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)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.3564305116656491, 0.07201282488003401, 4.671813836300796),
)
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)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.42444834528495234, 0.0898263632679403, 4.50255727350929),
)
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)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.3907992623559733, 0.0809285460933456, 4.581810501430255),
)
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)
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(),
(0.3908928033782067, 0.18999999995323363, 1.9520673866815672),
atol=1e-6,
)
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)
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(),
(0.3934093962620499, 0.18999999989011893, 1.9653126130421081),
atol=1e-6,
)
