Calculate the Mean Absolute Error (MAE) between every unordered pair of tickers’ series in the Toolkit instance.

Also known as: MAD (Mean Absolute Deviation).

See forecast_evaluation_model.get_mae for the formula, and get_rmse’s docstring for why this controller method compares two ASSETS directly (rather than an asset against an actual forecasting model’s output – see get_out_of_sample_validation for that).

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Calculate the Mean Absolute Error (MAE) in Python

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

pip install financetoolkit -U

Then call get_mae as shown below.

from financetoolkit import Toolkit

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

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

Which returns:

Ticker A Ticker B MAE
AAPL MSFT 0.0887

Parameters

get_mae 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.
Root Mean Squared Error (RMSE) 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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