def create_returns_tear_sheet(returns, positions=None,
                              transactions=None,
                              live_start_date=None,
                              cone_std=(1.0, 1.5, 2.0),
                              benchmark_rets=None,
                              bootstrap=False,
                              turnover_denom='AGB',
                              header_rows=None,
                              return_fig=False):
    """
    Generate a number of plots for analyzing a strategy's returns.

    - Fetches benchmarks, then creates the plots on a single figure.
    - Plots: rolling returns (with cone), rolling beta, rolling sharpe,
        rolling Fama-French risk factors, drawdowns, underwater plot, monthly
        and annual return plots, daily similarity plots,
        and return quantile box plot.
    - Will also print the start and end dates of the strategy,
        performance statistics, drawdown periods, and the return range.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame, optional
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame, optional
        Executed trade volumes and fill prices.
        - See full explanation in create_full_tear_sheet.
    live_start_date : datetime, optional
        The point in time when the strategy began live trading,
        after its backtest period.
    cone_std : float, or tuple, optional
        If float, The standard deviation to use for the cone plots.
        If tuple, Tuple of standard deviation values to use for the cone plots
         - The cone is a normal distribution with this standard deviation
             centered around a linear regression.
    benchmark_rets : pd.Series, optional
        Daily noncumulative returns of the benchmark.
         - This is in the same style as returns.
    bootstrap : boolean, optional
        Whether to perform bootstrap analysis for the performance
        metrics. Takes a few minutes longer.
    turnover_denom : str, optional
        Either AGB or portfolio_value, default AGB.
        - See full explanation in txn.get_turnover.
    header_rows : dict or OrderedDict, optional
        Extra rows to display at the top of the perf stats table.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
    """

    if benchmark_rets is not None:
        returns = utils.clip_returns_to_benchmark(returns, benchmark_rets)

    plotting.show_perf_stats(returns, benchmark_rets,
                             positions=positions,
                             transactions=transactions,
                             turnover_denom=turnover_denom,
                             bootstrap=bootstrap,
                             live_start_date=live_start_date,
                             header_rows=header_rows)

    plotting.show_worst_drawdown_periods(returns)

    vertical_sections = 11

    if live_start_date is not None:
        vertical_sections += 1
        live_start_date = ep.utils.get_utc_timestamp(live_start_date)

    if benchmark_rets is not None:
        vertical_sections += 1

    if bootstrap:
        vertical_sections += 1

    fig = plt.figure(figsize=(14, vertical_sections * 6))
    gs = gridspec.GridSpec(vertical_sections, 3, wspace=0.5, hspace=0.5)
    ax_rolling_returns = plt.subplot(gs[:2, :])

    i = 2
    ax_rolling_returns_vol_match = plt.subplot(gs[i, :],
                                               sharex=ax_rolling_returns)
    i += 1
    ax_rolling_returns_log = plt.subplot(gs[i, :],
                                         sharex=ax_rolling_returns)
    i += 1
    ax_returns = plt.subplot(gs[i, :],
                             sharex=ax_rolling_returns)
    i += 1
    if benchmark_rets is not None:
        ax_rolling_beta = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
        i += 1
    ax_rolling_volatility = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_rolling_sharpe = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_drawdown = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_underwater = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_monthly_heatmap = plt.subplot(gs[i, 0])
    ax_annual_returns = plt.subplot(gs[i, 1])
    ax_monthly_dist = plt.subplot(gs[i, 2])
    i += 1
    ax_return_quantiles = plt.subplot(gs[i, :])
    i += 1

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        live_start_date=live_start_date,
        cone_std=cone_std,
        ax=ax_rolling_returns)
    ax_rolling_returns.set_title(
        'Cumulative returns')

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        live_start_date=live_start_date,
        cone_std=None,
        volatility_match=(benchmark_rets is not None),
        legend_loc=None,
        ax=ax_rolling_returns_vol_match)
    ax_rolling_returns_vol_match.set_title(
        'Cumulative returns volatility matched to benchmark')

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        logy=True,
        live_start_date=live_start_date,
        cone_std=cone_std,
        ax=ax_rolling_returns_log)
    ax_rolling_returns_log.set_title(
        'Cumulative returns on logarithmic scale')

    plotting.plot_returns(
        returns,
        live_start_date=live_start_date,
        ax=ax_returns,
    )
    ax_returns.set_title(
        'Returns')

    if benchmark_rets is not None:
        plotting.plot_rolling_beta(
            returns, benchmark_rets, ax=ax_rolling_beta)

    plotting.plot_rolling_volatility(
        returns, factor_returns=benchmark_rets, ax=ax_rolling_volatility)

    plotting.plot_rolling_sharpe(
        returns, ax=ax_rolling_sharpe)

    # Drawdowns
    plotting.plot_drawdown_periods(
        returns, top=5, ax=ax_drawdown)

    plotting.plot_drawdown_underwater(
        returns=returns, ax=ax_underwater)

    plotting.plot_monthly_returns_heatmap(returns, ax=ax_monthly_heatmap)
    plotting.plot_annual_returns(returns, ax=ax_annual_returns)
    plotting.plot_monthly_returns_dist(returns, ax=ax_monthly_dist)

    plotting.plot_return_quantiles(
        returns,
        live_start_date=live_start_date,
        ax=ax_return_quantiles)

    if bootstrap and (benchmark_rets is not None):
        ax_bootstrap = plt.subplot(gs[i, :])
        plotting.plot_perf_stats(returns, benchmark_rets,
                                 ax=ax_bootstrap)
    elif bootstrap:
        raise ValueError('bootstrap requires passing of benchmark_rets.')

    for ax in fig.axes:
        plt.setp(ax.get_xticklabels(), visible=True)

    if return_fig:
        return fig
def create_returns_tear_sheet(returns, positions=None,
                              transactions=None,
                              live_start_date=None,
                              cone_std=(1.0, 1.5, 2.0),
                              benchmark_rets=None,
                              bootstrap=False,
                              turnover_denom='AGB',
                              header_rows=None,
                              return_fig=False):
    """
    Generate a number of plots for analyzing a strategy's returns.

    - Fetches benchmarks, then creates the plots on a single figure.
    - Plots: rolling returns (with cone), rolling beta, rolling sharpe,
        rolling Fama-French risk factors, drawdowns, underwater plot, monthly
        and annual return plots, daily similarity plots,
        and return quantile box plot.
    - Will also print the start and end dates of the strategy,
        performance statistics, drawdown periods, and the return range.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame, optional
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame, optional
        Executed trade volumes and fill prices.
        - See full explanation in create_full_tear_sheet.
    live_start_date : datetime, optional
        The point in time when the strategy began live trading,
        after its backtest period.
    cone_std : float, or tuple, optional
        If float, The standard deviation to use for the cone plots.
        If tuple, Tuple of standard deviation values to use for the cone plots
         - The cone is a normal distribution with this standard deviation
             centered around a linear regression.
    benchmark_rets : pd.Series, optional
        Daily noncumulative returns of the benchmark.
         - This is in the same style as returns.
    bootstrap : boolean, optional
        Whether to perform bootstrap analysis for the performance
        metrics. Takes a few minutes longer.
    turnover_denom : str, optional
        Either AGB or portfolio_value, default AGB.
        - See full explanation in txn.get_turnover.
    header_rows : dict or OrderedDict, optional
        Extra rows to display at the top of the perf stats table.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
    """

    if benchmark_rets is not None:
        returns = utils.clip_returns_to_benchmark(returns, benchmark_rets)

    plotting.show_perf_stats(returns, benchmark_rets,
                             positions=positions,
                             transactions=transactions,
                             turnover_denom=turnover_denom,
                             bootstrap=bootstrap,
                             live_start_date=live_start_date,
                             header_rows=header_rows)

    plotting.show_worst_drawdown_periods(returns)

    vertical_sections = 11

    if live_start_date is not None:
        vertical_sections += 1
        live_start_date = ep.utils.get_utc_timestamp(live_start_date)

    if benchmark_rets is not None:
        vertical_sections += 1

    if bootstrap:
        vertical_sections += 1

    fig = plt.figure(figsize=(14, vertical_sections * 6))
    gs = gridspec.GridSpec(vertical_sections, 3, wspace=0.5, hspace=0.5)
    ax_rolling_returns = plt.subplot(gs[:2, :])

    i = 2
    ax_rolling_returns_vol_match = plt.subplot(gs[i, :],
                                               sharex=ax_rolling_returns)
    i += 1
    ax_rolling_returns_log = plt.subplot(gs[i, :],
                                         sharex=ax_rolling_returns)
    i += 1
    ax_returns = plt.subplot(gs[i, :],
                             sharex=ax_rolling_returns)
    i += 1
    if benchmark_rets is not None:
        ax_rolling_beta = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
        i += 1
    ax_rolling_volatility = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_rolling_sharpe = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_drawdown = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_underwater = plt.subplot(gs[i, :], sharex=ax_rolling_returns)
    i += 1
    ax_monthly_heatmap = plt.subplot(gs[i, 0])
    ax_annual_returns = plt.subplot(gs[i, 1])
    ax_monthly_dist = plt.subplot(gs[i, 2])
    i += 1
    ax_return_quantiles = plt.subplot(gs[i, :])
    i += 1

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        live_start_date=live_start_date,
        cone_std=cone_std,
        ax=ax_rolling_returns)
    ax_rolling_returns.set_title(
        'Cumulative returns')

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        live_start_date=live_start_date,
        cone_std=None,
        volatility_match=(benchmark_rets is not None),
        legend_loc=None,
        ax=ax_rolling_returns_vol_match)
    ax_rolling_returns_vol_match.set_title(
        'Cumulative returns volatility matched to benchmark')

    plotting.plot_rolling_returns(
        returns,
        factor_returns=benchmark_rets,
        logy=True,
        live_start_date=live_start_date,
        cone_std=cone_std,
        ax=ax_rolling_returns_log)
    ax_rolling_returns_log.set_title(
        'Cumulative returns on logarithmic scale')

    plotting.plot_returns(
        returns,
        live_start_date=live_start_date,
        ax=ax_returns,
    )
    ax_returns.set_title(
        'Returns')

    if benchmark_rets is not None:
        plotting.plot_rolling_beta(
            returns, benchmark_rets, ax=ax_rolling_beta)

    plotting.plot_rolling_volatility(
        returns, factor_returns=benchmark_rets, ax=ax_rolling_volatility)

    plotting.plot_rolling_sharpe(
        returns, ax=ax_rolling_sharpe)

    # Drawdowns
    plotting.plot_drawdown_periods(
        returns, top=5, ax=ax_drawdown)

    plotting.plot_drawdown_underwater(
        returns=returns, ax=ax_underwater)

    plotting.plot_monthly_returns_heatmap(returns, ax=ax_monthly_heatmap)
    plotting.plot_annual_returns(returns, ax=ax_annual_returns)
    plotting.plot_monthly_returns_dist(returns, ax=ax_monthly_dist)

    plotting.plot_return_quantiles(
        returns,
        live_start_date=live_start_date,
        ax=ax_return_quantiles)

    if bootstrap and (benchmark_rets is not None):
        ax_bootstrap = plt.subplot(gs[i, :])
        plotting.plot_perf_stats(returns, benchmark_rets,
                                 ax=ax_bootstrap)
    elif bootstrap:
        raise ValueError('bootstrap requires passing of benchmark_rets.')

    for ax in fig.axes:
        plt.setp(ax.get_xticklabels(), visible=True)

    if return_fig:
        return fig
def create_returns_tear_sheet ( returns , positions = None , transactions = None , live_start_date = None , cone_std = ( 1.0 , 1.5 , 2.0 ) , benchmark_rets = None , bootstrap = False , turnover_denom = 'AGB' , header_rows = None , return_fig = False ) : if benchmark_rets is not None : returns = utils . clip_returns_to_benchmark ( returns , benchmark_rets ) plotting . show_perf_stats ( returns , benchmark_rets , positions = positions , transactions = transactions , turnover_denom = turnover_denom , bootstrap = bootstrap , live_start_date = live_start_date , header_rows = header_rows ) plotting . show_worst_drawdown_periods ( returns ) vertical_sections = 11 if live_start_date is not None : vertical_sections += 1 live_start_date = ep . utils . get_utc_timestamp ( live_start_date ) if benchmark_rets is not None : vertical_sections += 1 if bootstrap : vertical_sections += 1 fig = plt . figure ( figsize = ( 14 , vertical_sections * 6 ) ) gs = gridspec . GridSpec ( vertical_sections , 3 , wspace = 0.5 , hspace = 0.5 ) ax_rolling_returns = plt . subplot ( gs [ : 2 , : ] ) i = 2 ax_rolling_returns_vol_match = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_rolling_returns_log = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_returns = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 if benchmark_rets is not None : ax_rolling_beta = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_rolling_volatility = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_rolling_sharpe = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_drawdown = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_underwater = plt . subplot ( gs [ i , : ] , sharex = ax_rolling_returns ) i += 1 ax_monthly_heatmap = plt . subplot ( gs [ i , 0 ] ) ax_annual_returns = plt . subplot ( gs [ i , 1 ] ) ax_monthly_dist = plt . subplot ( gs [ i , 2 ] ) i += 1 ax_return_quantiles = plt . subplot ( gs [ i , : ] ) i += 1 plotting . plot_rolling_returns ( returns , factor_returns = benchmark_rets , live_start_date = live_start_date , cone_std = cone_std , ax = ax_rolling_returns ) ax_rolling_returns . set_title ( 'Cumulative returns' ) plotting . plot_rolling_returns ( returns , factor_returns = benchmark_rets , live_start_date = live_start_date , cone_std = None , volatility_match = ( benchmark_rets is not None ) , legend_loc = None , ax = ax_rolling_returns_vol_match ) ax_rolling_returns_vol_match . set_title ( 'Cumulative returns volatility matched to benchmark' ) plotting . plot_rolling_returns ( returns , factor_returns = benchmark_rets , logy = True , live_start_date = live_start_date , cone_std = cone_std , ax = ax_rolling_returns_log ) ax_rolling_returns_log . set_title ( 'Cumulative returns on logarithmic scale' ) plotting . plot_returns ( returns , live_start_date = live_start_date , ax = ax_returns , ) ax_returns . set_title ( 'Returns' ) if benchmark_rets is not None : plotting . plot_rolling_beta ( returns , benchmark_rets , ax = ax_rolling_beta ) plotting . plot_rolling_volatility ( returns , factor_returns = benchmark_rets , ax = ax_rolling_volatility ) plotting . plot_rolling_sharpe ( returns , ax = ax_rolling_sharpe ) # Drawdowns plotting . plot_drawdown_periods ( returns , top = 5 , ax = ax_drawdown ) plotting . plot_drawdown_underwater ( returns = returns , ax = ax_underwater ) plotting . plot_monthly_returns_heatmap ( returns , ax = ax_monthly_heatmap ) plotting . plot_annual_returns ( returns , ax = ax_annual_returns ) plotting . plot_monthly_returns_dist ( returns , ax = ax_monthly_dist ) plotting . plot_return_quantiles ( returns , live_start_date = live_start_date , ax = ax_return_quantiles ) if bootstrap and ( benchmark_rets is not None ) : ax_bootstrap = plt . subplot ( gs [ i , : ] ) plotting . plot_perf_stats ( returns , benchmark_rets , ax = ax_bootstrap ) elif bootstrap : raise ValueError ( 'bootstrap requires passing of benchmark_rets.' ) for ax in fig . axes : plt . setp ( ax . get_xticklabels ( ) , visible = True ) if return_fig : return fig
Generate a number of plots for analyzing a strategy's returns.

    - Fetches benchmarks, then creates the plots on a single figure.
    - Plots: rolling returns (with cone), rolling beta, rolling sharpe,
        rolling Fama-French risk factors, drawdowns, underwater plot, monthly
        and annual return plots, daily similarity plots,
        and return quantile box plot.
    - Will also print the start and end dates of the strategy,
        performance statistics, drawdown periods, and the return range.

    Parameters
    ----------
    returns : pd.Series
        Daily returns of the strategy, noncumulative.
         - See full explanation in create_full_tear_sheet.
    positions : pd.DataFrame, optional
        Daily net position values.
         - See full explanation in create_full_tear_sheet.
    transactions : pd.DataFrame, optional
        Executed trade volumes and fill prices.
        - See full explanation in create_full_tear_sheet.
    live_start_date : datetime, optional
        The point in time when the strategy began live trading,
        after its backtest period.
    cone_std : float, or tuple, optional
        If float, The standard deviation to use for the cone plots.
        If tuple, Tuple of standard deviation values to use for the cone plots
         - The cone is a normal distribution with this standard deviation
             centered around a linear regression.
    benchmark_rets : pd.Series, optional
        Daily noncumulative returns of the benchmark.
         - This is in the same style as returns.
    bootstrap : boolean, optional
        Whether to perform bootstrap analysis for the performance
        metrics. Takes a few minutes longer.
    turnover_denom : str, optional
        Either AGB or portfolio_value, default AGB.
        - See full explanation in txn.get_turnover.
    header_rows : dict or OrderedDict, optional
        Extra rows to display at the top of the perf stats table.
    return_fig : boolean, optional
        If True, returns the figure that was plotted on.
Generate a number of plots for analyzing a strategy s returns .