def test_cvar_example():
beta = 0.95
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df).dropna()
cv = EfficientCVaR(mu, historical_rets, beta=beta)
w = cv.min_cvar()
assert isinstance(w, dict)
assert set(w.keys()) == set(cv.tickers)
np.testing.assert_almost_equal(cv.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.17745746040573562, 0.017049502122532853),
rtol=1e-4,
atol=1e-4,
)
cvar = cv.portfolio_performance()[1]
portfolio_rets = historical_rets @ cv.weights
var_hist = portfolio_rets.quantile(1 - beta)
cvar_hist = -portfolio_rets[portfolio_rets < var_hist].mean()
np.testing.assert_almost_equal(cvar_hist, cvar, decimal=3)
def test_efficient_risk_market_neutral():
cv = EfficientCVaR(*setup_efficient_cvar(data_only=True), weight_bounds=(-1, 1))
w = cv.efficient_risk(0.025, market_neutral=True)
assert isinstance(w, dict)
assert set(w.keys()) == set(cv.tickers)
np.testing.assert_almost_equal(cv.weights.sum(), 0)
assert (cv.weights < 1).all() and (cv.weights > -1).all()
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.5895653670063358, 0.025),
rtol=1e-4,
atol=1e-4,
)
def test_cvar_example_short():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df).dropna()
cv = EfficientCVaR(
mu,
historical_rets,
weight_bounds=(-1, 1),
)
w = cv.efficient_return(0.2, market_neutral=True)
assert isinstance(w, dict)
assert set(w.keys()) == set(cv.tickers)
np.testing.assert_almost_equal(cv.weights.sum(), 0)
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.2, 0.013406209257292611),
rtol=1e-4,
atol=1e-4,
)
def test_efficient_return_short():
cv = EfficientCVaR(*setup_efficient_cvar(data_only=True),
weight_bounds=(-3.0, 3.0))
w = cv.efficient_return(0.26)
assert isinstance(w, dict)
assert set(w.keys()) == set(cv.tickers)
np.testing.assert_almost_equal(cv.weights.sum(), 1)
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.26, 0.01804624747353764),
rtol=1e-4,
atol=1e-4,
)
cvar = cv.portfolio_performance()[1]
ef_long_only = EfficientCVaR(*setup_efficient_cvar(data_only=True),
weight_bounds=(0.0, 1.0))
ef_long_only.efficient_return(0.26)
long_only_cvar = ef_long_only.portfolio_performance()[1]
assert long_only_cvar > cvar
def test_es_return_sample():
df = get_data()
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Generate a 1y sample of daily data
np.random.seed(0)
mu_daily = (1 + mu)**(1 / 252) - 1
S_daily = S / 252
sample_rets = pd.DataFrame(np.random.multivariate_normal(
mu_daily, S_daily, 300),
columns=mu.index)
cv = EfficientCVaR(mu, sample_rets)
w = cv.efficient_return(0.2)
assert isinstance(w, dict)
assert set(w.keys()) == set(cv.tickers)
np.testing.assert_almost_equal(cv.weights.sum(), 1)
assert all([i >= -1e-5 for i in w.values()])
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.20, 0.01789275427676941),
rtol=1e-4,
atol=1e-4,
)
# Cover verbose param case
np.testing.assert_equal(cv.portfolio_performance(verbose=True),
cv.portfolio_performance())
def test_cvar_example_weekly():
beta = 0.95
df = get_data()
df = df.resample("W").first()
mu = expected_returns.mean_historical_return(df, frequency=52)
historical_rets = expected_returns.returns_from_prices(df).dropna()
cv = EfficientCVaR(mu, historical_rets, beta=beta)
cv.efficient_return(0.2)
np.testing.assert_allclose(
cv.portfolio_performance(),
(0.2, 0.03447723250708958),
rtol=1e-4,
atol=1e-4,
)
cvar = cv.portfolio_performance()[1]
portfolio_rets = historical_rets @ cv.weights
var_hist = portfolio_rets.quantile(1 - beta)
cvar_hist = -portfolio_rets[portfolio_rets < var_hist].mean()
np.testing.assert_almost_equal(cvar_hist, cvar, decimal=3)
def test_cvar_no_returns():
# Issue 324
df = get_data()
historical_rets = expected_returns.returns_from_prices(df).dropna()
assert EfficientCVaR(None, historical_rets)
def test_cvar_errors():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df)
with pytest.warns(UserWarning):
EfficientCVaR(mu, historical_rets)
historical_rets = historical_rets.dropna(axis=0, how="any")
assert EfficientCVaR(mu, historical_rets)
cv = setup_efficient_cvar()
with pytest.raises(NotImplementedError):
cv.min_volatility()
with pytest.raises(NotImplementedError):
cv.max_sharpe()
with pytest.raises(NotImplementedError):
cv.max_quadratic_utility()
with pytest.raises(ValueError):
# Beta must be between 0 and 1
cv = EfficientCVaR(mu, historical_rets, 1)
with pytest.warns(UserWarning):
cv = EfficientCVaR(mu, historical_rets, 0.1)
with pytest.raises(OptimizationError):
# Must be <= max expected return
cv = EfficientCVaR(mu, historical_rets)
cv.efficient_return(target_return=np.abs(mu).max() + 0.01)
with pytest.raises(AttributeError):
# list not supported.
EfficientCVaR(mu, historical_rets.to_numpy().tolist())
historical_rets = historical_rets.iloc[:, :-1]
with pytest.raises(ValueError):
EfficientCVaR(mu, historical_rets)