allow users to buffer train data with buffer_train_data_candles parameter

freqtrade/freqai/data_kitchen.py

@@ -1562,3 +1562,25 @@ class FreqaiDataKitchen:
             dataframe.columns = dataframe.columns.str.replace(c, "")
 
         return dataframe
+
+    def buffer_timerange(self, timerange: TimeRange):
+        """
+        Buffer the start and end of the timerange. This is used *after* the indicators
+        are populated.
+
+        The main example use is when predicting maxima and minima, the argrelextrema
+        function  cannot know the maxima/minima at the edges of the timerange. To improve
+        model accuracy, it is best to compute argrelextrema on the full timerange
+        and then use this function to cut off the edges (buffer) by the kernel.
+
+        In another case, if the targets are set to a shifted price movement, this
+        buffer is unnecessary because the shifted candles at the end of the timerange
+        will be NaN and FreqAI will automatically cut those off of the training
+        dataset.
+        """
+        buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"]
+        if buffer:
+            timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"])
+            timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"])
+
+        return timerange