Merge pull request #8203 from freqtrade/add-bufer-train-data-candles

Add buffer_train_data_candles feature

freqtrade/constants.py

@@ -569,7 +569,8 @@ CONF_SCHEMA = {
                                            "nu": {"type": "number", "default": 0.1}
                                            },
                                        },
-                        "shuffle_after_split": {"type": "boolean", "default": False}
+                        "shuffle_after_split": {"type": "boolean", "default": False},
+                        "buffer_train_data_candles": {"type": "integer", "default": 0}
                     },
                     "required": ["include_timeframes", "include_corr_pairlist", ]
                 },

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

freqtrade/freqai/freqai_interface.py

@@ -330,6 +330,8 @@ class IFreqaiModel(ABC):
                 dataframe_base_backtest = strategy.set_freqai_targets(
                     dataframe_base_backtest, metadata=metadata)
 
+                tr_train = dk.buffer_timerange(tr_train)
+
                 dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
                 dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
 
@@ -614,6 +616,8 @@ class IFreqaiModel(ABC):
             strategy, corr_dataframes, base_dataframes, pair
         )
 
+        new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
+
         unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
         # find the features indicated by strategy and store in datakitchen