def test_cvoptimizer_with_cons_and_ieq(self):
objective = np.array([0.1, 0.2, 0.3])
cov = np.array([[0.05, 0.01, 0.02],
[0.01, 0.06, 0.03],
[0.02, 0.03, 0.07]])
lbound = np.array([-0.3, -0.3, -0.3])
ubound = np.array([0.5, 0.5, 0.5])
cons = np.array([[1., 1., 1.]])
clbound = np.array([0.])
cubound = np.array([0.])
target_vol = 0.1
optimizer = CVOptimizer(objective,
cov,
lbound,
ubound,
cons,
clbound,
cubound,
target_vol)
# check against known good result
np.testing.assert_array_almost_equal(optimizer.x_value(),
[-0.3, -0.10919033, 0.40919033],
4)
def test_cvoptimizer_with_factor_model(self):
objective = np.array([0.1, 0.2, 0.3])
lbound = np.array([0.0, 0.0, 0.0])
ubound = np.array([1.0, 1.0, 1.0])
factor_var = np.array([[0.5, -0.3], [-0.3, 0.7]])
factor_load = np.array([[0.8, 0.2], [0.5, 0.5], [0.2, 0.8]])
idsync = np.array([0.1, 0.3, 0.2])
cons = np.array([[1., 1., 1.]])
clbound = np.array([1.])
cubound = np.array([1.])
target_vol = 0.5
optimizer = CVOptimizer(objective,
None,
lbound,
ubound,
cons,
clbound,
cubound,
target_vol,
factor_var,
factor_load,
idsync)
# check against cvxpy result
np.testing.assert_array_almost_equal(optimizer.x_value(),
[0.26595552, 0.21675092, 0.51729356],
4)
def test_cvoptimizer_without_cons(self):
objective = np.array([0.1, 0.2, 0.3])
cov = np.array([[0.05, 0.01, 0.02], [0.01, 0.06, 0.03],
[0.02, 0.03, 0.07]])
lbound = np.array([-0.3, -0.3, -0.3])
ubound = np.array([0.5, 0.5, 0.5])
target_vol = 0.1
optimizer = CVOptimizer(objective, cov, lbound, ubound, None, None,
None, target_vol, target_vol)
# check against known good result
np.testing.assert_array_almost_equal(optimizer.x_value(),
[.0231776, 0.1274768, 0.30130881],
4)
def target_vol_builder(er: np.ndarray,
risk_model: Dict[str, Union[None, np.ndarray]],
bm: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
risk_exposure: Optional[np.ndarray],
risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
vol_target: float = 1.,
linear_solver: str = 'ma27')-> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
optimizer = CVOptimizer(er,
risk_model['cov'],
lbound,
ubound,
cons_mat,
clbound,
cubound,
vol_target,
risk_model['factor_cov'],
risk_model['factor_loading'],
risk_model['idsync'],
linear_solver=linear_solver)
return _create_result(optimizer, bm)
def target_vol_builder(er: np.ndarray,
cov: np.ndarray,
bm: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
risk_exposure: Optional[np.ndarray],
risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
vol_low: float = 0.,
vol_high: float = 1.)-> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
optimizer = CVOptimizer(er,
cov,
lbound,
ubound,
cons_mat,
clbound,
cubound,
vol_low,
vol_high)
return _create_result(optimizer, bm)