Choppiness Index
get_chop(quotes, lookback_periods=14)
Parameters
name | type | notes |
---|---|---|
quotes |
Iterable[Quote] | Iterable(such as list or an object having __iter__() ) of the Quote class or its sub-class. • Need help 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 ofChopResult
. - 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 haveNone
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_history_from_feed("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]