- Introduced a new `CollectorStateAndTelemetry` class to encapsulate the status, health checks, and statistics of the data collector, promoting modularity and separation of concerns.
- Updated `BaseDataCollector` to replace direct status management with calls to the new telemetry class, enhancing maintainability and readability.
- Refactored logging methods to utilize the telemetry class, ensuring consistent logging practices.
- Modified the `OKXCollector` to integrate with the new telemetry system for improved status reporting and error handling.
- Added comprehensive tests for the `CollectorStateAndTelemetry` class to ensure functionality and reliability.
These changes streamline the data collector's architecture, aligning with project standards for maintainability and performance.
- Updated all technical indicators to return pandas DataFrames instead of lists, improving consistency and usability.
- Modified the `calculate` method in `TechnicalIndicators` to directly return DataFrames with relevant indicator values.
- Enhanced the `data_integration.py` to utilize the new DataFrame outputs for better integration with charting.
- Updated documentation to reflect the new DataFrame-centric approach, including usage examples and output structures.
- Improved error handling to ensure empty DataFrames are returned when insufficient data is available.
These changes streamline the indicator calculations and improve the overall architecture, 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.
