def test_hrp_portfolio():
df = get_data()
returns = df.pct_change().dropna(how="all")
hrp = HRPOpt(returns)
w = hrp.optimize(linkage_method="single")
# uncomment this line if you want generating a new file
# pd.Series(w).to_csv(resource("weights_hrp.csv"))
x = pd.read_csv(resource("weights_hrp.csv"), squeeze=True, index_col=0)
pd.testing.assert_series_equal(x,
pd.Series(w),
check_names=False,
rtol=1e-2)
assert isinstance(w, dict)
assert set(w.keys()) == set(df.columns)
np.testing.assert_almost_equal(sum(w.values()), 1)
assert all([i >= 0 for i in w.values()])
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_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()
with pytest.warns(RuntimeWarning):
black_litterman.market_implied_prior_returns(mcaps, delta, S.values)
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_weights():
df = get_data()
S = risk_models.sample_cov(df)
viewdict = {
"AAPL": 0.20,
"BBY": -0.30,
"BAC": 0,
"SBUX": -0.2,
"T": 0.131321
}
bl = BlackLittermanModel(S, absolute_views=viewdict)
prices = pd.read_csv(resource("spy_prices.csv"),
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(prices)
bl.bl_weights(delta)
w = bl.clean_weights()
assert abs(sum(w.values()) - 1) < 1e-5
# check weights are allocated in same direction as views
# (in absence of priors)
assert all(viewdict[t] * w[t] >= 0 for t in viewdict)
# numerical check
test_weights = {
"GOOG": 0.0,
"AAPL": 1.40675,
"FB": 0.0,
"BABA": 0.0,
"AMZN": 0.0,
"GE": 0.0,
"AMD": 0.0,
"WMT": 0.0,
"BAC": 0.02651,
"GM": 0.0,
"T": 2.81117,
"UAA": 0.0,
"SHLD": 0.0,
"XOM": 0.0,
"RRC": 0.0,
"BBY": -1.44667,
"MA": 0.0,
"PFE": 0.0,
"JPM": 0.0,
"SBUX": -1.79776,
}
assert w == test_weights
bl = BlackLittermanModel(S, absolute_views=viewdict)
bl.optimize(delta)
w2 = bl.clean_weights()
assert w2 == w
bl = BlackLittermanModel(S, absolute_views=pd.Series(viewdict))
bl.optimize(delta)
w2 = bl.clean_weights()
assert w2 == w
