chore(Linter): reformatted code with black (#211)

* chore(Linter): reformatted code with black

* Create black.yaml

run_inference.py

@@ -4,7 +4,11 @@ from reporting.saving import load_models
 
 from run_pipeline import run_pipeline
 from config.types import Config, RawConfig
-from config.presets import get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
+from config.presets import (
+    get_dev_config,
+    get_default_ensemble_config,
+    get_lightweight_ensemble_config,
+)
 from labeling.process import label_data
 import pandas as pd
 
@@ -12,44 +16,73 @@ from training.directional_training import train_directional_model
 from training.bet_sizing import bet_sizing_with_meta_model
 from training.types import PipelineOutcome
 
-def run_inference(preload_models:bool, fallback_raw_config: RawConfig):
+
+def run_inference(preload_models: bool, fallback_raw_config: RawConfig):
     if preload_models:
         pipeline_outcome, config = load_models(None)
     else:
-        pipeline_outcome, config = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=fallback_raw_config)
-    
-    config.mode = 'inference'
+        pipeline_outcome, config = run_pipeline(
+            project_name="price-prediction",
+            with_wandb=False,
+            sweep=False,
+            raw_config=fallback_raw_config,
+        )
+
+    config.mode = "inference"
     __inference(config, pipeline_outcome)
 
 
 def __inference(config: Config, pipeline_outcome: PipelineOutcome):
-    
+
     # 1. Load data, check for validity and process data
     X, returns = load_data(
-        assets = config.assets,
-        other_assets = config.other_assets,
-        exogenous_data = config.exogenous_data,
-        target_asset = config.target_asset,
-        load_non_target_asset = config.load_non_target_asset,
-        own_features = config.own_features,
-        other_features = config.other_features,
-        exogenous_features = config.exogenous_features,
+        assets=config.assets,
+        other_assets=config.other_assets,
+        exogenous_data=config.exogenous_data,
+        target_asset=config.target_asset,
+        load_non_target_asset=config.load_non_target_asset,
+        own_features=config.own_features,
+        other_features=config.other_features,
+        exogenous_features=config.exogenous_features,
     )
-    assert check_data(X, config) == True, "Data is not valid. Cancelling Inference." 
+    assert check_data(X, config) == True, "Data is not valid. Cancelling Inference."
 
     # 2. Filter for significant events when we want to trade, and label data
-    events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns)
+    events, X, y, forward_returns = label_data(
+        config.event_filter, config.labeling, X, returns
+    )
 
     inference_from: pd.Timestamp = X.index[len(X.index) - 1]
 
     # 3. Train directional models
-    directional_training_outcome = train_directional_model(X, y, forward_returns, config, config.directional_model, from_index = inference_from, preloaded_training_step = pipeline_outcome.directional_training)
-    
+    directional_training_outcome = train_directional_model(
+        X,
+        y,
+        forward_returns,
+        config,
+        config.directional_model,
+        from_index=inference_from,
+        preloaded_training_step=pipeline_outcome.directional_training,
+    )
+
     # 4. Run bet sizing on primary model's output
-    bet_sizing_outcome = bet_sizing_with_meta_model(X, directional_training_outcome.training.predictions, y, forward_returns, config.meta_model, config, 'meta', from_index = inference_from, transformations_over_time = pipeline_outcome.bet_sizing.meta_transformations, preloaded_models = pipeline_outcome.bet_sizing.meta_training.model_over_time)
+    bet_sizing_outcome = bet_sizing_with_meta_model(
+        X,
+        directional_training_outcome.training.predictions,
+        y,
+        forward_returns,
+        config.meta_model,
+        config,
+        "meta",
+        from_index=inference_from,
+        transformations_over_time=pipeline_outcome.bet_sizing.meta_transformations,
+        preloaded_models=pipeline_outcome.bet_sizing.meta_training.model_over_time,
+    )
 
     return PipelineOutcome(directional_training_outcome, bet_sizing_outcome)
 
 
-if __name__ == '__main__':
-    run_inference(preload_models=True, fallback_raw_config=get_lightweight_ensemble_config())
+if __name__ == "__main__":
+    run_inference(
+        preload_models=True, fallback_raw_config=get_lightweight_ensemble_config()
+    )