added lambda parameter and its corresponding test

alphamind/analysis/factoranalysis.py

@@ -230,13 +230,19 @@ def factor_analysis(factors: pd.DataFrame,
         lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints(benchmark, **kwargs)
         cov = kwargs['cov']
 
+        if 'lambda' in kwargs:
+            lam = kwargs['lambda']
+        else:
+            lam = 1.
+
         status, _, weights = mean_variance_builder(er,
                                                    cov=cov,
                                                    bm=benchmark,
                                                    lbound=lbound,
                                                    ubound=ubound,
                                                    risk_exposure=cons_exp,
-                                                   risk_target=(risk_lbound, risk_ubound))
+                                                   risk_target=(risk_lbound, risk_ubound),
+                                                   lam=lam)
         if status != 'optimal':
             raise ValueError('mean variance optimizer in status: {0}'.format(status))
     else:

alphamind/tests/portfolio/test_meanvariancebuild.py

@@ -13,12 +13,11 @@ from alphamind.portfolio.meanvariancebuilder import mean_variance_builder
 class TestMeanVarianceBuild(unittest.TestCase):
 
     def test_mean_variance_builder(self):
-
-        er = np.random.randint(0, 10, size=3) / 10.
-        cov = np.array([[0.04, 0.01, 0.02],
-                        [0.01, 0.05, 0.03],
-                        [0.02, 0.03, 0.06]])
-        ids_var = np.diag(np.random.randint(2, 5, size=3) / 100.)
+        er = np.array([0.01, 0.02, 0.03])
+        cov = np.array([[0.02, 0.01, 0.02],
+                        [0.01, 0.02, 0.03],
+                        [0.02, 0.03, 0.02]])
+        ids_var = np.diag([0.01, 0.02, 0.03])
         cov += ids_var
 
         bm = np.array([0.3, 0.3, 0.4])
@@ -37,3 +36,30 @@ class TestMeanVarianceBuild(unittest.TestCase):
         self.assertTrue(np.all(x >= lbound) - 1.e-6)
         self.assertTrue(np.all(x @ risk_exposure <= risk_target[1] + 1.e-6))
         self.assertTrue(np.all(x @ risk_exposure >= risk_target[0] - 1.e-6))
+        np.testing.assert_array_almost_equal(x, [0.1, 0.4, 0.5])
+
+    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],
+                        [0.01, 0.02, 0.03],
+                        [0.02, 0.03, 0.02]])
+        ids_var = np.diag([0.01, 0.02, 0.03])
+        cov += ids_var
+
+        bm = np.array([0.3, 0.3, 0.4])
+        lbound = np.array([0., 0., 0.])
+        ubound = np.array([0.4, 0.4, 0.5])
+
+        risk_exposure = np.array([[1., 1., 1.],
+                                  [1., 0., 1.]]).T
+        risk_target = (np.array([bm.sum(), 0.3]), np.array([bm.sum(), 0.7]))
+
+        status, _, x = mean_variance_builder(er, cov, bm, lbound, ubound, risk_exposure, risk_target, lam=100)
+
+        self.assertTrue(status == 'optimal')
+        self.assertAlmostEqual(x.sum(), bm.sum())
+        self.assertTrue(np.all(x <= ubound + 1.e-6))
+        self.assertTrue(np.all(x >= lbound) - 1.e-6)
+        self.assertTrue(np.all(x @ risk_exposure <= risk_target[1] + 1.e-6))
+        self.assertTrue(np.all(x @ risk_exposure >= risk_target[0] - 1.e-6))
+        np.testing.assert_array_almost_equal(x, [0.2950, 0.3000, 0.4050])
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