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Merge branch 'freqtrade:feat/freqai' into feat/freqai

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lolong 2022-07-19 00:14:57 +02:00 committed by GitHub
commit cd9be396dc
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1 changed files with 12 additions and 10 deletions
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22
freqtrade/freqai/data_drawer.py
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@ -80,7 +80,7 @@ class FreqaiDataDrawer:
if exists: if exists:
with open(self.full_path / str("historic_predictions.pkl"), "rb") as fp: with open(self.full_path / str("historic_predictions.pkl"), "rb") as fp:
self.historic_predictions = pickle.load(fp) self.historic_predictions = pickle.load(fp)
logger.info(f"Found existing historic predictions at {self.full_path}, but beware of " logger.info(f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for " "that statistics may be inaccurate if the bot has been offline for "
"an extended period of time.") "an extended period of time.")
elif not self.follow_mode: elif not self.follow_mode:
@ -93,14 +93,14 @@ class FreqaiDataDrawer:
return exists return exists
def save_drawer_to_disk(self): def save_historic_predictions_to_disk(self):
""" """
Save data drawer full of all pair model metadata in present model folder. Save data drawer full of all pair model metadata in present model folder.
""" """
with open(self.full_path / str("historic_predictions.pkl"), "wb") as fp: with open(self.full_path / str("historic_predictions.pkl"), "wb") as fp:
pickle.dump(self.historic_predictions, fp, protocol=pickle.HIGHEST_PROTOCOL) pickle.dump(self.historic_predictions, fp, protocol=pickle.HIGHEST_PROTOCOL)
def save_historic_predictions_to_disk(self): def save_drawer_to_disk(self):
""" """
Save data drawer full of all pair model metadata in present model folder. Save data drawer full of all pair model metadata in present model folder.
""" """
@ -235,13 +235,14 @@ class FreqaiDataDrawer:
i = length_difference + 1 i = length_difference + 1
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i) df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
hp_df = self.historic_predictions[pair]
# here are some pandas hula hoops to accommodate the possibility of a series if pair in self.historic_predictions:
# or dataframe depending number of labels requested by user hp_df = self.historic_predictions[pair]
nan_df = pd.DataFrame(np.nan, index=hp_df.index[-2:] + 2, columns=hp_df.columns) # here are some pandas hula hoops to accommodate the possibility of a series
hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0) # or dataframe depending number of labels requested by user
hp_df = pd.concat([hp_df, nan_df[-2:-1]], axis=0) nan_df = pd.DataFrame(np.nan, index=hp_df.index[-2:] + 2, columns=hp_df.columns)
hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
self.historic_predictions[pair] = hp_df[:-1]
for label in dk.label_list: for label in dk.label_list:
df[label].iloc[-1] = predictions[label].iloc[-1] df[label].iloc[-1] = predictions[label].iloc[-1]
@ -254,7 +255,8 @@ class FreqaiDataDrawer:
df["DI_values"].iloc[-1] = dk.DI_values[-1] df["DI_values"].iloc[-1] = dk.DI_values[-1]
# append the new predictions to persistent storage # append the new predictions to persistent storage
hp_df.iloc[-1] = df[label].iloc[-1] if pair in self.historic_predictions:
self.historic_predictions[pair].iloc[-1] = df[label].iloc[-1]
if length_difference < 0: if length_difference < 0:
prepend_df = pd.DataFrame( prepend_df = pd.DataFrame(