diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py
index 0e9d2e605..db535862d 100644
--- a/freqtrade/freqai/data_drawer.py
+++ b/freqtrade/freqai/data_drawer.py
@@ -98,6 +98,7 @@ class FreqaiDataDrawer:
                 "model_filename": "", "trained_timestamp": 0,
                 "data_path": "", "extras": {}}
         self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
+        self.current_candle: datetime = datetime.fromtimestamp(637887600)
 
     def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
         """
@@ -636,6 +637,8 @@ class FreqaiDataDrawer:
                         axis=0,
                     )
 
+            self.current_candle = history_data[dk.pair][self.config['timeframe']].iloc[-1]['date']
+
     def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
         """
         Load pair histories for all whitelist and corr_pairlist pairs.
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index dcf902954..219cee893 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -75,7 +75,7 @@ class IFreqaiModel(ABC):
         if self.keras and self.ft_params.get("DI_threshold", 0):
             self.ft_params["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
-        self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
+        self.CONV_WIDTH = self.freqai_info.get("conv_width", 1)
         if self.ft_params.get("inlier_metric_window", 0):
             self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
         self.pair_it = 0
@@ -93,7 +93,7 @@ class IFreqaiModel(ABC):
         # get_corr_dataframes is controlling the caching of corr_dataframes
         # for improved performance. Careful with this boolean.
         self.get_corr_dataframes: bool = True
-
+        self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
 
@@ -339,6 +339,7 @@ class IFreqaiModel(ABC):
         if self.dd.historic_data:
             self.dd.update_historic_data(strategy, dk)
             logger.debug(f'Updating historic data on pair {metadata["pair"]}')
+            self.track_current_candle()
 
         if not self.follow_mode:
 
@@ -683,8 +684,8 @@ class IFreqaiModel(ABC):
                                    " avoid blinding open trades and degrading performance.")
                 self.pair_it = 0
                 self.inference_time = 0
-                if self.corr_pairlist:
-                    self.get_corr_dataframes = True
+                # if self.corr_pairlist:
+                #     self.get_corr_dataframes = True
         return
 
     def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
@@ -766,6 +767,18 @@ class IFreqaiModel(ABC):
 
         return dataframe
 
+    def track_current_candle(self):
+        """
+        Checks if the latest candle appended by the datadrawer is
+        equivalent to the latest candle seen by FreqAI. If not, it
+        asks to refresh the cached corr_dfs, and resets the pair
+        counter.
+        """
+        if self.dd.current_candle > self.current_candle:
+            self.get_corr_dataframes = True
+            self.pair_it = 0
+            self.current_candle = self.dd.current_candle
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 
diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py
index c46f9e815..99eefe31b 100644
--- a/tests/freqai/test_freqai_interface.py
+++ b/tests/freqai/test_freqai_interface.py
@@ -192,6 +192,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+    df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
     for i in range(5):
         df[f'%-constant_{i}'] = i
         # df.loc[:, f'%-constant_{i}'] = i