- 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
