code: def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
      
  
      if ax is None:
          ax = plt.gca()
  
      is_returns = returns if live_start_date is None \
          else returns.loc[returns.index < live_start_date]
      is_weekly = ep.aggregate_returns(is_returns, 'weekly')
      is_monthly = ep.aggregate_returns(is_returns, 'monthly')
      sns.boxplot(data=[is_returns, is_weekly, is_monthly],
                  palette=["#4c72B0", "#55A868", "#CCB974"],
                  ax=ax, **kwargs)
  
      if live_start_date is not None:
          oos_returns = returns.loc[returns.index >= live_start_date]
          oos_weekly = ep.aggregate_returns(oos_returns, 'weekly')
          oos_monthly = ep.aggregate_returns(oos_returns, 'monthly')
  
          sns.swarmplot(data=[oos_returns, oos_weekly, oos_monthly], ax=ax,
                        color="red",
                        marker="d", **kwargs)
          red_dots = matplotlib.lines.Line2D([], [], color="red", marker="d",
                                             label="Out-of-sample data",
                                             linestyle='')
          ax.legend(handles=[red_dots], frameon=True, framealpha=0.5)
      ax.set_xticklabels(['Daily', 'Weekly', 'Monthly'])
      ax.set_title('Return quantiles')
  
      return ax
docs: Creates a box plot of daily, weekly, and monthly return
      distributions.
  
      Parameters
      ----------
      returns : pd.Series
          Daily returns of the strategy, noncumulative.
           - See full explanation in tears.create_full_tear_sheet.
      live_start_date : datetime, optional
          The point in time when the strategy began live trading, after
          its backtest period.
      ax : matplotlib.Axes, optional
          Axes upon which to plot.
      **kwargs, optional
          Passed to seaborn plotting function.
  
      Returns
      -------
      ax : matplotlib.Axes
          The axes that were plotted on.