class_docstr: Defines a level of significance by taking into account the average of price
  bar components of the past period of a larger timeframe. For example when
  operating with days, the values are taking from the already "past" month
  fixed prices.
  
  Example of using this indicator:
  
    data = btfeeds.ADataFeed(dataname=x, timeframe=bt.TimeFrame.Days)
    cerebro.adddata(data)
    cerebro.resampledata(data, timeframe=bt.TimeFrame.Months)
  
  In the ``__init__`` method of the strategy:
  
    pivotindicator = btind.DemarkPivotPoiont(self.data1)  # the resampled data
  
  The indicator will try to automatically plo to the non-resampled data. To
  disable this behavior use the following during construction:
  
    - _autoplot=False
  
  Note:
  
    The example shows *days* and *months*, but any combination of timeframes
    can be used. See the literature for recommended combinations
  
  Formula:
    - if close < open x = high + (2 x low) + close
  
    - if close > open x = (2 x high) + low + close
  
    - if Close == open x = high + low + (2 x close)
  
    - p = x / 4
  
    - support1 = x / 2 - high
    - resistance1 = x / 2 - low
  
  See:
    - http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:pivot_points
code: def _plotinit(self):
      # Try to plot to the actual timeframe master
      if self.p._autoplot:
          if hasattr(self.data, "data"):
              self.plotinfo.plotmaster = self.data.data
  
file_path: backtrader/backtrader/indicators/pivotpoint.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from .None import Indicator, CmpEx
label_desc: Use image node_id 1 for calling the DemarkPivotPoint obj's underlying member method code with example usage: obj._plotinit() without return types