pyfolio/timeseries.py
def simulate_paths(is_returns, num_days,
                   starting_value=1, num_samples=1000, random_seed=None):
    """
    Gnerate alternate paths using available values from in-sample returns.

    Parameters
    ----------
    is_returns : pandas.core.frame.DataFrame
        Non-cumulative in-sample returns.
    num_days : int
        Number of days to project the probability cone forward.
    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
    -------
    samples : numpy.ndarray
    """

    samples = np.empty((num_samples, num_days))
    seed = np.random.RandomState(seed=random_seed)
    for i in range(num_samples):
        samples[i, :] = is_returns.sample(num_days, replace=True,
                                          random_state=seed)

    return samples
def simulate_paths(is_returns, num_days,
                   starting_value=1, num_samples=1000, random_seed=None):
    """
    Gnerate alternate paths using available values from in-sample returns.

    Parameters
    ----------
    is_returns : pandas.core.frame.DataFrame
        Non-cumulative in-sample returns.
    num_days : int
        Number of days to project the probability cone forward.
    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
    -------
    samples : numpy.ndarray
    """

    samples = np.empty((num_samples, num_days))
    seed = np.random.RandomState(seed=random_seed)
    for i in range(num_samples):
        samples[i, :] = is_returns.sample(num_days, replace=True,
                                          random_state=seed)

    return samples
def simulate_paths ( is_returns , num_days , starting_value = 1 , num_samples = 1000 , random_seed = None ) : samples = np . empty ( ( num_samples , num_days ) ) seed = np . random . RandomState ( seed = random_seed ) for i in range ( num_samples ) : samples [ i , : ] = is_returns . sample ( num_days , replace = True , random_state = seed ) return samples
Gnerate alternate paths using available values from in-sample returns.

    Parameters
    ----------
    is_returns : pandas.core.frame.DataFrame
        Non-cumulative in-sample returns.
    num_days : int
        Number of days to project the probability cone forward.
    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
    -------
    samples : numpy.ndarray
Gnerate alternate paths using available values from in - sample returns .