Volatility Cone
Retrieves the Volatility Cone for the specified period and assets.
The Volatility Cone summarizes the distribution of historical annualized realized volatility over a range of rolling windows, showing how the current realized volatility for each window compares to its own historical range. It is commonly used to judge whether current (or implied) volatility is cheap or expensive relative to history.
Also known as: volatility cone, realized volatility term structure.
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Calculate the Volatility Cone in Python
The Volatility Cone is available in the Technicals module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_volatility_cone as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(tickers=["AAPL", "MSFT"])
volatility_cone = toolkit.technicals.get_volatility_cone()
volatility_cone.xs("AAPL", level=1, axis="columns")
Which returns:
| Window | Min | Median | Max | Current |
|---|---|---|---|---|
| 10 | 0.12 | 0.24 | 0.58 | 0.27 |
| 20 | 0.14 | 0.23 | 0.52 | 0.25 |
| 30 | 0.15 | 0.22 | 0.47 | 0.24 |
Parameters
get_volatility_cone accepts the following parameters:
- windows (list[int] | None, optional): The rolling windows (in periods) to calculate realized volatility for. Defaults to [10, 20, 30, 60, 90, 120].
- period (str, optional): The time period to consider for historical data. Can be “daily”, “weekly”, “quarterly”, or “yearly”. Defaults to “daily”.
- close_column (str, optional): The column name for closing prices in the historical data. Defaults to “Adj Close”.
- rounding (int | None, optional): The number of decimals to round the results to. If None, the rounding value specified during the initialization of the Toolkit instance will be used. Defaults to None.
Related Volatility Indicators
The Technicals module page introduces the module, and the sidebar lists all of its functions.