class_docstr: RandomBenchmarkResult expands on Result to add methods specific
  to random strategy benchmarking.
  
  Args:
      * backtests (list): List of backtests
  
  Attributes:
      * base_name (str): Name of backtest being benchmarked
      * r_stats (Result): Stats for random strategies
      * b_stats (Result): Stats for benchmarked strategy
class_docstr_tok: ["RandomBenchmarkResult","expands","on","Result","to","add","methods","specific","to","random","strategy","benchmarking",".","Args",":","*","backtests","(","list",")",":","List","of","backtests","Attributes",":","*","base_name","(","str",")",":","Name","of","backtest","being","benchmarked","*","r_stats","(","Result",")",":","Stats","for","random","strategies","*","b_stats","(","Result",")",":","Stats","for","benchmarked","strategy"]
code: def __init__(self, *backtests):
      super(RandomBenchmarkResult, self).__init__(*backtests)
      self.base_name = backtests[0].name
      # seperate stats to make
      self.r_stats = self.stats.drop(self.base_name, axis=1)
      self.b_stats = self.stats[self.base_name]
  
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 1 to create a new RandomBenchmarkResult object from inherited base classes: Result with example: obj = RandomBenchmarkResult()