added with new api

alphamind/cython/optimizers.pyx

@@ -72,7 +72,6 @@ cdef extern from "tvoptimizer.hpp" namespace "pfopt":
                     double*,
                     double*,
                     double,
-                    double,
                     int,
                     double*,
                     double*,
@@ -96,8 +95,7 @@ cdef class CVOptimizer:
                   cnp.ndarray[double, ndim=2] cons_matrix=None,
                   double[:] clbound=None,
                   double[:] cubound=None,
-                  double target_low=0.0,
-                  double target_high=1.0,
+                  double target_vol=1.0,
                   cnp.ndarray[double, ndim=2] factor_cov_matrix=None,
                   cnp.ndarray[double, ndim=2] factor_loading_matrix=None,
                   double[:] idsync_risk=None):
@@ -123,8 +121,7 @@ cdef class CVOptimizer:
                                         &cons[0],
                                         &clbound[0],
                                         &cubound[0],
-                                        target_low,
-                                        target_high,
+                                        target_vol,
                                         self.f,
                                         &factor_cov[0] if factor_cov is not None else NULL,
                                         &factor_loading[0] if factor_loading is not None else NULL,
@@ -139,8 +136,7 @@ cdef class CVOptimizer:
                                         NULL,
                                         NULL,
                                         NULL,
-                                        target_low,
-                                        target_high,
+                                        target_vol,
                                         self.f,
                                         &factor_cov[0] if factor_cov is not None else NULL,
                                         &factor_loading[0] if factor_loading is not None else NULL,

pfopt @ b78ad8a5

-Subproject commit ffaf2153dfdce380c3d8aa1a69b328ab77665ad3
+Subproject commit b78ad8a55855c448a866f91a3876a271a52d68a1

alphamind/portfolio/meanvariancebuilder.py

@@ -109,7 +109,6 @@ def target_vol_builder(er: np.ndarray,
                             cons_mat,
                             clbound,
                             cubound,
-                            0.,
                             vol_target,
                             risk_model['factor_cov'],
                             risk_model['factor_loading'],

alphamind/tests/cython/test_optimizers.py

@@ -116,7 +116,6 @@ class TestOptimizers(unittest.TestCase):
                                 None,
                                 None,
                                 None,
-                                target_vol,
                                 target_vol)
 
         # check against known good result
@@ -144,7 +143,6 @@ class TestOptimizers(unittest.TestCase):
                                 cons,
                                 clbound,
                                 cubound,
-                                target_vol,
                                 target_vol)
 
         # check against known good result
@@ -173,7 +171,6 @@ class TestOptimizers(unittest.TestCase):
                                 cons,
                                 clbound,
                                 cubound,
-                                0.,
                                 target_vol,
                                 factor_var,
                                 factor_load,
@@ -204,7 +201,6 @@ class TestOptimizers(unittest.TestCase):
                                 cons,
                                 clbound,
                                 cubound,
-                                0.,
                                 target_vol)
 
         # check against known good result

alphamind/tests/portfolio/test_meanvariancebuild.py

@@ -57,6 +57,9 @@ class TestMeanVarianceBuild(unittest.TestCase):
         status, _, x = mean_variance_builder(er, model, bm, lbound, ubound, None, None, lam=1)
         np.testing.assert_array_almost_equal(x, np.linalg.inv(cov) @ er)
 
+    def test_mean_variance_builder_without_constraints_with_factor_model(self):
+        pass
+
     def test_mean_variance_builder_with_none_unity_lambda(self):
         er = np.array([0.01, 0.02, 0.03])
         cov = np.array([[0.02, 0.01, 0.02],