprehensive API for programmatic access
+- **Event Notifications**: Real-time notifications for data changes
+- **Bulk Operations**: Efficient handling of batch operations
+- **Extensibility**: Plugin architecture for custom extensions
+
+## Integration Points
+
+### Upstream Connections
+- Data Processor (for processed data assets)
+- Feature Store (for feature metadata)
+- Market Data Gateway (for market data assets)
+
+### Downstream Consumers
+- Strategy Development Environment
+- Data Analysis Tools
+- Machine Learning Pipeline
+- Reporting Systems
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Database**: Document database for flexible metadata storage
+- **Search**: Elasticsearch for advanced search capabilities
+- **API**: GraphQL for flexible querying
+- **UI**: React-based web interface
+
+### Architecture Pattern
+- Domain-driven design for complex metadata management
+- Microservice architecture for scalability
+- Event sourcing for change tracking
+- CQRS for optimized read/write operations
+
+## Development Guidelines
+
+### Metadata Standards
+- Adherence to common metadata standards
+- Required vs. optional metadata fields
+- Validation rules for metadata quality
+- Consistent naming conventions
+
+### Extension Development
+- Plugin architecture documentation
+- Custom metadata field guidelines
+- Integration hook documentation
+- Testing requirements for extensions
+
+### Performance Considerations
+- Indexing strategies for efficient search
+- Caching recommendations
+- Bulk operation best practices
+- Query optimization techniques
+
+## Future Enhancements
+- Automated metadata extraction
+- Machine learning for data classification
+- Advanced lineage visualization
+- Enhanced data quality scoring
+- Collaborative annotations and discussions
+- Integration with external data marketplaces

docs/data-services/data-processor/README.md

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+# Data Processor
+
+## Overview
+The Data Processor service provides robust data transformation, cleaning, and enrichment capabilities for the stock-bot platform. It serves as the ETL (Extract, Transform, Load) backbone, handling both batch and streaming data processing needs to prepare raw data for consumption by downstream services.
+
+## Key Features
+
+### Data Transformation
+- **Format Conversion**: Transforms data between different formats (JSON, CSV, Parquet, etc.)
+- **Schema Mapping**: Maps between different data schemas
+- **Normalization**: Standardizes data values and formats
+- **Aggregation**: Creates summary data at different time intervals
+
+### Data Quality Management
+- **Validation Rules**: Enforces data quality rules and constraints
+- **Cleansing**: Removes or corrects invalid data
+- **Missing Data Handling**: Strategies for handling incomplete data
+- **Anomaly Detection**: Identifies and flags unusual data patterns
+
+### Pipeline Orchestration
+- **Workflow Definition**: Configurable data processing workflows
+- **Scheduling**: Time-based and event-based pipeline execution
+- **Dependency Management**: Handles dependencies between processing steps
+- **Error Handling**: Graceful error recovery and retry mechanisms
+
+### Data Enrichment
+- **Reference Data Integration**: Enhances data with reference sources
+- **Feature Engineering**: Creates derived features for analysis
+- **Cross-source Joins**: Combines data from multiple sources
+- **Temporal Enrichment**: Adds time-based context and features
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for raw market data)
+- External Data Connectors (for alternative data)
+- Data Lake/Storage (for historical data)
+
+### Downstream Consumers
+- Feature Store (for processed features)
+- Data Catalog (for processed datasets)
+- Intelligence Services (for analysis input)
+- Data Warehouse (for reporting data)
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Processing Frameworks**: Apache Spark for batch, Kafka Streams for streaming
+- **Storage**: Object storage for intermediate data
+- **Orchestration**: Airflow for pipeline management
+- **Configuration**: YAML-based pipeline definitions
+
+### Architecture Pattern
+- Data pipeline architecture
+- Pluggable transformation components
+- Separation of pipeline definition from execution
+- Idempotent processing for reliability
+
+## Development Guidelines
+
+### Pipeline Development
+- Modular transformation development
+- Testing requirements for transformations
+- Performance optimization techniques
+- Documentation requirements
+
+### Data Quality Controls
+- Quality rule definition standards
+- Error handling and reporting
+- Data quality metric collection
+- Threshold-based alerting
+
+### Operational Considerations
+- Monitoring requirements
+- Resource utilization guidelines
+- Scaling recommendations
+- Failure recovery procedures
+
+## Future Enhancements
+- Machine learning-based data cleaning
+- Advanced schema evolution handling
+- Visual pipeline builder
+- Enhanced pipeline monitoring dashboard
+- Automated data quality remediation
+- Real-time processing optimizations

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