def portfolio_bounds(er, variance_bound, u_bounds, extended_return=True):
logging.info("Initialize and calculate variance bound portfolio with bounded portfolio weights.")
n = er.shape[1]
mu = ps.first_moment(er)
sigma = ps.covariance(er)
sol = eval_portfolio_bounds(mu, sigma, variance_bound, u_bounds)
logging.info("Solution = {0}".format(sol))
if extended_return:
return sol
else:
return sol[0]
def portfolio_logbar(er, variance_bound, extended_return=True):
logging.info("Initialize parameters and calculate variance bound portfolio.")
n = er.shape[1]
mu = ps.first_moment(er)
sigma = ps.covariance(er)
sol = eval_portfolio_logbar(mu, sigma, variance_bound)
logging.info("Solution = {0}".format(sol))
if extended_return:
return sol
else:
return sol[0]
def bench_exact_hess(x):
test_prob.eval_hess_bar_g(x)
def benchmark_fd_exact_hess_bar_g():
print(timeit.timeit('bench_fd_hess()', setup='from dccsupport import bench_fd_hess',
number=1000))
print(timeit.timeit('bench_exact_hess()', setup='from dccsupport import bench_exact_hess',
number=1000))
# test_xm = np.concatenate([np.array([[ -1.3125 , -1.125 , -1.0625 , -0.5625 ],
# [ -1.125 , -1.8125 , -1.25 , -0.90625],
# [ -1.0625 , -1.25 , -1.3125 , -0.625 ],
# [ -0.5625 , -0.90625, -0.625 , -1.025 ]]).reshape(-1),
# np.array([0.1667, 0.3333, 0.5])]) / 10.
# test_xm = init_tau(test_xm, 3, 1)/100.
# test_xm_extended = init_tau(test_xm, 3, 2)/100.
if __name__ == '__main__':
test_er = loaddata.load_fama_french_10_industry().values[-300:, :3]
mu, sigma = ps.first_moment(test_er), ps.covariance(test_er)
# mu, P = ps.shrinkage_moments(test_er.values[300:,:3], bootstrap_size=100)
variance_bound = 0.03
u_bounds = {'lower': -0.1*np.ones(3), 'upper': 0.5*np.ones(3)}
test_prob = MVProblem(mu, sigma, variance_bound, u_bounds)
# test_prob_extended = DccSupportProblem(mu, P, s, y, eta, extended_Ws)
def portfolio(excess_returns, mean_weight):
mu = ps.first_moment(excess_returns)
sigma = ps.covariance(excess_returns)
return mean_weight * 0.5 * la.solve(sigma, mu)