code: def plot_slippage_sweep(returns, positions, transactions,
                          slippage_params=(3, 8, 10, 12, 15, 20, 50),
                          ax=None, **kwargs):
      
  
      if ax is None:
          ax = plt.gca()
  
      slippage_sweep = pd.DataFrame()
      for bps in slippage_params:
          adj_returns = txn.adjust_returns_for_slippage(returns, positions,
                                                        transactions, bps)
          label = str(bps) + " bps"
          slippage_sweep[label] = ep.cum_returns(adj_returns, 1)
  
      slippage_sweep.plot(alpha=1.0, lw=0.5, ax=ax)
  
      ax.set_title('Cumulative returns given additional per-dollar slippage')
      ax.set_ylabel('')
  
      ax.legend(loc='center left', frameon=True, framealpha=0.5)
  
      return ax
docs: Plots equity curves at different per-dollar slippage assumptions.
  
      Parameters
      ----------
      returns : pd.Series
          Timeseries of portfolio returns to be adjusted for various
          degrees of slippage.
      positions : pd.DataFrame
          Daily net position values.
           - See full explanation in tears.create_full_tear_sheet.
      transactions : pd.DataFrame
          Prices and amounts of executed trades. One row per trade.
           - See full explanation in tears.create_full_tear_sheet.
      slippage_params: tuple
          Slippage pameters to apply to the return time series (in
          basis points).
      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.