code: def compute_mean_returns_spread(mean_returns,
                                  upper_quant,
                                  lower_quant,
                                  std_err=None):
      
  
      mean_return_difference = mean_returns.xs(upper_quant,
                                               level='factor_quantile') \
          - mean_returns.xs(lower_quant, level='factor_quantile')
  
      if std_err is None:
          joint_std_err = None
      else:
          std1 = std_err.xs(upper_quant, level='factor_quantile')
          std2 = std_err.xs(lower_quant, level='factor_quantile')
          joint_std_err = np.sqrt(std1**2 + std2**2)
  
      return mean_return_difference, joint_std_err
docs: Computes the difference between the mean returns of
      two quantiles. Optionally, computes the standard error
      of this difference.
  
      Parameters
      ----------
      mean_returns : pd.DataFrame
          DataFrame of mean period wise returns by quantile.
          MultiIndex containing date and quantile.
          See mean_return_by_quantile.
      upper_quant : int
          Quantile of mean return from which we
          wish to subtract lower quantile mean return.
      lower_quant : int
          Quantile of mean return we wish to subtract
          from upper quantile mean return.
      std_err : pd.DataFrame, optional
          Period wise standard error in mean return by quantile.
          Takes the same form as mean_returns.
  
      Returns
      -------
      mean_return_difference : pd.Series
          Period wise difference in quantile returns.
      joint_std_err : pd.Series
          Period wise standard error of the difference in quantile returns.
          if std_err is None, this will be None