class_docstr: Hurst exponent filter.
  
  :param dataSeries: The DataSeries instance being filtered.
  :type dataSeries: :class:`pyalgotrade.dataseries.DataSeries`.
  :param period: The number of values to use to calculate the hurst exponent.
  :type period: int.
  :param minLags: The minimum number of lags to use. Must be >= 2.
  :type minLags: int.
  :param maxLags: The maximum number of lags to use. Must be > minLags.
  :type maxLags: int.
  :param maxLen: The maximum number of values to hold.
      Once a bounded length is full, when new items are added, a corresponding number of items are discarded
      from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.
  :type maxLen: int.
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code: def __init__(
      self,
      dataSeries,
      period,
      minLags=2,
      maxLags=20,
      logValues=True,
      maxLen=None,
  ):
      assert period > 0, "period must be > 0"
      assert minLags >= 2, "minLags must be >= 2"
      assert maxLags > minLags, "maxLags must be > minLags"
  
      super(HurstExponent, self).__init__(
          dataSeries,
          HurstExponentEventWindow(period, minLags, maxLags, logValues),
          maxLen,
      )
  
file_path: pyalgotrade/pyalgotrade/technical/hurst.py
imports: import numpy
  from pyalgotrade import technical
label_desc: Use image node_id 1 to create a new HurstExponent object from inherited base classes: technical with example: obj = HurstExponent(dataSeries, period, minLags, maxLags, logValues, maxLen)