def test_cla_max_sharpe_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.44859872371106785, 0.26762066559448255, 1.601515797589826),
)
sharpe = cla.portfolio_performance()[2]
cla_long_only = setup_cla()
cla_long_only.max_sharpe()
long_only_sharpe = cla_long_only.portfolio_performance()[2]
assert sharpe > long_only_sharpe
def test_cla_max_sharpe_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
w = cla.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.3799273115521356, 0.23115368271125736, 1.5570909679242886),
)
sharpe = cla.portfolio_performance()[2]
cla_long_only = setup_cla()
cla_long_only.max_sharpe()
long_only_sharpe = cla_long_only.portfolio_performance()[2]
assert sharpe > long_only_sharpe
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.23215576461823062, 0.1325959061825329, 1.6000174569958052),
)
def test_cla_min_volatility_exp_cov_short():
cla = CLA(*setup_cla(data_only=True), weight_bounds=(-1, 1))
df = get_data()
cla.cov_matrix = risk_models.exp_cov(df).values
w = cla.min_volatility()
assert isinstance(w, dict)
assert set(w.keys()) == set(cla.tickers)
np.testing.assert_almost_equal(cla.weights.sum(), 1)
np.testing.assert_allclose(
cla.portfolio_performance(),
(0.2634735528776959, 0.13259590618253303, 1.8362071642131053),
)
'JPM': 0.017675709092515708,
'MA': 0.03812737349732021,
'PFE': 0.07786528342813454,
'RRC': 0.03161528695094597,
'SBUX': 0.039844436656239136,
'SHLD': 0.027113184241298865,
'T': 0.11138956508836476,
'UAA': 0.02711590957075009,
'WMT': 0.10569551148587905,
'XOM': 0.11175337115721229}
"""
# Crticial Line Algorithm
cla = CLA(mu, S)
print(cla.max_sharpe())
cla.portfolio_performance(verbose=True)
"""
{'GOOG': 0.020889868669945022,
'AAPL': 0.08867994115132602,
'FB': 0.19417572932251745,
'BABA': 0.10492386821217001,
'AMZN': 0.0644908140418782,
'GE': 0.0,
'AMD': 0.0,
'WMT': 0.0034898157701416382,
'BAC': 0.0,
'GM': 0.0,
'T': 2.4138966206946562e-19,
'UAA': 0.0,
'SHLD': 0.0,
'XOM': 0.0005100736411646903,