fix(Training): major bug in scaling, we only scaled training data, not the ones we're predicting from (#45)

training/walk_forward.py

@@ -21,16 +21,14 @@ def walk_forward_train_test(
     first_nonzero_return = max(get_first_valid_return_index(target_returns), get_first_valid_return_index(X.iloc[:,0]))
     train_from = first_nonzero_return + window_size + 1
     train_till = len(y)
-    
-    iterations_since_retrain = 0
+    iterations_before_retrain = 0
+
     if scaler is not None:
         scaler = clone(scaler)
 
     for index in range(train_from, train_till):
 
-        iterations_since_retrain += 1
-
-        if iterations_since_retrain >= retrain_every or pd.isna(models[index-1]):
+        if iterations_before_retrain <= 0 or pd.isna(models[index-1]):
             train_window_start = index - window_size - 1
             train_window_end = index - 1
             
@@ -38,26 +36,30 @@ def walk_forward_train_test(
                 # First we need to fit on the expanding window data slice
                 # This is our only way to avoid lookahead bia
                 X_expanding_window = X[first_nonzero_return:train_window_end]
-                scaler.fit(X_expanding_window)
+                scaler.fit(X_expanding_window.values)
 
             X_slice = X[train_window_start:train_window_end]
             y_slice = y[train_window_start:train_window_end]
 
             if scaler is not None:
-                X_slice = scaler.transform(X_slice)
+                X_slice = scaler.transform(X_slice.values)
             else:
                 X_slice = X_slice.to_numpy()
 
             current_model = clone(model)
             current_model.fit(X_slice, y_slice.to_numpy())
-            iterations_since_retrain = 0
+            iterations_before_retrain = retrain_every
         else:
             current_model = models[index-1]
 
         models[index] = current_model
 
         next_timestep = X.iloc[index].to_numpy().reshape(1, -1)
+        if scaler is not None:
+            next_timestep = scaler.transform(next_timestep)
+
         prediction = current_model.predict(next_timestep).item()
         predictions[index] = prediction
+        iterations_before_retrain -= 1
 
     return models, predictions