* 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(Tests): added basic unit tests for walk_forward_train_test()
* fix(Tests): inherit from BaseEstimator, fix index problems in walk_forward_train_test
* fix(WalkForward): predictions were mistakenly removed, oops
* fix(WalkForward): mistakenly re-assiging model
* 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()
* feat: Started implementing pytorch-forecasting.
* feat(Forecasting): pytorch-forecasting scaffolding is now working, added narrow data format, fixed missing `time` column index name
Co-authored-by: Daniel Szemerey <szemy2@gmail.com>
Mark Aron Szulyovszky