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bring back auto DF resizing for okx

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This commit is contained in:
robcaulk 2022-08-08 01:13:13 +02:00
parent 67c722c9c8
commit ea64f43e52
1 changed files with 12 additions and 13 deletions
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25
freqtrade/freqai/data_drawer.py
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@ -279,15 +279,15 @@ class FreqaiDataDrawer:
# own return array in the same shape, we need to figure out how the size has changed # own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly. # and adapt our stored/returned info accordingly.
# length_difference = len(self.model_return_values[pair]) - len_df length_difference = len(self.model_return_values[pair]) - len_df
# i = 0 i = 0
# if length_difference == 0: if length_difference == 0:
# i = 1 i = 1
# elif length_difference > 0: elif length_difference > 0:
# i = length_difference + 1 i = length_difference + 1
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-1) df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
if pair in self.historic_predictions: if pair in self.historic_predictions:
hp_df = self.historic_predictions[pair] hp_df = self.historic_predictions[pair]
@ -320,11 +320,11 @@ class FreqaiDataDrawer:
for key in df.keys(): for key in df.keys():
self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1] self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1]
# if length_difference < 0: if length_difference < 0:
# prepend_df = pd.DataFrame( prepend_df = pd.DataFrame(
# np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
# ) )
# df = pd.concat([prepend_df, df], axis=0) df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe( def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame: self, pair: str, dataframe: DataFrame) -> DataFrame:
@ -355,7 +355,6 @@ class FreqaiDataDrawer:
dataframe[f"{label}_mean"] = 0 dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0 dataframe[f"{label}_std"] = 0
# dataframe['prediction'] = 0
dataframe["do_predict"] = 0 dataframe["do_predict"] = 0
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: