diff --git a/docs/freqai-running.md b/docs/freqai-running.md
index bb84bd533..42c56c06d 100644
--- a/docs/freqai-running.md
+++ b/docs/freqai-running.md
@@ -67,6 +67,12 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
     *want* to retrain a new model with the same config file, you should simply change the `identifier`.
     This way, you can return to using any model you wish by simply specifying the `identifier`.
 
+!!! Note
+    Backtesting calls the `set_freqai_targets()` function for every window defined in `backtest_period_days` parameter 
+    to better simulate the dry/run live behavior, but it's analyzes the whole time-range at once in `feature_engineering_*()` for performance reasons. 
+    Because of this, strategy authors need to make sure that strategies do not look-ahead into the future at `feature_engineering_*()` functions. 
+    Strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies)
+
 ---
 
 ### Saving prediction data
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index a0f29a301..c4e87176c 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -313,14 +313,8 @@ class IFreqaiModel(ABC):
                 dk.append_predictions(append_df)
             else:
                 if populate_indicators:
-                    tr_from_main_df = (f'{dataframe["date"].min().strftime("%Y%m%d")}'
-                                       f'-{dataframe["date"].max().strftime("%Y%m%d")}')
-                    timerange = TimeRange.parse_timerange(tr_from_main_df)
-                    self.dd.load_all_pair_histories(timerange, self.dk)
-                    corr_df, base_df = self.dd.get_base_and_corr_dataframes(timerange, pair, dk)
                     dataframe = self.dk.use_strategy_to_populate_indicators(
-                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"],
-                        corr_dataframes=corr_df, base_dataframes=base_df
+                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
                     )
                     populate_indicators = False