- Suppressed SQLAlchemy logging in `app.py` and `main.py` to reduce console verbosity.
- Introduced a new modular chart system in `components/charts/` with a `ChartBuilder` class for flexible chart creation.
- Added utility functions for data processing and validation in `components/charts/utils.py`.
- Implemented indicator definitions and configurations in `components/charts/config/indicator_defs.py`.
- Created a comprehensive documentation structure for the new chart system, ensuring clarity and maintainability.
- Added unit tests for the `ChartBuilder` class to verify functionality and robustness.
- Updated existing components to integrate with the new chart system, enhancing overall architecture and user experience.
- Introduced `app.py` as the main entry point for the dashboard, providing real-time visualization and bot management interface.
- Implemented layout components including header, navigation tabs, and content areas for market data, bot management, performance analytics, and system health.
- Added callbacks for dynamic updates of market data charts and statistics, ensuring real-time interaction.
- Created reusable UI components in `components` directory for modularity and maintainability.
- Enhanced database operations for fetching market data and checking data availability.
- Updated `main.py` to start the dashboard application with improved user instructions and error handling.
- Documented components and functions for clarity and future reference.
- 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.
- 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.
- 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.
