Update docstring format

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -78,7 +78,7 @@ class BaseClassifierModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -77,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/data_drawer.py

@@ -412,9 +412,8 @@ class FreqaiDataDrawer:
     def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
         """
         Saves all data associated with a model for a single sub-train time range
-        :params:
+        :param model: User trained model which can be reused for inferencing to generate
-        :model: User trained model which can be reused for inferencing to generate
+                      predictions
-        predictions
         """
 
         if not dk.data_path.is_dir():
@@ -532,8 +531,7 @@ class FreqaiDataDrawer:
         Append new candles to our stores historic data (in memory) so that
         we do not need to load candle history from disk and we dont need to
         pinging exchange multiple times for the same candle.
-        :params:
+        :param dataframe: DataFrame = strategy provided dataframe
-        dataframe: DataFrame = strategy provided dataframe
         """
         feat_params = self.freqai_info["feature_parameters"]
         with self.history_lock:
@@ -579,9 +577,8 @@ class FreqaiDataDrawer:
         """
         Load pair histories for all whitelist and corr_pairlist pairs.
         Only called once upon startup of bot.
-        :params:
+        :param timerange: TimeRange = full timerange required to populate all indicators
-        timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
-        for training according to user defined train_period_days
         """
         history_data = self.historic_data
 
@@ -604,10 +601,9 @@ class FreqaiDataDrawer:
         """
         Searches through our historic_data in memory and returns the dataframes relevant
         to the present pair.
-        :params:
+        :param timerange: TimeRange = full timerange required to populate all indicators
-        timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
-        for training according to user defined train_period_days
+        :param metadata: dict = strategy furnished pair metadata
-        metadata: dict = strategy furnished pair metadata
         """
         with self.history_lock:
             corr_dataframes: Dict[Any, Any] = {}

freqtrade/freqai/data_kitchen.py

@@ -107,9 +107,8 @@ class FreqaiDataKitchen:
     ) -> None:
         """
         Set the paths to the data for the present coin/botloop
-        :params:
+        :param metadata: dict = strategy furnished pair metadata
-        metadata: dict = strategy furnished pair metadata
+        :param trained_timestamp: int = timestamp of most recent training
-        trained_timestamp: int = timestamp of most recent training
         """
         self.full_path = Path(
             self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
@@ -129,8 +128,8 @@ class FreqaiDataKitchen:
         Given the dataframe for the full history for training, split the data into
         training and test data according to user specified parameters in configuration
         file.
-        :filtered_dataframe: cleaned dataframe ready to be split.
+        :param filtered_dataframe: cleaned dataframe ready to be split.
-        :labels: cleaned labels ready to be split.
+        :param labels: cleaned labels ready to be split.
         """
         feat_dict = self.freqai_config["feature_parameters"]
 
@@ -189,13 +188,14 @@ class FreqaiDataKitchen:
         remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
         0s in the prediction dataset. However, prediction dataset do_predict will reflect any
         row that had a NaN and will shield user from that prediction.
-        :params:
+
-        :unfiltered_df: the full dataframe for the present training period
+        :param unfiltered_df: the full dataframe for the present training period
-        :training_feature_list: list, the training feature list constructed by
+        :param training_feature_list: list, the training feature list constructed by
-        self.build_feature_list() according to user specified parameters in the configuration file.
+                                      self.build_feature_list() according to user specified
-        :labels: the labels for the dataset
+                                      parameters in the configuration file.
-        :training_filter: boolean which lets the function know if it is training data or
+        :param labels: the labels for the dataset
-        prediction data to be filtered.
+        :param training_filter: boolean which lets the function know if it is training data or
+                                prediction data to be filtered.
         :returns:
         :filtered_df: dataframe cleaned of NaNs and only containing the user
         requested feature set.
@@ -285,8 +285,8 @@ class FreqaiDataKitchen:
     def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
         """
         Normalize all data in the data_dictionary according to the training dataset
-        :params:
+        :param data_dictionary: dictionary containing the cleaned and
-        :data_dictionary: dictionary containing the cleaned and split training/test data/labels
+                                split training/test data/labels
         :returns:
         :data_dictionary: updated dictionary with standardized values.
         """
@@ -516,8 +516,7 @@ class FreqaiDataKitchen:
     def pca_transform(self, filtered_dataframe: DataFrame) -> None:
         """
         Use an existing pca transform to transform data into components
-        :params:
+        :param filtered_dataframe: DataFrame = the cleaned dataframe
-        filtered_dataframe: DataFrame = the cleaned dataframe
         """
         pca_components = self.pca.transform(filtered_dataframe)
         self.data_dictionary["prediction_features"] = pd.DataFrame(
@@ -561,8 +560,7 @@ class FreqaiDataKitchen:
         """
         Build/inference a Support Vector Machine to detect outliers
         in training data and prediction
-        :params:
+        :param predict: bool = If true, inference an existing SVM model, else construct one
-        predict: bool = If true, inference an existing SVM model, else construct one
         """
 
         if self.keras:
@@ -647,11 +645,11 @@ class FreqaiDataKitchen:
         Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
         User controls this via the config param `DBSCAN_outlier_pct` which indicates the
         pct of training data that they want to be considered outliers.
-        :params:
+        :param predict: bool = If False (training), iterate to find the best hyper parameters
-        predict: bool = If False (training), iterate to find the best hyper parameters to match
+                        to match user requested outlier percent target.
-        user requested outlier percent target. If True (prediction), use the parameters
+                        If True (prediction), use the parameters determined from
-        determined from the previous training to estimate if the current prediction point
+                        the previous training to estimate if the current prediction point
-        is an outlier.
+                        is an outlier.
         """
 
         if predict:
@@ -1118,15 +1116,13 @@ class FreqaiDataKitchen:
         prediction_dataframe: DataFrame = pd.DataFrame(),
     ) -> DataFrame:
         """
-        Use the user defined strategy for populating indicators during
+        Use the user defined strategy for populating indicators during retrain
-        retrain
+        :param strategy: IStrategy = user defined strategy object
-        :params:
+        :param corr_dataframes: dict = dict containing the informative pair dataframes
-        strategy: IStrategy = user defined strategy object
+                                (for user defined timeframes)
-        corr_dataframes: dict = dict containing the informative pair dataframes
+        :param base_dataframes: dict = dict containing the current pair dataframes
-        (for user defined timeframes)
+                                (for user defined timeframes)
-        base_dataframes: dict = dict containing the current pair dataframes
+        :param metadata: dict = strategy furnished pair metadata
-        (for user defined timeframes)
-        metadata: dict = strategy furnished pair metadata
         :returns:
         dataframe: DataFrame = dataframe containing populated indicators
         """

freqtrade/freqai/freqai_interface.py

@@ -603,11 +603,11 @@ class IFreqaiModel(ABC):
         If the user reuses an identifier on a subsequent instance,
         this function will not be called. In that case, "real" predictions
         will be appended to the loaded set of historic predictions.
-        :param: df: DataFrame = the dataframe containing the training feature data
+        :param df: DataFrame = the dataframe containing the training feature data
-        :param: model: Any = A model which was `fit` using a common library such as
+        :param model: Any = A model which was `fit` using a common library such as
-        catboost or lightgbm
+                      catboost or lightgbm
-        :param: dk: FreqaiDataKitchen = object containing methods for data analysis
+        :param dk: FreqaiDataKitchen = object containing methods for data analysis
-        :param: pair: str = current pair
+        :param pair: str = current pair
         """
 
         self.dd.historic_predictions[pair] = pred_df

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -64,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove