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_multi_asset_pipeline.py

@@ -6,13 +6,13 @@ from config.presets import get_default_config
 
 
 def run_multi_asset_pipeline(
-    project_name: str, with_wandb: bool, sweep: bool, raw_config: RawConfig
+    project_name: str, with_wandb: bool, raw_config: RawConfig
 ):
     collection = data_collections["fivemin_crypto"]
     for asset in collection:
-        print(f"# Predicting asset: {asset[1]}\n")
-        raw_config.target_asset = asset[1]
-        wandb, config = setup_config(project_name, with_wandb, sweep, raw_config)
+        print(f"# Predicting asset: {asset.file_name}\n")
+        raw_config.target_asset = asset.file_name
+        wandb, config = setup_config(project_name, with_wandb, raw_config)
         pipeline_outcome = run_training(config)
         report_results(
             pipeline_outcome.directional_training.training.stats,
@@ -20,7 +20,6 @@ def run_multi_asset_pipeline(
             pipeline_outcome.get_output_weights(),
             config,
             wandb,
-            sweep,
         )
 
 
@@ -28,6 +27,5 @@ if __name__ == "__main__":
     run_multi_asset_pipeline(
         project_name="price-prediction",
         with_wandb=False,
-        sweep=False,
         raw_config=get_default_config(),
     )