class_docstr: A new result type to help compare results generated from
  :class:`FixedIncomeStrategy <bt.core.FixedIncomeStrategy>`.
  Recall that in a fixed income strategy, the normalized prices are computed
  using additive returns expressed as a percentage of current outstanding
  notional (i.e. fixed-notional equivalent).
  In strategies where the notional is varying, this may lead to counter-
  intuitive results because the different terms in the sum are being scaled by
  different notionals in the denominator (i.e. price could be below par, but
  overall change in value is positive).
  
  This class provides a way to "renormalize" the results with a different
  denominator value or series, i.e. using max or average notional exposure,
  or the risk exposure of the strategy.
  
  Args:
      * normalizing_value: pd.Series, float or dict thereof(by strategy name)
      * backtests (list): List of backtests (i.e. from Result.backtest_list)
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docstr: Compute the new price series from the strategy (s) and the
  normalizing value (v)
docstr_tok: ["Compute","the","new","price","series","from","the","strategy","(","s",")","and","the","normalizing","value","(","v",")"]
code: def _price(self, s, v):
      """
      Compute the new price series from the strategy (s) and the
      normalizing value (v)
      """
      # Compute additive returns net of flows
      returns = s.values.diff() - s.flows
      prices = bt.core.PAR * (1.0 + (returns / v).cumsum())
      prices.iloc[0] = bt.core.PAR
      return prices
  
imports: from __future__ import division
  from copy import deepcopy
  import bt
  import ffn
  import pandas
  import numpy
  from matplotlib import pyplot
  import pyprind
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the RenormalizedFixedIncomeResult obj's underlying member method code with example usage: obj._price(s, v) and returns: prices