- Deleted `install_cron.sh`, `setup_schedule.sh`, and `train_daily.sh` as part of the transition to a new scheduling mechanism.
- Removed associated Systemd service and timer files for daily model training.
- Updated `live_regime_strategy.py` and `main.py` to reflect changes in model training and scheduling logic.
- Adjusted `regime_strategy.py` to align with new target calculation methods and updated optimal parameters.
- Enhanced `regime_detection.py` to incorporate path-dependent labeling for target calculations.
- Introduced `train_daily.sh` for automating daily model retraining, including data download and model training steps.
- Added `install_cron.sh` for setting up a cron job to run the daily training script.
- Created `setup_schedule.sh` for configuring Systemd timers for daily training tasks.
- Implemented a terminal UI using Rich for real-time monitoring of trading performance, including metrics display and log handling.
- Updated `pyproject.toml` to include the `rich` dependency for UI functionality.
- Enhanced `.gitignore` to exclude model and log files.
- Added database support for trade persistence and metrics calculation.
- Updated README with installation and usage instructions for the new features.
- Updated `calculate_sl_tp` method to handle invalid entry prices and sides, returning (None, None) when necessary.
- Improved logging for SL/TP values in `LiveTradingBot` to display "N/A" for invalid values.
- Refined order placement in `OKXClient` to ensure guaranteed fill price retrieval, with fallback mechanisms for fetching order details and ticker prices if needed.
- Added error handling for scenarios where fill prices cannot be determined.
- Changed `max_position_usdt` to -1.0 to indicate that all available funds should be used if the value is less than or equal to zero.
- Modified the base size calculation in `LiveRegimeStrategy` to accommodate the new logic for `max_position_usdt`, ensuring it uses all available funds when applicable.
- Added FastAPI backend with core API endpoints for strategies, backtests, and data management.
- Introduced Vue 3 frontend with a dark theme, enabling users to run backtests, adjust parameters, and compare results.
- Implemented Pydantic schemas for request/response validation and SQLAlchemy models for database interactions.
- Enhanced project structure with dedicated modules for services, routers, and components.
- Updated dependencies in `pyproject.toml` and `frontend/package.json` to include FastAPI, SQLAlchemy, and Vue-related packages.
- Improved `.gitignore` to exclude unnecessary files and directories.
- Introduced `check_symbols.py` to load and filter ETH perpetual markets from the OKX exchange using CCXT.
- Updated the backtester to normalize signals to a 5-tuple format, incorporating size management for trades.
- Enhanced portfolio functions to support variable size and leverage adjustments based on initial capital.
- Added a new method in `CryptoQuantClient` for chunked historical data fetching to avoid API limits.
- Improved market symbol normalization in `market.py` to handle different formats.
- Updated regime strategy parameters based on recent research findings for optimal performance.
- Introduced `RegimeReversionStrategy` for ML-based regime detection and mean reversion trading.
- Added feature engineering and model training logic within the new strategy.
- Removed the deprecated `regime_detection.py` file to streamline the codebase.
- Updated the strategy factory to include the new regime strategy configuration.
- Introduced `CryptoQuantClient` for fetching data from the CryptoQuant API.
- Added `regime_detection.py` for advanced regime detection analysis using machine learning.
- Updated dependencies in `pyproject.toml` and `uv.lock` to include `scikit-learn`, `matplotlib`, `plotly`, `requests`, and `python-dotenv`.
- Enhanced `.gitignore` to exclude `regime_results.html` and CSV files.
- Created an interactive HTML plot for regime detection results and saved it as `regime_results.html`.
