- Updated README.md to reflect new features including dynamic margin ratio, model hot-reload, and multi-symbol streaming.
- Modified bot logic to ensure raw signals are passed to the `_close_and_reenter` method, even when the ML filter is loaded.
- Introduced a new script `run_tests.sh` for streamlined test execution.
- Improved test coverage for signal processing and re-entry logic, ensuring correct behavior under various conditions.
- Added `_close_and_reenter` method to handle immediate re-entry after closing a position when a reverse signal is detected, contingent on passing the ML filter.
- Updated `process_candle` to call `_close_and_reenter` instead of `_close_position` for reverse signals.
- Enhanced test coverage for the new functionality, ensuring correct behavior under various conditions, including ML filter checks and position limits.
- Introduced a new markdown document detailing the plan to transition the entire pipeline from a 1-minute to a 15-minute timeframe, aiming to improve model AUC from 0.49-0.50 to over 0.53.
- Updated key parameters across multiple scripts, including `LOOKAHEAD` adjustments and default data paths to reflect the new 15-minute interval.
- Modified data fetching and training scripts to ensure compatibility with the new timeframe, including changes in `fetch_history.py`, `train_model.py`, and `train_and_deploy.sh`.
- Enhanced the bot's data stream configuration to operate on a 15-minute interval, ensuring real-time data processing aligns with the new model training strategy.
- Updated training logs to capture new model performance metrics under the revised timeframe.
- Added a new stage to the Jenkins pipeline to notify Discord when a build starts, succeeds, or fails, improving communication during the CI/CD process.
- Implemented model hot-reload functionality in the MLFilter class, allowing automatic reloading of models when file changes are detected, enhancing responsiveness to updates.
- Updated deployment scripts to provide clearer messaging regarding model loading and container status, improving user experience and debugging capabilities.
- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.
This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.
- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.
Made-with: Cursor
