- 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.
- Introduced `service_config.py` to manage configuration loading, validation, and schema management, enhancing modularity and security.
- Created a `ServiceConfig` class for handling configuration with robust error handling and default values.
- Refactored `DataCollectionService` to utilize the new `ServiceConfig`, streamlining configuration management and improving readability.
- Added a `CollectorFactory` to encapsulate collector creation logic, promoting separation of concerns.
- Updated `CollectorManager` and related components to align with the new architecture, ensuring better maintainability.
- Enhanced logging practices across the service for improved monitoring and debugging.
These changes significantly improve the architecture and maintainability of the data collection service, aligning with project standards for modularity and performance.
- Deleted `example_complete_series_aggregation.py` as it is no longer needed.
- Introduced `data_collection_service.py`, a production-ready service for cryptocurrency market data collection with clean logging and robust error handling.
- Added configuration management for multiple trading pairs and exchanges, supporting health monitoring and graceful shutdown.
- Created `data_collection.json` for service configuration, including exchange settings and logging preferences.
- Updated `CandleProcessingConfig` to reflect changes in timeframes for candle processing.
- Enhanced documentation to cover the new data collection service and its configuration, ensuring clarity for users.
- Introduced `example_complete_series_aggregation.py` to demonstrate time series aggregation, emitting candles even when no trades occur.
- Implemented `CompleteSeriesProcessor` extending `RealTimeCandleProcessor` to handle time-based candle emission and empty candle creation.
- Refactored `OKXCollector` to utilize the new repository pattern for database operations, enhancing modularity and maintainability.
- Updated database operations to centralize data handling through `DatabaseOperations`, improving error handling and logging.
- Enhanced documentation to include details on the new aggregation example and repository pattern implementation, ensuring clarity for users.
- Added logger parameter to the OKXCollector to enable detailed ping/pong logging.
- Updated message processing methods to maintain heartbeat and track data reception timestamps.
- Adjusted ProductionManager to disable auto-restart and enable full logging for debugging WebSocket issues.
- Enhanced overall logging capabilities to facilitate better monitoring and troubleshooting of data collection processes.
- Added optional logger parameter to various classes including `BaseDataCollector`, `CollectorManager`, `RealTimeCandleProcessor`, and `BatchCandleProcessor` to support conditional logging.
- Implemented error-only logging mode, allowing components to log only error and critical messages when specified.
- Updated logging calls to utilize new helper methods for improved readability and maintainability.
- Enhanced documentation to include details on the new logging system and its usage across components.
- Ensured that child components inherit the logger from their parent components for consistent logging behavior.
- Introduced `monitor_clean.py` for monitoring database status with detailed logging and status updates.
- Added `production_clean.py` for running OKX data collection with minimal console output and comprehensive logging.
- Implemented command-line argument parsing for both scripts to customize monitoring intervals and collection durations.
- Enhanced logging capabilities to provide clear insights into data collection and monitoring processes.
- Updated documentation to include usage examples and descriptions for the new scripts, ensuring clarity for users.
- Updated `docker-compose.yml` to remove hardcoded passwords, relying on environment variables for PostgreSQL and Redis configurations.
- Modified `env.template` to reflect new password settings and ensure secure handling of sensitive information.
- Introduced a new `database/connection.py` file for improved database connection management, including connection pooling and session handling.
- Updated `database/models.py` to align with the new schema in `schema_clean.sql`, utilizing JSONB for optimized data storage.
- Enhanced `setup.md` documentation to clarify the initialization process and emphasize the importance of the `.env` file for configuration.
- Added a new `scripts/init_database.py` script for automated database initialization and verification, ensuring all tables are created as expected.
