Feat/freqai (#7105)

Vectorize weight setting, log training dates

Co-authored-by: robcaulk <rob.caulk@gmail.com>

freqtrade/freqai/data_kitchen.py

@@ -680,12 +680,9 @@ class FreqaiDataKitchen:
         Set weights so that recent data is more heavily weighted during
         training than older data.
         """
-
-        weights = np.zeros(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)
-            )
+        wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
+        weights = np.exp(
+            - np.arange(num_weights) / (wfactor * num_weights))[::-1]
         return weights
 
     def append_predictions(self, predictions, do_predict, len_dataframe):

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -39,7 +39,7 @@ class BaseRegressionModel(IFreqaiModel):
         :model: Trained model which can be used to inference (self.predict)
         """
 
-        logger.info("--------------------Starting training " f"{pair} --------------------")
+        logger.info("-------------------- Starting training " f"{pair} --------------------")
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
@@ -49,6 +49,10 @@ class BaseRegressionModel(IFreqaiModel):
             training_filter=True,
         )
 
+        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
+        logger.info(f"-------------------- Training on data from {start_date} to "
+                    f"{end_date}--------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
         if not self.freqai_info.get('fit_live_predictions', 0):