def simulate_mean_sortino_ratio(portfolio, test_data,
parameters = default_parameters):
test_bounds = generate_bounds(0.01,
len(test_data))
test_data_sets = [simulate_test_data(test_data,
test_bounds,
factor=1.5)
for _ in range(0,100)]
return np.mean([pstats.sortino_base(simulate_portfolio(portfolio,
td),
parameters.target_rate)
for td in test_data_sets])
def generate_ratios_experiment_20151027(
portfolios,
parameters = default_parameters):
test_data = excess_returns[120:] + parameters.risk_free_rate
test_bounds = generate_bounds(0.05,
len(test_data))
msv_results = simulate_portfolio(portfolios['msv'],
test_data)
pu_results = [(s, simulate_portfolio(u, test_data))
for (s,u) in portfolios['pu']]
naive_results = simulate_portfolio(portfolios['naive'],
test_data)
return {'Sharpe': {'msv': pstats.sharpe_base(msv_results),
'pu': [[s, pstats.sharpe_base(v)]
for (s, v) in pu_results],
'naive': pstats.sharpe_base(naive_results)},
'Sortino':
{'msv': pstats.sortino_base(msv_results,
parameters.target_rate),
'pu': [[s, pstats.sortino_base(v, parameters.target_rate)]
for (s, v) in pu_results],
'naive': pstats.sortino_base(naive_results,
parameters.target_rate)}}