def test_parametrization():
cd = setup_efficient_cdar()
cd.efficient_risk(0.08)
cd.efficient_risk(0.07)
cd = setup_efficient_cdar()
cd.efficient_return(0.08)
cd.efficient_return(0.07)
def test_min_cdar_extra_constraints():
cd = setup_efficient_cdar()
w = cd.min_cdar()
assert w["GOOG"] < 0.02 and w["MA"] > 0.02
cd = setup_efficient_cdar()
cd.add_constraint(lambda x: x[0] >= 0.03)
cd.add_constraint(lambda x: x[16] <= 0.03)
w = cd.min_cdar()
assert w["GOOG"] >= 0.025 and w["MA"] <= 0.035
def test_cdar_beta():
# cdar should decrease (i.e higher loss) as beta increases
cd = setup_efficient_cdar()
cd._beta = 0.5
cd.min_cdar()
cdar = cd.portfolio_performance()[1]
for beta in np.arange(0.55, 1, 0.05):
cd = setup_efficient_cdar()
cd._beta = beta
cd.min_cdar()
cdar_test = cd.portfolio_performance()[1]
assert cdar_test >= cdar
cdar = cdar_test
def test_min_cdar_tx_costs():
# Baseline
cd = setup_efficient_cdar()
cd.min_cdar()
w1 = cd.weights
# Pretend we were initally equal weight
cd = setup_efficient_cdar()
prev_w = np.array([1 / cd.n_assets] * cd.n_assets)
cd.add_objective(objective_functions.transaction_cost, w_prev=prev_w)
cd.min_cdar()
w2 = cd.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_risk_low_risk():
cd = setup_efficient_cdar()
cd.min_cdar()
min_value = cd.portfolio_performance()[1]
# Should fail below
with pytest.raises(OptimizationError):
cd = setup_efficient_cdar()
cd.efficient_risk(min_value - 0.01)
cd = setup_efficient_cdar()
cd.efficient_risk(min_value + 0.01)
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.212772, min_value + 0.01),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return_short():
cd = EfficientCDaR(*setup_efficient_cdar(data_only=True),
weight_bounds=(-3.0, 3.0))
w = cd.efficient_return(0.28)
assert isinstance(w, dict)
assert set(w.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.28, 0.045999),
rtol=1e-4,
atol=1e-4,
)
cdar = cd.portfolio_performance()[1]
ef_long_only = EfficientCDaR(*setup_efficient_cdar(data_only=True),
weight_bounds=(0.0, 1.0))
ef_long_only.efficient_return(0.26)
long_only_cdar = ef_long_only.portfolio_performance()[1]
assert long_only_cdar > cdar
def test_min_cdar_different_solver():
cd = setup_efficient_cdar(solver="ECOS")
w = cd.min_cdar()
assert isinstance(w, dict)
assert set(w.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
test_performance = (0.14798, 0.056433)
np.testing.assert_allclose(cd.portfolio_performance(),
test_performance,
rtol=1e-2,
atol=1e-2)
def test_efficient_return_L2_reg():
cd = setup_efficient_cdar()
cd.add_objective(objective_functions.L2_reg, gamma=1)
w = cd.efficient_return(0.25)
assert isinstance(w, dict)
assert set(w.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.25, 0.101115),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return():
cd = setup_efficient_cdar()
w = cd.efficient_return(0.25)
assert isinstance(w, dict)
assert set(w.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.25, 0.076193),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_risk_L2_reg():
cd = setup_efficient_cdar()
cd.add_objective(objective_functions.L2_reg, gamma=1)
weights = cd.efficient_risk(0.18)
assert isinstance(weights, dict)
assert set(weights.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
np.testing.assert_array_less(np.zeros(len(weights)), cd.weights + 1e-4)
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.288999, 0.178443),
rtol=1e-4,
atol=1e-4,
)
cd2 = setup_efficient_cdar()
cd2.efficient_risk(0.18)
# L2_reg should pull close to equal weight
equal_weight = np.full((cd.n_assets, ), 1 / cd.n_assets)
assert (np.abs(equal_weight - cd.weights).sum() <
np.abs(equal_weight - cd2.weights).sum())
def test_efficient_risk_market_neutral():
cd = EfficientCDaR(*setup_efficient_cdar(data_only=True),
weight_bounds=(-1, 1))
w = cd.efficient_risk(0.025, market_neutral=True)
assert isinstance(w, dict)
assert set(w.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 0)
assert (cd.weights < 1).all() and (cd.weights > -1).all()
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.219306, 0.025),
rtol=1e-4,
atol=1e-4,
)
def test_min_cdar_L2_reg():
cd = setup_efficient_cdar(solver="ECOS")
cd.add_objective(objective_functions.L2_reg, gamma=0.1)
weights = cd.min_cdar()
assert isinstance(weights, dict)
assert set(weights.keys()) == set(cd.tickers)
np.testing.assert_almost_equal(cd.weights.sum(), 1)
assert all([i >= -1e-5 for i in weights.values()])
cd2 = setup_efficient_cdar()
cd2.min_cdar()
# L2_reg should pull close to equal weight
equal_weight = np.full((cd.n_assets, ), 1 / cd.n_assets)
assert (np.abs(equal_weight - cd.weights).sum() <
np.abs(equal_weight - cd2.weights).sum())
np.testing.assert_allclose(
cd.portfolio_performance(),
(0.135105, 0.060849),
rtol=1e-4,
atol=1e-4,
)
def test_min_cdar_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}
cd = setup_efficient_cdar()
cd.add_sector_constraints(sector_mapper, sector_lower, sector_upper)
weights = cd.min_cdar()
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
def test_cdar_errors():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df)
with pytest.warns(UserWarning):
EfficientCDaR(mu, historical_rets)
historical_rets = historical_rets.dropna(axis=0, how="any")
assert EfficientCDaR(mu, historical_rets)
cd = setup_efficient_cdar()
with pytest.raises(NotImplementedError):
cd.min_volatility()
with pytest.raises(NotImplementedError):
cd.max_sharpe()
with pytest.raises(NotImplementedError):
cd.max_quadratic_utility()
with pytest.raises(ValueError):
# Beta must be between 0 and 1
cd = EfficientCDaR(mu, historical_rets, 1)
with pytest.raises(OptimizationError):
# Must be <= max expected return
cd = EfficientCDaR(mu, historical_rets)
cd.efficient_return(target_return=np.abs(mu).max() + 0.01)
with pytest.raises(TypeError):
# list not supported.
EfficientCDaR(mu, historical_rets.to_numpy().tolist())
historical_rets = historical_rets.iloc[:, :-1]
with pytest.raises(ValueError):
EfficientCDaR(mu, historical_rets)
