Merge branch 'develop' into release/3.2.1

4 files changed, 111 insertions, 121 deletions

diff --git a/openbb_terminal/common/behavioural_analysis/reddit_view.py b/openbb_terminal/common/behavioural_analysis/reddit_view.py
index 8fc49af7506..2855c5349cc 100644
--- a/openbb_terminal/common/behavioural_analysis/reddit_view.py
+++ b/openbb_terminal/common/behavioural_analysis/reddit_view.py
@@ -282,8 +282,8 @@ def display_redditsent(
         Optionally specify the name of the sheet the data is exported to.
     export: str
         Format to export data
-    external_axes: Optional[List[plt.Axes]]
-        If supplied, expect 1 external axis
+    external_axes : bool, optional
+        Whether to return the figure object or not, by default False
     """
     fig = OpenBBFigure()
 
diff --git a/openbb_terminal/fixedincome/fred_view.py b/openbb_terminal/fixedincome/fred_view.py
index 97b279c0128..b5ba7cb9b0a 100644
--- a/openbb_terminal/fixedincome/fred_view.py
+++ b/openbb_terminal/fixedincome/fred_view.py
@@ -975,8 +975,8 @@ def display_yield_curve(
     ----------
     date: str
         Date to get curve for. If None, gets most recent date (format yyyy-mm-dd)
-    external_axes : Optional[List[plt.Axes]], optional
-        External axes (1 axis is expected in the list), by default None
+    external_axes : bool, optional
+        Whether to return the figure object or not, by default False
     raw : bool
         Output only raw data
     export : str
diff --git a/openbb_terminal/forecast/brnn_view.py b/openbb_terminal/forecast/brnn_view.py
index 9fcf49e5428..b59c06abefc 100644
--- a/openbb_terminal/forecast/brnn_view.py
+++ b/openbb_terminal/forecast/brnn_view.py
@@ -48,72 +48,67 @@ def display_brnn_forecast(
 ) -> Union[OpenBBFigure, None]:
     """Display BRNN forecast
 
-        Parameters
-        ----------
-        data: Union[pd.Series, pd.DataFrame]
-            Input Data
-        target_column: str
-            Target column to forecast. Defaults to "close".
-        dataset_name: str
-            The name of the ticker to be predicted
-        n_predict: int
-            Days to predict. Defaults to 5.
-        train_split: float
-            Train/val split. Defaults to 0.85.
-        past_covariates: str
-            Multiple secondary columns to factor in when forecasting. Defaults to None.
-        forecast_horizon: int
-            Forecast horizon when performing historical forecasting. Defaults to 5.
-        input_chunk_length: int
-            Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14.
-        output_chunk_length: int
-            The length of the forecast of the model. Defaults to 5.
-        model_type: str
-            Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM".
-        n_rnn_layers: int
-                Number of layers in the RNN module. Defaults to 1.
-        dropout: float
-            Fraction of neurons affected by Dropout. Defaults to 0.0.
-        batch_size: int
-            Number of time series (input and output sequences) used in each training pass. Defaults to 32.
-        n_epochs: int
-            Number of epochs over which to train the model. Defaults to 101.
-        learning_rate: float
-            Defaults to 1e-3.
-        model_save_name: str
-            Name for model. Defaults to "brnn_model".
-        force_reset: bool
-            If set to True, any previously-existing model with the same name will be reset
-            (all checkpoints will be discarded). Defaults to True.
-        save_checkpoints: bool
-            Whether or not to automatically save the untrained model and checkpoints from training.
-            Defaults to True.
-        sheet_name: str
-            Optionally specify the name of the sheet the data is exported to.
-        export: str
-            Format to export data
-        residuals: bool
-            Whether to show residuals for the model. Defaults to False.
-        forecast_only: bool
-            Whether to only show dates in the forecasting range. Defaults to False.
-        start_date: Optional[datetime]
-            The starting date to perform analysis, data before this is trimmed. Defaults to None.
-        end_date: Optional[datetime]
-            The ending date to perform analysis, data after this is trimmed. Defaults to None.
-        naive: bool
-            Whether to show the naive baseline. This just assumes the closing price will be the same
-            as the previous day's closing price. Defaults to False.
-    <<<<<<< HEAD
-        external_axes : bool, optional
-            Whether to return the figure object or not, by default False
-    =======
-        export_pred_raw: bool
-            Whether to export the raw predicted values. Defaults to False.
-        metric: str
-            The metric to use for the model. Defaults to "mape".
-        external_axes: Optional[List[plt.axes]]
-            External axes to plot on
-    >>>>>>> OpenBBTerminal-main
+    Parameters
+    ----------
+    data: Union[pd.Series, pd.DataFrame]
+        Input Data
+    target_column: str
+        Target column to forecast. Defaults to "close".
+    dataset_name: str
+        The name of the ticker to be predicted
+    n_predict: int
+        Days to predict. Defaults to 5.
+    train_split: float
+        Train/val split. Defaults to 0.85.
+    past_covariates: str
+        Multiple secondary columns to factor in when forecasting. Defaults to None.
+    forecast_horizon: int
+        Forecast horizon when performing historical forecasting. Defaults to 5.
+    input_chunk_length: int
+        Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14.
+    output_chunk_length: int
+        The length of the forecast of the model. Defaults to 5.
+    model_type: str
+        Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM".
+    n_rnn_layers: int
+            Number of layers in the RNN module. Defaults to 1.
+    dropout: float
+        Fraction of neurons affected by Dropout. Defaults to 0.0.
+    batch_size: int
+        Number of time series (input and output sequences) used in each training pass. Defaults to 32.
+    n_epochs: int
+        Number of epochs over which to train the model. Defaults to 101.
+    learning_rate: float
+        Defaults to 1e-3.
+    model_save_name: str
+        Name for model. Defaults to "brnn_model".
+    force_reset: bool
+        If set to True, any previously-existing model with the same name will be reset
+        (all checkpoints will be discarded). Defaults to True.
+    save_checkpoints: bool
+        Whether or not to automatically save the untrained model and checkpoints from training.
+        Defaults to True.
+    sheet_name: str
+        Optionally specify the name of the sheet the data is exported to.
+    export: str
+        Format to export data
+    residuals: bool
+        Whether to show residuals for the model. Defaults to False.
+    forecast_only: bool
+        Whether to only show dates in the forecasting range. Defaults to False.
+    start_date: Optional[datetime]
+        The starting date to perform analysis, data before this is trimmed. Defaults to None.
+    end_date: Optional[datetime]
+        The ending date to perform analysis, data after this is trimmed. Defaults to None.
+    naive: bool
+        Whether to show the naive baseline. This just assumes the closing price will be the same
+        as the previous day's closing price. Defaults to False.
+    export_pred_raw: bool
+        Whether to export the raw predicted values. Defaults to False.
+    metric: str
+        The metric to use for the model. Defaults to "mape".
+    external_axes : bool, optional
+        Whether to return the figure object or not, by default False
     """
     data = helpers.clean_data(
         data, start_date, end_date, target_column, past_covariates
diff --git a/openbb_terminal/forecast/expo_view.py b/openbb_terminal/forecast/expo_view.py
index 7231cd76aed..ab6d82b30b4 100644
--- a/openbb_terminal/forecast/expo_view.py
+++ b/openbb_terminal/forecast/expo_view.py
@@ -41,57 +41,52 @@ def display_expo_forecast(
 ) -> Union[OpenBBFigure, None]:
     """Display Probabilistic Exponential Smoothing forecast
 
-        Parameters
-        ----------
-        data : Union[pd.Series, np.array]
-            Data to forecast
-        dataset_name: str
-            The name of the ticker to be predicted
-        target_column: Optional[str]:
-            Target column to forecast. Defaults to "close".
-        trend: str
-            Trend component.  One of [N, A, M]
-            Defaults to ADDITIVE.
-        seasonal: str
-            Seasonal component.  One of [N, A, M]
-            Defaults to ADDITIVE.
-        seasonal_periods: int
-            Number of seasonal periods in a year
-            If not set, inferred from frequency of the series.
-        dampen: str
-            Dampen the function
-        n_predict: int
-            Number of days to forecast
-        start_window: float
-            Size of sliding window from start of timeseries and onwards
-        forecast_horizon: int
-            Number of days to forecast when backtesting and retraining historical
-        sheet_name: str
-            Optionally specify the name of the sheet the data is exported to.
-        export: str
-            Format to export data
-        residuals: bool
-            Whether to show residuals for the model. Defaults to False.
-        forecast_only: bool
-            Whether to only show dates in the forecasting range. Defaults to False.
-        start_date: Optional[datetime]
-            The starting date to perform analysis, data before this is trimmed. Defaults to None.
-        end_date: Optional[datetime]
-            The ending date to perform analysis, data after this is trimmed. Defaults to None.
-        naive: bool
-            Whether to show the naive baseline. This just assumes the closing price will be the same
-            as the previous day's closing price. Defaults to False.
-    <<<<<<< HEAD
-        external_axes : bool, optional
-            Whether to return the figure object or not, by default False
-    =======
-        export_pred_raw: bool
-            Whether to export the raw predicted values. Defaults to False.
-        metric: str
-            The metric to use when backtesting. Defaults to "mape".
-        external_axes: Optional[List[plt.axes]]
-            External axes to plot on
-    >>>>>>> OpenBBTerminal-main
+    Parameters
+    ----------
+    data : Union[pd.Series, np.array]
+        Data to forecast
+    dataset_name: str
+        The name of the ticker to be predicted
+    target_column: Optional[str]:
+        Target column to forecast. Defaults to "close".
+    trend: str
+        Trend component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal: str
+        Seasonal component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal_periods: int
+        Number of seasonal periods in a year
+        If not set, inferred from frequency of the series.
+    dampen: str
+        Dampen the function
+    n_predict: int
+        Number of days to forecast
+    start_window: float
+        Size of sliding window from start of timeseries and onwards
+    forecast_horizon: int
+        Number of days to forecast when backtesting and retraining historical
+    sheet_name: str
+        Optionally specify the name of the sheet the data is exported to.
+    export: str
+        Format to export data
+    residuals: bool
+        Whether to show residuals for the model. Defaults to False.
+    forecast_only: bool
+        Whether to only show dates in the forecasting range. Defaults to False.
+    start_date: Optional[datetime]
+        The starting date to perform analysis, data before this is trimmed. Defaults to None.
+    end_date: Optional[datetime]
+        The ending date to perform analysis, data after this is trimmed. Defaults to None.
+    naive: bool
+        Whether to show the naive baseline. This just assumes the closing price will be the same
+        as the previous day's closing price. Defaults to False.
+    export_pred_raw: bool
+        Whether to export the raw predicted values. Defaults to False.
+    metric: str
+        The metric to use when backtesting. Defaults to "mape".
+    external_axes : bool, optional
+        Whether to return the figure object or not, by default False
     """
     data = helpers.clean_data(data, start_date, end_date, target_column, None)
     if not helpers.check_data(data, target_column, None):