Hurst Exponent

get_hurst(quotes, lookback_periods=100)


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.




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


See Utilities and Helpers for more information.


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] 💬