Calculate a two-sample t-test for a difference in mean column between every unordered pair of tickers in the Toolkit instance.

Also known as: independent samples t-test, Welch’s t-test (default), Student’s t-test (equal_variance=True).

For more information about the method, see hypothesis_testing_model.get_two_sample_t_test.

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Calculate the Two-Sample t-Test in Python

The Two-Sample t-Test is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_two_sample_t_test as shown below.

from financetoolkit import Toolkit

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

toolkit.econometrics.get_two_sample_t_test(period="weekly")

Which returns:

Ticker A Ticker B T-Statistic Degrees of Freedom P-Value Mean A Mean B
AAPL MSFT 0.2318 306.6549 0.8168 0.0047 0.0036

Parameters

get_two_sample_t_test 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”.
  • equal_variance (bool, optional): Whether to assume the two samples share a common variance (Student’s pooled t-test) instead of Welch’s (unequal-variance) t-test. Defaults to False.
  • 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.
F-Test Likelihood Ratio Test Wald Test

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

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