fix typos - live predictions candles

docs/freqai-configuration.md

@@ -192,11 +192,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
 ```
 
-To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
+To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
 
 ```json
     "freqai": {
-        "fit_live_prediction_candles": 300,
+        "fit_live_predictions_candles": 300,
     }
 ```
 

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
                     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) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
                     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) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -47,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
                     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) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)