diff --git a/openbb_terminal/forecast/theta_model.py b/openbb_terminal/forecast/theta_model.py
index 74ef6523933..5f0e266b094 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, rmse, mse
 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 +138,19 @@ 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
+        )
+
+    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: