diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py
index 68e7ea49a..02cfdd882 100644
--- a/tests/freqai/conftest.py
+++ b/tests/freqai/conftest.py
@@ -83,6 +83,22 @@ def make_rl_config(conf):
     return conf
 
 
+def mock_pytorch_mlp_model_training_parameters(conf):
+    return {
+            "learning_rate": 3e-4,
+            "trainer_kwargs": {
+                "max_iters": 1,
+                "batch_size": 64,
+                "max_n_eval_batches": 1,
+            },
+            "model_kwargs": {
+                "hidden_dim": 32,
+                "dropout_percent": 0.2,
+                "n_layer": 1,
+            }
+        }
+
+
 def get_patched_data_kitchen(mocker, freqaiconf):
     dk = FreqaiDataKitchen(freqaiconf)
     return dk
diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py
index b4d808af2..5b460cda1 100644
--- a/tests/freqai/test_freqai_interface.py
+++ b/tests/freqai/test_freqai_interface.py
@@ -89,19 +89,8 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
 
     if 'PyTorchMLPRegressor' in model:
         model_save_ext = 'zip'
-        freqai_conf['freqai']['model_training_parameters'].update({
-            "learning_rate": 3e-4,
-            "trainer_kwargs": {
-                "max_iters": 1,
-                "batch_size": 64,
-                "max_n_eval_batches": 1,
-            },
-            "model_kwargs": {
-                "hidden_dim": 32,
-                "dropout_percent": 0.2,
-                "n_layer": 1,
-            }
-        })
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
 
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
@@ -214,19 +203,8 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
                                         strategy, freqai.dk, data_load_timerange)
 
     if 'PyTorchMLPClassifier':
-        freqai_conf['freqai']['model_training_parameters'].update({
-            "learning_rate": 3e-4,
-            "trainer_kwargs": {
-                "max_iters": 1,
-                "batch_size": 64,
-                "max_n_eval_batches": 1,
-            },
-            "model_kwargs": {
-                "hidden_dim": 32,
-                "dropout_percent": 0.2,
-                "n_layer": 1,
-            }
-        })
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
 
     if freqai.dd.model_type == 'joblib':
         model_file_extension = ".joblib"
@@ -251,10 +229,12 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
         ("LightGBMRegressor", 2, "freqai_test_strat"),
         ("XGBoostRegressor", 2, "freqai_test_strat"),
         ("CatboostRegressor", 2, "freqai_test_strat"),
+        ("PyTorchMLPRegressor", 2, "freqai_test_strat"),
         ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
         ("XGBoostClassifier", 2, "freqai_test_classifier"),
         ("LightGBMClassifier", 2, "freqai_test_classifier"),
-        ("CatboostClassifier", 2, "freqai_test_classifier")
+        ("CatboostClassifier", 2, "freqai_test_classifier"),
+        ("PyTorchMLPClassifier", 2, "freqai_test_classifier")
     ],
     )
 def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
@@ -275,6 +255,10 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     if 'test_4ac' in model:
         freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
 
+    if 'PyTorchMLP' in model:
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
+
     freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
         {"indicator_periods_candles": [2]})