def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
    """
    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.
    """

    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
def plot_return_quantiles(returns, live_start_date=None, ax=None, **kwargs):
    """
    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.
    """

    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
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
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.
Creates a box plot of daily weekly and monthly return distributions .