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

run_hpo.py

@@ -0,0 +1,39 @@
+from config.types import RawConfig
+from run_pipeline import run_pipeline
+from config.presets import get_default_config
+import optuna
+from utils.helpers import powerset
+
+config = get_default_config()
+config.save_models = False
+
+
+def train(trial):
+    search_space = {
+        "n_features_to_select": trial.suggest_int("n_features_to_select", 10, 200),
+        "forecasting_horizon": trial.suggest_int("forecasting_horizon", 3, 300),
+        "own_features": trial.suggest_categorical(
+            "own_features", powerset(["z_score", "level_2", "level_1"])
+        ),
+        "other_features": trial.suggest_categorical(
+            "other_features", powerset(["z_score", "level_2", "level_1"])
+        ),
+        "labeling": trial.suggest_categorical(
+            "labeling", ["two_class", "three_class_balanced", "three_class_imbalanced"]
+        ),
+        "scaler": trial.suggest_categorical(
+            "scaler",
+            ["normalize", "minmax", "standardize", "robust", "box-cox", "quantile"],
+        ),
+    }
+    trial_config = RawConfig(**vars(config) | search_space)
+    outcome, _ = run_pipeline(
+        project_name="test", with_wandb=False, raw_config=trial_config
+    )
+    return 1 - outcome.get_output_stats()["sharpe"]
+
+
+study = optuna.create_study()
+study.optimize(train, n_trials=40)
+print(study.best_params)
+study.trials_dataframe().to_csv("output/trials.csv")