def test_idzorek_with_priors():
df = get_data()
S = risk_models.sample_cov(df)
mcaps = get_market_caps()
viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
bl = BlackLittermanModel(
S,
pi="market",
market_caps=mcaps,
absolute_views=viewdict,
omega="idzorek",
view_confidences=[1, 1, 0.25, 0.25],
)
rets = bl.bl_returns()
assert bl.omega[0, 0] == 0
np.testing.assert_almost_equal(rets["AAPL"], -0.3)
with pytest.raises(ValueError):
bl.portfolio_performance()
bl.bl_weights()
np.testing.assert_allclose(
bl.portfolio_performance(),
(0.943431295405105, 0.5361412623208567, 1.722365653051476),
)
# Check that bl.cov() has been called and used
assert bl.posterior_cov is not None
def test_bl_market_prior():
df = get_data()
S = risk_models.sample_cov(df)
prices = pd.read_csv(resource("spy_prices.csv"),
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = get_market_caps()
prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)
viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
bl = BlackLittermanModel(S, pi=prior, absolute_views=viewdict)
rets = bl.bl_returns()
# compare posterior with prior
for v in viewdict:
assert (prior[v] <= rets[v] <= viewdict[v]) or (viewdict[v] <= rets[v]
<= prior[v])
with pytest.raises(ValueError):
bl.portfolio_performance()
bl.bl_weights(delta)
np.testing.assert_allclose(
bl.portfolio_performance(),
(0.2580693114409672, 0.265445955488424, 0.8968654692926723),
)
# Check that bl.cov() has been called and used
assert bl.posterior_cov is not None
def test_bl_tau():
df = get_data()
S = risk_models.sample_cov(df)
prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
)
delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = get_market_caps()
prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)
viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
# Need to change omega for this test to work
omega = np.diag([0.01, 0.01, 0.01, 0.01])
bl0 = BlackLittermanModel(
S, pi=prior, absolute_views=viewdict, tau=1e-10, omega=omega
)
bl1 = BlackLittermanModel(
S, pi=prior, absolute_views=viewdict, tau=0.01, omega=omega
)
bl2 = BlackLittermanModel(
S, pi=prior, absolute_views=viewdict, tau=0.1, omega=omega
)
# For tiny tau, posterior should roughly equal prior
np.testing.assert_allclose(bl0.bl_returns(), bl0.pi.flatten(), rtol=1e-5)
# For bigger tau, GOOG should be given more weight
assert bl1.bl_returns()["GOOG"] > bl0.bl_returns()["GOOG"]
assert bl2.bl_returns()["GOOG"] > bl1.bl_returns()["GOOG"]
def test_market_implied_prior():
df = get_data()
S = risk_models.sample_cov(df)
prices = pd.read_csv(resource("spy_prices.csv"),
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = get_market_caps()
pi = black_litterman.market_implied_prior_returns(mcaps, delta, S)
assert isinstance(pi, pd.Series)
assert list(pi.index) == list(df.columns)
assert pi.notnull().all()
assert pi.dtype == "float64"
np.testing.assert_array_almost_equal(
pi.values,
np.array([
0.14933293,
0.2168623,
0.11219185,
0.10362374,
0.28416295,
0.12196098,
0.19036819,
0.08860159,
0.17724273,
0.08779627,
0.0791797,
0.16460474,
0.12854665,
0.08657863,
0.11230036,
0.13875465,
0.15017163,
0.09066484,
0.1696369,
0.13270213,
]),
)
mcaps = pd.Series(mcaps)
pi2 = black_litterman.market_implied_prior_returns(mcaps, delta, S)
pd.testing.assert_series_equal(pi, pi2, check_exact=False)
# Test alternate syntax
bl = BlackLittermanModel(
S,
pi="market",
market_caps=mcaps,
absolute_views={"AAPL": 0.1},
risk_aversion=delta,
)
pi = black_litterman.market_implied_prior_returns(mcaps,
delta,
S,
risk_free_rate=0)
np.testing.assert_array_almost_equal(bl.pi, pi.values.reshape(-1, 1))
def test_bl_market_automatic():
df = get_data()
S = risk_models.sample_cov(df)
mcaps = get_market_caps()
viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
bl = BlackLittermanModel(S, pi="market", absolute_views=viewdict, market_caps=mcaps)
rets = bl.bl_returns()
# Compare with explicit
prior = black_litterman.market_implied_prior_returns(mcaps, 1, S, 0)
bl2 = BlackLittermanModel(S, pi=prior, absolute_views=viewdict)
rets2 = bl2.bl_returns()
pd.testing.assert_series_equal(rets, rets2)
