remove populate_any_indicators
This commit is contained in:
robcaulk
@@ -1315,123 +1315,54 @@ class FreqaiDataKitchen:
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dataframe: DataFrame = dataframe containing populated indicators
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"""
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# this is a hack to check if the user is using the populate_any_indicators function
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# check if the user is using the deprecated populate_any_indicators function
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new_version = inspect.getsource(strategy.populate_any_indicators) == (
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inspect.getsource(IStrategy.populate_any_indicators))
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if new_version:
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tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
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pairs: List[str] = self.freqai_config["feature_parameters"].get(
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"include_corr_pairlist", [])
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if not new_version:
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raise OperationalException(
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"You are using the `populate_any_indicators()` function"
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" which was deprecated on March 1, 2023. Please refer "
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"to the strategy migration guide to use the new "
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"feature_engineering_* methods: \n"
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"https://www.freqtrade.io/en/stable/strategy_migration/#freqai-strategy \n"
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"And the feature_engineering_* documentation: \n"
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"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
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)
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for tf in tfs:
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if tf not in base_dataframes:
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base_dataframes[tf] = pd.DataFrame()
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for p in pairs:
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if p not in corr_dataframes:
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corr_dataframes[p] = {}
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if tf not in corr_dataframes[p]:
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corr_dataframes[p][tf] = pd.DataFrame()
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if not prediction_dataframe.empty:
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dataframe = prediction_dataframe.copy()
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else:
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dataframe = base_dataframes[self.config["timeframe"]].copy()
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corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
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"include_corr_pairlist", [])
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dataframe = self.populate_features(dataframe.copy(), pair, strategy,
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corr_dataframes, base_dataframes)
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metadata = {"pair": pair}
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dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
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# ensure corr pairs are always last
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for corr_pair in corr_pairs:
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if pair == corr_pair:
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continue # dont repeat anything from whitelist
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if corr_pairs and do_corr_pairs:
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dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
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corr_dataframes, base_dataframes, True)
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dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
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self.get_unique_classes_from_labels(dataframe)
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dataframe = self.remove_special_chars_from_feature_names(dataframe)
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if self.config.get('reduce_df_footprint', False):
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dataframe = reduce_dataframe_footprint(dataframe)
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return dataframe
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else:
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# the user is using the populate_any_indicators functions which is deprecated
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df = self.use_strategy_to_populate_indicators_old_version(
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strategy, corr_dataframes, base_dataframes, pair,
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prediction_dataframe, do_corr_pairs)
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return df
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def use_strategy_to_populate_indicators_old_version(
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self,
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strategy: IStrategy,
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corr_dataframes: dict = {},
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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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:param strategy: IStrategy = user defined strategy object
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:param corr_dataframes: dict = dict containing the df pair dataframes
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(for user defined timeframes)
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:param base_dataframes: dict = dict containing the current pair dataframes
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(for user defined timeframes)
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:param metadata: dict = strategy furnished pair metadata
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:return:
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dataframe: DataFrame = dataframe containing populated indicators
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"""
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# for prediction dataframe creation, we let dataprovider handle everything in the strategy
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# so we create empty dictionaries, which allows us to pass None to
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# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
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tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
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pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
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pairs: List[str] = self.freqai_config["feature_parameters"].get(
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"include_corr_pairlist", [])
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for tf in tfs:
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if tf not in base_dataframes:
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base_dataframes[tf] = pd.DataFrame()
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for p in pairs:
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if p not in corr_dataframes:
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corr_dataframes[p] = {}
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if tf not in corr_dataframes[p]:
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corr_dataframes[p][tf] = pd.DataFrame()
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if not prediction_dataframe.empty:
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dataframe = prediction_dataframe.copy()
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for tf in tfs:
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base_dataframes[tf] = None
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for p in pairs:
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if p not in corr_dataframes:
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corr_dataframes[p] = {}
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corr_dataframes[p][tf] = None
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else:
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dataframe = base_dataframes[self.config["timeframe"]].copy()
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sgi = False
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for tf in tfs:
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if tf == tfs[-1]:
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sgi = True # doing this last allows user to use all tf raw prices in labels
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dataframe = strategy.populate_any_indicators(
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pair,
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dataframe.copy(),
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tf,
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informative=base_dataframes[tf],
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set_generalized_indicators=sgi
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)
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corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
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"include_corr_pairlist", [])
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dataframe = self.populate_features(dataframe.copy(), pair, strategy,
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corr_dataframes, base_dataframes)
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metadata = {"pair": pair}
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dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
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# ensure corr pairs are always last
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for corr_pair in pairs:
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for corr_pair in corr_pairs:
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if pair == corr_pair:
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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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corr_pair,
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dataframe.copy(),
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tf,
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informative=corr_dataframes[corr_pair][tf]
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)
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if corr_pairs and do_corr_pairs:
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dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
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corr_dataframes, base_dataframes, True)
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dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
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self.get_unique_classes_from_labels(dataframe)
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