def test_custom_nonconvex_objective_market_neutral_efficient_risk():
# Recreate the market-neutral efficient_risk optimiser using this API
target_risk = 0.19
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(-1, 1))
weight_constr = {"type": "eq", "fun": lambda w: np.sum(w)}
risk_constr = {
"type": "eq",
"fun":
lambda w: target_risk**2 - np.dot(w.T, np.dot(ef.cov_matrix, w)),
}
constraints = [weight_constr, risk_constr]
ef.nonconvex_objective(
lambda w, mu: -w.T.dot(mu),
objective_args=(ef.expected_returns),
weights_sum_to_one=False,
constraints=constraints,
)
np.testing.assert_allclose(
ef.portfolio_performance(),
(0.2591296227818582, target_risk, 1.258574109251818),
atol=1e-6,
)
def calculEF(dataframe):
mu = expected_returns.mean_historical_return(dataframe)
S = risk_models.sample_cov(dataframe)
ef = EfficientFrontier(mu, S)
weights = ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)
ef.portfolio_performance(verbose=True)
return pd.DataFrame([weights])
parse_dates=True,
index_col="date")
returns = df.pct_change().dropna()
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Now try with a nonconvex objective from Kolm et al (2014)
def deviation_risk_parity(w, cov_matrix):
diff = w * np.dot(cov_matrix, w) - (w * np.dot(cov_matrix, w)).reshape(
-1, 1)
return (diff**2).sum().sum()
ef = EfficientFrontier(mu, S)
weights = ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)
ef.portfolio_performance(verbose=True)
"""
Expected annual return: 22.9%
Annual volatility: 19.2%
Sharpe Ratio: 1.09
"""
# Black-Litterman
spy_prices = pd.read_csv("tests/spy_prices.csv",
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(spy_prices)
mcaps = {
