- 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.
- Introduced a dedicated sub-package for technical indicators under `data/common/indicators/`, improving modularity and maintainability.
- Moved `TechnicalIndicators` and `IndicatorResult` classes to their respective files, along with utility functions for configuration management.
- Updated import paths throughout the codebase to reflect the new structure, ensuring compatibility.
- Added comprehensive safety net tests for the indicators module to verify core functionality and prevent regressions during refactoring.
- Enhanced documentation to provide clear usage examples and details on the new package structure.
These changes improve the overall architecture of the technical indicators module, making it more scalable and easier to manage.
