Add technical indicators module for OHLCV data analysis
- 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.
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- `data/__init__.py` - Data collection package initialization
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- `data/okx_collector.py` - OKX API integration for real-time market data collection
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- `data/aggregator.py` - OHLCV candle aggregation and processing
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- `data/common/indicators.py` - Technical indicators module with SMA, EMA, RSI, MACD, and Bollinger Bands calculations optimized for sparse OHLCV data
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- `strategies/base_strategy.py` - Base strategy class and interface
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- `strategies/ema_crossover.py` - Example EMA crossover strategy implementation
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- `components/dashboard.py` - Dashboard UI components and layouts
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- `tests/test_base_collector.py` - Comprehensive unit tests for the BaseDataCollector abstract class (13 tests)
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- `tests/test_collector_manager.py` - Comprehensive unit tests for the CollectorManager with health monitoring (14 tests)
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- `tests/test_logging_enhanced.py` - Comprehensive unit tests for enhanced logging features (16 tests)
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- `tests/test_indicators.py` - Comprehensive unit tests for technical indicators module (18 tests)
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- `docs/setup.md` - Comprehensive setup guide for new machines and environments
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- `docs/logging.md` - Complete documentation for the enhanced unified logging system
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- `docs/components/technical-indicators.md` - Complete documentation for the technical indicators module with usage examples and integration guide
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## Tasks
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- [x] 2.3 Build data validation and error handling for market data
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- [x] 2.4 Implement Redis channels for real-time data distribution
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- [x] 2.5 Create data storage layer for OHLCV data in PostgreSQL
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- [ ] 2.6 Add technical indicators calculation (SMA, EMA, RSI, MACD, Bollinger Bands)
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- [x] 2.6 Add technical indicators calculation (SMA, EMA, RSI, MACD, Bollinger Bands)
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- [ ] 2.7 Implement data recovery and reconnection logic for API failures
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- [ ] 2.8 Create data collection service with proper logging
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- [ ] 2.9 Unit test data collection and aggregation logic
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