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Merge branch 'feat/freqai' of github.com:lolongcovas/freqtrade into refactor_set_weights_higher_recent

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longyu
2022-07-19 00:15:15 +02:00
parent 2fdea6d43a cd9be396dc
commit 703d913a3c
42 changed files with 13339 additions and 12232 deletions
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24
freqtrade/freqai/data_drawer.py
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@@ -76,11 +76,11 @@ class FreqaiDataDrawer:
:returns:
exists: bool = whether or not the drawer was located
"""
exists = Path(self.full_path / str("historic_predictions.json")).resolve().exists()
exists = Path(self.full_path / str("historic_predictions.pkl")).resolve().exists()
if exists:
with open(self.full_path / str("historic_predictions.pkl"), "rb") as 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 "
"an extended period of time.")
elif not self.follow_mode:
@@ -93,14 +93,14 @@ class FreqaiDataDrawer:
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.
"""
with open(self.full_path / str("historic_predictions.pkl"), "wb") as fp:
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.
"""
@@ -235,13 +235,14 @@ class FreqaiDataDrawer:
i = length_difference + 1
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
# or dataframe depending number of labels requested by user
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)
hp_df = pd.concat([hp_df, nan_df[-2:-1]], axis=0)
if pair in self.historic_predictions:
hp_df = self.historic_predictions[pair]
# here are some pandas hula hoops to accommodate the possibility of a series
# or dataframe depending number of labels requested by user
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:
df[label].iloc[-1] = predictions[label].iloc[-1]
@@ -254,7 +255,8 @@ class FreqaiDataDrawer:
df["DI_values"].iloc[-1] = dk.DI_values[-1]
# 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:
prepend_df = pd.DataFrame(