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freqai bt fix populate any indicators call - first commit

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This commit is contained in:
Wagner Costa
2022-12-20 20:26:27 -03:00
parent 3012c55ec5
commit cc796cc594
4 changed files with 104 additions and 63 deletions
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7
freqtrade/freqai/data_kitchen.py
View File

@@ -1184,6 +1184,7 @@ class FreqaiDataKitchen:
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
set_only_targets: bool = False
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
@@ -1222,7 +1223,8 @@ class FreqaiDataKitchen:
dataframe.copy(),
tf,
informative=base_dataframes[tf],
set_generalized_indicators=sgi
set_generalized_indicators=sgi,
set_only_targets=set_only_targets
)
# ensure corr pairs are always last
@@ -1235,7 +1237,8 @@ class FreqaiDataKitchen:
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[corr_pair][tf]
informative=corr_dataframes[corr_pair][tf],
set_only_targets=set_only_targets
)
self.get_unique_classes_from_labels(dataframe)

48
freqtrade/freqai/freqai_interface.py
View File

@@ -149,12 +149,13 @@ class IFreqaiModel(ABC):
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
# dataframe = self.dk.use_strategy_to_populate_indicators(
# strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
# )
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
@@ -255,7 +256,7 @@ class IFreqaiModel(ABC):
self.dd.save_metric_tracker_to_disk()
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
) -> FreqaiDataKitchen:
"""
The main broad execution for backtesting. For backtesting, each pair enters and then gets
@@ -267,12 +268,14 @@ class IFreqaiModel(ABC):
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param strategy: Strategy to train on
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
self.pair_it += 1
train_it = 0
set_only_targets = False
# Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly
@@ -301,14 +304,39 @@ class IFreqaiModel(ABC):
dk.set_new_model_names(pair, timestamp_model_id)
if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
self.dd.load_metadata(dk)
dk.find_features(dataframe)
self.check_if_feature_list_matches_strategy(dk)
# self.dd.load_metadata(dk)
# dk.find_features(dataframe)
# self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
if set_only_targets is False:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe,
pair=metadata["pair"], set_only_targets=set_only_targets
)
set_only_targets = True
dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
dataframe_base_train = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe_base_train,
pair=metadata["pair"], set_only_targets=set_only_targets
)
# region need to verify if this block is needed
# (or slice dataframe_backtest from dataframe)
dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
dataframe_base_backtest = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe_base_backtest,
pair=metadata["pair"], set_only_targets=set_only_targets
)
# endregion
set_only_targets = True
dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)