- Updated `fetch_history.py` to collect open interest (OI) and funding rate data from Binance, improving the dataset for model training.
- Modified `train_and_deploy.sh` to include options for OI and funding rate collection during data fetching.
- Enhanced `dataset_builder.py` to incorporate OI change and funding rate features with rolling z-score normalization.
- Updated training logs to reflect new metrics and features, ensuring comprehensive tracking of model performance.
- Adjusted feature columns in `ml_features.py` to include OI and funding rate for improved model robustness.
- 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 markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation.
- Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes.
- Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance.
- Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility.
- Adjusted model training parameters for better performance and included detailed validation results in the training log.
- 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.
- Introduced a new function `_split_combined` to separate XRP, BTC, and ETH data from a combined DataFrame.
- Updated `train_mlx` to utilize the new function, improving data management and feature handling.
- Adjusted dataset generation to accommodate BTC and ETH features, with warnings for missing features.
- Changed default data path in `train_mlx` and `train_model` to point to the combined dataset for consistency.
- Increased `LOOKAHEAD` from 60 to 90 and adjusted `ATR_TP_MULT` for better model performance.
- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.
- Renamed stages for clarity, changing 'Checkout' to 'Git Clone from Gitea' and 'Build Image' to 'Build Docker Image'.
- Updated Git checkout step to use specific branch and credentials for Gitea.
- Enhanced Docker login process with `withCredentials` for better security.
- Added a new stage for deploying to production LXC, including SSH commands for directory creation and Docker management.
- Updated success and failure messages to include Korean language support for better localization.
- 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 a new entry to the training log for the LightGBM model, including date, AUC, sample count, and model path.
- This entry mirrors an existing one, potentially for tracking model performance over time.
- Added comprehensive plans for training a LightGBM model on M4 Mac Mini and deploying it to an LXC container.
- Created scripts for model training, deployment, and a full pipeline execution.
- Enhanced model transfer with error handling and logging for better tracking.
- Introduced profiling for training time analysis and dataset generation optimization.
Made-with: Cursor
- Added a new function to accurately retrieve the number of allocated CPUs in containerized environments, improving parallel processing efficiency.
- Updated the dataset generation function to utilize the new CPU count function, ensuring optimal resource usage during model training.
Made-with: Cursor
- Created README.md to document project features, structure, and setup instructions.
- Updated fetch_history.py to include path adjustments for module imports.
- Enhanced train_model.py for parallel processing of dataset generation and added command-line argument for specifying worker count.
Made-with: Cursor
- 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
