appease mypy

freqtrade/freqai/data_kitchen.py

@@ -8,6 +8,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Tuple
 
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 from joblib import dump, load
 from pandas import DataFrame
@@ -35,14 +36,14 @@ class FreqaiDataKitchen:
         self.data_dictionary: Dict[Any, Any] = {}
         self.config = config
         self.freqai_config = config["freqai"]
-        self.predictions = np.array([])
-        self.do_predict = np.array([])
-        self.target_mean = np.array([])
-        self.target_std = np.array([])
-        self.full_predictions = np.array([])
-        self.full_do_predict = np.array([])
-        self.full_target_mean = np.array([])
-        self.full_target_std = np.array([])
+        self.predictions: npt.ArrayLike = np.array([])
+        self.do_predict: npt.ArrayLike = np.array([])
+        self.target_mean: npt.ArrayLike = np.array([])
+        self.target_std: npt.ArrayLike = np.array([])
+        self.full_predictions: npt.ArrayLike = np.array([])
+        self.full_do_predict: npt.ArrayLike = np.array([])
+        self.full_target_mean: npt.ArrayLike = np.array([])
+        self.full_target_std: npt.ArrayLike = np.array([])
         self.model_path = Path()
         self.model_filename = ""
 
@@ -123,6 +124,7 @@ class FreqaiDataKitchen:
         :labels: cleaned labels ready to be split.
         """
 
+        weights: npt.ArrayLike
         if self.config["freqai"]["feature_parameters"]["weight_factor"] > 0:
             weights = self.set_weights_higher_recent(len(filtered_dataframe))
         else:
@@ -519,12 +521,13 @@ class FreqaiDataKitchen:
         self.do_predict += do_predict
         self.do_predict -= 1
 
-    def set_weights_higher_recent(self, num_weights: int) -> int:
+    def set_weights_higher_recent(self, num_weights: int) -> npt.ArrayLike:
         """
         Set weights so that recent data is more heavily weighted during
         training than older data.
         """
-        weights = np.zeros(num_weights)
+
+        weights = np.zeros_like(num_weights)
         for i in range(1, len(weights)):
             weights[len(weights) - i] = np.exp(
                 -i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights)

freqtrade/freqai/freqai_interface.py

@@ -4,10 +4,12 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Any, Dict, Tuple
 
-import numpy as np
+import numpy.typing as npt
 import pandas as pd
 from pandas import DataFrame
 
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RunMode
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 
 
@@ -37,8 +39,9 @@ class IFreqaiModel(ABC):
         self.current_time = None
         self.model = None
         self.predictions = None
+        self.live_trained_timerange = None
 
-    def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def start(self, dataframe: DataFrame, metadata: dict, dp: DataProvider) -> DataFrame:
         """
         Entry point to the FreqaiModel, it will train a new model if
         necesssary before making the prediction.
@@ -57,6 +60,9 @@ class IFreqaiModel(ABC):
         self.pair = metadata["pair"]
         self.dh = FreqaiDataKitchen(self.config, dataframe)
 
+        if dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            logger.info('testing live')
+
         logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
 
         # Loop enforcing the sliding window training/backtesting paragigm
@@ -99,7 +105,7 @@ class IFreqaiModel(ABC):
         :dataframe: the full dataframe for the present training period
         """
 
-        return dataframe
+        return
 
     @abstractmethod
     def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
@@ -124,8 +130,10 @@ class IFreqaiModel(ABC):
         all the training and test data/labels.
         """
 
+        return
+
     @abstractmethod
-    def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]:
+    def predict(self, dataframe: DataFrame) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
         """
         Filter the prediction features data and predict with it.
         :param: unfiltered_dataframe: Full dataframe for the current backtest period.