avoid redundant indicator population for corr_pair list
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@@ -1126,6 +1126,49 @@ class FreqaiDataKitchen:
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if pair not in self.all_pairs:
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self.all_pairs.append(pair)
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def extract_corr_pair_columns_from_populated_indicators(
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self,
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dataframe: DataFrame
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) -> Dict[str, DataFrame]:
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"""
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Find the columns of the dataframe corresponding to the corr_pairlist, save them
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in a dictionary to be reused and attached to other pairs.
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:params:
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:dataframe: fully populated dataframe (current pair + corr_pairs)
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:return:
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:corr_dataframes: dictionary of dataframes to be attached to other pairs in same candle.
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"""
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corr_dataframes: Dict[str, DataFrame] = {}
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pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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for pair in pairs:
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coin = pair.split('/')[0]
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pair_cols = [col for col in dataframe.columns if coin in col]
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corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
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return corr_dataframes
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def attach_corr_pair_columns(self, dataframe: DataFrame,
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corr_dataframes: Dict[str, DataFrame],
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current_pair: str) -> DataFrame:
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"""
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Attach the existing corr_pair dataframes to the current pair dataframe before training
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:params:
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:dataframe: current pair strategy dataframe, indicators populated already
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:corr_dataframes: dictionary of saved dataframes from earlier in the same candle
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:current_pair: current pair to which we will attach corr pair dataframe
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:return:
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:dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
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ready for training
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"""
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pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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for pair in pairs:
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if current_pair not in pair:
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dataframe = pd.concat([dataframe, corr_dataframes[pair]], axis=1)
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return dataframe
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def use_strategy_to_populate_indicators(
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self,
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strategy: IStrategy,
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@@ -1133,6 +1176,7 @@ class FreqaiDataKitchen:
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base_dataframes: dict = {},
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pair: str = "",
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prediction_dataframe: DataFrame = pd.DataFrame(),
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do_corr_pairs: bool = True,
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) -> DataFrame:
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"""
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Use the user defined strategy for populating indicators during retrain
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@@ -1173,10 +1217,13 @@ class FreqaiDataKitchen:
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informative=base_dataframes[tf],
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set_generalized_indicators=sgi
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)
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if pairs:
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for i in pairs:
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if pair in i:
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continue # dont repeat anything from whitelist
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# ensure corr pairs are always last
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for i in pairs:
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if pair in i:
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continue # dont repeat anything from whitelist
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for tf in tfs:
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if pairs and do_corr_pairs:
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dataframe = strategy.populate_any_indicators(
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i,
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dataframe.copy(),
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