feat: implement LightGBM model improvement plan with feature normalization and walk-forward validation

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- 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.

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21in7

2026-03-01 22:02:32 +09:00

parent c6428af64e

commit a6697e7cca

7 changed files with 487 additions and 22 deletions

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