diff --git a/.gitignore b/.gitignore
index f34aa31f0eb..502886f06cc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,3 +61,6 @@ darts_logs/
 # User data
 custom_imports/*.csv
 custom_imports/*/*.csv
+
+# lightning logs
+lightning_logs/
diff --git a/build/conda/conda-3-9-env-full.yaml b/build/conda/conda-3-9-env-full.yaml
index 33392f4c800..634a3c6692e 100644
--- a/build/conda/conda-3-9-env-full.yaml
+++ b/build/conda/conda-3-9-env-full.yaml
@@ -5,4 +5,4 @@ dependencies:
   - python=3.9.6
   - pip
   - poetry=1.1.13
-  - lightgbm=3.3.3
+  - lightgbm=3.3.5
diff --git a/openbb_terminal/forecast/brnn_model.py b/openbb_terminal/forecast/brnn_model.py
index 4a1341f0a65..2cef8210b7c 100644
--- a/openbb_terminal/forecast/brnn_model.py
+++ b/openbb_terminal/forecast/brnn_model.py
@@ -38,6 +38,7 @@ def get_brnn_data(
     model_save_name: str = "brnn_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -84,6 +85,8 @@ def get_brnn_data(
         discarded). Defaults to True.
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -174,4 +177,5 @@ def get_brnn_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/brnn_view.py b/openbb_terminal/forecast/brnn_view.py
index 34b4225001d..1bfdde3f3c2 100644
--- a/openbb_terminal/forecast/brnn_view.py
+++ b/openbb_terminal/forecast/brnn_view.py
@@ -43,6 +43,7 @@ def display_brnn_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display BRNN forecast
@@ -102,6 +103,10 @@ def display_brnn_forecast(
     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: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -137,6 +142,7 @@ def display_brnn_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -160,6 +166,7 @@ def display_brnn_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/expo_model.py b/openbb_terminal/forecast/expo_model.py
index 4c117a90f78..74ace57dd59 100644
--- a/openbb_terminal/forecast/expo_model.py
+++ b/openbb_terminal/forecast/expo_model.py
@@ -8,7 +8,7 @@ from typing import List, Optional, Tuple, Union
 
 import pandas as pd
 from darts import TimeSeries
-from darts.metrics import mape
+from darts.metrics import mape, mse, rmse, smape
 from darts.models import ExponentialSmoothing
 from darts.utils.utils import ModelMode, SeasonalityMode
 from numpy import ndarray
@@ -40,6 +40,7 @@ def get_expo_data(
     n_predict: int = 5,
     start_window: float = 0.85,
     forecast_horizon: int = 5,
+    metric: str = "mape",
 ) -> Tuple[
     List[TimeSeries],
     List[TimeSeries],
@@ -76,6 +77,8 @@ def get_expo_data(
         Size of sliding window from start of timeseries and onwards
     forecast_horizon: int
         Number of days to forecast when backtesting and retraining historical
+    metric: str
+        Metric to use for backtesting. Defaults to MAPE.
 
     Returns
     -------
@@ -149,8 +152,19 @@ def get_expo_data(
     # we have the historical fcast, now lets train on entire set and predict.
     best_model.fit(ticker_series)
     probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500)
-    precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)
-    console.print(f"Exponential smoothing obtains MAPE: {precision:.2f}% \n")
+
+    if metric == "rmse":
+        precision = rmse(actual_series=ticker_series, pred_series=historical_fcast_es)
+    elif metric == "mse":
+        precision = mse(actual_series=ticker_series, pred_series=historical_fcast_es)
+    elif metric == "mape":
+        precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es)
+    elif metric == "smape":
+        precision = smape(actual_series=ticker_series, pred_series=historical_fcast_es)
+
+    console.print(
+        f"Exponential smoothing obtains {metric.upper()}: {precision:.2f}% \n"
+    )
 
     return (
         ticker_series,
diff --git a/openbb_terminal/forecast/expo_view.py b/openbb_terminal/forecast/expo_view.py
index de41d84f49c..79771b537c7 100644
--- a/openbb_terminal/forecast/expo_view.py
+++ b/openbb_terminal/forecast/expo_view.py
@@ -36,6 +36,7 @@ def display_expo_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Probabilistic Exponential Smoothing forecast
@@ -80,6 +81,10 @@ def display_expo_forecast(
     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: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -103,6 +108,7 @@ def display_expo_forecast(
         n_predict=n_predict,
         start_window=start_window,
         forecast_horizon=forecast_horizon,
+        metric=metric,
     )
 
     if ticker_series == []:
@@ -127,6 +133,7 @@ def display_expo_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/forecast_controller.py b/openbb_terminal/forecast/forecast_controller.py
index 1252f27731c..2da91ffbfa5 100644
--- a/openbb_terminal/forecast/forecast_controller.py
+++ b/openbb_terminal/forecast/forecast_controller.py
@@ -356,6 +356,7 @@ class ForecastController(BaseController):
         naive: bool = False,
         explainability_raw: bool = False,
         export_pred_raw: bool = False,
+        metric: bool = False,
     ):
         if hidden_size:
             parser.add_argument(
@@ -531,7 +532,7 @@ class ForecastController(BaseController):
                 action="store",
                 dest="model_type",
                 default="LSTM",
-                help='Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU")',
+                help='Enter a string specifying the RNN module type ("RNN", "LSTM" or "GRU")',
             )
         if dropout is not None:
             parser.add_argument(
@@ -631,6 +632,17 @@ class ForecastController(BaseController):
                 help="Export predictions to a csv file.",
             )
 
+        if metric:
+            parser.add_argument(
+                "--metric",
+                type=str,
+                action="store",
+                dest="metric",
+                default="mape",
+                choices=["rmse", "mse", "mape", "smape"],
+                help="Calculate precision based on a specific metric (rmse, mse, mape)",
+            )
+
             # if user does not put in --dataset
         return parser
 
@@ -2114,6 +2126,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2143,6 +2156,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2175,6 +2189,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2202,6 +2217,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2261,6 +2277,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2297,6 +2314,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2373,6 +2391,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2415,6 +2434,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2482,6 +2502,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2525,6 +2546,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2561,6 +2583,7 @@ class ForecastController(BaseController):
             naive=True,
             explainability_raw=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2595,6 +2618,7 @@ class ForecastController(BaseController):
                 naive=ns_parser.naive,
                 explainability_raw=ns_parser.explainability_raw,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2630,6 +2654,7 @@ class ForecastController(BaseController):
             naive=True,
             explainability_raw=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2663,6 +2688,7 @@ class ForecastController(BaseController):
                 naive=ns_parser.naive,
                 explainability_raw=ns_parser.explainability_raw,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2714,6 +2740,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2755,6 +2782,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2842,6 +2870,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2885,6 +2914,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -2958,6 +2988,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -3001,6 +3032,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
@@ -3092,6 +3124,7 @@ class ForecastController(BaseController):
             end=True,
             naive=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -3136,6 +3169,7 @@ class ForecastController(BaseController):
                 end_date=ns_parser.s_end_date,
                 naive=ns_parser.naive,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)
diff --git a/openbb_terminal/forecast/helpers.py b/openbb_terminal/forecast/helpers.py
index 19df1151904..6e6750bcd23 100644
--- a/openbb_terminal/forecast/helpers.py
+++ b/openbb_terminal/forecast/helpers.py
@@ -11,7 +11,7 @@ import pandas as pd
 from darts import TimeSeries
 from darts.dataprocessing.transformers import MissingValuesFiller, Scaler
 from darts.explainability.shap_explainer import ShapExplainer
-from darts.metrics import mape
+from darts.metrics import mape, mse, rmse, smape
 from darts.models.forecasting.torch_forecasting_model import GlobalForecastingModel
 from darts.utils.statistics import plot_residuals_analysis
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@@ -477,6 +477,7 @@ def plot_forecast(
     forecast_only: bool = False,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     quant_kwargs = {}
@@ -503,7 +504,16 @@ def plot_forecast(
     if naive:
         # show naive forecast shift timeseries by 1
         naive_fcast = naive_fcast.drop_before(historical_fcast.start_time())
-        naive_precision = mape(ticker_series, naive_fcast)
+
+        # calculate precision based on metric
+        if metric == "rsme":
+            naive_precision = rmse(ticker_series, naive_fcast)
+        elif metric == "mse":
+            naive_precision = mse(ticker_series, naive_fcast)
+        elif metric == "mape":
+            naive_precision = mape(ticker_series, naive_fcast)
+        elif metric == "smape":
+            naive_precision = smape(ticker_series, naive_fcast)
 
         naive_fcast.plot(
             label=f"Naive+1: {naive_precision:.2f}%",
@@ -516,7 +526,7 @@ def plot_forecast(
         pred_label += " w/ past covs"
     predicted_values.plot(label=pred_label, **quant_kwargs, color="#00AAFF")
     ax.set_title(
-        f"{name} for <{ticker_name}> for next [{n_predict}] days (MAPE={precision:.2f}%)"
+        f"{name} for <{ticker_name}> for next [{n_predict}] days ({metric.upper()}={precision:.2f}%)"
     )
     ax.set_ylabel(target_col)
     ax.set_xlabel("Date")
@@ -719,6 +729,7 @@ def get_prediction(
     train_split: float,
     forecast_horizon: int,
     n_predict: int,
+    metric: str,
 ):
     _, val = ticker_series.split_before(train_split)
 
@@ -771,10 +782,17 @@ def get_prediction(
         else:
             prediction = best_model.predict(series=ticker_series, n=n_predict)
 
-    precision = mape(
-        actual_series=val, pred_series=historical_fcast
-    )  # mape = mean average percentage error
-    console.print(f"{model_name} model obtains MAPE: {precision:.2f}% \n")
+    # calculate precision based on metric (rmse, mse, mape)
+    if metric == "rmse":
+        precision = rmse(actual_series=val, pred_series=historical_fcast)
+    elif metric == "mse":
+        precision = mse(actual_series=val, pred_series=historical_fcast)
+    elif metric == "mape":
+        precision = mape(actual_series=val, pred_series=historical_fcast)
+    elif metric == "smape":
+        precision = smape(actual_series=val, pred_series=historical_fcast)
+
+    console.print(f"{model_name} model obtains {metric.upper()}: {precision:.2f}% \n")
 
     # scale back
     if use_scalers and isinstance(scaler, Scaler):
diff --git a/openbb_terminal/forecast/linregr_model.py b/openbb_terminal/forecast/linregr_model.py
index 5e4e4d91fc4..70a60654cd0 100644
--- a/openbb_terminal/forecast/linregr_model.py
+++ b/openbb_terminal/forecast/linregr_model.py
@@ -27,6 +27,7 @@ def get_linear_regression_data(
     output_chunk_length: int = 5,
     lags: Union[int, List[int]] = 14,
     random_state: Optional[int] = None,
+    metric: str = "mape",
 ) -> Tuple[
     List[TimeSeries],
     List[TimeSeries],
@@ -56,6 +57,8 @@ def get_linear_regression_data(
         lagged target values to predict the next time step
     random_state: Optional[int]
         The state for the model
+    metric: str
+        The metric to use for the model. Defaults to "mape".
 
     Returns
     -------
@@ -112,4 +115,5 @@ def get_linear_regression_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/linregr_view.py b/openbb_terminal/forecast/linregr_view.py
index 6bc09879edc..2b3815338cd 100644
--- a/openbb_terminal/forecast/linregr_view.py
+++ b/openbb_terminal/forecast/linregr_view.py
@@ -35,6 +35,7 @@ def display_linear_regression(
     naive: bool = False,
     explainability_raw: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Linear Regression Forecasting
@@ -74,6 +75,12 @@ def display_linear_regression(
     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.
+    explainability_raw: bool
+        Whether to show the raw explainability data. Defaults to False.
+    export_pred_raw: bool
+        Whether to export the raw prediction data. 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
     """
@@ -100,6 +107,7 @@ def display_linear_regression(
         forecast_horizon=forecast_horizon,
         output_chunk_length=output_chunk_length,
         lags=lags,
+        metric=metric,
     )
 
     probabilistic = True
@@ -121,6 +129,7 @@ def display_linear_regression(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/nbeats_model.py b/openbb_terminal/forecast/nbeats_model.py
index acb8480bfbe..57bca236fce 100644
--- a/openbb_terminal/forecast/nbeats_model.py
+++ b/openbb_terminal/forecast/nbeats_model.py
@@ -37,6 +37,7 @@ def get_NBEATS_data(
     model_save_name: str = "nbeats_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -88,6 +89,8 @@ def get_NBEATS_data(
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training.
         Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -168,4 +171,5 @@ def get_NBEATS_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/nbeats_view.py b/openbb_terminal/forecast/nbeats_view.py
index 283f6dac602..e790ce09aff 100644
--- a/openbb_terminal/forecast/nbeats_view.py
+++ b/openbb_terminal/forecast/nbeats_view.py
@@ -44,6 +44,7 @@ def display_nbeats_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display NBEATS forecast
@@ -108,6 +109,10 @@ def display_nbeats_forecast(
     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
+        Metric to use for evaluation. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -145,6 +150,7 @@ def display_nbeats_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -168,6 +174,7 @@ def display_nbeats_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/nhits_model.py b/openbb_terminal/forecast/nhits_model.py
index 43a3d1ed0f4..312b1d6e0ad 100644
--- a/openbb_terminal/forecast/nhits_model.py
+++ b/openbb_terminal/forecast/nhits_model.py
@@ -43,6 +43,7 @@ def get_nhits_data(
     model_save_name: str = "nhits_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -109,6 +110,8 @@ def get_nhits_data(
         discarded). Defaults to True.
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -197,4 +200,5 @@ def get_nhits_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/nhits_view.py b/openbb_terminal/forecast/nhits_view.py
index d4ec81ecdee..42fc8d06d79 100644
--- a/openbb_terminal/forecast/nhits_view.py
+++ b/openbb_terminal/forecast/nhits_view.py
@@ -49,6 +49,7 @@ def display_nhits_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Nhits forecast
@@ -128,6 +129,10 @@ def display_nhits_forecast(
     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
+        Metric to use for evaluation. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -169,6 +174,7 @@ def display_nhits_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -192,6 +198,7 @@ def display_nhits_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/regr_model.py b/openbb_terminal/forecast/regr_model.py
index 19871d10f24..12057e4bea3 100644
--- a/openbb_terminal/forecast/regr_model.py
+++ b/openbb_terminal/forecast/regr_model.py
@@ -26,6 +26,7 @@ def get_regression_data(
     forecast_horizon: int = 5,
     output_chunk_length: int = 5,
     lags: Union[int, List[int]] = 14,
+    metric: str = "mape",
 ) -> Tuple[
     List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]
 ]:
@@ -49,6 +50,8 @@ def get_regression_data(
         The length of the forecast of the model. Defaults to 1.
     lags: Union[int, List[int]]
         lagged target values to predict the next time step
+    metric: str
+        Metric to use for evaluation. Defaults to "mape".
 
     Returns
     -------
@@ -99,4 +102,5 @@ def get_regression_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/regr_view.py b/openbb_terminal/forecast/regr_view.py
index 71e34bf4ea5..f680dedb590 100644
--- a/openbb_terminal/forecast/regr_view.py
+++ b/openbb_terminal/forecast/regr_view.py
@@ -35,6 +35,7 @@ def display_regression(
     naive: bool = False,
     explainability_raw: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Regression Forecasting
@@ -74,6 +75,8 @@ def display_regression(
     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.
+    metric: str
+        The metric to use for the forecast. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -100,6 +103,7 @@ def display_regression(
         forecast_horizon=forecast_horizon,
         output_chunk_length=output_chunk_length,
         lags=lags,
+        metric=metric,
     )
     probabilistic = False
     helpers.plot_forecast(
@@ -120,6 +124,7 @@ def display_regression(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/rnn_model.py b/openbb_terminal/forecast/rnn_model.py
index 5f860bb43e0..8744ef98338 100644
--- a/openbb_terminal/forecast/rnn_model.py
+++ b/openbb_terminal/forecast/rnn_model.py
@@ -36,6 +36,7 @@ def get_rnn_data(
     input_chunk_size: int = 14,
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[type[TimeSeries]]],
     Optional[List[type[TimeSeries]]],
@@ -77,6 +78,8 @@ def get_rnn_data(
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training.
         Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -145,4 +148,5 @@ def get_rnn_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/rnn_view.py b/openbb_terminal/forecast/rnn_view.py
index 31f25916575..cbf1360f8cd 100644
--- a/openbb_terminal/forecast/rnn_view.py
+++ b/openbb_terminal/forecast/rnn_view.py
@@ -42,6 +42,7 @@ def display_rnn_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display RNN forecast
@@ -94,6 +95,10 @@ def display_rnn_forecast(
     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 predictions. Defaults to False.
+    metric: str
+        The metric to use for the forecast. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -124,6 +129,7 @@ def display_rnn_forecast(
         input_chunk_size=input_chunk_size,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -148,6 +154,7 @@ def display_rnn_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/tcn_model.py b/openbb_terminal/forecast/tcn_model.py
index 1d0c7467d39..fe4604b1404 100644
--- a/openbb_terminal/forecast/tcn_model.py
+++ b/openbb_terminal/forecast/tcn_model.py
@@ -37,6 +37,7 @@ def get_tcn_data(
     model_save_name: str = "tcn_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     List[Optional[TimeSeries]],
     List[Optional[TimeSeries]],
@@ -86,6 +87,8 @@ def get_tcn_data(
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training.
         Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -166,4 +169,5 @@ def get_tcn_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/tcn_view.py b/openbb_terminal/forecast/tcn_view.py
index 5e7cf5aec94..13ff4003761 100644
--- a/openbb_terminal/forecast/tcn_view.py
+++ b/openbb_terminal/forecast/tcn_view.py
@@ -44,6 +44,7 @@ def display_tcn_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display TCN forecast
@@ -104,6 +105,10 @@ def display_tcn_forecast(
     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: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -141,6 +146,7 @@ def display_tcn_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -165,6 +171,7 @@ def display_tcn_forecast(
         naive=naive,
         export_pred_raw=export_pred_raw,
         external_axes=external_axes,
+        metric=metric,
     )
     if residuals:
         helpers.plot_residuals(
diff --git a/openbb_terminal/forecast/tft_model.py b/openbb_terminal/forecast/tft_model.py
index 136ab21f074..41420ee2af3 100644
--- a/openbb_terminal/forecast/tft_model.py
+++ b/openbb_terminal/forecast/tft_model.py
@@ -43,6 +43,7 @@ def get_tft_data(
     model_save_name: str = "tft_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -101,6 +102,8 @@ def get_tft_data(
     save_checkpoints: (bool, optional)
         Whether or not to automatically save the untrained model and checkpoints from training.
         Defaults to True.
+    metric: (str, optional)
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -204,4 +207,5 @@ def get_tft_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/tft_view.py b/openbb_terminal/forecast/tft_view.py
index 3064027500c..8264b921d2a 100644
--- a/openbb_terminal/forecast/tft_view.py
+++ b/openbb_terminal/forecast/tft_view.py
@@ -45,6 +45,7 @@ def display_tft_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Temporal Fusion Transformer forecast
@@ -105,6 +106,8 @@ def display_tft_forecast(
     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.
+    metric: str
+        The metric to use for the model. Defaults to "mape".
     external_axes:Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -143,6 +146,7 @@ def display_tft_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -166,6 +170,7 @@ def display_tft_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals:
diff --git a/openbb_terminal/forecast/theta_model.py b/openbb_terminal/forecast/theta_model.py
index 74ef6523933..0f3f1c1ebe3 100644
--- a/openbb_terminal/forecast/theta_model.py
+++ b/openbb_terminal/forecast/theta_model.py
@@ -8,7 +8,7 @@ from typing import List, Optional, Tuple, Union
 import numpy as np
 import pandas as pd
 from darts import TimeSeries
-from darts.metrics import mape
+from darts.metrics import mape, mse, rmse, smape
 from darts.models import Theta
 from darts.utils.utils import SeasonalityMode
 from statsmodels.tools.sm_exceptions import ConvergenceWarning
@@ -34,6 +34,7 @@ def get_theta_data(
     n_predict: int = 5,
     start_window: float = 0.85,
     forecast_horizon: int = 5,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -64,7 +65,9 @@ def get_theta_data(
     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
+        Number of days to forecast when backtesting and retraining historical data
+    metric: str
+        Metric to use when backtesting and retraining historical data. Defaults to "mape".
 
     Returns
     -------
@@ -135,10 +138,23 @@ def get_theta_data(
     # fit model on entire series for final prediction
     best_theta_model_final.fit(ticker_series)
     prediction = best_theta_model_final.predict(int(n_predict))
-    precision = mape(
-        actual_series=ticker_series, pred_series=historical_fcast_theta
-    )  # mape = mean average precision error
-    console.print(f"Theta Model obtains MAPE: {precision:.2f}% \n")
+
+    if metric == "rmse":
+        precision = rmse(
+            actual_series=ticker_series, pred_series=historical_fcast_theta
+        )
+    elif metric == "mse":
+        precision = mse(actual_series=ticker_series, pred_series=historical_fcast_theta)
+    elif metric == "mape":
+        precision = mape(
+            actual_series=ticker_series, pred_series=historical_fcast_theta
+        )
+    elif metric == "smape":
+        precision = smape(
+            actual_series=ticker_series, pred_series=historical_fcast_theta
+        )
+
+    console.print(f"Theta Model obtains {metric.upper()}: {precision:.2f}% \n")
 
     return (
         ticker_series,
diff --git a/openbb_terminal/forecast/theta_view.py b/openbb_terminal/forecast/theta_view.py
index 428e95c1663..041b865fbf7 100644
--- a/openbb_terminal/forecast/theta_view.py
+++ b/openbb_terminal/forecast/theta_view.py
@@ -34,6 +34,7 @@ def display_theta_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Theta forecast
@@ -73,6 +74,10 @@ def display_theta_forecast(
     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 backtesting. Defaults to "mape".
     external_axes:Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -95,6 +100,7 @@ def display_theta_forecast(
         target_column=target_column,
         start_window=start_window,
         forecast_horizon=forecast_horizon,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -119,6 +125,7 @@ def display_theta_forecast(
         naive=naive,
         export_pred_raw=export_pred_raw,
         external_axes=external_axes,
+        metric=metric,
     )
 
     if residuals:
diff --git a/openbb_terminal/forecast/trans_model.py b/openbb_terminal/forecast/trans_model.py
index ac89b255820..21cac569567 100644
--- a/openbb_terminal/forecast/trans_model.py
+++ b/openbb_terminal/forecast/trans_model.py
@@ -40,6 +40,7 @@ def get_trans_data(
     model_save_name: str = "trans_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[TimeSeries]],
     Optional[List[TimeSeries]],
@@ -94,6 +95,8 @@ def get_trans_data(
         discarded). Defaults to True.
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -176,4 +179,5 @@ def get_trans_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )
diff --git a/openbb_terminal/forecast/trans_view.py b/openbb_terminal/forecast/trans_view.py
index fda46a687a6..4ca64c1b5b4 100644
--- a/openbb_terminal/forecast/trans_view.py
+++ b/openbb_terminal/forecast/trans_view.py
@@ -47,6 +47,7 @@ def display_trans_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Transformer forecast
@@ -112,6 +113,10 @@ def display_trans_forecast(
     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: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -152,6 +157,7 @@ def display_trans_forecast(
         model_save_name=model_save_name,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -176,6 +182,7 @@ def display_trans_forecast(
         naive=naive,
         export_pred_raw=export_pred_raw,
         external_axes=external_axes,
+        metric=metric,
     )
     if residuals:
         helpers.plot_residuals(