Calculate the Root Mean Squared Error (RMSE) between every unordered pair of tickers’ series in the Toolkit instance.

Also known as: RMSD (Root Mean Squared Deviation).

See forecast_evaluation_model.get_rmse for the formula. This controller method compares two ASSETS’ series directly, treating one as a naive “forecast” proxy for the other – a simple, tracking-error style measure of how closely two series move together in absolute deviation terms (e.g. a portfolio versus a benchmark, or one asset as a naive stand-in forecast for a similar one). For evaluating an actual FORECASTING MODEL (ARIMA/VAR) rather than one asset as a naive proxy for another, use get_out_of_sample_validation instead.

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Calculate the Root Mean Squared Error (RMSE) in Python

The Root Mean Squared Error (RMSE) is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_rmse as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.econometrics.get_rmse(period="quarterly")

Which returns:

Ticker A Ticker B RMSE
AAPL MSFT 0.1084

Parameters

get_rmse accepts the following parameters:

  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • column (str, optional): The historical data column to compare. Defaults to “Return”.
  • include_benchmark (bool, optional): Whether to include “Benchmark” among the tickers paired up. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Mean Absolute Error (MAE) Out-of-Sample Validation Diebold-Mariano Test

The Econometrics module page introduces the module, and the sidebar lists all of its functions.

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