Choppiness Index

get_chop(quotes, lookback_periods=14)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`lookback_periods`	int, default 14	Number of periods (`N`) for the lookback evaluation. Must be greater than 1.

Historical quotes requirements

You must have at least N+1 periods of quotes to cover the warmup periods.

quotes is an Iterable[Quote] collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

Return

ChopResults[ChopResult]

This method returns a time series of all available indicator values for the quotes provided.
ChopResults is just a list of ChopResult.
It always returns the same number of elements as there are in the historical quotes.
It does not return a single incremental indicator value.
The first N periods will have None values since there’s not enough data to calculate.

ChopResult

name	type	notes
`date`	datetime	Date
`chop`	float, Optional	Choppiness Index

Utilities

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# Calculate CHOP(14)
results = indicators.get_chop(quotes, 14)

About Choppiness Index

Created by E.W. Dreiss, the Choppiness Index measures the trendiness or choppiness on a scale of 0 to 100, to depict steady trends versus conditions of choppiness. [Discuss] 💬