* feat(Data): add option to predict 3 classes
* feat(Evaluation): added ability to evaluate 3 class predictions
* chore(Config): set sensible config for regression models
* feat(Data): added option to use balanced or imbalanced three-class data
* feat(Evaluate): correctly track "no_of_samples" now that we have three classes
* chore(Sweep): remove probably not useful scaler values from sweep
* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available
* feat(Utils): added get_first_valid_return_index()
* feat(Ensemble): return models from `run_whole_pipeline`
* feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion,
* chore(Pipeline): remove unnecessary extra ensemble results dataframe
* refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file
* feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio
* feat(CI): added environment.yml file
* chore(Environment): update env.yml
* feat(CI): added testing workflow
* fix(CI): renamed enviroment.yml
* fix(Tests): added missing new parameter to walk_forward_train_test()
* fix(Evaluate): ignore empty data at evaluation time, so we don't inflate the model's performance
* refactor(Pipeline): pass in data_loader arguments to the pipeline
* feat(Evaluation): added sharpe, sortino, etc
* fix: Took out the method to fill NaN numbers with 0s. This way in evaluation we can ignore NaN values.
* fix: Fix of the fix added fillna back. Either we root out NaN lines in the very beginning or we stick with the method you created.
Co-authored-by: Daniel Szemerey <szemy2@gmail.com>
* feat(Eval): added format_data_for_backtest()
* feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO
* feat(Core): added walk forward method of training/testing
* fix(Model): remove the unnecessary softmax activation from the keras models
* feat(Core): added walk_forward_train_test()
Mark Aron Szulyovszky