def test_cla_max_sharpe_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.44859872371106785, 0.26762066559448255, 1.601515797589826),
)
sharpe = cla.portfolio_performance()[2]
cla_long_only = setup_cla()
cla_long_only.max_sharpe()
long_only_sharpe = cla_long_only.portfolio_performance()[2]
assert sharpe > long_only_sharpe
def test_cla_max_sharpe_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3799273115521356, 0.23115368271125736, 1.5570909679242886),
)
sharpe = cla.portfolio_performance()[2]
cla_long_only = setup_cla()
cla_long_only.max_sharpe()
long_only_sharpe = cla_long_only.portfolio_performance()[2]
assert sharpe > long_only_sharpe
def test_cla_efficient_frontier():
cla = setup_cla()
cla.efficient_frontier()
mu, sigma, weights = cla.efficient_frontier()
assert len(mu) == len(sigma) and len(sigma) == len(weights)
# higher return = higher risk
assert sigma[-1] < sigma[0] and mu[-1] < mu[0]
assert weights[0].shape == (20, 1)
def test_cla_min_volatility():
cla = setup_cla()
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.1505682139948257, 0.15915084514118688, 0.8204054077060994),
)
def test_min_volatility():
cla = setup_cla()
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.1793123248125915, 0.15915084514118688, 1.00101463282373),
)
def test_min_volatility():
cla = setup_cla()
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.1793123248125915, 0.15915084514118688, 0.9981061956268638),
)
def test_cla_custom_bounds():
bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
cla = CLA(*setup_cla(data_only=True), weight_bounds=bounds)
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
assert (0.01 <= cla.weights[::2]).all() and (cla.weights[::2] <=
0.13).all()
assert (0.02 <= cla.weights[1::2]).all() and (cla.weights[1::2] <=
0.11).all()
# Test polymorphism of the weight_bounds param.
bounds2 = ([bounds[0][0], bounds[1][0]] * 10,
[bounds[0][1], bounds[1][1]] * 10)
cla2 = CLA(*setup_cla(data_only=True), weight_bounds=bounds2)
cla2.cov_matrix = risk_models.exp_cov(df).values
w2 = cla2.min_volatility()
assert dict(w2) == dict(w)
def test_max_sharpe_long_only():
cla = setup_cla()
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3253436663900292, 0.21333530089904357, 1.4312852355106793),
)
def test_max_sharpe_long_only():
cla = setup_cla()
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3253436657555845, 0.2133353004830236, 1.4281588303044812),
)
def test_cla_max_sharpe_long_only():
cla = setup_cla()
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2994470912768992, 0.21764331657015668, 1.283968171780824),
)
def test_cla_efficient_frontier():
cla = setup_cla()
with pytest.raises(ValueError):
cla.efficient_frontier()
cla.max_sharpe()
mu, sigma, weights = cla.efficient_frontier()
assert len(mu) == len(sigma) and len(sigma) == len(weights)
# higher return = higher risk
assert sigma[-1] < sigma[0] and mu[-1] < mu[0]
assert weights[0].shape == (20, 1)
def test_cla_max_sharpe_exp_cov():
df = get_data()
cla = setup_cla()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.32971891062187103, 0.17670121760851704, 1.7527831149871063),
)
def test_cla_max_sharpe_semicovariance():
df = get_data()
cla = setup_cla()
cla.cov_matrix = risk_models.semicovariance(df, benchmark=0).values
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2936179968144084, 0.06362345488289835, 4.300583759841616),
)
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2634735528776959, 0.13259590618253303, 1.8362071642131053),
)
def test_cla_max_sharpe_exp_cov():
df = get_data()
cla = setup_cla()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3619453128519127, 0.1724297730592084, 1.9830990135009723),
)
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.23215576461823062, 0.1325959061825329, 1.6000174569958052),
)
def test_cla_max_sharpe_semicovariance():
df = get_data()
cla = setup_cla()
cla.cov_matrix = risk_models.semicovariance(df, benchmark=0).values
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2686858719299194, 0.06489248187610204, 3.8322755539652578),
)
def test_max_sharpe_semicovariance():
df = get_data()
cla = setup_cla()
cla.covar = risk_models.semicovariance(df, benchmark=0)
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3253436663900292, 0.21333530089904357, 1.4312852355106793),
)
def test_max_sharpe_semicovariance():
# f
df = get_data()
cla = setup_cla()
cla.covar = risk_models.semicovariance(df, benchmark=0)
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3253436657555845, 0.2133353004830236, 1.4281588303044812),
)
def test_cla_custom_bounds():
bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
cla = CLA(*setup_cla(data_only=True), weight_bounds=bounds)
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
assert (0.01 <= cla.weights[::2]).all() and (cla.weights[::2] <=
0.13).all()
assert (0.02 <= cla.weights[1::2]).all() and (cla.weights[1::2] <=
0.11).all()
def test_cla_max_sharpe_semicovariance():
df = get_data()
cla = setup_cla()
cla.cov_matrix = risk_models.semicovariance(df, benchmark=0).values
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2721798377099145, 0.07258537193305141, 3.474251505420551),
atol=1e-4,
rtol=1e-4,
)
def test_portfolio_performance():
cla = setup_cla()
with pytest.raises(ValueError):
cla.portfolio_performance()
cla.max_sharpe()
assert cla.portfolio_performance()
def test_portfolio_performance():
cla = setup_cla()
with pytest.raises(ValueError):
cla.portfolio_performance()
cla.max_sharpe()
assert cla.portfolio_performance()
def test_cla_inheritance():
cla = setup_cla()
assert cla.clean_weights
assert cla.set_weights
def test_cla_error():
cla = setup_cla()
w = cla.min_volatility()
with pytest.raises(NotImplementedError):
cla.set_weights(w)
def test_cla_inheritance():
cla = setup_cla()
assert cla.clean_weights
assert cla.set_weights
