def plot_slippage_sensitivity(returns, positions, transactions,<EOL>ax=None, **kwargs):
if ax is None:<EOL><INDENT>ax = plt.gca()<EOL><DEDENT>avg_returns_given_slippage = pd.Series()<EOL>for bps in range(<NUM_LIT:1>, <NUM_LIT:100>):<EOL><INDENT>adj_returns = txn.adjust_returns_for_slippage(returns, positions,<EOL>transactions, bps)<EOL>avg_returns = ep.annual_return(adj_returns)<EOL>avg_returns_given_slippage.loc[bps] = avg_returns<EOL><DEDENT>avg_returns_given_slippage.plot(alpha=<NUM_LIT:1.0>, lw=<NUM_LIT:2>, ax=ax)<EOL>ax.set_title('<STR_LIT>')<EOL>ax.set_xticks(np.arange(<NUM_LIT:0>, <NUM_LIT:100>, <NUM_LIT:10>))<EOL>ax.set_ylabel('<STR_LIT>')<EOL>ax.set_xlabel('<STR_LIT>')<EOL>return ax<EOL>
Plots curve relating per-dollar slippage to average annual returns.

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.
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.