work on market-data-gateway

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