def test_efficient_semivariance_vs_heuristic():
benchmark = 0
es = setup_efficient_semivariance()
es.efficient_return(0.20)
mu_es, semi_deviation, _ = es.portfolio_performance()
np.testing.assert_almost_equal(mu_es, 0.2)
mean_return, historic_returns = setup_efficient_semivariance(
data_only=True)
pairwise_semivariance = risk_models.semicovariance(historic_returns,
returns_data=True,
benchmark=0,
frequency=1)
ef = EfficientFrontier(mean_return, pairwise_semivariance)
ef.efficient_return(0.20)
mu_ef, _, _ = ef.portfolio_performance()
# mu_ef *= 252
portfolio_returns = historic_returns @ ef.weights
drops = np.fmin(portfolio_returns - benchmark, 0)
T = historic_returns.shape[0]
semivariance = np.sum(np.square(drops)) / T * 252
semi_deviation_ef = np.sqrt(semivariance)
assert semi_deviation < semi_deviation_ef
assert mu_es / semi_deviation > mu_ef / semi_deviation_ef
def test_min_semivariance_extra_constraints():
es = setup_efficient_semivariance()
w = es.min_semivariance()
assert w["GOOG"] < 0.02 and w["AAPL"] > 0.02
es = setup_efficient_semivariance()
es.add_constraint(lambda x: x[0] >= 0.03)
es.add_constraint(lambda x: x[1] <= 0.03)
w = es.min_semivariance()
assert w["GOOG"] >= 0.025 and w["AAPL"] <= 0.035
def test_parametrization():
es = setup_efficient_semivariance()
es.efficient_risk(0.19)
es.efficient_risk(0.19)
es = setup_efficient_semivariance()
es.efficient_return(0.25)
es.efficient_return(0.25)
es = setup_efficient_semivariance()
es.max_quadratic_utility(1)
es.max_quadratic_utility(1)
def test_max_quadratic_utility_range():
# increasing risk_aversion should lower both vol and return
es = setup_efficient_semivariance()
es.solver = "ECOS"
es.max_quadratic_utility(risk_aversion=0.01)
prev_ret, prev_semivar, _ = es.portfolio_performance()
for delta in [0.1, 0.5, 1, 3, 5, 10]:
es = setup_efficient_semivariance()
es.max_quadratic_utility(risk_aversion=delta)
print(es.portfolio_performance())
ret, semivar, _ = es.portfolio_performance()
assert ret < prev_ret and semivar < prev_semivar
prev_ret = ret
prev_semivar = semivar
def test_min_semivariance_tx_costs():
# Baseline
es = setup_efficient_semivariance()
es.min_semivariance()
w1 = es.weights
# Pretend we were initally equal weight
es = setup_efficient_semivariance()
prev_w = np.array([1 / es.n_assets] * es.n_assets)
es.add_objective(objective_functions.transaction_cost, w_prev=prev_w)
es.min_semivariance()
w2 = es.weights
# TX cost should pull closer to prev portfolio
assert np.abs(prev_w - w2).sum() < np.abs(prev_w - w1).sum()
def test_efficient_semivariance_vs_heuristic_weekly():
benchmark = 0
_, historic_returns = setup_efficient_semivariance(data_only=True)
weekly_returns = historic_returns.resample("W").sum()
mean_weekly_returns = weekly_returns.mean(axis=0)
es = EfficientSemivariance(mean_weekly_returns,
weekly_returns,
frequency=52)
es.efficient_return(0.20 / 52)
mu_es, semi_deviation, _ = es.portfolio_performance()
pairwise_semivariance = risk_models.semicovariance(weekly_returns,
returns_data=True,
benchmark=0,
frequency=1)
ef = EfficientFrontier(mean_weekly_returns, pairwise_semivariance)
ef.efficient_return(0.20 / 52)
mu_ef, _, _ = ef.portfolio_performance()
portfolio_returns = historic_returns @ ef.weights
drops = np.fmin(portfolio_returns - benchmark, 0)
T = weekly_returns.shape[0]
semivariance = np.sum(np.square(drops)) / T * 52
semi_deviation_ef = np.sqrt(semivariance)
assert semi_deviation < semi_deviation_ef
assert mu_es / semi_deviation > mu_ef / semi_deviation_ef
def test_efficient_risk_low_risk():
es = setup_efficient_semivariance()
es.min_semivariance()
min_value = es.portfolio_performance()[1]
# Should fail below
with pytest.raises(SolverError):
es = setup_efficient_semivariance()
es.efficient_risk(min_value - 0.01)
es = setup_efficient_semivariance()
es.efficient_risk(min_value + 0.01)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.22187394011764086, min_value + 0.01, 2.1248023794940822),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return_short():
es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True),
weight_bounds=(None, None))
w = es.efficient_return(0.25)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.25, 0.09073654273906914, 2.534811096726317),
rtol=1e-4,
atol=1e-4,
)
sortino = es.portfolio_performance()[2]
ef_long_only = setup_efficient_semivariance()
ef_long_only.efficient_return(0.25)
long_only_sortino = ef_long_only.portfolio_performance()[2]
assert sortino > long_only_sortino
def test_min_semivariance_different_solver():
es = setup_efficient_semivariance(solver="ECOS")
w = es.min_semivariance()
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= 0 for i in w.values()])
test_performance = (0.10258693150198975, 0.08495982152369484,
0.9720704448391136)
np.testing.assert_allclose(es.portfolio_performance(),
test_performance,
rtol=1e-2,
atol=1e-2)
es = setup_efficient_semivariance(solver="OSQP")
w = es.min_semivariance()
np.testing.assert_allclose(es.portfolio_performance(),
test_performance,
rtol=1e-2,
atol=1e-2)
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_max_quadratic_utility():
es = setup_efficient_semivariance()
w = es.max_quadratic_utility(risk_aversion=2)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.45386915148502716, 0.1730946893389541, 2.506542246570167),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_risk_L2_reg():
es = setup_efficient_semivariance()
es.add_objective(objective_functions.L2_reg, gamma=1)
weights = es.efficient_risk(0.19)
assert isinstance(weights, dict)
assert set(weights.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
np.testing.assert_array_less(np.zeros(len(weights)), es.weights + 1e-4)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.31339234055845905, 0.14360466712901232, 2.0430557475884794),
rtol=1e-4,
atol=1e-4,
)
ef2 = setup_efficient_semivariance()
ef2.efficient_risk(0.19)
# L2_reg should pull close to equal weight
equal_weight = np.full((es.n_assets, ), 1 / es.n_assets)
assert (np.abs(equal_weight - es.weights).sum() <
np.abs(equal_weight - ef2.weights).sum())
def test_min_semivariance():
es = setup_efficient_semivariance()
w = es.min_semivariance()
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
es.portfolio_performance(),
(0.1024524845740464, 0.08497381732237187, 0.970328121911246),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return_L2_reg():
es = setup_efficient_semivariance()
es.add_objective(objective_functions.L2_reg, gamma=1)
w = es.efficient_return(0.25)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= 0 for i in w.values()])
np.testing.assert_allclose(
es.portfolio_performance(),
(0.25, 0.12068369208695134, 1.9058084487031388),
rtol=1e-4,
atol=1e-4,
)
def test_min_semivariance_L2_reg():
es = setup_efficient_semivariance()
es.add_objective(objective_functions.L2_reg, gamma=1)
weights = es.min_semivariance()
assert isinstance(weights, dict)
assert set(weights.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= 0 for i in weights.values()])
ef2 = setup_efficient_semivariance()
ef2.min_semivariance()
# L2_reg should pull close to equal weight
equal_weight = np.full((es.n_assets, ), 1 / es.n_assets)
assert (np.abs(equal_weight - es.weights).sum() <
np.abs(equal_weight - ef2.weights).sum())
np.testing.assert_allclose(
es.portfolio_performance(),
(0.11684586840374926, 0.11286393006990993, 0.8580763432902009),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return():
es = setup_efficient_semivariance()
w = es.efficient_return(0.25)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
es.portfolio_performance(),
(0.25, 0.10035962239578998, 2.291760094911752),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_risk_market_neutral():
es = EfficientSemivariance(*setup_efficient_semivariance(data_only=True),
weight_bounds=(-1, 1))
w = es.efficient_risk(0.21, market_neutral=True)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 0)
assert (es.weights < 1).all() and (es.weights > -1).all()
np.testing.assert_allclose(
es.portfolio_performance(),
(0.9257112257221027, 0.21, 4.312873624163129),
rtol=1e-4,
atol=1e-4,
)
def test_max_quadratic_utility_L2_reg():
es = setup_efficient_semivariance()
es.add_objective(objective_functions.L2_reg, gamma=5)
weights = es.max_quadratic_utility()
assert isinstance(weights, dict)
assert set(weights.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= 0 for i in weights.values()])
np.testing.assert_allclose(
es.portfolio_performance(),
(0.11717839698599568, 0.11286470279298752, 0.8610167269410781),
rtol=1e-4,
atol=1e-4,
)
ef2 = setup_efficient_semivariance()
ef2.max_quadratic_utility()
# L2_reg should pull close to equal weight
equal_weight = np.full((es.n_assets, ), 1 / es.n_assets)
assert (np.abs(equal_weight - es.weights).sum() <
np.abs(equal_weight - ef2.weights).sum())
def test_efficient_risk():
es = setup_efficient_semivariance()
w = es.efficient_risk(0.2)
assert isinstance(w, dict)
assert set(w.keys()) == set(es.tickers)
np.testing.assert_almost_equal(es.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
es.portfolio_performance(),
(0.4567521774612227, 0.2, 2.183678863194344),
rtol=1e-4,
atol=1e-4,
)
def test_min_semivariance_sector_constraints():
sector_mapper = {
"GOOG": "tech",
"AAPL": "tech",
"FB": "tech",
"AMZN": "tech",
"BABA": "tech",
"GE": "utility",
"AMD": "tech",
"WMT": "retail",
"BAC": "fig",
"GM": "auto",
"T": "auto",
"UAA": "airline",
"SHLD": "retail",
"XOM": "energy",
"RRC": "energy",
"BBY": "retail",
"MA": "fig",
"PFE": "pharma",
"JPM": "fig",
"SBUX": "retail",
}
sector_upper = {
"tech": 0.2,
"utility": 0.1,
"retail": 0.2,
"fig": 0.4,
"airline": 0.05,
"energy": 0.2,
}
sector_lower = {"utility": 0.01, "fig": 0.02, "airline": 0.01}
es = setup_efficient_semivariance()
es.add_sector_constraints(sector_mapper, sector_lower, sector_upper)
weights = es.min_semivariance()
for sector in list(set().union(sector_upper, sector_lower)):
sector_sum = 0
for t, v in weights.items():
if sector_mapper[t] == sector:
sector_sum += v
assert sector_sum <= sector_upper.get(sector, 1) + 1e-5
assert sector_sum >= sector_lower.get(sector, 0) - 1e-5