class_docstr: Calculates the ``zscore`` for data0 and data1. Although it doesn't directly
  uses any external package it relies on ``OLS_SlopeInterceptN`` which uses
  ``pandas`` and ``statsmodels``
class_docstr_tok: ["Calculates","the","``","zscore","``","for","data0","and","data1",".","Although","it","does","n't","directly","uses","any","external","package","it","relies","on","``","OLS_SlopeInterceptN","``","which","uses","``","pandas","``","and","``","statsmodels","``"]
code: def __init__(self):
      slint = OLS_Slope_InterceptN(*self.datas)
  
      spread = self.data0 - (slint.slope * self.data1 + slint.intercept)
      self.l.spread = spread
  
      self.l.spread_mean = bt.ind.SMA(spread, period=self.p.period)
      self.l.spread_std = bt.ind.StdDev(spread, period=self.p.period)
      self.l.zscore = (spread - self.l.spread_mean) / self.l.spread_std
  
file_path: backtrader/backtrader/indicators/ols.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import backtrader
  from .None import PeriodN
label_desc: Use image node_id 1 to create a new OLS_TransformationN object from inherited base classes: PeriodN with example: obj = OLS_TransformationN()