- Introduced a new module for live trading based on the Multi-Pair Divergence Strategy.
- Implemented configuration classes for OKX API and multi-pair settings.
- Developed data feed functionality to fetch real-time OHLCV and funding data for multiple assets.
- Created a trading bot orchestrator to manage trading cycles, including entry and exit signals based on ML model predictions.
- Added comprehensive logging and error handling for robust operation.
- Included a README with setup instructions and usage guidelines for the new module.
- Extend regime detection to top 10 cryptocurrencies (45 pairs)
- Dynamic pair selection based on divergence score (|z_score| * probability)
- Universal ML model trained on all pairs
- Correlation-based filtering to avoid redundant positions
- Funding rate integration from OKX for all 10 assets
- ATR-based dynamic stop-loss and take-profit
- Walk-forward training with 70/30 split
Performance: +35.69% return (vs +28.66% baseline), 63.6% win rate
- 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`.
