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Rehaul organization of return values

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This commit is contained in:
robcaulk
2022-07-02 18:09:38 +02:00
parent 93e1410ed9
commit 106131ff0f
7 changed files with 429 additions and 292 deletions
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71
freqtrade/freqai/data_drawer.py
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@@ -163,21 +163,30 @@ class FreqaiDataDrawer:
# send pair to end of queue
self.pair_dict[pair]['priority'] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, dh, dataframe: DataFrame) -> None:
def set_initial_return_values(self, pair: str, dk, pred_df, do_preds) -> None:
"""
Set the initial return values to a persistent dataframe. This avoids needing to repredict on
historical candles, and also stores historical predictions despite retrainings (so stored
predictions are true predictions, not just inferencing on trained data)
"""
self.model_return_values[pair] = pd.DataFrame()
for label in dk.label_list:
self.model_return_values[pair][label] = pred_df[label]
self.model_return_values[pair][f'{label}_mean'] = dk.data['labels_mean'][label]
self.model_return_values[pair][f'{label}_std'] = dk.data['labels_std'][label]
self.model_return_values[pair] = dataframe
self.model_return_values[pair]['target_mean'] = dh.data['target_mean']
self.model_return_values[pair]['target_std'] = dh.data['target_std']
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = dh.DI_values
self.model_return_values[pair]['DI_values'] = dk.DI_values
self.model_return_values[pair]['do_predict'] = do_preds
def append_model_predictions(self, pair: str, predictions, do_preds,
target_mean, target_std, dh, len_df) -> None:
dk, len_df) -> None:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]['prediction']) - len_df
length_difference = len(self.model_return_values[pair]) - len_df
i = 0
if length_difference == 0:
@@ -185,30 +194,56 @@ class FreqaiDataDrawer:
elif length_difference > 0:
i = length_difference + 1
df = self.model_return_values[pair].shift(-i)
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
df['prediction'].iloc[-1] = predictions[-1]
for label in dk.label_list:
df[label].iloc[-1] = predictions[label].iloc[-1]
df[f"{label}_mean"].iloc[-1] = dk.data['labels_mean'][label]
df[f"{label}_std"].iloc[-1] = dk.data['labels_std'][label]
# df['prediction'].iloc[-1] = predictions[-1]
df['do_predict'].iloc[-1] = do_preds[-1]
df['target_mean'].iloc[-1] = target_mean
df['target_std'].iloc[-1] = target_std
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
df['DI_values'].iloc[-1] = dh.DI_values[-1]
df['DI_values'].iloc[-1] = dk.DI_values[-1]
if length_difference < 0:
prepend_df = pd.DataFrame(np.zeros((abs(length_difference) - 1, len(df.columns))),
columns=df.columns)
df = pd.concat([prepend_df, df], axis=0)
def return_null_values_to_strategy(self, dataframe: DataFrame, dh) -> None:
def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
"""
Attach the return values to the strat dataframe
:params:
dataframe: DataFrame = strat dataframe
:returns:
dataframe: DataFrame = strat dataframe with return values attached
"""
df = self.model_return_values[pair]
to_keep = [col for col in dataframe.columns if not col.startswith('&')]
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
return dataframe
dataframe['prediction'] = 0
def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
"""
Build 0 filled dataframe to return to strategy
"""
dk.find_features(dataframe)
for label in dk.label_list:
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0
# dataframe['prediction'] = 0
dataframe['do_predict'] = 0
dataframe['target_mean'] = 0
dataframe['target_std'] = 0
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dataframe['DI_value'] = 0
dk.return_dataframe = dataframe
def purge_old_models(self) -> None:
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
@@ -257,7 +292,7 @@ class FreqaiDataDrawer:
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
# json.dump(self.model_return_values, fp, default=self.np_encoder)
# def load_model_return_values_from_disk(self, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
# if exists:
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
@@ -268,4 +303,4 @@ class FreqaiDataDrawer:
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
# 'sending null values back to strategy')
# return exists, dh
# return exists, dk