Logistic Regression

Fit a Logistic Regression (Logit model) of whether dependent_ticker’s return is positive on independent_tickers.

Also known as: logit model, logit regression.

For more information about the method, see regression_model.get_logistic_regression.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Logistic Regression for you. Just ask in plain English.

Calculate the Logistic Regression in Python

The Logistic Regression is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_logistic_regression as shown below.

from financetoolkit import Toolkit

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

# AAPL (the first ticker) is dependent; MSFT and Benchmark are independent
toolkit.econometrics.get_logistic_regression(
    independent_tickers=["MSFT", "Benchmark"], period="weekly"
)

Which returns:

Parameters

get_logistic_regression accepts the following parameters:

• dependent_ticker (str | None, optional): The dependent asset (whose up/down direction is predicted). Defaults to None, meaning the Toolkit instance’s first ticker.
• independent_tickers (str | list[str] | None, optional): The independent (predictor) asset(s). Defaults to None, meaning every other ticker in the Toolkit instance besides dependent_ticker.
• include_benchmark (bool, optional): Whether to include “Benchmark” in the default independent ticker(s) (has no effect when independent_tickers is given explicitly). Defaults to False.
• period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
• column (str, optional): The historical data column to derive returns from. Defaults to “Return”.
• add_constant (bool, optional): Whether to include an intercept. Defaults to True.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Regression

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