- 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.
- 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.
- Deleted the `okx_config.json` file as part of the configuration refactor.
- Updated `BaseDataCollector` to include an optional `timeframes` parameter for more flexible data collection.
- Modified `DataCollectionService` and `OKXCollector` to pass and utilize the new `timeframes` parameter.
- Enhanced `ExchangeCollectorConfig` to validate timeframes, ensuring they are provided and correctly formatted.
- Updated documentation to reflect the new configurable timeframes feature, improving clarity for users.
These changes streamline the configuration process and improve the flexibility of data collection, aligning with project standards for maintainability and usability.
- Introduced a new `exceptions.py` file containing custom exceptions for the exchanges module, improving error specificity and handling.
- Updated the `factory.py` and `registry.py` files to utilize the new exceptions, enhancing robustness in error reporting and logging.
- Implemented validation logic in `ExchangeCollectorConfig` to ensure proper configuration, raising appropriate exceptions when validation fails.
- Enhanced logging throughout the factory methods to provide better insights into the collector creation process and error scenarios.
- Added comprehensive documentation for the exchanges module, detailing the architecture, error handling, and usage examples.
These changes significantly improve the error handling and maintainability of the exchanges module, aligning with project standards and enhancing developer experience.
- Introduced a new transformation module that includes safety limits for trade operations, enhancing data integrity and preventing errors.
- Refactored existing transformation logic into dedicated classes and functions, improving modularity and maintainability.
- Added detailed validation for trade sizes, prices, and symbol formats, ensuring compliance with trading rules.
- Implemented logging for significant operations and validation checks, aiding in monitoring and debugging.
- Created a changelog to document the new features and changes, providing clarity for future development.
- Developed extensive unit tests to cover the new functionality, ensuring reliability and preventing regressions.
These changes significantly enhance the architecture of the transformation module, making it more robust and easier to manage.
- 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.
- 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.
- 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.
- Removed the `RawDataManager` class and integrated its functionality directly into the `RawTradeRepository`, streamlining the management of raw trade data.
- Implemented the `cleanup_old_raw_data` method to delete outdated records, preventing table bloat and improving performance.
- Added the `get_raw_data_stats` method to retrieve statistics about raw data storage, enhancing data management capabilities.
- Updated documentation to reflect the new methods and their usage, ensuring clarity for future developers.
These changes improve the maintainability and efficiency of the database operations related to raw trade data.
- Updated the `MarketDataRepository` and `RawTradeRepository` classes to exclusively utilize SQLAlchemy ORM for all database interactions, enhancing maintainability and type safety.
- Removed raw SQL queries in favor of ORM methods, ensuring a consistent and database-agnostic approach across the repository layer.
- Revised documentation to reflect these changes, emphasizing the importance of using the ORM for database operations.
These modifications improve the overall architecture of the database layer, making it more scalable and easier to manage.
- Introduced a modular repository structure by creating separate repository classes for `Bot`, `MarketData`, and `RawTrade`, improving code organization and maintainability.
- Updated the `DatabaseOperations` class to utilize the new repository classes, enhancing the abstraction of database interactions.
- Refactored the `.env` file to update database connection parameters and add new logging and health monitoring configurations.
- Modified the `okx_config.json` to change default timeframes for trading pairs, aligning with updated requirements.
- Added comprehensive unit tests for the new repository classes, ensuring robust functionality and reliability.
These changes improve the overall architecture of the database layer, making it more scalable and easier to manage.
- Updated the logging documentation to reflect changes in the unified log message format, including the addition of pathname, line number, and function name for better traceability.
- Modified the `get_logger` function to set a default value for `component_name`, improving usability for users who may not specify a component name.
- Ensured consistency in the documentation regarding the parameters and their descriptions.
These updates improve the clarity and ease of use of the logging system, making it more accessible for developers.
- Revised the logging documentation to clarify the unified logging system's features and usage patterns.
- Simplified the logger implementation by removing the custom `DateRotatingFileHandler` and utilizing the standard library's `TimedRotatingFileHandler` for date-based log rotation.
- Enhanced the `get_logger` function to ensure thread-safe logger configuration and prevent duplicate handlers.
- Introduced a new `cleanup_old_logs` function for age-based log cleanup, while retaining the existing count-based cleanup mechanism.
- Improved error handling and logging setup to ensure robust logging behavior across components.
These changes enhance the clarity and maintainability of the logging system, making it easier for developers to implement and utilize logging in their components.
