* 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()
* 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