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)