def markowitz(tickers, start=None, end=None, regularize=False):
prices = load_prices(tickers, start, end)
mu = expected_returns.mean_historical_return(prices, frequency=12)
S = risk_models.sample_cov(prices, frequency=12)
ef = EfficientFrontier(mu, S)
if regularize:
ef.gamma = 1
return ef
def test_max_sharpe_L2_reg_with_shorts():
ef_no_reg = setup_efficient_frontier()
ef_no_reg.max_sharpe()
initial_number = sum(ef_no_reg.weights > 0.01)
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(None, None))
ef.gamma = 1
w = ef.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(ef.tickers)
assert set(w.keys()) == set(ef.expected_returns.index)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.32360478341793864, 0.20241509658051923, 1.499911758296975),
)
new_number = sum(ef.weights > 0.01)
assert new_number >= initial_number
def test_max_sharpe_L2_reg_with_shorts():
ef_no_reg = setup_efficient_frontier()
ef_no_reg.max_sharpe()
initial_number = sum(ef_no_reg.weights > 0.01)
ef = EfficientFrontier(
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
)
ef.gamma = 1
w = ef.max_sharpe()
assert isinstance(w, dict)
assert set(w.keys()) == set(ef.tickers)
assert set(w.keys()) == set(ef.expected_returns.index)
np.testing.assert_almost_equal(ef.weights.sum(), 1)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.3236047844566581, 0.20241509723550233, 1.4969817524033966),
)
new_number = sum(ef.weights > 0.01)
assert new_number >= initial_number
