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refactoring backtesting_fit_live_predictions function

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Wagner Costa Santos 2022-11-09 09:51:42 -03:00
parent 3e57c18ac6
commit 8ee95db927
1 changed files with 14 additions and 25 deletions
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39
freqtrade/freqai/freqai_interface.py
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@ -827,37 +827,31 @@ class IFreqaiModel(ABC):
) )
def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen): def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
start_time = time.perf_counter() """
Apply fit_live_predictions function in backtesting with a dummy historic_predictions
:param dk: datakitchen object
"""
fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0) fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
if fit_live_predictions_candles: if fit_live_predictions_candles:
predictions_columns = [col for col in dk.full_df.columns if ( label_columns = [col for col in dk.full_df.columns if (
col.startswith("&") and col.startswith("&") and
'_mean' not in col and not (col.startswith("&") and col.endswith("_mean")) and
'_std' not in col and not (col.startswith("&") and col.endswith("_std")) and
col not in self.dk.data["extra_returns_per_train"]) col not in self.dk.data["extra_returns_per_train"])
] ]
self.dd.historic_predictions[self.dk.pair] = pd.DataFrame( self.dd.historic_predictions[self.dk.pair] = pd.DataFrame(
columns=dk.full_df.columns).astype(dk.full_df.dtypes) columns=dk.full_df.columns).astype(dk.full_df.dtypes)
# for index, row in dk.full_df.iterrows():
for index in range(len(dk.full_df)): for index in range(len(dk.full_df)):
if index > fit_live_predictions_candles: if index >= fit_live_predictions_candles:
self.dd.historic_predictions[self.dk.pair] = ( self.dd.historic_predictions[self.dk.pair] = (
dk.full_df.iloc[index - fit_live_predictions_candles + 1:index + 1]) dk.full_df.iloc[index - fit_live_predictions_candles:index])
else: else:
self.dd.historic_predictions[self.dk.pair] = dk.full_df.iloc[:index + 1] self.dd.historic_predictions[self.dk.pair] = dk.full_df.iloc[:index]
# self.dd.historic_predictions[self.dk.pair].loc[index] = row.values.tolist()
# pd.concat(self.dd.historic_predictions[self.dk.pair], row.values)
self.fit_live_predictions(self.dk, self.dk.pair) self.fit_live_predictions(self.dk, self.dk.pair)
if index > fit_live_predictions_candles: if index >= fit_live_predictions_candles:
print(index) for label in label_columns:
if index <= fit_live_predictions_candles:
dk.full_df.at[index, "warmed_up"] = 0
else:
dk.full_df.at[index, "warmed_up"] = 1
for label in predictions_columns:
if dk.full_df[label].dtype == object: if dk.full_df[label].dtype == object:
continue continue
if "labels_mean" in self.dk.data: if "labels_mean" in self.dk.data:
@ -869,13 +863,8 @@ class IFreqaiModel(ABC):
for extra_col in self.dk.data["extra_returns_per_train"]: for extra_col in self.dk.data["extra_returns_per_train"]:
dk.full_df.at[index, f"{extra_col}"] = ( dk.full_df.at[index, f"{extra_col}"] = (
self.dk.data["extra_returns_per_train"][extra_col]) self.dk.data["extra_returns_per_train"][extra_col])
end_time = time.perf_counter()
logger.info(f"Downloaded the tutorial in {start_time - end_time:0.4f} seconds")
# print(f"Downloaded the tutorial in {start_time - end_time:0.4f} seconds")
return return
# Following methods which are overridden by user made prediction models. # Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example. # See freqai/prediction_models/CatboostPredictionModel.py for an example.