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

* chore(Linter): reformatted code with black * Create black.yaml
2026-08-19 05:48:09 +00:00 · 2022-02-17 19:22:17 +01:00
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
@@ -13,75 +13,92 @@ from transformations.scaler import get_scaler
 from transformations.rfe import RFETransformation
 from transformations.pca import PCATransformation

+
 def bet_sizing_with_meta_model(
-                            X: XDataFrame,
-                            input_predictions: pd.Series,
-                            y: ySeries,
-                            forward_returns: ForwardReturnSeries,
-                            model: Model,
-                            config: Config,
-                            model_suffix: str,
-                            from_index: Optional[pd.Timestamp],
-                            transformations_over_time: Optional[TransformationsOverTime] = None,
-                            preloaded_models: Optional[ModelOverTime] = None
-                        ) -> BetSizingWithMetaOutcome:
+    X: XDataFrame,
+    input_predictions: pd.Series,
+    y: ySeries,
+    forward_returns: ForwardReturnSeries,
+    model: Model,
+    config: Config,
+    model_suffix: str,
+    from_index: Optional[pd.Timestamp],
+    transformations_over_time: Optional[TransformationsOverTime] = None,
+    preloaded_models: Optional[ModelOverTime] = None,
+) -> BetSizingWithMetaOutcome:

    input_predictions.name = "model_predictions"
-    discretized_predictions = input_predictions.apply(discretize_threeway_threshold(0.33))
+    discretized_predictions = input_predictions.apply(
+        discretize_threeway_threshold(0.33)
+    )
    discretized_predictions.name = "model_discretized_predictions"

-    meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(equal_except_nan, axis = 1)
-    meta_X = pd.concat([X, input_predictions, discretized_predictions], axis = 1)
+    meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(
+        equal_except_nan, axis=1
+    )
+    meta_X = pd.concat([X, input_predictions, discretized_predictions], axis=1)

    if transformations_over_time is None:
        print("Preprocess transformations")
        transformations_over_time = walk_forward_process_transformations(
-            X = meta_X,
-            y = meta_y,
-            forward_returns = forward_returns,
-            expanding_window = config.expanding_window_meta,
-            window_size = config.sliding_window_size_meta,
-            retrain_every = config.retrain_every,
-            from_index = from_index,
-            transformations= [
+            X=meta_X,
+            y=meta_y,
+            forward_returns=forward_returns,
+            expanding_window=config.expanding_window_meta,
+            window_size=config.sliding_window_size_meta,
+            retrain_every=config.retrain_every,
+            from_index=from_index,
+            transformations=[
                get_scaler(config.scaler),
-                PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=config.sliding_window_size_meta),
-                RFETransformation(n_feature_to_select=40, model=default_feature_selector_classification)
+                PCATransformation(
+                    ratio_components_to_keep=0.5,
+                    sliding_window_size=config.sliding_window_size_meta,
+                ),
+                RFETransformation(
+                    n_feature_to_select=40,
+                    model=default_feature_selector_classification,
+                ),
            ],
        )

    meta_outcome = train_model(
-        ticker_to_predict = "prediction_correct",
-        X = meta_X,
-        y = meta_y,
-        forward_returns = forward_returns,
-        model = model,
-        expanding_window = config.expanding_window_meta,
-        sliding_window_size = config.sliding_window_size_meta,
-        retrain_every = config.retrain_every,
-        from_index = from_index,
-        no_of_classes = 'two',
-        level = 'meta',
-        output_stats = config.mode == 'training',
-        transformations_over_time = transformations_over_time,
-        model_over_time = preloaded_models,
+        ticker_to_predict="prediction_correct",
+        X=meta_X,
+        y=meta_y,
+        forward_returns=forward_returns,
+        model=model,
+        expanding_window=config.expanding_window_meta,
+        sliding_window_size=config.sliding_window_size_meta,
+        retrain_every=config.retrain_every,
+        from_index=from_index,
+        no_of_classes="two",
+        level="meta",
+        output_stats=config.mode == "training",
+        transformations_over_time=transformations_over_time,
+        model_over_time=preloaded_models,
    )

    meta_predictions = meta_outcome.predictions
-    bet_size = meta_outcome.probabilities.iloc[:,1]
+    bet_size = meta_outcome.probabilities.iloc[:, 1]
    avg_predictions_with_sizing = input_predictions * meta_predictions * bet_size

-    if config.mode == 'training':
+    if config.mode == "training":
        stats = evaluate_predictions(
-            forward_returns = forward_returns,
-            y_pred = avg_predictions_with_sizing,
-            y_true = y,
-            no_of_classes = 'three-balanced',
-            discretize=False
+            forward_returns=forward_returns,
+            y_pred=avg_predictions_with_sizing,
+            y_true=y,
+            no_of_classes="three-balanced",
+            discretize=False,
        )
        print(stats)
    else:
        stats = None
    model_id = "model_" + config.target_asset[1] + "_" + model_suffix

-    return BetSizingWithMetaOutcome(model_id, meta_outcome, transformations_over_time, avg_predictions_with_sizing, stats)
+    return BetSizingWithMetaOutcome(
+        model_id,
+        meta_outcome,
+        transformations_over_time,
+        avg_predictions_with_sizing,
+        stats,
+    )