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4.0 - 4.0 Implement real-time strategy execution and data integration features

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- Added `realtime_execution.py` for real-time strategy execution, enabling live signal generation and integration with the dashboard's chart refresh cycle.
- Introduced `data_integration.py` to manage market data orchestration, caching, and technical indicator calculations for strategy signal generation.
- Implemented `validation.py` for comprehensive validation and quality assessment of strategy-generated signals, ensuring reliability and consistency.
- Developed `batch_processing.py` to facilitate efficient backtesting of multiple strategies across large datasets with memory management and performance optimization.
- Updated `__init__.py` files to include new modules and ensure proper exports, enhancing modularity and maintainability.
- Enhanced unit tests for the new features, ensuring robust functionality and adherence to project standards.

These changes establish a solid foundation for real-time strategy execution and data integration, aligning with project goals for modularity, performance, and maintainability.
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Vasily.onl
2025-06-12 18:29:39 +08:00
parent f09864d61b
commit 8c23489ff0
13 changed files with 6429 additions and 11 deletions
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tasks/4.0-strategy-engine-foundation.md
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@@ -22,12 +22,21 @@
- `database/migrations/versions/add_strategy_signals_table.py` - Alembic migration for strategy signals table
- `components/charts/layers/strategy_signals.py` - Strategy signal chart layer for visualization
- `components/charts/data_integration.py` - Updated to include strategy data integration
- `strategies/data_integration.py` - Strategy data integration with indicator orchestration and caching
- `strategies/validation.py` - Strategy signal validation and quality assurance
- `strategies/batch_processing.py` - Batch processing engine for backtesting multiple strategies across large datasets
- `strategies/realtime_execution.py` - Real-time strategy execution pipeline for live signal generation
- `dashboard/callbacks/realtime_strategies.py` - Dashboard callbacks for real-time strategy integration
- `tests/strategies/test_base_strategy.py` - Unit tests for BaseStrategy abstract class
- `tests/strategies/test_strategy_factory.py` - Unit tests for strategy factory system
- `tests/strategies/test_strategy_manager.py` - Unit tests for StrategyManager class
- `tests/strategies/implementations/test_ema_crossover.py` - Unit tests for EMA Crossover strategy
- `tests/strategies/implementations/test_rsi.py` - Unit tests for RSI strategy
- `tests/strategies/implementations/test_macd.py` - Unit tests for MACD strategy
- `tests/strategies/test_data_integration.py` - Unit tests for strategy data integration
- `tests/strategies/test_validation.py` - Unit tests for strategy signal validation
- `tests/strategies/test_batch_processing.py` - Unit tests for batch processing capabilities
- `tests/strategies/test_realtime_execution.py` - Unit tests for real-time execution pipeline
- `tests/database/test_strategy_repository.py` - Unit tests for strategy repository
### Notes
@@ -73,6 +82,26 @@
- **Reasoning**: Maintains consistency with existing database access patterns, ensures proper session management, and provides a clean API for strategy data operations.
- **Impact**: All strategy database operations follow the same patterns as other modules, with proper error handling, logging, and transaction management.
### 7. Vectorized Data Integration
- **Decision**: Implement vectorized approaches in `StrategyDataIntegrator` for DataFrame construction, indicator batching, and multi-strategy processing while maintaining iterative interfaces for backward compatibility.
- **Reasoning**: Significant performance improvements for backtesting and bulk analysis scenarios, better memory efficiency with pandas operations, and preparation for multi-strategy batch processing capabilities.
- **Impact**: Enhanced performance for large datasets while maintaining existing single-strategy interfaces. Sets foundation for efficient multi-strategy and multi-timeframe processing in future phases.
### 8. Single-Strategy Orchestration Focus
- **Decision**: Implement strategy calculation orchestration focused on single-strategy optimization with indicator dependency resolution, avoiding premature multi-strategy complexity.
- **Reasoning**: Multi-strategy coordination is better handled at the backtesting layer or through parallelization. Single-strategy optimization provides immediate benefits while keeping code maintainable and focused.
- **Impact**: Cleaner, more maintainable code with optimized single-strategy performance. Provides foundation for future backtester-level parallelization without architectural complexity.
### 9. Indicator Warm-up Handling for Streaming Batch Processing
- **Decision**: Implemented dynamic warm-up period calculation and overlapping windows with result trimming for streaming batch processing.
- **Reasoning**: To ensure accurate indicator calculations and prevent false signals when processing large datasets in chunks, as indicators require a certain amount of historical data to 'warm up'.
- **Impact**: Guarantees correct backtest results for strategies relying on indicators with warm-up periods, even when using memory-efficient streaming. Automatically adjusts chunk processing to include necessary historical context and removes duplicate/invalid initial signals.
### 10. Real-time Strategy Execution Architecture
- **Decision**: Implemented event-driven real-time strategy execution pipeline with signal broadcasting, chart integration, and concurrent processing capabilities.
- **Reasoning**: Real-time strategy execution requires different architecture than batch processing - event-driven triggers, background signal processing, throttled chart updates, and integration with existing dashboard refresh cycles.
- **Impact**: Enables live strategy signal generation that integrates seamlessly with the existing chart system. Provides concurrent strategy execution, real-time signal storage, error handling with automatic strategy disabling, and performance monitoring for production use.
## Tasks
- [x] 1.0 Core Strategy Foundation Setup
@@ -109,16 +138,16 @@
- [x] 3.8 Add data retention policies for strategy signals (configurable cleanup of old analysis data)
- [x] 3.9 Implement strategy signal aggregation queries for performance analysis
- [ ] 4.0 Strategy Data Integration
- [ ] 4.1 Create `StrategyDataIntegrator` class in new `strategies/data_integration.py` module
- [ ] 4.2 Implement data loading interface that leverages existing `TechnicalIndicators` class for indicator dependencies
- [x] 4.0 Strategy Data Integration
- [x] 4.1 Create `StrategyDataIntegrator` class in new `strategies/data_integration.py` module
- [x] 4.2 Implement data loading interface that leverages existing `TechnicalIndicators` class for indicator dependencies
- [x] 4.3 Add multi-timeframe data handling for strategies that require indicators from different timeframes
- [ ] 4.4 Implement strategy calculation orchestration with proper indicator dependency resolution
- [ ] 4.5 Create caching layer for computed indicator results to avoid recalculation across strategies
- [ ] 4.6 Add strategy signal generation and validation pipeline
- [ ] 4.7 Implement batch processing capabilities for backtesting large datasets
- [ ] 4.8 Create real-time strategy execution pipeline that integrates with existing chart data refresh
- [ ] 4.9 Add error handling and recovery mechanisms for strategy calculation failures
- [x] 4.4 Implement strategy calculation orchestration with proper indicator dependency resolution
- [x] 4.5 Create caching layer for computed indicator results to avoid recalculation across strategies
- [x] 4.6 Add strategy signal generation and validation pipeline
- [x] 4.7 Implement batch processing capabilities for backtesting large datasets
- [x] 4.8 Create real-time strategy execution pipeline that integrates with existing chart data refresh
- [x] 4.9 Add error handling and recovery mechanisms for strategy calculation failures
- [ ] 5.0 Chart Integration and Visualization
- [ ] 5.1 Create `StrategySignalLayer` class in `components/charts/layers/strategy_signals.py`