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add fix_live_predictions function to backtesting

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Wagner Costa Santos 2022-11-08 18:20:39 -03:00
parent 8d9988a942
commit 3e57c18ac6
2 changed files with 53 additions and 1 deletions
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2
freqtrade/freqai/data_kitchen.py
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@ -1014,7 +1014,7 @@ class FreqaiDataKitchen:
if self.full_df.empty:
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, append_df], axis=0)
self.full_df = pd.concat([self.full_df, append_df], axis=0, ignore_index=True)
def fill_predictions(self, dataframe):
"""

52
freqtrade/freqai/freqai_interface.py
View File

@ -305,6 +305,7 @@ class IFreqaiModel(ABC):
dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
self.backtesting_fit_live_predictions(dk)
dk.fill_predictions(dataframe)
return dk
@ -824,6 +825,57 @@ class IFreqaiModel(ABC):
f"to {tr_train_stopts_str}, {train_it}/{total_trains} "
"trains"
)
def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
start_time = time.perf_counter()
fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
if fit_live_predictions_candles:
predictions_columns = [col for col in dk.full_df.columns if (
col.startswith("&") and
'_mean' not in col and
'_std' not in col and
col not in self.dk.data["extra_returns_per_train"])
]
self.dd.historic_predictions[self.dk.pair] = pd.DataFrame(
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)):
if index > fit_live_predictions_candles:
self.dd.historic_predictions[self.dk.pair] = (
dk.full_df.iloc[index - fit_live_predictions_candles + 1:index + 1])
else:
self.dd.historic_predictions[self.dk.pair] = dk.full_df.iloc[:index + 1]
# 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)
if index > fit_live_predictions_candles:
print(index)
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:
continue
if "labels_mean" in self.dk.data:
dk.full_df.at[index, f"{label}_mean"] = (
self.dk.data["labels_mean"][label])
if "labels_std" in self.dk.data:
dk.full_df.at[index, f"{label}_std"] = self.dk.data["labels_std"][label]
for extra_col in self.dk.data["extra_returns_per_train"]:
dk.full_df.at[index, f"{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
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.