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Update missing typehints

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This commit is contained in:
Matthias 2022-07-28 07:07:40 +02:00
parent 3273881282
commit a2a0d35a24
2 changed files with 7 additions and 6 deletions
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9
freqtrade/freqai/data_drawer.py
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@ -8,10 +8,10 @@ from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from joblib import dump, load
from joblib.externals import cloudpickle
from numpy.typing import ArrayLike
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@ -219,7 +219,7 @@ class FreqaiDataDrawer:
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen,
pred_df: DataFrame, do_preds: npt.ArrayLike) -> None:
pred_df: DataFrame, do_preds: ArrayLike) -> 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
@ -238,7 +238,8 @@ class FreqaiDataDrawer:
mrv_df["do_predict"] = do_preds
def append_model_predictions(self, pair: str, predictions, do_preds, dk, len_df) -> None:
def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: ArrayLike,
dk: FreqaiDataKitchen, len_df: int) -> 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
@ -293,7 +294,7 @@ class FreqaiDataDrawer:
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
return dataframe
def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None:
"""
Build 0 filled dataframe to return to strategy
"""

4
freqtrade/freqai/freqai_interface.py
View File

@ -11,8 +11,8 @@ from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from numpy.typing import ArrayLike
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@ -548,7 +548,7 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
) -> Tuple[DataFrame, npt.ArrayLike]:
) -> Tuple[DataFrame, ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.