- 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 `indicators.py` containing implementations for SMA, EMA, RSI, MACD, and Bollinger Bands, optimized for handling sparse OHLCV data.
- Added `IndicatorResult` dataclass to encapsulate results of indicator calculations.
- Implemented methods for calculating multiple indicators efficiently with JSON configuration support and validation.
- Updated `__init__.py` to include new indicators in the module's exports.
- Enhanced documentation to cover the new technical indicators module, including usage examples and integration details.
- Added comprehensive unit tests to ensure accuracy and robustness of the indicators module.
- 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.
- Introduced `force_update_candles` option in `okx_config.json` to control candle update behavior.
- Updated `OKXCollector` to handle candle storage based on the `force_update_candles` setting, allowing for either updating existing records or preserving them.
- Enhanced logging to reflect the action taken during candle storage, improving traceability.
- Updated database schema to include `updated_at` timestamp for better tracking of data changes.
- Increased health check interval from 30s to 120s in `okx_config.json`.
- Added support for additional timeframes (1s, 5s, 10s, 15s, 30s) in the aggregation logic across multiple components.
- Updated `CandleProcessingConfig` and `RealTimeCandleProcessor` to handle new timeframes.
- Enhanced validation and parsing functions to include new second-based timeframes.
- Updated database schema to support new timeframes in `schema_clean.sql`.
- Improved documentation to reflect changes in multi-timeframe aggregation capabilities.
- 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.
- Introduced a modular architecture for data processing, including common utilities for validation, transformation, and aggregation.
- Implemented `StandardizedTrade`, `OHLCVCandle`, and `TimeframeBucket` classes for unified data handling across exchanges.
- Developed `OKXDataProcessor` for OKX-specific data validation and processing, leveraging the new common framework.
- Enhanced `OKXCollector` to utilize the common data processing utilities, improving modularity and maintainability.
- Updated documentation to reflect the new architecture and provide guidance on the data processing framework.
- Created comprehensive tests for the new data processing components to ensure reliability and functionality.
- Introduced the `OKXCollector` and `OKXWebSocketClient` classes for real-time market data collection from the OKX exchange.
- Implemented a factory pattern for creating exchange-specific collectors, enhancing modularity and scalability.
- Added configuration support for the OKX collector in `config/okx_config.json`.
- Updated documentation to reflect the new modular architecture and provide guidance on using the OKX collector.
- Created unit tests for the OKX collector and exchange factory to ensure functionality and reliability.
- Enhanced logging and error handling throughout the new implementation for improved monitoring and debugging.
- Introduced `BaseDataCollector` and `CollectorManager` classes for standardized data collection and centralized management.
- Added health monitoring features, including auto-restart capabilities and detailed status reporting for collectors.
- Updated `env.template` to include new logging and health check configurations.
- Enhanced documentation in `docs/data_collectors.md` to provide comprehensive guidance on the new data collection system.
- Added unit tests for `BaseDataCollector` and `CollectorManager` to ensure reliability and functionality.
- Updated `.gitignore` to exclude log files from version control.
- Added `pytest` as a dependency in `pyproject.toml` for testing purposes.
- Included `pytest` in `uv.lock` to ensure consistent dependency management.
- Introduced comprehensive documentation for the new unified logging system in `docs/logging.md`, detailing features, usage, and configuration options.
- Introduced `alembic.ini` for Alembic configuration, enabling structured database migrations.
- Created `database/migrations/env.py` to manage migration environment and database URL retrieval.
- Added migration script template `database/migrations/script.py.mako` for generating migration scripts.
- Updated `.gitignore` to exclude migration versions from version control.
- Enhanced `setup.md` documentation to include details on the migration system and commands for managing migrations.
- Introduced `database/redis_manager.py` to manage Redis connections, including synchronous and asynchronous clients.
- Implemented pub/sub messaging capabilities for real-time data distribution, with structured channel definitions for market data, bot signals, and system health.
- Added configuration options for Redis connection pooling and error handling, ensuring robust integration with the Crypto Trading Bot Platform.
- 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.
- Added new SQLAlchemy models in `database/models.py` for market data, trades, bots, signals, and performance tracking.
- Updated `docker-compose.yml` to use TimescaleDB for PostgreSQL and configured shared preload libraries.
- Created new schema files: `schema.sql` for the complete database setup and `schema_clean.sql` for a simplified version without hypertables.
- Updated documentation in `setup.md` to reflect changes in database initialization and service setup.
