diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py
index e6638d4fd..1692b4acf 100644
--- a/freqtrade/freqai/torch/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@@ -59,6 +59,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.data_convertor = data_convertor
         self.window_size: int = window_size
         self.tb_logger = tb_logger
+        self.test_batch_counter = 0
 
     def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
         """
@@ -83,8 +84,10 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             batch_size=self.batch_size,
             n_iters=self.max_iters,
         )
-        for epoch in range(1, n_epochs + 1):
-            for i, batch_data in enumerate(data_loaders_dictionary["train"]):
+
+        batch_counter = 0
+        for epoch in range(n_epochs):
+            for _, batch_data in enumerate(data_loaders_dictionary["train"]):
                 xb, yb = batch_data
                 xb = xb.to(self.device)
                 yb = yb.to(self.device)
@@ -94,7 +97,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
                 self.optimizer.zero_grad(set_to_none=True)
                 loss.backward()
                 self.optimizer.step()
-                self.tb_logger.log_scalar("train_loss", loss.item(), i)
+                self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
+                batch_counter += 1
 
             # evaluation
             if "test" in splits:
@@ -107,14 +111,15 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             split: str,
     ) -> None:
         self.model.eval()
-        for i, batch_data in enumerate(data_loader_dictionary[split]):
+        for _, batch_data in enumerate(data_loader_dictionary[split]):
             xb, yb = batch_data
             xb.to(self.device)
             yb.to(self.device)
 
             yb_pred = self.model(xb)
             loss = self.criterion(yb_pred, yb)
-            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), i)
+            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
+            self.test_batch_counter += 1
 
         self.model.train()