Tracking Error is a financial metric that quantifies the volatility or dispersion of the difference between the returns of an investment portfolio or asset and the returns of a benchmark index. It measures how closely the portfolio tracks its benchmark and provides insights into the consistency of the portfolio’s performance relative to the benchmark. A higher Tracking Error indicates greater divergence from the benchmark, while a lower Tracking Error suggests that the portfolio closely follows the benchmark.

The formula is as follows:

\[\text{Tracking Error} (\text{TE}) = \text{Standard Deviation of} (\text{Portfolio Returns} - \text{Benchmark Returns})\]

See definition: https://en.wikipedia.org/wiki/Tracking_error

Also known as: active risk, benchmark deviation.

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Calculate the Tracking Error in Python

The Tracking Error is available in the Performance module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_tracking_error as shown below.

from financetoolkit import Toolkit

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

toolkit.performance.get_tracking_error()

Which returns:

Date AAPL TSLA
2021 0.0118 0.0317
2022 0.0115 0.0344
2023 0.009 0.0304
2024 0.0121 0.0369
2025 0.0139 0.0328
2026 0.0154 0.0226

Parameters

get_tracking_error accepts the following parameters:

  • period (str, optional): The period to use for the calculation. Defaults to “quarterly” if the Toolkit is initialised with quarterly=True, otherwise “yearly”.
  • rolling (int, optional): The rolling window size to use for the calculation. If set, Tracking Error is calculated over a rolling window of this many periods across the full return history instead of per period. Defaults to None.
  • rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the ratios. Defaults to False.
  • lag (int | str, optional): The lag to use for the growth calculation. Defaults to 1.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
Sortino Ratio Ulcer Performance Index (UPI) Calmar Ratio Sterling Ratio Burke Ratio M2 Ratio Information Ratio Upside Capture Ratio Downside Capture Ratio Win Rate Kappa Ratio Omega Ratio

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

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