def test_max_sharpe_short_semicovariance():
df = get_data()
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(-1, 1))
ef.cov_matrix = risk_models.semicovariance(df, benchmark=0)
w = ef.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(ef.tickers)
assert set(w.keys()) == set(ef.expected_returns.index)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.3564654865246848, 0.07202031837368413, 4.671813373260894))
def test_efficient_risk_exp_cov_market_neutral():
df = get_data()
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(-1, 1))
ef.cov_matrix = risk_models.exp_cov(df)
w = ef.efficient_risk(0.19, market_neutral=True)
assert isinstance(w, dict)
assert set(w.keys()) == set(ef.tickers)
assert set(w.keys()) == set(ef.expected_returns.index)
np.testing.assert_almost_equal(ef.weights.sum(), 0)
assert (ef.weights < 1).all() and (ef.weights > -1).all()
np.testing.assert_allclose(ef.portfolio_performance(),
(0.39089308906686077, 0.19, 1.9520670176494717),
atol=1e-6)
