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/walk_forward/inference.py

@@ -20,10 +20,13 @@ def walk_forward_inference(
     expanding_window: bool,
     window_size: int,
     retrain_every: int,
+    class_labels: list[int],
     from_index: Optional[pd.Timestamp],
 ) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
     predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
-    probabilities = pd.DataFrame(index=X.index)
+    probabilities = pd.DataFrame(
+        index=X.index, columns=[str(label) for label in class_labels]
+    )
 
     inference_from = (
         max(