pyfolio/timeseries.py
def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
                            starting_value=1, num_samples=1000,
                            random_seed=None):
    """
    Determines the upper and lower bounds of an n standard deviation
    cone of forecasted cumulative returns. Future cumulative mean and
    standard devation are computed by repeatedly sampling from the
    in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
    meaning it does not assume that returns are normally distributed.

    Parameters
    ----------
    is_returns : pd.Series
        In-sample daily returns of the strategy, noncumulative.
         - See full explanation in tears.create_full_tear_sheet.
    num_days : int
        Number of days to project the probability cone forward.
    cone_std : int, float, or list of int/float
        Number of standard devations to use in the boundaries of
        the cone. If multiple values are passed, cone bounds will
        be generated for each value.
    starting_value : int or float
        Starting value of the out of sample period.
    num_samples : int
        Number of samples to draw from the in-sample daily returns.
        Each sample will be an array with length num_days.
        A higher number of samples will generate a more accurate
        bootstrap cone.
    random_seed : int
        Seed for the pseudorandom number generator used by the pandas
        sample method.

    Returns
    -------
    pd.DataFrame
        Contains upper and lower cone boundaries. Column names are
        strings corresponding to the number of standard devations
        above (positive) or below (negative) the projected mean
        cumulative returns.
    """

    samples = simulate_paths(
        is_returns=is_returns,
        num_days=num_days,
        starting_value=starting_value,
        num_samples=num_samples,
        random_seed=random_seed
    )

    cone_bounds = summarize_paths(
        samples=samples,
        cone_std=cone_std,
        starting_value=starting_value
    )

    return cone_bounds
def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
                            starting_value=1, num_samples=1000,
                            random_seed=None):
    """
    Determines the upper and lower bounds of an n standard deviation
    cone of forecasted cumulative returns. Future cumulative mean and
    standard devation are computed by repeatedly sampling from the
    in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
    meaning it does not assume that returns are normally distributed.

    Parameters
    ----------
    is_returns : pd.Series
        In-sample daily returns of the strategy, noncumulative.
         - See full explanation in tears.create_full_tear_sheet.
    num_days : int
        Number of days to project the probability cone forward.
    cone_std : int, float, or list of int/float
        Number of standard devations to use in the boundaries of
        the cone. If multiple values are passed, cone bounds will
        be generated for each value.
    starting_value : int or float
        Starting value of the out of sample period.
    num_samples : int
        Number of samples to draw from the in-sample daily returns.
        Each sample will be an array with length num_days.
        A higher number of samples will generate a more accurate
        bootstrap cone.
    random_seed : int
        Seed for the pseudorandom number generator used by the pandas
        sample method.

    Returns
    -------
    pd.DataFrame
        Contains upper and lower cone boundaries. Column names are
        strings corresponding to the number of standard devations
        above (positive) or below (negative) the projected mean
        cumulative returns.
    """

    samples = simulate_paths(
        is_returns=is_returns,
        num_days=num_days,
        starting_value=starting_value,
        num_samples=num_samples,
        random_seed=random_seed
    )

    cone_bounds = summarize_paths(
        samples=samples,
        cone_std=cone_std,
        starting_value=starting_value
    )

    return cone_bounds
def forecast_cone_bootstrap ( is_returns , num_days , cone_std = ( 1. , 1.5 , 2. ) , starting_value = 1 , num_samples = 1000 , random_seed = None ) : samples = simulate_paths ( is_returns = is_returns , num_days = num_days , starting_value = starting_value , num_samples = num_samples , random_seed = random_seed ) cone_bounds = summarize_paths ( samples = samples , cone_std = cone_std , starting_value = starting_value ) return cone_bounds
Determines the upper and lower bounds of an n standard deviation
    cone of forecasted cumulative returns. Future cumulative mean and
    standard devation are computed by repeatedly sampling from the
    in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
    meaning it does not assume that returns are normally distributed.

    Parameters
    ----------
    is_returns : pd.Series
        In-sample daily returns of the strategy, noncumulative.
         - See full explanation in tears.create_full_tear_sheet.
    num_days : int
        Number of days to project the probability cone forward.
    cone_std : int, float, or list of int/float
        Number of standard devations to use in the boundaries of
        the cone. If multiple values are passed, cone bounds will
        be generated for each value.
    starting_value : int or float
        Starting value of the out of sample period.
    num_samples : int
        Number of samples to draw from the in-sample daily returns.
        Each sample will be an array with length num_days.
        A higher number of samples will generate a more accurate
        bootstrap cone.
    random_seed : int
        Seed for the pseudorandom number generator used by the pandas
        sample method.

    Returns
    -------
    pd.DataFrame
        Contains upper and lower cone boundaries. Column names are
        strings corresponding to the number of standard devations
        above (positive) or below (negative) the projected mean
        cumulative returns.
Determines the upper and lower bounds of an n standard deviation cone of forecasted cumulative returns . Future cumulative mean and standard devation are computed by repeatedly sampling from the in - sample daily returns ( i . e . bootstrap ) . This cone is non - parametric meaning it does not assume that returns are normally distributed .