Calculates and collects various volatility indicators based on the provided data.

All Volatility Indicators in Python

collect_volatility_indicators is part of the Technicals module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call collect_volatility_indicators as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(tickers=["AAPL", "MSFT"])

toolkit.technicals.collect_volatility_indicators().xs("AAPL", level=1, axis="columns")

Which returns:

Date Bollinger Band Upper Bollinger Band Middle Bollinger Band Lower True Range
2026-06-18 316.843 300.742 284.642 4.95
2026-06-22 315.955 300.078 284.201 5.66
2026-06-23 312.09 298.585 285.08 7.46
2026-06-24 309.328 297.358 285.388 6.76
2026-06-25 309.176 294.781 280.386 19.33
2026-06-26 306.652 293.098 279.544 11.74
2026-06-29 305.571 291.684 277.796 8.52
2026-06-30 305.53 291.599 277.667 9.24
2026-07-01 305.81 291.799 277.788 7.39
2026-07-02 309.318 292.727 276.137 15.74

Parameters

collect_volatility_indicators accepts the following parameters:

  • period (str, optional): The time period to consider for historical data. Can be “daily”, “weekly”, “quarterly”, or “yearly”. Defaults to “daily”.
  • window (int, optional): The window size for calculating indicators. Defaults to 14.
  • close_column (str, optional): The name of the column containing the close prices. Defaults to “Adj Close”.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the indicator values. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation.
  • 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. Defaults to 1.
Share