diff --git a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
index 846d6df2e..b1f2eecc6 100644
--- a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@@ -27,6 +27,12 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         ...
         "freqai": {
             ...
+            "conv_width": 30,  // PyTorchTransformer is based on windowing
+            "feature_parameters": {
+                ...
+                "include_shifted_candles": 0,  // which removes the need for shifted candles
+                ...
+            },
             "model_training_parameters" : {
                 "learning_rate": 3e-4,
                 "trainer_kwargs": {
@@ -120,16 +126,16 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         # create empty torch tensor
         self.model.model.eval()
         yb = torch.empty(0).to(self.device)
-        if x.shape[1] > 1:
+        if x.shape[1] > self.window_size:
             ws = self.window_size
             for i in range(0, x.shape[1] - ws):
                 xb = x[:, i:i + ws, :].to(self.device)
                 y = self.model.model(xb)
-                yb = torch.cat((yb, y), dim=0)
+                yb = torch.cat((yb, y), dim=1)
         else:
             yb = self.model.model(x)
 
-        yb = yb.cpu().squeeze()
+        yb = yb.cpu().squeeze(0)
         pred_df = pd.DataFrame(yb.detach().numpy(), columns=dk.label_list)
         pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)