class_docstr: Defined by John F. Ehlers in `Cybernetic Analysis for Stock and Futures`,
  2004, published by Wiley. `ISBN: 978-0-471-46307-8`
  
  The Laguerre RSI tries to implements a better RSI by providing a sort of
  *Time Warp without Time Travel* using a Laguerre filter. This provides for
  faster reactions to price changes
  
  ``gamma`` is meant to have values between ``0.2`` and ``0.8``, with the
  best balance found theoretically at the default of ``0.5``
code: def next(self):
      l0_1 = self.l0  # cache previous intermediate values
      l1_1 = self.l1
      l2_1 = self.l2
  
      g = self.p.gamma  # avoid more lookups
      self.l0 = l0 = (1.0 - g) * self.data + g * l0_1
      self.l1 = l1 = -g * l0 + l0_1 + g * l1_1
      self.l2 = l2 = -g * l1 + l1_1 + g * l2_1
      self.l3 = l3 = -g * l2 + l2_1 + g * self.l3
  
      cu = 0.0
      cd = 0.0
      if l0 >= l1:
          cu = l0 - l1
      else:
          cd = l1 - l0
  
      if l1 >= l2:
          cu += l1 - l2
      else:
          cd += l2 - l1
  
      if l2 >= l3:
          cu += l2 - l3
      else:
          cd += l3 - l2
  
      den = cu + cd
      self.lines.lrsi[0] = 1.0 if not den else cu / den
  
file_path: backtrader/backtrader/indicators/lrsi.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from .None import PeriodN
label_desc: Use image node_id 1 for calling the LaguerreRSI obj's underlying member method code with example usage: obj.next() without return types