* feature(MetaLabeling): added hacky prototype
* fix(MetaLabeling): drop index until first valid X & y
* fix(MetaLabeling): transform both X & y before feature selection
* fix(MetaLabeling): got feature selection to work
* fix(MetaLabeling): correct values for meta_y
* feat(MetaLabeling): created predictions multiplied by bet sizes
* feat(Pipeline): print out averaged result
* fix(Evaluation): correctly deal with non-discretized data
* fix(Pipeline): use the right column names
* refactor(Pipeline): move out meta-labeling
* refactor(Pipeline): complete refactoring
* feat(CI): post results to PR
* fix(Pipeline): use the correct filename
* chore(Config): removed now redundant feature_selection flag
* feat(Models): added SVC
* fix(Pipeline): accidentally switched two return values
* feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file
* fix(Pipeline): wrong function name
* fix(Sweep): yaml + run_sweep
* fix(Sweep): typo in name
* fix(Reporting): only save averaged results
* feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging
* feat(Reporting): print out sharpe improvement in meta-labeling step
* fix(Sweep): adjusted config, defaulted to good defaults
* fix(Sweep): adjusted sweep
* feat(Selection): added prototype feature selection python script
* feat(Utils): added some helpers for the future from Advances in Financial ML book
* feat(Selection): added RFECV
* feat(Selection): added configurable feature selection step into pipeline
* feat(Config): added level_1 & level_2 default config, PCA before feature selection process starts
* feat(Selection): added backup feature selector models if current one can't output feature importance, removed unnecessary array for level-2 models
* fix(Training): deal with zero first value coming out of static models
* feat(Sweep): added feature selection sweep
* fix(Sweep): config problem
* fix(Sweep): config
* chore(Utils): removed unnecessary purged k-fold crossval class
* feat(Config): added dimensionality_reduction as a separate flag
* fix(Sweep): config updated
* fix(Sweep): sweep name
* chore(Config): updated level_2 config to the best performing configuation
* fix(Reporting): use weighted average (with no_of_samples as weights) and only report level-1 OR level-2 model performance
* chore(Config): updated sweep config
* fix(Reporting): missing import
* fix(Evaluation): get_first_valid_return_index can deal with zero valid indexes
* fix(Training): increase threshold for skipping assets
* fix(DataLoader): target asset should be always the first column
* feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit()
* fix(Sweep): removed unused `other_features` parameter that fails sweep
* feat(Config): using preset names for defining feature extractors again
* fix(Tests): fixed model stub classes
* 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()
Mark Aron Szulyovszky