code: def plot_bayes_cone(returns_train, returns_test, ppc,
                      plot_train_len=50, ax=None):
      
  
      score = compute_consistency_score(returns_test,
                                        ppc)
  
      ax = _plot_bayes_cone(
          returns_train,
          returns_test,
          ppc,
          plot_train_len=plot_train_len,
          ax=ax)
      ax.text(
          0.40,
          0.90,
          'Consistency score: %.1f' %
          score,
          verticalalignment='bottom',
          horizontalalignment='right',
          transform=ax.transAxes,
      )
  
      ax.set_ylabel('Cumulative returns')
      return score
docs: Generate cumulative returns plot with Bayesian cone.
  
      Parameters
      ----------
      returns_train : pd.Series
          Timeseries of simple returns
      returns_test : pd.Series
          Out-of-sample returns. Datetimes in returns_test will be added to
          returns_train as missing values and predictions will be generated
          for them.
      ppc : np.array
          Posterior predictive samples of shape samples x
          len(returns_test).
      plot_train_len : int (optional)
          How many data points to plot of returns_train. Useful to zoom in on
          the prediction if there is a long backtest period.
      ax : matplotlib.Axis (optional)
          Axes upon which to plot.
  
      Returns
      -------
      score : float
          Consistency score (see compute_consistency_score)
      trace : pymc3.sampling.BaseTrace
          A PyMC3 trace object that contains samples for each parameter
          of the posterior.