- 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`.
