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TCPDashboard/tasks/refactor-common-package.md
Ajasra 96ee25bd01 Refactor data validation module for improved modularity and functionality 
- Removed the existing `validation.py` file and replaced it with a modular structure, introducing separate files for validation results, field validators, and the base validator class.
- Implemented comprehensive validation functions for common data types, enhancing reusability and maintainability.
- Added a new `__init__.py` to expose the validation utilities, ensuring a clean public interface.
- Created detailed documentation for the validation module, including usage examples and architectural details.
- Introduced extensive unit tests to cover the new validation framework, ensuring reliability and preventing regressions.

These changes enhance the overall architecture of the data validation module, making it more scalable and easier to manage.
2025-06-07 12:31:47 +08:00

3.6 KiB
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Relevant Files

  • data/common/aggregation.py - To be broken into a sub-package.
  • data/common/indicators.py - To be broken into a sub-package and have a bug fixed.
  • data/common/validation.py - To be refactored for better modularity.
  • data/common/transformation.py - To be refactored for better modularity.
  • data/common/data_types.py - To be updated with new types from other modules.
  • data/common/__init__.py - To be updated to reflect the new package structure.
  • tests/ - Existing tests will need to be run after each step to ensure no regressions.

Notes

  • This refactoring focuses on improving modularity by splitting large files into smaller, more focused modules, as outlined in the refactoring.mdc guide.
  • Each major step will be followed by a verification phase to ensure the application remains stable.

Tasks

  • 1.0 Refactor aggregation.py into a dedicated sub-package.

    • 1.1 Create safety net tests to ensure the aggregation logic still works as expected.
    • 1.2 Create a new directory data/common/aggregation.
    • 1.3 Create data/common/aggregation/__init__.py to mark it as a package.
    • 1.4 Move the TimeframeBucket class to data/common/aggregation/bucket.py.
    • 1.5 Move the RealTimeCandleProcessor class to data/common/aggregation/realtime.py.
    • 1.6 Move the BatchCandleProcessor class to data/common/aggregation/batch.py.
    • 1.7 Move the utility functions to data/common/aggregation/utils.py.
    • 1.8 Update data/common/aggregation/__init__.py to expose all public classes and functions.
    • 1.9 Delete the original data/common/aggregation.py file.
    • 1.10 Run tests to verify the aggregation logic still works as expected.

  • 2.0 Refactor indicators.py into a dedicated sub-package.

    • 2.1 Create safety net tests for indicators module.
    • 2.2 Create a new directory data/common/indicators.
    • 2.3 Create data/common/indicators/__init__.py to mark it as a package.
    • 2.4 Move the TechnicalIndicators class to data/common/indicators/technical.py.
    • 2.5 Move the IndicatorResult class to data/common/indicators/result.py.
    • 2.6 Move the utility functions to data/common/indicators/utils.py.
    • 2.7 Update data/common/indicators/__init__.py to expose all public classes and functions.
    • 2.8 Delete the original data/common/indicators.py file.
    • 2.9 Run tests to verify the indicators logic still works as expected.

  • 3.0 Refactor validation.py for better modularity.

    • 3.1 Create safety net tests for validation module.
    • 3.2 Extract common validation logic into separate functions.
    • 3.3 Improve error handling and validation messages.
    • 3.4 Run tests to verify validation still works as expected.

  • 4.0 Refactor transformation.py for better modularity.

    • 4.1 Create safety net tests for transformation module.
    • 4.2 Extract common transformation logic into separate functions.
    • 4.3 Improve error handling and transformation messages.
    • 4.4 Run tests to verify transformation still works as expected.

  • 5.0 Update data_types.py with new types.

    • 5.1 Review and document all data types.
    • 5.2 Add any missing type hints.
    • 5.3 Add validation for data types.
    • 5.4 Run tests to verify data types still work as expected.

  • 6.0 Final verification and cleanup.

    • 6.1 Run all tests to ensure no regressions.
    • 6.2 Update documentation to reflect new structure.
    • 6.3 Review and clean up any remaining TODOs.
    • 6.4 Create PR with changes.