From a2a0d35a24ebc55fa6be095d72dd8e59f1aea796 Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Thu, 28 Jul 2022 07:07:40 +0200
Subject: [PATCH] Update missing typehints

---
 freqtrade/freqai/data_drawer.py      | 9 +++++----
 freqtrade/freqai/freqai_interface.py | 4 ++--
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py
index 4f3c0062f..4d37ef8c1 100644
--- a/freqtrade/freqai/data_drawer.py
+++ b/freqtrade/freqai/data_drawer.py
@@ -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
         """
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 4bee3fefd..4c74be7ab 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -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.