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adjust backtest to new feature engineering functions

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This commit is contained in:
Wagner Costa 2022-12-28 19:03:41 -03:00
parent 6f7eb71bbb
commit 2b89f643b7
1 changed files with 21 additions and 11 deletions
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32
freqtrade/freqai/freqai_interface.py
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@ -149,12 +149,9 @@ 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"]
)
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 +252,7 @@ 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 +264,14 @@ 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
populate_indicators = True
# 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 +300,26 @@ 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 populate_indicators:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
populate_indicators = False
dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
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)
@ -913,7 +924,6 @@ class IFreqaiModel(ABC):
dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
dk.return_dataframe = pd.merge(
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
return dk
# Following methods which are overridden by user made prediction models.