work on market-data-gateway

docs/core-services/README.md

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+# Core Services
+
+Core services provide fundamental infrastructure and foundational capabilities for the stock trading platform.
+
+## Services
+
+### Market Data Gateway
+- **Purpose**: Real-time market data processing and distribution
+- **Key Functions**:
+  - WebSocket streaming for live market data
+  - Multi-source data aggregation (Alpaca, Yahoo Finance, etc.)
+  - Data caching and normalization
+  - Rate limiting and connection management
+  - Error handling and reconnection logic
+
+### Risk Guardian
+- **Purpose**: Real-time risk monitoring and controls
+- **Key Functions**:
+  - Position monitoring and risk threshold enforcement
+  - Portfolio risk assessment and alerting
+  - Real-time risk metric calculations
+  - Automated risk controls and circuit breakers
+  - Risk reporting and compliance monitoring
+
+## Architecture
+
+Core services form the backbone of the trading platform, providing essential data flow and risk management capabilities that all other services depend upon. They handle the most critical and time-sensitive operations requiring high reliability and performance.

docs/core-services/market-data-gateway/README.md

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+# Market Data Gateway
+
+## Overview
+The Market Data Gateway (MDG) service serves as the central hub for real-time market data processing and distribution within the stock-bot platform. It acts as the intermediary between external market data providers and internal platform services, ensuring consistent, normalized, and reliable market data delivery.
+
+## Key Features
+
+### Real-time Data Processing
+- **WebSocket Streaming**: Provides low-latency data streams for market updates
+- **Multi-source Aggregation**: Integrates data from multiple providers (Alpaca, Yahoo Finance, etc.)
+- **Normalized Data Model**: Transforms varied provider formats into a unified platform data model
+- **Subscription Management**: Allows services to subscribe to specific data streams
+
+### Data Quality Management
+- **Validation & Sanitization**: Ensures data integrity through validation rules
+- **Anomaly Detection**: Identifies unusual price movements or data issues
+- **Gap Filling**: Interpolation strategies for missing data points
+- **Data Reconciliation**: Cross-validates data from multiple sources
+
+### Performance Optimization
+- **Caching Layer**: In-memory cache for frequently accessed data
+- **Rate Limiting**: Protects against API quota exhaustion
+- **Connection Pooling**: Efficiently manages provider connections
+- **Compression**: Minimizes data transfer size for bandwidth efficiency
+
+### Operational Resilience
+- **Automatic Reconnection**: Handles provider disconnections gracefully
+- **Circuit Breaking**: Prevents cascade failures during outages
+- **Failover Mechanisms**: Switches to alternative data sources when primary sources fail
+- **Health Monitoring**: Self-reports service health metrics
+
+## Integration Points
+
+### Upstream Connections
+- Alpaca Markets API (primary data source)
+- Yahoo Finance API (secondary data source)
+- Potential future integrations with IEX, Polygon, etc.
+
+### Downstream Consumers
+- Strategy Orchestrator
+- Risk Guardian
+- Trading Dashboard
+- Data Persistence Layer
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Messaging**: WebSockets for real-time streaming
+- **Caching**: Redis for shared cache
+- **Metrics**: Prometheus metrics for monitoring
+- **Configuration**: Environment-based with runtime updates
+
+### Architecture Pattern
+- Event-driven microservice with publisher-subscriber model
+- Horizontally scalable to handle increased data volumes
+- Stateless design with external state management
+
+## Development Guidelines
+
+### Error Handling
+- Detailed error classification and handling strategy
+- Graceful degradation during partial outages
+- Comprehensive error logging with context
+
+### Testing Strategy
+- Unit tests for data transformation logic
+- Integration tests with mock data providers
+- Performance tests for throughput capacity
+- Chaos testing for resilience verification
+
+### Observability
+- Detailed logs for troubleshooting
+- Performance metrics for optimization
+- Health checks for system monitoring
+- Tracing for request flow analysis
+
+## Future Enhancements
+- Support for options and derivatives data
+- Real-time news and sentiment integration
+- Machine learning-based data quality improvements
+- Enhanced historical data query capabilities

docs/core-services/risk-guardian/README.md

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+# Risk Guardian
+
+## Overview
+The Risk Guardian service provides real-time risk monitoring and control mechanisms for the stock-bot platform. It serves as the protective layer that ensures trading activities remain within defined risk parameters, safeguarding the platform and its users from excessive market exposure and potential losses.
+
+## Key Features
+
+### Real-time Risk Monitoring
+- **Position Tracking**: Continuously monitors all open positions
+- **Risk Metric Calculation**: Calculates key risk metrics (VaR, volatility, exposure)
+- **Threshold Management**: Configurable risk thresholds with multiple severity levels
+- **Aggregated Risk Views**: Risk assessment at portfolio, strategy, and position levels
+
+### Automated Risk Controls
+- **Pre-trade Validation**: Validates orders against risk limits before execution
+- **Circuit Breakers**: Automatically halts trading when thresholds are breached
+- **Position Liquidation**: Controlled unwinding of positions when necessary
+- **Trading Restrictions**: Enforces instrument, size, and frequency restrictions
+
+### Risk Alerting
+- **Real-time Notifications**: Immediate alerts for threshold breaches
+- **Escalation Paths**: Multi-level alerting based on severity
+- **Alert History**: Maintains historical record of all risk events
+- **Custom Alert Rules**: Configurable alerting conditions and criteria
+
+### Compliance Management
+- **Regulatory Reporting**: Assists with required regulatory reporting
+- **Audit Trails**: Comprehensive logging of risk-related decisions
+- **Rule-based Controls**: Implements compliance-driven trading restrictions
+- **Documentation**: Maintains evidence of risk control effectiveness
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for price data)
+- Strategy Orchestrator (for active strategies)
+- Order Management System (for position tracking)
+
+### Downstream Consumers
+- Trading Dashboard (for risk visualization)
+- Strategy Orchestrator (for trading restrictions)
+- Notification Service (for alerting)
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Database**: Time-series database for risk metrics
+- **Messaging**: Event-driven architecture with message bus
+- **Math Libraries**: Specialized libraries for risk calculations
+- **Caching**: In-memory risk state management
+
+### Architecture Pattern
+- Reactive microservice with event sourcing
+- Command Query Responsibility Segregation (CQRS)
+- Rule engine for risk evaluation
+- Stateful service with persistence
+
+## Development Guidelines
+
+### Risk Calculation Approach
+- Clear documentation of all risk formulas
+- Validation against industry standard calculations
+- Performance optimization for real-time processing
+- Regular backtesting of risk models
+
+### Testing Strategy
+- Unit tests for risk calculation logic
+- Scenario-based testing for specific market conditions
+- Stress testing with extreme market movements
+- Performance testing for high-frequency updates
+
+### Calibration Process
+- Documented process for risk model calibration
+- Historical data validation
+- Parameter sensitivity analysis
+- Regular recalibration schedule
+
+## Future Enhancements
+- Machine learning for anomaly detection
+- Scenario analysis and stress testing
+- Custom risk models per strategy type
+- Enhanced visualization of risk exposures
+- Factor-based risk decomposition

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

docs/execution-services/README.md

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+# Execution Services
+
+Execution services handle trade execution, order management, and broker integrations for the trading platform.
+
+## Services
+
+*Currently in planning phase - no active services deployed*
+
+## Planned Capabilities
+
+### Order Management System (OMS)
+- **Purpose**: Centralized order lifecycle management
+- **Planned Functions**:
+  - Order routing and execution
+  - Order validation and risk checks
+  - Execution quality monitoring
+  - Fill reporting and trade confirmations
+
+### Broker Gateway
+- **Purpose**: Multi-broker connectivity and abstraction
+- **Planned Functions**:
+  - Broker API integration and management
+  - Order routing optimization
+  - Execution venue selection
+  - Trade settlement and clearing
+
+### Portfolio Manager
+- **Purpose**: Position tracking and portfolio management
+- **Planned Functions**:
+  - Real-time position tracking
+  - Portfolio rebalancing
+  - Corporate actions processing
+  - P&L calculation and reporting
+
+## Architecture
+
+Execution services will form the operational core of trade execution, ensuring reliable and efficient order processing while maintaining proper risk controls and compliance requirements.

docs/execution-services/broker-gateway/README.md

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+# Broker Gateway
+
+## Overview
+The Broker Gateway service will provide a unified interface for connecting to multiple broker APIs and trading venues within the stock-bot platform. It will abstract the complexities of different broker systems, providing a standardized way to route orders, receive executions, and manage account information across multiple execution venues.
+
+## Planned Features
+
+### Broker Integration
+- **Multi-broker Support**: Connectivity to multiple brokerage platforms
+- **Unified API**: Standardized interface across different brokers
+- **Credential Management**: Secure handling of broker authentication
+- **Connection Management**: Monitoring and maintenance of broker connections
+- **Error Handling**: Standardized error processing across providers
+
+### Order Routing
+- **Smart Routing**: Intelligent selection of optimal execution venues
+- **Route Optimization**: Cost and execution quality-based routing decisions
+- **Failover Routing**: Automatic rerouting in case of broker issues
+- **Split Orders**: Distribution of large orders across multiple venues
+- **Order Translation**: Mapping platform orders to broker-specific formats
+
+### Account Management
+- **Balance Tracking**: Real-time account balance monitoring
+- **Position Reconciliation**: Verification of position data with brokers
+- **Margin Calculation**: Standardized margin requirement calculation
+- **Account Limits**: Enforcement of account-level trading restrictions
+- **Multi-account Support**: Management of multiple trading accounts
+
+### Market Access
+- **Market Data Proxying**: Standardized access to broker market data
+- **Instrument Coverage**: Management of tradable instrument universe
+- **Trading Hours**: Handling of exchange trading calendars
+- **Fee Structure**: Tracking of broker-specific fee models
+- **Corporate Actions**: Processing of splits, dividends, and other events
+
+## Planned Integration Points
+
+### Upstream Connections
+- Order Management System (for order routing)
+- Risk Guardian (for account risk monitoring)
+- Authentication Service (for user permissions)
+
+### Downstream Connections
+- External Broker APIs (e.g., Alpaca, Interactive Brokers)
+- Market Data Providers
+- Clearing Systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Database**: Fast key-value store for state management
+- **Messaging**: Message bus for order events
+- **Authentication**: Secure credential vault
+- **Monitoring**: Real-time connection monitoring
+
+### Architecture Pattern
+- Adapter pattern for broker integrations
+- Circuit breaker for fault tolerance
+- Rate limiting for API compliance
+- Idempotent operations for reliability
+
+## Development Guidelines
+
+### Broker Integration
+- API implementation requirements
+- Authentication methods
+- Error mapping standards
+- Testing requirements
+
+### Performance Considerations
+- Latency expectations
+- Throughput requirements
+- Resource utilization guidelines
+- Connection pooling recommendations
+
+### Reliability Measures
+- Retry strategies
+- Circuit breaker configurations
+- Monitoring requirements
+- Failover procedures
+
+## Implementation Roadmap
+1. Core integration with primary broker (Alpaca)
+2. Order routing and execution tracking
+3. Account management and position reconciliation
+4. Additional broker integrations
+5. Smart routing and optimization features

docs/execution-services/order-management-system/README.md

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+# Order Management System
+
+## Overview
+The Order Management System (OMS) will provide centralized order lifecycle management for the stock-bot platform. It will handle the entire order process from creation through routing, execution, and settlement, ensuring reliable and efficient trade processing while maintaining proper audit trails.
+
+## Planned Features
+
+### Order Lifecycle Management
+- **Order Creation**: Clean API for creating various order types
+- **Order Validation**: Pre-execution validation and risk checks
+- **Order Routing**: Intelligent routing to appropriate brokers/venues
+- **Execution Tracking**: Real-time tracking of order status
+- **Fill Management**: Processing of full and partial fills
+- **Cancellation & Modification**: Handling order changes and cancellations
+
+### Order Types & Algorithms
+- **Market & Limit Orders**: Basic order type handling
+- **Stop & Stop-Limit Orders**: Risk-controlling conditional orders
+- **Time-in-Force Options**: Day, GTC, IOC, FOK implementations
+- **Algorithmic Orders**: TWAP, VWAP, Iceberg, and custom algorithms
+- **Bracket Orders**: OCO (One-Cancels-Other) and other complex orders
+
+### Execution Quality
+- **Best Execution**: Strategies for achieving optimal execution prices
+- **Transaction Cost Analysis**: Measurement and optimization of execution costs
+- **Slippage Monitoring**: Tracking of execution vs. expected prices
+- **Fill Reporting**: Comprehensive reporting on execution quality
+
+### Operational Features
+- **Audit Trail**: Complete history of all order events
+- **Reconciliation**: Matching of orders with executions
+- **Exception Handling**: Management of rejected or failed orders
+- **Compliance Rules**: Implementation of regulatory requirements
+
+## Planned Integration Points
+
+### Upstream Connections
+- Strategy Orchestrator (order requests)
+- Risk Guardian (risk validation)
+- Authentication Services (permission validation)
+
+### Downstream Consumers
+- Broker Gateway (order routing)
+- Portfolio Manager (position impact)
+- Trading Dashboard (order visualization)
+- Data Warehouse (execution analytics)
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Database**: High-performance transactional database
+- **Messaging**: Low-latency message bus for order events
+- **API**: RESTful and WebSocket interfaces
+- **Persistence**: Event sourcing for order history
+
+### Architecture Pattern
+- Event-driven architecture for real-time processing
+- CQRS for optimized read/write operations
+- Microservice decomposition by functionality
+- High availability and fault tolerance design
+
+## Development Guidelines
+
+### Order Management
+- Order state machine definitions
+- Order type specifications
+- Validation rule implementation
+- Exception handling procedures
+
+### Performance Requirements
+- Order throughput expectations
+- Latency budgets by component
+- Scaling approaches
+- Resource utilization guidelines
+
+### Testing Strategy
+- Unit testing requirements
+- Integration testing approach
+- Performance testing methodology
+- Compliance verification procedures
+
+## Implementation Roadmap
+1. Core order types and lifecycle management
+2. Basic routing and execution tracking
+3. Advanced order types and algorithms
+4. Execution quality analytics and optimization
+5. Compliance and regulatory reporting features

docs/execution-services/portfolio-manager/README.md

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+# Portfolio Manager
+
+## Overview
+The Portfolio Manager service will provide comprehensive position tracking, portfolio analysis, and management capabilities for the stock-bot platform. It will maintain the current state of all trading portfolios, calculate performance metrics, and support portfolio-level decision making.
+
+## Planned Features
+
+### Position Management
+- **Real-time Position Tracking**: Accurate tracking of all open positions
+- **Position Reconciliation**: Validation against broker records
+- **Average Price Calculation**: Tracking of position entry prices
+- **Lot Management**: FIFO/LIFO/average cost basis tracking
+- **Multi-currency Support**: Handling positions across different currencies
+
+### Portfolio Analytics
+- **Performance Metrics**: Return calculation (absolute, relative, time-weighted)
+- **Risk Metrics**: Volatility, beta, correlation, VaR calculations
+- **Attribution Analysis**: Performance attribution by sector, strategy, asset class
+- **Scenario Analysis**: What-if analysis for portfolio changes
+- **Benchmark Comparison**: Performance vs. standard benchmarks
+
+### Corporate Action Processing
+- **Dividend Processing**: Impact of cash and stock dividends
+- **Split Adjustments**: Handling of stock splits and reverse splits
+- **Merger & Acquisition Handling**: Position adjustments for M&A events
+- **Rights & Warrants**: Processing of corporate rights events
+- **Spin-offs**: Management of position changes from spin-off events
+
+### Portfolio Construction
+- **Rebalancing Tools**: Portfolio rebalancing against targets
+- **Optimization**: Portfolio optimization for various objectives
+- **Constraint Management**: Enforcement of portfolio constraints
+- **Tax-aware Trading**: Consideration of tax implications
+- **Cash Management**: Handling of cash positions and forecasting
+
+## Planned Integration Points
+
+### Upstream Connections
+- Order Management System (for executed trades)
+- Broker Gateway (for position verification)
+- Market Data Gateway (for pricing)
+- Strategy Orchestrator (for allocation decisions)
+
+### Downstream Consumers
+- Risk Guardian (for portfolio risk assessment)
+- Trading Dashboard (for portfolio visualization)
+- Reporting System (for performance reporting)
+- Tax Reporting (for tax calculations)
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Database**: Fast database with transaction support
+- **Calculation Engine**: Optimized financial calculation libraries
+- **API**: RESTful interface with WebSocket updates
+- **Caching**: In-memory position cache for performance
+
+### Architecture Pattern
+- Event sourcing for position history
+- CQRS for optimized read/write operations
+- Eventual consistency for distributed state
+- Snapshotting for performance optimization
+
+## Development Guidelines
+
+### Position Calculations
+- Cost basis methodologies
+- Corporate action handling
+- FX conversion approaches
+- Performance calculation standards
+
+### Data Consistency
+- Reconciliation procedures
+- Error detection and correction
+- Data validation requirements
+- Audit trail requirements
+
+### Performance Optimization
+- Caching strategies
+- Calculation optimization
+- Query patterns
+- Batch processing approaches
+
+## Implementation Roadmap
+1. Basic position tracking and P&L calculation
+2. Portfolio analytics and performance metrics
+3. Corporate action processing
+4. Advanced portfolio construction tools
+5. Tax and regulatory reporting features

docs/integration-services/README.md

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+# Integration Services
+
+Integration services provide connectivity, messaging, and interoperability between internal services and external systems.
+
+## Services
+
+*Currently in planning phase - no active services deployed*
+
+## Planned Capabilities
+
+### Message Bus
+- **Purpose**: Event-driven communication between services
+- **Planned Functions**:
+  - Event publishing and subscription
+  - Message routing and transformation
+  - Dead letter queue handling
+  - Event sourcing and replay capabilities
+
+### API Gateway
+- **Purpose**: Unified API management and routing
+- **Planned Functions**:
+  - API endpoint consolidation
+  - Authentication and authorization
+  - Rate limiting and throttling
+  - Request/response transformation
+
+### External Data Connectors
+- **Purpose**: Third-party data source integration
+- **Planned Functions**:
+  - Alternative data provider connections
+  - News and sentiment data feeds
+  - Economic indicator integrations
+  - Social media sentiment tracking
+
+### Notification Service
+- **Purpose**: Multi-channel alerting and notifications
+- **Planned Functions**:
+  - Email, SMS, and push notifications
+  - Alert routing and escalation
+  - Notification templates and personalization
+  - Delivery tracking and analytics
+
+## Architecture
+
+Integration services will provide the connectivity fabric that enables seamless communication between all platform components and external systems, ensuring loose coupling and high scalability.

docs/integration-services/api-gateway/README.md

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+# API Gateway
+
+## Overview
+The API Gateway service will provide a unified entry point for all external API requests to the stock-bot platform. It will handle request routing, composition, protocol translation, authentication, and other cross-cutting concerns, providing a simplified interface for clients while abstracting the internal microservice architecture.
+
+## Planned Features
+
+### Request Management
+- **Routing**: Direct requests to appropriate backend services
+- **Aggregation**: Combine results from multiple microservices
+- **Transformation**: Convert between different data formats and protocols
+- **Parameter Validation**: Validate request parameters before forwarding
+- **Service Discovery**: Dynamically locate service instances
+
+### Security Features
+- **Authentication**: Centralized authentication for all API requests
+- **Authorization**: Role-based access control for API endpoints
+- **API Keys**: Management of client API keys and quotas
+- **JWT Validation**: Token-based authentication handling
+- **OAuth Integration**: Support for OAuth 2.0 flows
+
+### Traffic Management
+- **Rate Limiting**: Protect services from excessive requests
+- **Throttling**: Client-specific request throttling
+- **Circuit Breaking**: Prevent cascading failures
+- **Load Balancing**: Distribute requests among service instances
+- **Retries**: Automatic retry of failed requests
+
+### Operational Features
+- **Request Logging**: Comprehensive logging of API activity
+- **Metrics Collection**: Performance and usage metrics
+- **Caching**: Response caching for improved performance
+- **Documentation**: Auto-generated API documentation
+- **Versioning**: Support for multiple API versions
+
+## Planned Integration Points
+
+### Frontend Connections
+- Trading Dashboard (web client)
+- Mobile applications
+- Third-party integrations
+- Partner systems
+
+### Backend Services
+- All platform microservices
+- Authentication services
+- Monitoring and logging systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **API Gateway**: Kong, AWS API Gateway, or custom solution
+- **Runtime**: Node.js with TypeScript
+- **Documentation**: OpenAPI/Swagger
+- **Cache**: Redis for response caching
+- **Storage**: Database for API configurations
+
+### Architecture Pattern
+- Backend for Frontend (BFF) pattern
+- API Gateway pattern
+- Circuit breaker pattern
+- Bulkhead pattern for isolation
+
+## Development Guidelines
+
+### API Design
+- RESTful API design standards
+- Error response format
+- Versioning strategy
+- Resource naming conventions
+
+### Security Implementation
+- Authentication requirements
+- Authorization approach
+- API key management
+- Rate limit configuration
+
+### Performance Optimization
+- Caching strategies
+- Request batching techniques
+- Response compression
+- Timeout configurations
+
+## Implementation Roadmap
+1. Core routing and basic security features
+2. Traffic management and monitoring
+3. Request aggregation and transformation
+4. Advanced security features
+5. Developer portal and documentation

docs/integration-services/message-bus/README.md

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+# Message Bus
+
+## Overview
+The Message Bus service will provide the central event-driven communication infrastructure for the stock-bot platform. It will enable reliable, scalable, and decoupled interaction between services through asynchronous messaging, event streaming, and publish-subscribe patterns.
+
+## Planned Features
+
+### Messaging Infrastructure
+- **Topic-based Messaging**: Publish-subscribe communication model
+- **Message Queuing**: Reliable message delivery with persistence
+- **Event Streaming**: Real-time event processing with replay capabilities
+- **Message Routing**: Dynamic routing based on content and metadata
+- **Quality of Service**: Various delivery guarantee levels (at-least-once, exactly-once)
+
+### Message Processing
+- **Message Transformation**: Content transformation and enrichment
+- **Message Filtering**: Rules-based filtering of messages
+- **Schema Validation**: Enforcement of message format standards
+- **Serialization Formats**: Support for JSON, Protocol Buffers, Avro
+- **Compression**: Message compression for efficiency
+
+### Operational Features
+- **Dead Letter Handling**: Management of unprocessable messages
+- **Message Tracing**: End-to-end tracing of message flow
+- **Event Sourcing**: Event storage and replay capability
+- **Rate Limiting**: Protection against message floods
+- **Back-pressure Handling**: Flow control mechanisms
+
+### Integration Capabilities
+- **Service Discovery**: Dynamic discovery of publishers/subscribers
+- **Protocol Bridging**: Support for multiple messaging protocols
+- **External System Integration**: Connectors for external message systems
+- **Legacy System Adapters**: Integration with non-event-driven systems
+- **Web Integration**: WebSocket and SSE support for web clients
+
+## Planned Integration Points
+
+### Service Connections
+- All platform microservices as publishers and subscribers
+- Trading Dashboard (for real-time updates)
+- External Data Sources (for ingestion)
+- Monitoring Systems (for operational events)
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Messaging Platform**: Kafka or RabbitMQ
+- **Client Libraries**: Native TypeScript SDK
+- **Monitoring**: Prometheus integration for metrics
+- **Management**: Admin interface for topic/queue management
+- **Storage**: Optimized storage for event persistence
+
+### Architecture Pattern
+- Event-driven architecture
+- Publish-subscribe pattern
+- Command pattern for request-response
+- Circuit breaker for resilience
+
+## Development Guidelines
+
+### Message Design
+- Event schema standards
+- Versioning approach
+- Backward compatibility requirements
+- Message size considerations
+
+### Integration Patterns
+- Event notification pattern
+- Event-carried state transfer
+- Command messaging pattern
+- Request-reply pattern implementations
+
+### Operational Considerations
+- Monitoring requirements
+- Scaling guidelines
+- Disaster recovery approach
+- Message retention policies
+
+## Implementation Roadmap
+1. Core messaging infrastructure
+2. Service integration patterns
+3. Operational tooling and monitoring
+4. Advanced features (replay, transformation)
+5. External system connectors and adapters

docs/intelligence-services/README.md

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+# Intelligence Services
+
+Intelligence services provide AI/ML capabilities, strategy execution, and algorithmic trading intelligence for the platform.
+
+## Services
+
+### Backtest Engine
+- **Purpose**: Historical strategy testing and performance analysis
+- **Key Functions**:
+  - Strategy backtesting with historical data
+  - Performance analytics and metrics calculation
+  - Vectorized and event-based processing modes
+  - Risk-adjusted return analysis
+  - Strategy comparison and optimization
+
+### Signal Engine
+- **Purpose**: Trading signal generation and processing
+- **Key Functions**:
+  - Technical indicator calculations
+  - Signal generation from multiple sources
+  - Signal aggregation and filtering
+  - Real-time signal processing
+  - Signal quality assessment
+
+### Strategy Orchestrator
+- **Purpose**: Trading strategy execution and management
+- **Key Functions**:
+  - Strategy lifecycle management
+  - Event-driven strategy execution
+  - Multi-strategy coordination
+  - Strategy performance monitoring
+  - Risk integration and position management
+
+## Architecture
+
+Intelligence services form the "brain" of the trading platform, combining market analysis, machine learning, and algorithmic decision-making to generate actionable trading insights. They work together to create a comprehensive trading intelligence pipeline from signal generation to strategy execution.

docs/intelligence-services/backtest-engine/README.md

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+# Backtest Engine
+
+## Overview
+The Backtest Engine service provides comprehensive historical simulation capabilities for trading strategies within the stock-bot platform. It enables strategy developers to evaluate performance, risk, and robustness of trading algorithms using historical market data before deploying them to production.
+
+## Key Features
+
+### Simulation Framework
+- **Event-based Processing**: True event-driven simulation of market activities
+- **Vectorized Processing**: High-performance batch processing for speed
+- **Multi-asset Support**: Simultaneous testing across multiple instruments
+- **Historical Market Data**: Access to comprehensive price and volume history
+
+### Performance Analytics
+- **Return Metrics**: CAGR, absolute return, risk-adjusted metrics
+- **Risk Metrics**: Drawdown, volatility, VaR, expected shortfall
+- **Transaction Analysis**: Slippage modeling, fee impact, market impact
+- **Statistical Analysis**: Win rate, profit factor, Sharpe/Sortino ratios
+
+### Realistic Simulation
+- **Order Book Simulation**: Realistic market depth modeling
+- **Latency Modeling**: Simulates execution and market data delays
+- **Fill Probability Models**: Realistic order execution simulation
+- **Market Impact Models**: Adjusts prices based on order sizes
+
+### Development Tools
+- **Parameter Optimization**: Grid search and genetic algorithm optimization
+- **Walk-forward Testing**: Time-based validation with parameter stability
+- **Monte Carlo Analysis**: Probability distribution of outcomes
+- **Sensitivity Analysis**: Impact of parameter changes on performance
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for historical data)
+- Feature Store (for historical feature values)
+- Strategy Repository (for strategy definitions)
+
+### Downstream Consumers
+- Strategy Orchestrator (for optimized parameters)
+- Risk Guardian (for risk model validation)
+- Trading Dashboard (for backtest visualization)
+- Strategy Development Environment
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Computation Engine**: Optimized numerical libraries
+- **Storage**: Time-series database for results
+- **Visualization**: Interactive performance charts
+- **Distribution**: Parallel processing for large backtests
+
+### Architecture Pattern
+- Pipeline architecture for data flow
+- Plugin system for custom components
+- Separation of strategy logic from simulation engine
+- Reproducible random state management
+
+## Development Guidelines
+
+### Strategy Development
+- Strategy interface definition
+- Testing harness documentation
+- Performance optimization guidelines
+- Validation requirements
+
+### Simulation Configuration
+- Parameter specification format
+- Simulation control options
+- Market assumption configuration
+- Execution model settings
+
+### Results Analysis
+- Standard metrics calculation
+- Custom metric development
+- Visualization best practices
+- Comparative analysis techniques
+
+## Future Enhancements
+- Agent-based simulation for market microstructure
+- Cloud-based distributed backtesting
+- Real market data replay with tick data
+- Machine learning for parameter optimization
+- Strategy combination and portfolio optimization
+- Enhanced visualization and reporting capabilities

docs/intelligence-services/signal-engine/README.md

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+# Signal Engine
+
+## Overview
+The Signal Engine service generates, processes, and manages trading signals within the stock-bot platform. It transforms raw market data and feature inputs into actionable trading signals that inform strategy execution decisions, serving as the analytical brain of the trading system.
+
+## Key Features
+
+### Signal Generation
+- **Technical Indicators**: Comprehensive library of technical analysis indicators
+- **Statistical Models**: Mean-reversion, momentum, and other statistical signals
+- **Pattern Recognition**: Identification of chart patterns and formations
+- **Custom Signal Definition**: Framework for creating proprietary signals
+
+### Signal Processing
+- **Filtering**: Noise reduction and signal cleaning
+- **Aggregation**: Combining multiple signals into composite indicators
+- **Normalization**: Standardizing signals across different instruments
+- **Ranking**: Relative strength measurement across instruments
+
+### Quality Management
+- **Signal Strength Metrics**: Quantitative assessment of signal reliability
+- **Historical Performance**: Tracking of signal predictive power
+- **Decay Modeling**: Time-based degradation of signal relevance
+- **Correlation Analysis**: Identifying redundant or correlated signals
+
+### Operational Features
+- **Real-time Processing**: Low-latency signal generation
+- **Batch Processing**: Overnight/weekend comprehensive signal computation
+- **Signal Repository**: Historical storage of generated signals
+- **Signal Subscription**: Event-based notification of new signals
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for price and volume data)
+- Feature Store (for derived trading features)
+- Alternative Data Services (for sentiment, news factors)
+- Data Processor (for preprocessed data)
+
+### Downstream Consumers
+- Strategy Orchestrator (for signal consumption)
+- Backtest Engine (for signal effectiveness analysis)
+- Trading Dashboard (for signal visualization)
+- Risk Guardian (for risk factor identification)
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Calculation Engine**: Optimized numerical libraries
+- **Storage**: Time-series database for signal storage
+- **Messaging**: Event-driven notification system
+- **Parallel Processing**: Multi-threaded computation for intensive signals
+
+### Architecture Pattern
+- Pipeline architecture for signal flow
+- Pluggable signal component design
+- Separation of data preparation from signal generation
+- Event sourcing for signal versioning
+
+## Development Guidelines
+
+### Signal Development
+- Signal specification format
+- Performance optimization techniques
+- Testing requirements and methodology
+- Documentation standards
+
+### Quality Controls
+- Validation methodology
+- Backtesting requirements
+- Correlation thresholds
+- Signal deprecation process
+
+### Operational Considerations
+- Computation scheduling
+- Resource utilization guidelines
+- Monitoring requirements
+- Failover procedures
+
+## Future Enhancements
+- Machine learning-based signal generation
+- Adaptive signal weighting
+- Real-time signal quality feedback
+- Advanced signal visualization
+- Cross-asset class signals
+- Alternative data integration

docs/intelligence-services/strategy-orchestrator/README.md

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+# Strategy Orchestrator
+
+## Overview
+The Strategy Orchestrator service coordinates the execution and lifecycle management of trading strategies within the stock-bot platform. It serves as the central orchestration engine that translates trading signals into executable orders while managing strategy state, performance monitoring, and risk integration.
+
+## Key Features
+
+### Strategy Lifecycle Management
+- **Strategy Registration**: Onboarding and configuration of trading strategies
+- **Version Control**: Management of strategy versions and deployments
+- **State Management**: Tracking of strategy execution state
+- **Activation/Deactivation**: Controlled enabling and disabling of strategies
+
+### Execution Coordination
+- **Signal Processing**: Consumes and processes signals from Signal Engine
+- **Order Generation**: Translates signals into executable trading orders
+- **Execution Timing**: Optimizes order timing based on market conditions
+- **Multi-strategy Coordination**: Manages interactions between strategies
+
+### Performance Monitoring
+- **Real-time Metrics**: Tracks strategy performance metrics in real-time
+- **Alerting**: Notifies on strategy performance anomalies
+- **Execution Quality**: Measures and reports on execution quality
+- **Strategy Attribution**: Attributes P&L to specific strategies
+
+### Risk Integration
+- **Pre-trade Risk Checks**: Validates orders against risk parameters
+- **Position Tracking**: Monitors strategy position and exposure
+- **Risk Limit Enforcement**: Ensures compliance with risk thresholds
+- **Circuit Breakers**: Implements strategy-specific circuit breakers
+
+## Integration Points
+
+### Upstream Connections
+- Signal Engine (for trading signals)
+- Feature Store (for real-time feature access)
+- Market Data Gateway (for market data)
+- Backtest Engine (for optimized parameters)
+
+### Downstream Consumers
+- Order Management System (for order execution)
+- Risk Guardian (for risk monitoring)
+- Trading Dashboard (for strategy visualization)
+- Data Catalog (for strategy performance data)
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **State Management**: Redis for distributed state
+- **Messaging**: Event-driven architecture with message bus
+- **Database**: Time-series database for performance metrics
+- **API**: RESTful API for management functions
+
+### Architecture Pattern
+- Event-driven architecture for reactive processing
+- Command pattern for strategy operations
+- State machine for strategy lifecycle
+- Circuit breaker pattern for fault tolerance
+
+## Development Guidelines
+
+### Strategy Integration
+- Strategy interface specification
+- Required callback implementations
+- Configuration schema definition
+- Testing and validation requirements
+
+### Performance Optimization
+- Event processing efficiency
+- State management best practices
+- Resource utilization guidelines
+- Latency minimization techniques
+
+### Operational Procedures
+- Strategy deployment process
+- Monitoring requirements
+- Troubleshooting guidelines
+- Failover procedures
+
+## Future Enhancements
+- Advanced multi-strategy optimization
+- Machine learning for execution optimization
+- Enhanced strategy analytics dashboard
+- Dynamic parameter adjustment
+- Auto-scaling based on market conditions
+- Strategy recommendation engine

docs/interface-services/README.md

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+# Interface Services
+
+Interface services provide user-facing applications and APIs for interacting with the trading platform.
+
+## Services
+
+### Trading Dashboard
+- **Purpose**: Web-based user interface for trading operations
+- **Key Functions**:
+  - Real-time portfolio monitoring and visualization
+  - Trading strategy configuration and management
+  - Performance analytics and reporting dashboards
+  - Risk monitoring and alerting interface
+  - Market data visualization and charting
+  - Order management and execution tracking
+
+## Architecture
+
+Interface services create the user experience layer of the trading platform, providing intuitive and responsive interfaces for traders, analysts, and administrators. They translate complex backend operations into accessible visual interfaces and interactive workflows.
+
+## Technology Stack
+
+- **Frontend**: React-based single-page applications
+- **Visualization**: Real-time charts and data visualization
+- **State Management**: Modern React patterns and state libraries
+- **API Integration**: RESTful and WebSocket connections to backend services

docs/interface-services/trading-dashboard/README.md

@@ -0,0 +1,87 @@
+# Trading Dashboard
+
+## Overview
+The Trading Dashboard service provides a comprehensive web-based user interface for monitoring, analyzing, and controlling trading activities within the stock-bot platform. It serves as the primary visual interface for traders, analysts, and administrators to interact with the platform's capabilities and visualize market data and trading performance.
+
+## Key Features
+
+### Portfolio Monitoring
+- **Position Dashboard**: Real-time view of all open positions
+- **P&L Tracking**: Current and historical profit/loss visualization
+- **Risk Metrics**: Visual representation of key risk indicators
+- **Performance Analytics**: Strategy and portfolio performance charts
+
+### Market Visualization
+- **Real-time Charts**: Interactive price and volume charts
+- **Technical Indicators**: Overlay of key technical indicators
+- **Market Depth**: Order book visualization where available
+- **Multi-instrument View**: Customizable multi-asset dashboards
+
+### Trading Controls
+- **Order Management**: Create, track, and modify orders
+- **Strategy Controls**: Enable, disable, and configure strategies
+- **Risk Parameters**: Adjust risk thresholds and limits
+- **Alert Configuration**: Set up custom trading alerts
+
+### Advanced Analytics
+- **Strategy Performance**: Detailed analytics on strategy execution
+- **Attribution Analysis**: Performance attribution by strategy/instrument
+- **Historical Backtests**: Visualization of backtest results
+- **Market Correlation**: Relationship analysis between instruments
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for real-time market data)
+- Strategy Orchestrator (for strategy status and control)
+- Risk Guardian (for risk metrics)
+- Order Management System (for order status)
+
+### Downstream Components
+- User Authentication Service
+- Notification Service
+- Export/Reporting Services
+
+## Technical Implementation
+
+### Technology Stack
+- **Framework**: Angular with RxJS for reactive programming
+- **UI Components**: Angular Material and custom components
+- **Styling**: Tailwind CSS for responsive design
+- **Charting**: Lightweight charting libraries for performance
+- **Real-time Updates**: WebSocket connections for live data
+
+### Architecture Pattern
+- Component-based architecture
+- State management with services and observables
+- Lazy-loaded modules for performance
+- Responsive design for multiple device types
+
+## Development Guidelines
+
+### Component Structure
+- Smart/presentational component pattern
+- Reusable UI component library
+- State management best practices
+- Performance optimization techniques
+
+### Data Visualization
+- Chart configuration standards
+- Data refresh strategies
+- Animation guidelines
+- Accessibility requirements
+
+### User Experience
+- Consistent UI/UX patterns
+- Keyboard shortcuts and navigation
+- Form validation approach
+- Error handling and feedback
+
+## Future Enhancements
+- Advanced customization capabilities
+- User-defined dashboards and layouts
+- Mobile-optimized interface
+- Collaborative features (comments, annotations)
+- AI-powered insights and recommendations
+- Enhanced export and reporting capabilities
+- Dark/light theme support

docs/platform-services/README.md

@@ -0,0 +1,53 @@
+# Platform Services
+
+Platform services provide foundational infrastructure, monitoring, and operational capabilities that support all other services.
+
+## Services
+
+*Currently in planning phase - no active services deployed*
+
+## Planned Capabilities
+
+### Service Discovery
+- **Purpose**: Dynamic service registration and discovery
+- **Planned Functions**:
+  - Service health monitoring
+  - Load balancing and routing
+  - Service mesh coordination
+  - Configuration management
+
+### Logging & Monitoring
+- **Purpose**: Observability and operational insights
+- **Planned Functions**:
+  - Centralized logging aggregation
+  - Metrics collection and analysis
+  - Distributed tracing
+  - Performance monitoring and alerting
+
+### Configuration Management
+- **Purpose**: Centralized configuration and secrets management
+- **Planned Functions**:
+  - Environment-specific configurations
+  - Secrets encryption and rotation
+  - Dynamic configuration updates
+  - Configuration versioning and rollback
+
+### Authentication & Authorization
+- **Purpose**: Security and access control
+- **Planned Functions**:
+  - User authentication and session management
+  - Role-based access control (RBAC)
+  - API security and token management
+  - Audit logging and compliance
+
+### Backup & Recovery
+- **Purpose**: Data protection and disaster recovery
+- **Planned Functions**:
+  - Automated backup scheduling
+  - Point-in-time recovery
+  - Cross-region replication
+  - Disaster recovery orchestration
+
+## Architecture
+
+Platform services provide the operational foundation that enables reliable, secure, and observable operation of the entire trading platform. They implement cross-cutting concerns and best practices for production deployments.

docs/platform-services/authentication-authorization/README.md

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+# Authentication & Authorization
+
+## Overview
+The Authentication & Authorization service will provide comprehensive security controls for the stock-bot platform. It will manage user identity, authentication, access control, and security policy enforcement across all platform components, ensuring proper security governance and compliance with regulatory requirements.
+
+## Planned Features
+
+### User Management
+- **User Provisioning**: Account creation and management
+- **Identity Sources**: Local and external identity providers
+- **User Profiles**: Customizable user attributes
+- **Group Management**: User grouping and organization
+- **Account Lifecycle**: Comprehensive user lifecycle management
+
+### Authentication
+- **Multiple Factors**: Support for MFA/2FA
+- **Single Sign-On**: Integration with enterprise SSO solutions
+- **Social Login**: Support for third-party identity providers
+- **Session Management**: Secure session handling and expiration
+- **Password Policies**: Configurable password requirements
+
+### Authorization
+- **Role-Based Access Control**: Fine-grained permission management
+- **Attribute-Based Access**: Context-aware access decisions
+- **Permission Management**: Centralized permission administration
+- **Dynamic Policies**: Rule-based access policies
+- **Delegated Administration**: Hierarchical permission management
+
+### Security Features
+- **Token Management**: JWT and OAuth token handling
+- **API Security**: Protection of API endpoints
+- **Rate Limiting**: Prevention of brute force attacks
+- **Audit Logging**: Comprehensive security event logging
+- **Compliance Reporting**: Reports for regulatory requirements
+
+## Planned Integration Points
+
+### Service Integration
+- All platform microservices
+- API Gateway
+- Frontend applications
+- External systems and partners
+
+### Identity Providers
+- Internal identity store
+- Enterprise directory services
+- Social identity providers
+- OAuth/OIDC providers
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Identity Server**: Keycloak or Auth0
+- **API Protection**: OAuth 2.0 and OpenID Connect
+- **Token Format**: JWT with appropriate claims
+- **Storage**: Secure credential and policy storage
+- **Encryption**: Industry-standard encryption for sensitive data
+
+### Architecture Pattern
+- Identity as a service
+- Policy-based access control
+- Token-based authentication
+- Layered security model
+
+## Development Guidelines
+
+### Authentication Integration
+- Authentication flow implementation
+- Token handling best practices
+- Session management requirements
+- Credential security standards
+
+### Authorization Implementation
+- Permission modeling approach
+- Policy definition format
+- Access decision points
+- Contextual authorization techniques
+
+### Security Considerations
+- Token security requirements
+- Key rotation procedures
+- Security event monitoring
+- Penetration testing requirements
+
+## Implementation Roadmap
+1. Core user management and authentication
+2. Basic role-based authorization
+3. API security and token management
+4. Advanced access control policies
+5. Compliance reporting and auditing

docs/platform-services/backup-recovery/README.md

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+# Backup & Recovery
+
+## Overview
+The Backup & Recovery service will provide comprehensive data protection, disaster recovery, and business continuity capabilities for the stock-bot platform. It will ensure that critical data and system configurations are preserved, with reliable recovery options in case of system failures, data corruption, or catastrophic events.
+
+## Planned Features
+
+### Backup Management
+- **Automated Backups**: Scheduled backup of all critical data
+- **Incremental Backups**: Efficient storage of incremental changes
+- **Multi-tier Backup**: Different retention policies by data importance
+- **Backup Verification**: Automated testing of backup integrity
+- **Backup Catalog**: Searchable index of available backups
+
+### Recovery Capabilities
+- **Point-in-time Recovery**: Restore to specific moments in time
+- **Granular Recovery**: Restore specific objects or datasets
+- **Self-service Recovery**: User portal for simple recovery operations
+- **Recovery Testing**: Regular validation of recovery procedures
+- **Recovery Performance**: Optimized for minimal downtime
+
+### Disaster Recovery
+- **Cross-region Replication**: Geographic data redundancy
+- **Recovery Site**: Standby environment for critical services
+- **Failover Automation**: Scripted failover procedures
+- **Recovery Orchestration**: Coordinated multi-system recovery
+- **DR Testing**: Regular disaster scenario testing
+
+### Data Protection
+- **Encryption**: At-rest and in-transit data encryption
+- **Access Controls**: Restricted access to backup data
+- **Retention Policies**: Compliance with data retention requirements
+- **Immutable Backups**: Protection against ransomware
+- **Air-gapped Storage**: Ultimate protection for critical backups
+
+## Planned Integration Points
+
+### Data Sources
+- Platform databases (MongoDB, PostgreSQL)
+- Object storage and file systems
+- Service configurations
+- Message queues and event streams
+- User data and preferences
+
+### System Integration
+- Infrastructure as Code systems
+- Monitoring and alerting
+- Compliance reporting
+- Operations management tools
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Backup Tools**: Cloud-native backup solutions
+- **Storage**: Object storage with versioning
+- **Orchestration**: Infrastructure as Code for recovery
+- **Monitoring**: Backup health and status monitoring
+- **Automation**: Scripted recovery procedures
+
+### Architecture Pattern
+- Centralized backup management
+- Distributed backup agents
+- Immutable backup storage
+- Recovery validation automation
+
+## Development Guidelines
+
+### Backup Strategy
+- Backup frequency guidelines
+- Retention period standards
+- Versioning requirements
+- Validation procedures
+
+### Recovery Procedures
+- Recovery time objectives
+- Recovery point objectives
+- Testing frequency requirements
+- Documentation standards
+
+### Security Requirements
+- Encryption standards
+- Access control implementation
+- Audit requirements
+- Secure deletion procedures
+
+## Implementation Roadmap
+1. Core database backup capabilities
+2. Basic recovery procedures
+3. Cross-region replication
+4. Automated recovery testing
+5. Advanced protection features

docs/platform-services/configuration-management/README.md

@@ -0,0 +1,90 @@
+# Configuration Management
+
+## Overview
+The Configuration Management service will provide centralized management of application and service configurations across the stock-bot platform. It will handle environment-specific settings, dynamic configuration updates, secrets management, and configuration versioning to ensure consistent and secure system configuration.
+
+## Planned Features
+
+### Configuration Storage
+- **Hierarchical Configuration**: Nested configuration structure
+- **Environment Separation**: Environment-specific configurations
+- **Schema Validation**: Configuration format validation
+- **Default Values**: Fallback configuration defaults
+- **Configuration as Code**: Version-controlled configuration
+
+### Dynamic Configuration
+- **Runtime Updates**: Changes without service restart
+- **Configuration Propagation**: Real-time distribution of changes
+- **Subscription Model**: Configuration change notifications
+- **Batch Updates**: Atomic multi-value changes
+- **Feature Flags**: Dynamic feature enablement
+
+### Secrets Management
+- **Secure Storage**: Encrypted storage of sensitive values
+- **Access Control**: Fine-grained access to secrets
+- **Secret Versioning**: Historical versions of secrets
+- **Automatic Rotation**: Scheduled credential rotation
+- **Key Management**: Management of encryption keys
+
+### Operational Features
+- **Configuration History**: Tracking of configuration changes
+- **Rollbacks**: Revert to previous configurations
+- **Audit Trail**: Comprehensive change logging
+- **Configuration Comparison**: Diff between configurations
+- **Import/Export**: Bulk configuration operations
+
+## Planned Integration Points
+
+### Service Integration
+- All platform microservices
+- CI/CD pipelines
+- Infrastructure components
+- Development environments
+
+### External Systems
+- Secret management services
+- Source control systems
+- Operational monitoring
+- Compliance systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Configuration Server**: Spring Cloud Config or custom solution
+- **Secret Store**: HashiCorp Vault or AWS Secrets Manager
+- **Storage**: Git-backed or database storage
+- **API**: RESTful interface with versioning
+- **SDK**: Client libraries for service integration
+
+### Architecture Pattern
+- Configuration as a service
+- Event-driven configuration updates
+- Layered access control model
+- High-availability design
+
+## Development Guidelines
+
+### Configuration Structure
+- Naming conventions
+- Hierarchy organization
+- Type validation
+- Documentation requirements
+
+### Secret Management
+- Secret classification
+- Rotation requirements
+- Access request process
+- Emergency access procedures
+
+### Integration Approach
+- Client library usage
+- Caching recommendations
+- Failure handling
+- Update processing
+
+## Implementation Roadmap
+1. Static configuration management
+2. Basic secrets storage
+3. Dynamic configuration updates
+4. Advanced secret management features
+5. Operational tooling and integration

docs/platform-services/logging-monitoring/README.md

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+# Logging & Monitoring
+
+## Overview
+The Logging & Monitoring service will provide comprehensive observability capabilities for the stock-bot platform. It will collect, process, store, and visualize logs, metrics, and traces from all platform components, enabling effective operational monitoring, troubleshooting, and performance optimization.
+
+## Planned Features
+
+### Centralized Logging
+- **Log Aggregation**: Collection of logs from all services
+- **Structured Logging**: Standardized log format across services
+- **Log Processing**: Parsing, enrichment, and transformation
+- **Log Storage**: Efficient storage with retention policies
+- **Log Search**: Advanced search capabilities with indexing
+
+### Metrics Collection
+- **System Metrics**: CPU, memory, disk, network usage
+- **Application Metrics**: Custom application-specific metrics
+- **Business Metrics**: Trading and performance indicators
+- **SLI/SLO Tracking**: Service level indicators and objectives
+- **Alerting Thresholds**: Metric-based alert configuration
+
+### Distributed Tracing
+- **Request Tracing**: End-to-end tracing of requests
+- **Span Collection**: Detailed operation timing
+- **Trace Correlation**: Connect logs, metrics, and traces
+- **Latency Analysis**: Performance bottleneck identification
+- **Dependency Mapping**: Service dependency visualization
+
+### Alerting & Notification
+- **Alert Rules**: Multi-condition alert definitions
+- **Notification Channels**: Email, SMS, chat integrations
+- **Alert Grouping**: Intelligent alert correlation
+- **Escalation Policies**: Tiered notification escalation
+- **On-call Management**: Rotation and scheduling
+
+## Planned Integration Points
+
+### Data Sources
+- All platform microservices
+- Infrastructure components
+- Databases and storage systems
+- Message bus and event streams
+- External dependencies
+
+### Consumers
+- Operations team dashboards
+- Incident management systems
+- Capacity planning tools
+- Automated remediation systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Logging**: ELK Stack (Elasticsearch, Logstash, Kibana) or similar
+- **Metrics**: Prometheus and Grafana
+- **Tracing**: Jaeger or Zipkin
+- **Alerting**: AlertManager or PagerDuty
+- **Collection**: Vector, Fluentd, or similar collectors
+
+### Architecture Pattern
+- Centralized collection with distributed agents
+- Push and pull metric collection models
+- Sampling for high-volume telemetry
+- Buffering for resilient data collection
+
+## Development Guidelines
+
+### Instrumentation Standards
+- Logging best practices
+- Metric naming conventions
+- Trace instrumentation approach
+- Cardinality management
+
+### Performance Impact
+- Sampling strategies
+- Buffer configurations
+- Resource utilization limits
+- Batching recommendations
+
+### Data Management
+- Retention policies
+- Aggregation strategies
+- Storage optimization
+- Query efficiency guidelines
+
+## Implementation Roadmap
+1. Core logging infrastructure
+2. Basic metrics collection
+3. Critical alerting capability
+4. Distributed tracing
+5. Advanced analytics and visualization

docs/platform-services/service-discovery/README.md

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+# Service Discovery
+
+## Overview
+The Service Discovery component will provide dynamic registration, discovery, and health monitoring of services within the stock-bot platform. It will enable services to locate and communicate with each other without hardcoded endpoints, supporting a flexible and resilient microservices architecture.
+
+## Planned Features
+
+### Service Registration
+- **Automatic Registration**: Self-registration of services on startup
+- **Metadata Management**: Service capabilities and endpoint information
+- **Instance Tracking**: Multiple instances of the same service
+- **Version Information**: Service version and compatibility data
+- **Registration Expiry**: TTL-based registration with renewal
+
+### Service Discovery
+- **Name-based Lookup**: Find services by logical names
+- **Filtering**: Discovery based on metadata and attributes
+- **Load Balancing**: Client or server-side load balancing
+- **Caching**: Client-side caching of service information
+- **DNS Integration**: Optional DNS-based discovery
+
+### Health Monitoring
+- **Health Checks**: Customizable health check protocols
+- **Automatic Deregistration**: Removal of unhealthy instances
+- **Status Propagation**: Health status notifications
+- **Dependency Health**: Cascading health status for dependencies
+- **Self-healing**: Automatic recovery procedures
+
+### Configuration Management
+- **Dynamic Configuration**: Runtime configuration updates
+- **Environment-specific Settings**: Configuration by environment
+- **Configuration Versioning**: History and rollback capabilities
+- **Secret Management**: Secure handling of sensitive configuration
+- **Configuration Change Events**: Notifications of config changes
+
+## Planned Integration Points
+
+### Service Integration
+- All platform microservices
+- External service dependencies
+- Infrastructure components
+- Monitoring systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Service Registry**: Consul, etcd, or ZooKeeper
+- **Client Libraries**: TypeScript SDK for services
+- **Health Check**: HTTP, TCP, and custom health checks
+- **Configuration Store**: Distributed key-value store
+- **Load Balancer**: Client-side or service mesh integration
+
+### Architecture Pattern
+- Service registry pattern
+- Client-side discovery pattern
+- Health check pattern
+- Circuit breaker integration
+
+## Development Guidelines
+
+### Service Integration
+- Registration process
+- Discovery implementation
+- Health check implementation
+- Configuration consumption
+
+### Resilience Practices
+- Caching strategy
+- Fallback mechanisms
+- Retry configuration
+- Circuit breaker settings
+
+### Operational Considerations
+- High availability setup
+- Disaster recovery approach
+- Scaling guidelines
+- Monitoring requirements
+
+## Implementation Roadmap
+1. Core service registry implementation
+2. Basic health checking
+3. Service discovery integration
+4. Configuration management
+5. Advanced health monitoring with dependency tracking