- 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.
- 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.
