- Introduced dynamic generation of parameter fields and callback handling for indicators, enhancing modularity and maintainability.
- Updated `config_utils.py` with new utility functions to load indicator templates and generate dynamic outputs and states for parameter fields.
- Refactored `indicators.py` to utilize these utilities, streamlining the callback logic and improving user experience by reducing hardcoded elements.
- Modified `indicator_modal.py` to create parameter fields dynamically based on JSON templates, eliminating the need for manual updates when adding new indicators.
- Added documentation outlining the new data-driven architecture for indicators, improving clarity and guidance for future development.
These changes significantly enhance the flexibility and scalability of the indicator system, aligning with project goals for maintainability and performance.
- Updated `pyproject.toml` to include the new `data` package in the build configuration, ensuring all components are properly included.
- Introduced `ADR-004` documentation outlining the rationale for refactoring the data collection system into a modular architecture, addressing complexity and maintainability issues.
- Enhanced `data_collectors.md` to reflect the new component structure, detailing responsibilities of `CollectorLifecycleManager`, `ManagerHealthMonitor`, `ManagerStatsTracker`, and `ManagerLogger`.
- Refactored `DataCollectionService` to utilize the new modular components, improving orchestration and error handling.
- Removed the obsolete `collector-service-tasks-optimization.md` and `refactor-common-package.md` files, streamlining the tasks documentation.
These changes significantly improve the architecture and maintainability of the data collection service, aligning with project standards for modularity, performance, and documentation clarity.
- Extracted `OHLCVData` and validation logic into a new `common/ohlcv_data.py` module, promoting better organization and reusability.
- Updated `BaseDataCollector` to utilize the new `validate_ohlcv_data` function for improved data validation, enhancing code clarity and maintainability.
- Refactored imports in `data/__init__.py` to reflect the new structure, ensuring consistent access to common data types and exceptions.
- Removed redundant data validation logic from `BaseDataCollector`, streamlining its responsibilities.
- Added unit tests for `OHLCVData` and validation functions to ensure correctness and reliability.
These changes improve the architecture of the data module, aligning with project standards for maintainability and performance.
- Split the `aggregation.py` file into a dedicated sub-package, improving modularity and maintainability.
- Moved `TimeframeBucket`, `RealTimeCandleProcessor`, and `BatchCandleProcessor` classes into their respective files within the new `aggregation` sub-package.
- Introduced utility functions for trade aggregation and validation, enhancing code organization.
- Updated import paths throughout the codebase to reflect the new structure, ensuring compatibility.
- Added safety net tests for the aggregation package to verify core functionality and prevent regressions during refactoring.
These changes enhance the overall architecture of the aggregation module, making it more scalable and easier to manage.
