diff --git a/docs/freqai-running.md b/docs/freqai-running.md
index 1eaee1bf2..f3ccc546f 100644
--- a/docs/freqai-running.md
+++ b/docs/freqai-running.md
@@ -128,6 +128,9 @@ The FreqAI specific parameter `label_period_candles` defines the offset (number
 
 You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `False` which means that all new models are trained from scratch, without input from previous models.
 
+???+ danger "Continual learning enforces a constant parameter space"
+    Since `continual_learning` means that the model parameter space *cannot* change between trainings, `principal_component_analysis` is automatically disabled when `continual_learning` is enabled. Hint: PCA changes the parameter space and the number of features, learn more about PCA [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
+
 ## Hyperopt
 
 You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 07c357de3..b657bd811 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -105,6 +105,9 @@ class IFreqaiModel(ABC):
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
         self.can_short = True  # overridden in start() with strategy.can_short
         self.model: Any = None
+        if self.ft_params.get('principal_component_analysis', False) and self.continual_learning:
+            self.ft_params.update({'principal_component_analysis': False})
+            logger.warning('User tried to use PCA with continual learning. Deactivating PCA.')
 
         record_params(config, self.full_path)
 
@@ -154,8 +157,7 @@ class IFreqaiModel(ABC):
                 dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
                 dataframe = dk.remove_features_from_df(dk.return_dataframe)
             else:
-                logger.info(
-                    "Backtesting using historic predictions (live models)")
+                logger.info("Backtesting using historic predictions (live models)")
                 dk = self.start_backtesting_from_historic_predictions(
                     dataframe, metadata, self.dk)
                 dataframe = dk.return_dataframe