def test_max_quadratic_utility_with_shorts():
es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True),
weight_bounds=(-1, 1))
es.max_quadratic_utility()
np.testing.assert_almost_equal(es.weights.sum(), 1)
np.testing.assert_allclose(
es.portfolio_performance(),
(3.2806086360944846, 1.0219729896939227, 3.190503730505662),
rtol=1e-4,
atol=1e-4,
)
def test_max_quadratic_utility_market_neutral():
es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True),
weight_bounds=(-1, 1),
solver="ECOS")
es.max_quadratic_utility(market_neutral=True)
np.testing.assert_almost_equal(es.weights.sum(), 0)
np.testing.assert_allclose(
es.portfolio_performance(),
(3.106826930927578, 0.9789014670659041, 3.153358161960706),
rtol=1e-4,
atol=1e-4,
)
def test_es_errors():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df)
with pytest.warns(UserWarning):
EfficientSemivariance(mu, historical_rets)
historical_rets = historical_rets.dropna(axis=0, how="any")
es = EfficientSemivariance(mu, historical_rets)
with pytest.raises(NotImplementedError):
es.min_volatility()
with pytest.raises(NotImplementedError):
es.max_sharpe()
with pytest.raises(ValueError):
# Must be > 0
es.max_quadratic_utility(risk_aversion=-0.01)
with pytest.raises(ValueError):
# Must be > 0
es.efficient_return(target_return=-0.01)
with pytest.raises(ValueError):
# Must be <= max expected return
es.efficient_return(target_return=np.abs(mu).max() + 0.01)
with pytest.raises(AttributeError):
# list not supported.
EfficientSemivariance(mu, historical_rets.to_numpy().tolist())
historical_rets = historical_rets.iloc[:, :-1]
with pytest.raises(ValueError):
EfficientSemivariance(mu, historical_rets)