def test_es_example_weekly():
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()
es = EfficientSemivariance(mu, historical_rets, frequency=52)
es.efficient_return(0.2)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.2000000562544616, 0.07667633475531543, 2.3475307841574087),
rtol=1e-4,
atol=1e-4,
)
def test_es_example_monthly():
df = get_data()
df = df.resample("M").first()
mu = expected_returns.mean_historical_return(df, frequency=12)
historical_rets = expected_returns.returns_from_prices(df).dropna()
es = EfficientSemivariance(mu, historical_rets, frequency=12)
es.efficient_return(0.3)
np.testing.assert_allclose(
es.portfolio_performance(),
(0.3, 0.04746519522734184, 5.899059271933824),
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_es_errors():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df)
with pytest.warns(UserWarning):
EfficientSemivariance(mu, historical_rets)
historical_rets = historical_rets.dropna(axis=0, how="any")
assert EfficientSemivariance(mu, historical_rets)
historical_rets = historical_rets.iloc[:, :-1]
with pytest.raises(ValueError):
EfficientSemivariance(mu, historical_rets)
def test_es_example():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df).dropna()
es = EfficientSemivariance(mu, historical_rets)
w = es.efficient_return(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(),
(0.20, 0.08558991313395496, 2.1030523036993265),
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_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)
es = EfficientSemivariance(mu, sample_rets)
w = es.efficient_return(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(),
(0.20, 0.10050247458629837, 1.7910016727029479),
rtol=1e-4,
atol=1e-4,
)
# Cover verbose param case
np.testing.assert_equal(es.portfolio_performance(verbose=True),
es.portfolio_performance())
def test_es_example_short():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df).dropna()
es = EfficientSemivariance(mu, historical_rets, weight_bounds=(-1, 1))
w = es.efficient_return(0.2, market_neutral=True)
goog_weight = w["GOOG"]
historical_rets["GOOG"] -= historical_rets["GOOG"].quantile(0.75)
es = EfficientSemivariance(mu, historical_rets, weight_bounds=(-1, 1))
w = es.efficient_return(0.2, market_neutral=True)
goog_weight2 = w["GOOG"]
assert abs(goog_weight2) >= abs(goog_weight)
def test_es_errors():
df = get_data()
mu = expected_returns.mean_historical_return(df)
historical_rets = expected_returns.returns_from_prices(df)
with pytest.warns(UserWarning):
EfficientSemivariance(mu, historical_rets)
historical_rets = historical_rets.dropna(axis=0, how="any")
es = EfficientSemivariance(mu, historical_rets)
with pytest.raises(NotImplementedError):
es.min_volatility()
with pytest.raises(NotImplementedError):
es.max_sharpe()
with pytest.raises(ValueError):
# Must be > 0
es.max_quadratic_utility(risk_aversion=-0.01)
with pytest.raises(ValueError):
# Must be > 0
es.efficient_return(target_return=-0.01)
with pytest.raises(ValueError):
# Must be <= max expected return
es.efficient_return(target_return=np.abs(mu).max() + 0.01)
with pytest.raises(AttributeError):
# list not supported.
EfficientSemivariance(mu, historical_rets.to_numpy().tolist())
historical_rets = historical_rets.iloc[:, :-1]
with pytest.raises(ValueError):
EfficientSemivariance(mu, historical_rets)
def test_es_no_returns():
# Issue 324
df = get_data()
historical_rets = expected_returns.returns_from_prices(df).dropna()
assert EfficientSemivariance(None, historical_rets)