remove populate_any_indicators
This commit is contained in:
robcaulk
parent
352f4962da
commit
fd4e27d889
@ -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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@ -1,4 +1,3 @@
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import inspect
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import logging
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import threading
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import time
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@ -106,8 +105,6 @@ class IFreqaiModel(ABC):
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self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
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self.can_short = True # overridden in start() with strategy.can_short
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self.warned_deprecated_populate_any_indicators = False
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record_params(config, self.full_path)
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def __getstate__(self):
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@ -138,9 +135,6 @@ class IFreqaiModel(ABC):
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self.data_provider = strategy.dp
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self.can_short = strategy.can_short
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# check if the strategy has deprecated populate_any_indicators function
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self.check_deprecated_populate_any_indicators(strategy)
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if self.live:
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self.inference_timer('start')
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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@ -489,7 +483,7 @@ class IFreqaiModel(ABC):
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"strategy is furnishing the same features as the pretrained"
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"model. In case of --strategy-list, please be aware that FreqAI "
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"requires all strategies to maintain identical "
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"populate_any_indicator() functions"
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"feature_engineering_* functions"
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)
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def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
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@ -601,7 +595,7 @@ class IFreqaiModel(ABC):
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:param strategy: IStrategy = user defined strategy object
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:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
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:param data_load_timerange: TimeRange = the amount of data to be loaded
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for populate_any_indicators
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for populating indicators
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(larger than new_trained_timerange so that
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new_trained_timerange does not contain any NaNs)
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"""
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@ -806,7 +800,7 @@ class IFreqaiModel(ABC):
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logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
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"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
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"is included in the column names when you are creating features "
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"in `populate_any_indicators()`.")
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"in `feature_engineering_*` functions.")
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self.get_corr_dataframes = not bool(self.corr_dataframes)
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elif self.corr_dataframes:
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dataframe = dk.attach_corr_pair_columns(
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@ -933,26 +927,6 @@ class IFreqaiModel(ABC):
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dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
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return dk
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def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
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"""
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Check and warn if the deprecated populate_any_indicators function is used.
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:param strategy: strategy object
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"""
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if not self.warned_deprecated_populate_any_indicators:
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self.warned_deprecated_populate_any_indicators = True
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old_version = inspect.getsource(strategy.populate_any_indicators) != (
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inspect.getsource(IStrategy.populate_any_indicators))
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if old_version:
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logger.warning("DEPRECATION WARNING: "
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"You are using the deprecated populate_any_indicators function. "
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"This function will raise an error on March 1 2023. "
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"Please update your strategy by using "
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"the new feature_engineering functions. See \n"
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"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
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"for details.")
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# Following methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModel.py for an example.
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@ -93,7 +93,7 @@ class Backtesting:
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if self.config.get('strategy_list'):
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if self.config.get('freqai', {}).get('enabled', False):
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logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
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"to have identical populate_any_indicators.")
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"to have identical feature_engineering_* functions.")
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for strat in list(self.config['strategy_list']):
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stratconf = deepcopy(self.config)
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stratconf['strategy'] = strat
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@ -35,8 +35,8 @@ def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir, c
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args = get_args(args)
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bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
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Backtesting(bt_config)
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assert log_has_re('Using --strategy-list with FreqAI REQUIRES all strategies to have identical '
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'populate_any_indicators.', caplog)
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assert log_has_re('Using --strategy-list with FreqAI REQUIRES all strategies to have identical',
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caplog)
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Backtesting.cleanup()
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@ -291,18 +291,6 @@ def test_advise_all_indicators(default_conf, testdatadir) -> None:
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assert len(processed['UNITTEST/BTC']) == 103
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def test_populate_any_indicators(default_conf, testdatadir) -> None:
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strategy = StrategyResolver.load_strategy(default_conf)
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timerange = TimeRange.parse_timerange('1510694220-1510700340')
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data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
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fill_up_missing=True)
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processed = strategy.populate_any_indicators('UNITTEST/BTC', data, '5m')
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assert processed == data
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assert id(processed) == id(data)
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assert len(processed['UNITTEST/BTC']) == 103
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def test_freqai_not_initialized(default_conf) -> None:
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strategy = StrategyResolver.load_strategy(default_conf)
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strategy.ft_bot_start()
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