def bootstrap(block_size, data, nbootstrap, sig_level=0.05, custom_func=None):
"""
Description
----
Stationary bootstrap for different block sizes
Inputs
----
:block_size: Number of points per block
:nbootstrap: Number of bootstrap samples
:data: Data to find confidence intervals for
Outputs
----
List containing mean, standard error
"""
bs = StationaryBootstrap(block_size, data)
try:
bs_results = eval('bs.apply(' + custom_func + ', nbootstrap)')
except TypeError:
bs_results = bs.apply(np.mean, nbootstrap)
mn = np.mean(bs_results, axis=0)
se = np.std(bs_results, ddof=1, axis=0)
return list(np.hstack((mn, se)))
def _bootstrap(self, block_size):
"""
Description
----
Stationary bootstrap for different block sizes
Inputs
----
:block_size: Number of points per block
Outputs
----
List containing mean, standard error
"""
bs = StationaryBootstrap(block_size, self._data)
try:
bs_results = eval('bs.apply(' + self._custom_func +
', nbootstrap)')
except TypeError:
bs_results = bs.apply(np.mean, self._nbootstrap)
mn = np.mean(bs_results, axis=0)
se = np.std(bs_results, ddof=1, axis=0)
return list(np.hstack((mn, se)))
def bootstrap(x, f):
if len(x) == 0:
return np.nan
from arch.bootstrap import StationaryBootstrap
bs = StationaryBootstrap(50, x)
return bs.apply(f,100).mean()
def bootstrap(x, f):
if len(x) == 0:
return np.nan
from arch.bootstrap import StationaryBootstrap
bs = StationaryBootstrap(50, x)
return bs.apply(f, 100).mean()
def sharpe_ratio(x):
mu, sigma = x.mean(), np.sqrt(x.var())
values = np.array([x.sum(), sigma, mu / sigma]).squeeze()
index = ['CR', 'sigma', 'SR']
return pd.Series(values, index=index)
R = import_data('results/Yearly_portfolio/yearly_portfolio_returns_CDAX_mp')
R1 = R.iloc[:, 2]
params = sharpe_ratio(R1)
bs = StationaryBootstrap(12, R1)
results = bs.apply(sharpe_ratio, 100000)
delta_CR = results[:, 0] - params[0]
delta_sigma = results[:, 1] - params[1]
delta_SR = results[:, 2] - params[2]
def CI(delta, q=0.95):
delta.sort()
abs_sorted = np.abs(delta)
bound = abs_sorted[int(q * 100000)]
return bound
CR_bound = CI(delta_CR)
sigma_bound = CI(delta_sigma)
SR_bound = CI(delta_SR)
