- Added `StrategyRun` and `StrategySignal` models to track strategy execution sessions and generated signals, respectively, ensuring a clear separation from live trading data.
- Introduced `StrategyRepository` for managing database operations related to strategy runs and signals, including methods for creating, updating, and retrieving strategy data.
- Updated `DatabaseOperations` to integrate the new repository, enhancing the overall architecture and maintaining consistency with existing database access patterns.
- Enhanced documentation to reflect the new database schema and repository functionalities, ensuring clarity for future development and usage.
These changes establish a robust foundation for strategy analysis and backtesting, aligning with project goals for modularity, performance, and maintainability.
- Deleted `app_new.py`, which was previously the main entry point for the dashboard application, to streamline the codebase.
- Consolidated the application initialization and callback registration logic into `main.py`, enhancing modularity and maintainability.
- Updated the logging and error handling practices in `main.py` to ensure consistent application behavior and improved debugging capabilities.
These changes simplify the application structure, aligning with project standards for modularity and maintainability.
- Introduced a comprehensive data collection framework, including `CollectorServiceConfig`, `BaseDataCollector`, and `CollectorManager`, enhancing modularity and maintainability.
- Developed `CollectorFactory` for streamlined collector creation, promoting separation of concerns and improved configuration handling.
- Enhanced `DataCollectionService` to utilize the new architecture, ensuring robust error handling and logging practices.
- Added `TaskManager` for efficient management of asynchronous tasks, improving performance and resource management.
- Implemented health monitoring and auto-recovery features in `CollectorManager`, ensuring reliable operation of data collectors.
- Updated imports across the codebase to reflect the new structure, ensuring consistent access to components.
These changes significantly improve the architecture and maintainability of the data collection service, aligning with project standards for modularity, performance, and error handling.
- 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.
