feat(HPO): added run_hpo script (#237)

* feat(HPO): added `run_hpo` script

* fix(Linter): ran

* feat(HPO): removed any reference to sweep (superseeded by optuna)

* fix(HPO): optimize for sharpe

* fix(Config): removed glassnode data, save trials from hpo

* feat(Labelling): added three-balanced method works again

* fix(BetSizing): set the correct class labels

* fix(HPO): powerset should return what's expected, added two new normalization methods

* fix(Linter): ran

* fix(DataLoader): sort the dataframe when fetching data

* fix(Config): only take z-score of other assets

training/bet_sizing.py

@@ -66,6 +66,7 @@ def bet_sizing_with_meta_model(
         retrain_every=config.retrain_every,
         from_index=from_index,
         level="meta",
+        class_labels=[0, 1],
         transformations_over_time=transformations_over_time,
         model_over_time=preloaded_models,
     )