- Added feature selection using SelectKBest with f_regression
- Introduced ensemble weighting based on validation performance
- Implemented a function to calculate and display overfitting scores
- Added more regularization parameters for Random Forest and XGBoost in hyperparameter tuning
- Modified `prepare_data` function to create X and y with a one-step offset for proper time series forecasting
- Updated Random Forest and XGBoost prediction in `train_and_evaluate_model` to predict one step ahead
- Modified `ensemble_predict` and `weighted_ensemble_predict` functions to predict one step ahead
- Updated hyperparameter tuning for Random Forest and XGBoost to use TimeSeriesSplit for cross-validation
- Adjusted the quick test mode to use a smaller parameter space and fewer iterations for faster tuning
- Corrected `calculate_ensemble_weights` function to properly handle model tuples
- Ensured consistent one-step-ahead prediction across all models and evaluation steps
- Enhanced the README with more detailed information about recent modifications and tool capabilities
- Optimized hyperparameter tuning process, resulting in significantly faster tuning times for Random Forest and XGBoost models
- Implemented early stopping for neural networks to prevent overfitting.
- Added more technical indicators for enhanced feature engineering.
- Introduced a weighted ensemble method for combining model predictions.
- Implemented a simple trading strategy based on predictions.
- Enhanced visualization to include trading strategy performance.
