small modification to mean variance builder interface

alphamind/portfolio/meanvariancebuilder.py

@@ -52,7 +52,8 @@ def mean_variance_builder(er: np.ndarray,
                           ubound: Union[np.ndarray, float],
                           risk_exposure: Optional[np.ndarray],
                           risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
-                          lam: float=1.) -> Tuple[str, float, np.ndarray]:
+                          lam: 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)
 
     if np.all(lbound == -np.inf) and np.all(ubound == np.inf) and cons_mat is None:
@@ -87,7 +88,8 @@ def mean_variance_builder(er: np.ndarray,
                                 lam,
                                 risk_model['factor_cov'],
                                 risk_model['factor_loading'],
-                                risk_model['idsync'])
+                                risk_model['idsync'],
+                                linear_solver=linear_solver)
 
         return _create_result(optimizer, bm)
 
@@ -99,7 +101,8 @@ def target_vol_builder(er: np.ndarray,
                        ubound: Union[np.ndarray, float],
                        risk_exposure: Optional[np.ndarray],
                        risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
-                       vol_target: float = 1.)-> Tuple[str, float, 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,
@@ -112,7 +115,8 @@ def target_vol_builder(er: np.ndarray,
                             vol_target,
                             risk_model['factor_cov'],
                             risk_model['factor_loading'],
-                            risk_model['idsync'])
+                            risk_model['idsync'],
+                            linear_solver=linear_solver)
 
     return _create_result(optimizer, bm)
 

alphamind/tests/cython/test_optimizers.py

@@ -150,6 +150,34 @@ class TestOptimizers(unittest.TestCase):
                                              [-0.3, -0.10919033, 0.40919033],
                                              4)
 
+    def test_cvoptimizer_with_cons_with_different_solver(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,
+                                linear_solver='ma97')
+
+        # 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])