def test_min_volatility_sector_constraints():
sector_mapper = {
"T": "auto",
"UAA": "airline",
"SHLD": "retail",
"XOM": "energy",
"RRC": "energy",
"BBY": "retail",
"MA": "fig",
"PFE": "pharma",
"JPM": "fig",
"SBUX": "retail",
"GOOG": "tech",
"AAPL": "tech",
"FB": "tech",
"AMZN": "tech",
"BABA": "tech",
"GE": "utility",
"AMD": "tech",
"WMT": "retail",
"BAC": "fig",
"GM": "auto",
}
sector_upper = {
"tech": 0.2,
"utility": 0.1,
"retail": 0.2,
"fig": 0.4,
"airline": 0.05,
"energy": 0.2,
}
sector_lower = {"utility": 0.01, "fig": 0.02, "airline": 0.01}
# ef = setup_efficient_frontier()
ef = EfficientFrontier(
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
)
ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)
weights = ef.min_volatility()
for sector in list(set().union(sector_upper, sector_lower)):
sector_sum = 0
for t, v in weights.items():
if sector_mapper[t] == sector:
sector_sum += v
assert sector_sum <= sector_upper.get(sector, 1) + 1e-5
assert sector_sum >= sector_lower.get(sector, 0) - 1e-5
:param skew: expected return of each asset
:type skew: np.ndarray
:param cov_matrix: covariance matrix
:type cov_matrix: np.ndarray
:param negative: whether quantity should be made negative (so we can minimise)
:type negative: boolean
:return: (negative) Sharpe ratio
:rtype: float
"""
sk = w @ skew
sign = -1 if negative else 1
return sign * sk
ef_skew = EfficientFrontier(exp_ret, S, weight_bounds=(0.0, 0.15))
ef_skew.add_sector_constraints(sector_mapper, sector_lower, sector_upper)
ef_skew.add_objective(objective_functions.L2_reg, gamma=20)
#ef.add_objective(minimize_negative_skew, skew= skw)
ef_skew.convex_objective(minimize_negative_skew, skew=skw)
#ef.max_sharpe()
weights_skew = ef_skew.clean_weights()
weights_skew
ef_skew.portfolio_performance(verbose=True)
# %%
weight_dict = {
"Miniumum Variance" : weights_minvol,
"Maximum Sharpe Ratio" : weights_maxsharpe,
"Maximum Quadratic Utility" : weights_maxquad,
"Efficient Target Risk" : weights_effrisk,
