24b6a3feed
- Introduced `indicators.py` containing implementations for SMA, EMA, RSI, MACD, and Bollinger Bands, optimized for handling sparse OHLCV data. - Added `IndicatorResult` dataclass to encapsulate results of indicator calculations. - Implemented methods for calculating multiple indicators efficiently with JSON configuration support and validation. - Updated `__init__.py` to include new indicators in the module's exports. - Enhanced documentation to cover the new technical indicators module, including usage examples and integration details. - Added comprehensive unit tests to ensure accuracy and robustness of the indicators module.
Components Documentation
This section contains detailed technical documentation for all system components in the TCP Dashboard platform.
📋 Contents
Data Collection System
- Data Collectors - Comprehensive guide to the enhanced data collector system
- BaseDataCollector abstract class with health monitoring
- CollectorManager for centralized management
- Exchange Factory Pattern for standardized collector creation
- Modular Exchange Architecture for scalable implementation
- Auto-restart and failure recovery mechanisms
- Health monitoring and alerting systems
- Performance optimization techniques
- Integration examples and patterns
- Comprehensive troubleshooting guide
Database Operations
- Database Operations - Repository pattern for clean database interactions
- Repository Pattern implementation for data access abstraction
- MarketDataRepository for candle/OHLCV operations
- RawTradeRepository for WebSocket data storage
- Automatic transaction management and session cleanup
- Configurable duplicate handling with force update options
- Custom error handling with DatabaseOperationError
- Database health monitoring and performance statistics
- Migration guide from direct SQL to repository pattern
Technical Analysis
- Technical Indicators - Comprehensive technical analysis module
- Five Core Indicators: SMA, EMA, RSI, MACD, and Bollinger Bands
- Sparse Data Handling: Optimized for the platform's aggregation strategy
- Vectorized Calculations: High-performance pandas and numpy implementation
- Flexible Configuration: JSON-based parameter configuration with validation
- Integration Ready: Seamless integration with OHLCV data and real-time processing
- Batch processing for multiple indicators
- Support for different price columns (open, high, low, close)
- Comprehensive unit testing and documentation
Logging & Monitoring
- Enhanced Logging System - Unified logging framework
- Multi-level logging with automatic cleanup
- Console and file output with formatting
- Performance monitoring integration
- Cross-component logging standards
- Log aggregation and analysis
🔧 Component Architecture
Core Components
┌─────────────────────────────────────────────────────────────┐
│ CollectorManager │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Global Health Monitor │ │
│ │ • System-wide health checks │ │
│ │ • Auto-restart coordination │ │
│ │ • Performance analytics │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │
│ │ OKX Collector │ │Binance Collector│ │ Custom │ │
│ │ │ │ │ │ Collector │ │
│ │ • Health Monitor│ │ • Health Monitor│ │ • Health Mon │ │
│ │ • Auto-restart │ │ • Auto-restart │ │ • Auto-resta │ │
│ │ • Data Validate │ │ • Data Validate │ │ • Data Valid │ │
│ └─────────────────┘ └─────────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
Design Patterns
- Factory Pattern: Standardized component creation across exchanges
- Observer Pattern: Event-driven data processing and callbacks
- Strategy Pattern: Pluggable data processing strategies
- Singleton Pattern: Centralized logging and configuration management
🚀 Quick Start
Using Components
# Data Collector usage
from data.exchanges import create_okx_collector
from data.base_collector import DataType
collector = create_okx_collector(
symbol='BTC-USDT',
data_types=[DataType.TRADE, DataType.ORDERBOOK]
)
# Logging usage
from utils.logger import get_logger
logger = get_logger("my_component")
logger.info("Component initialized")
Component Integration
# Integrating multiple components
from data.collector_manager import CollectorManager
from utils.logger import get_logger
manager = CollectorManager("production_system")
logger = get_logger("system_manager")
# Components work together seamlessly
await manager.start()
logger.info("System started successfully")
📊 Performance & Monitoring
Health Monitoring
All components include built-in health monitoring:
- Real-time Status: Component state and performance metrics
- Auto-Recovery: Automatic restart on failures
- Performance Tracking: Message rates, uptime, error rates
- Alerting: Configurable alerts for component health
Logging Integration
Unified logging across all components:
- Structured Logging: JSON-formatted logs for analysis
- Multiple Levels: Debug, Info, Warning, Error levels
- Automatic Cleanup: Log rotation and old file cleanup
- Performance Metrics: Built-in performance tracking
🔗 Related Documentation
- Exchange Documentation - Exchange-specific implementations
- Architecture Overview - System design and patterns
- Setup Guide - Component configuration and deployment
- API Reference - Technical specifications
📈 Future Components
Planned component additions:
- Strategy Engine: Trading strategy execution framework
- Portfolio Manager: Position and risk management
- Dashboard UI: Web-based monitoring and control interface
- Alert Manager: Advanced alerting and notification system
- Data Analytics: Historical data analysis and reporting
For the complete documentation index, see the main documentation README.