def test_cov_ndarray():
df = get_data()
prior_df = df.pct_change().mean()
S = risk_models.sample_cov(df)
views = pd.Series(0.1, index=S.columns)
bl = BlackLittermanModel(S, pi=prior_df, Q=views)
bl_nd = BlackLittermanModel(S.to_numpy(), pi=prior_df.to_numpy(), Q=views)
# Compare without missing ticker index values.
np.testing.assert_equal(bl_nd.bl_returns().to_numpy(),
bl.bl_returns().to_numpy())
np.testing.assert_equal(bl_nd.bl_cov().to_numpy(), bl.bl_cov().to_numpy())
assert list(bl_nd.bl_weights().values()) == list(bl.bl_weights().values())
def test_bl_cov_default():
df = get_data()
cov_matrix = risk_models.CovarianceShrinkage(df).ledoit_wolf()
viewdict = {"AAPL": 0.20, "BBY": -0.30, "BAC": 0, "SBUX": -0.2, "T": 0.131321}
bl = BlackLittermanModel(cov_matrix, absolute_views=viewdict)
S = bl.bl_cov()
assert S.shape == (20, 20)
assert S.index.equals(df.columns)
assert S.index.equals(S.columns)
assert S.notnull().all().all()
