Hurst Exponent

get_hurst(quotes, lookback_periods=100)

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 100	Number of periods (`N`) in the Hurst Analysis. Must be greater than 20.

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

HurstResults[HurstResult]

This method returns a time series of all available indicator values for the quotes provided.
HurstResults is just a list of HurstResult.
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.

HurstResult

name	type	notes
`date`	datetime	Date
`hurst_exponent`	float, Optional	Hurst Exponent (`H`)

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 20-period Hurst
results = indicators.get_hurst(quotes, 20)

About Hurst Exponent

The Hurst Exponent (H) is part of a Rescaled Range Analysis, a random-walk path analysis that measures trending and mean-reverting tendencies of incremental return values. When H is greater than 0.5 it depicts trending. When H is less than 0.5 it is is more likely to revert to the mean. When H is around 0.5 it represents a random walk. [Discuss] 💬