- Enhanced the `UserIndicator` class to include an optional `timeframe` attribute for custom indicator timeframes.
- Updated the `get_indicator_data` method in `MarketDataIntegrator` to fetch and calculate indicators based on the specified timeframe, ensuring proper data alignment and handling.
- Modified the `ChartBuilder` to pass the correct DataFrame for plotting indicators with different timeframes.
- Added UI elements in the indicator modal for selecting timeframes, improving user experience.
- Updated relevant JSON templates to include the new `timeframe` field for all indicators.
- Refactored the `prepare_chart_data` function to ensure it returns a DataFrame with a `DatetimeIndex` for consistent calculations.
This commit enhances the flexibility and usability of the indicator system, allowing users to analyze data across various timeframes.
- Added type conversion for relevant columns in `VolumeAnalyzer` and `PriceMovementAnalyzer` to ensure consistent data handling and avoid type errors.
- Refactored the `create_data_analysis_panel` function to implement a tabbed interface for volume and price movement analysis, improving user experience and organization of analysis tools.
- Updated styles in `indicator_modal.py` for better layout and responsiveness of the modal component.
- Marked unit testing of dashboard components as complete in the task list.
- Updated `ChartBuilder` to support dynamic indicator integration, allowing users to specify overlay and subplot indicators for enhanced chart analysis.
- Implemented a new `get_indicator_data` method in `MarketDataIntegrator` for fetching indicator data based on user configurations.
- Added `create_export_controls` in `chart_controls.py` to facilitate data export options (CSV/JSON) for user analysis.
- Enhanced error handling and logging throughout the chart and data analysis processes to improve reliability and user feedback.
- Updated documentation to reflect new features and usage guidelines for indicator management and data export functionalities.
- Updated `app_new.py` to run the application in debug mode for stability.
- Introduced a new time range control panel in `dashboard/components/chart_controls.py`, allowing users to select predefined time ranges and custom date ranges.
- Enhanced chart callbacks in `dashboard/callbacks/charts.py` to handle time range inputs, ensuring accurate market statistics and analysis based on user selections.
- Implemented logic to preserve chart state during updates, preventing resets of zoom/pan settings.
- Updated market statistics display to reflect the selected time range, improving user experience and data relevance.
- Added a clear button for custom date ranges to reset selections easily.
- Enhanced documentation to reflect the new time range features and usage guidelines.
- Added `psutil` dependency for system performance metrics.
- Implemented a new layout in `dashboard/layouts/system_health.py` using Mantine components for real-time monitoring of data collection services, database health, Redis status, and system performance.
- Enhanced callbacks in `dashboard/callbacks/system_health.py` for detailed status updates and error handling.
- Introduced quick status indicators for data collection, database, Redis, and performance metrics with auto-refresh functionality.
- Created modals for viewing detailed data collection information and service logs.
- Updated documentation to reflect the new features and usage guidelines.
- Introduced `BotIntegratedSignalLayer` and `BotIntegratedTradeLayer` to facilitate automated data fetching and visualization of bot signals and trades.
- Implemented `BotDataService` for efficient retrieval of bot-related data, including filtering and performance summaries.
- Added support for various bot-enhanced layers, including support/resistance and custom strategy layers, to improve trading analysis.
- Updated existing signal layer components to integrate with the new bot functionalities, ensuring seamless operation.
- Enhanced logging and error handling for better debugging and user feedback during bot operations.
- Included comprehensive tests for new functionalities to ensure reliability and maintainability.
- Updated documentation to reflect the new bot integration features and usage guidelines.
- Introduced `TradingSignalLayer` and `TradeExecutionLayer` for visualizing buy/sell signals and trade entries/exits on charts.
- Implemented signal validation and filtering mechanisms to ensure data integrity and user-configurable options.
- Enhanced market data layout to support new timeframes for improved user experience.
- Updated documentation to reflect the new signal layer architecture and its integration with the dashboard.
- Ensured compatibility with existing components while maintaining a modular structure for future enhancements.
- Deleted `app.py`, consolidating the main application logic into a modular structure for improved maintainability.
- Added `dash-mantine-components` dependency to enhance UI component capabilities.
- Updated `pyproject.toml` and `uv.lock` to reflect the new dependency.
- Adjusted imports in `components/__init__.py` and `chart_controls.py` to align with the new modular design.
- Cleaned up unused parameter controls in the market data layout to streamline the user interface.
- Introduced a new modular structure for the dashboard, enhancing maintainability and scalability.
- Created main application entry point in `app_new.py`, integrating all components and callbacks.
- Developed layout modules for market data, bot management, performance analytics, and system health in the `layouts` directory.
- Implemented callback modules for navigation, charts, indicators, and system health in the `callbacks` directory.
- Established reusable UI components in the `components` directory, including chart controls and indicator modals.
- Enhanced documentation to reflect the new modular structure and provide clear usage guidelines.
- Ensured all components are under 300-400 lines for better readability and maintainability.
- Introduced a comprehensive user indicator management system in `components/charts/indicator_manager.py`, allowing users to create, edit, and manage custom indicators with JSON persistence.
- Added new default indicators in `components/charts/indicator_defaults.py` to provide users with immediate options for technical analysis.
- Enhanced the chart rendering capabilities by implementing the `create_chart_with_indicators` function in `components/charts/builder.py`, supporting both overlay and subplot indicators.
- Updated the main application layout in `app.py` to include a modal for adding and editing indicators, improving user interaction.
- Enhanced documentation to cover the new indicator system, including a quick guide for adding new indicators and detailed usage examples.
- Added unit tests to ensure the reliability and functionality of the new indicator management features.
Implement comprehensive chart configuration and validation system
- Introduced a modular chart configuration system in `components/charts/config/` to manage indicator definitions, default configurations, and strategy-specific setups.
- Added new modules for error handling and validation, enhancing user guidance and error reporting capabilities.
- Implemented detailed schema validation for indicators and strategies, ensuring robust configuration management.
- Created example strategies and default configurations to facilitate user onboarding and usage.
- Enhanced documentation to provide clear guidelines on the configuration system, validation rules, and usage examples.
- Added unit tests for all new components to ensure functionality and reliability across the configuration system.
Implement modular chart layers and error handling for Crypto Trading Bot Dashboard
- Introduced a comprehensive chart layer system in `components/charts/layers/` to support various technical indicators and subplots.
- Added base layer components including `BaseLayer`, `CandlestickLayer`, and `VolumeLayer` for flexible chart rendering.
- Implemented overlay indicators such as `SMALayer`, `EMALayer`, and `BollingerBandsLayer` with robust error handling.
- Created subplot layers for indicators like `RSILayer` and `MACDLayer`, enhancing visualization capabilities.
- Developed a `MarketDataIntegrator` for seamless data fetching and validation, improving data quality assurance.
- Enhanced error handling utilities in `components/charts/error_handling.py` to manage insufficient data scenarios effectively.
- Updated documentation to reflect the new chart layer architecture and usage guidelines.
- Added unit tests for all chart layer components to ensure functionality and reliability.
- 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.
- Marked task 2.9 as complete in the project documentation by adding comprehensive unit tests for data collection and aggregation functionality.
- Created `test_data_collection_aggregation.py` to cover OKX data collection, real-time candle aggregation, data validation, and transformation.
- Included tests for error handling, edge cases, and performance to ensure robustness and reliability of the data processing components.
- Enhanced documentation within the test module to provide clarity on the testing approach and coverage.
- 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.
- 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.
- 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.
