code: def compute_consistency_score(returns_test, preds):
      
  
      returns_test_cum = cum_returns(returns_test, starting_value=1.)
      cum_preds = np.cumprod(preds + 1, 1)
  
      q = [sp.stats.percentileofscore(cum_preds[:, i],
                                      returns_test_cum.iloc[i],
                                      kind='weak')
           for i in range(len(returns_test_cum))]
      # normalize to be from 100 (perfect median line) to 0 (completely outside
      # of cone)
      return 100 - np.abs(50 - np.mean(q)) / .5
docs: Compute Bayesian consistency score.
  
      Parameters
      ----------
      returns_test : pd.Series
          Observed cumulative returns.
      preds : numpy.array
          Multiple (simulated) cumulative returns.
  
      Returns
      -------
      Consistency score
          Score from 100 (returns_test perfectly on the median line of the
          Bayesian cone spanned by preds) to 0 (returns_test completely
          outside of Bayesian cone.)