OpenBB/website/content/sdk/reference/ta/clenow.md

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clenow	This page offers thorough knowledge of Clenow Volatility Adjusted Momentum model in OpenBB. It describes the function, parameters, returns and provides examples of the feature in python code for aspiring users.	Clenow Volatility Adjusted Momentum Technical analysis Function description Parameters Python code example Data series Regression Momentum chart

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Gets the Clenow Volatility Adjusted Momentum. this is defined as the regression coefficient on log prices

Source Code: [link]

openbb.ta.clenow(values: pd.Series, window: int = 90)

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Parameters

Name	Type	Description	Default	Optional
values	pd.Series	Values to perform regression for	None	False
window	int	Length of lookback period	90	True

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Returns

Type	Description
	R2 of fit to log data

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Examples

from openbb_terminal.sdk import openbb
df = openbb.stocks.load("AAPL")
openbb.ta.clenow(df["Close"])

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Prints table and plots clenow momentum

Source Code: [link]

openbb.ta.clenow_chart(data: pd.Series, symbol: str = "", window: int = 90, export: str = "", external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None)

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Parameters

Name	Type	Description	Default	Optional
data	pd.Series	Series of values	None	False
symbol	str	Symbol that the data corresponds to		True
window	int	Length of window	90	True
export	str	Format to export data		True
external_axes	Optional[List[plt.Axes]]	External axes (2 axes are expected in the list), by default None	None	True

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Returns

This function does not return anything

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Examples

from openbb_terminal.sdk import openbb
df = openbb.stocks.load("AAPL")
openbb.ta.clenow_chart(df["Close"])

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