added target_vol_builder

alphamind/portfolio/meanvariancebuilder.py

@@ -8,7 +8,40 @@ Created on 2017-6-27
 import numpy as np
 from typing import Union
 from typing import Tuple
+from typing import Optional
 from alphamind.cython.optimizers import QPOptimizer
+from alphamind.cython.optimizers import CVOptimizer
+
+
+def _create_bounds(lbound,
+                   ubound,
+                   bm,
+                   risk_exposure,
+                   risk_target):
+    lbound = lbound - bm
+    ubound = ubound - bm
+
+    if risk_exposure is not None:
+        cons_mat = risk_exposure.T
+        bm_risk = cons_mat @ bm
+
+        clbound = risk_target[0] - bm_risk
+        cubound = risk_target[1] - bm_risk
+    else:
+        cons_mat = None
+        clbound = None
+        cubound = None
+
+    return lbound, ubound, cons_mat, clbound, cubound
+
+
+def _create_result(optimizer, bm):
+    if optimizer.status() == 0 or optimizer.status() == 1:
+        status = 'optimal'
+    else:
+        status = optimizer.status()
+
+    return status, optimizer.feval(), optimizer.x_value() + bm
 
 
 def mean_variance_builder(er: np.ndarray,
@@ -16,33 +49,45 @@ def mean_variance_builder(er: np.ndarray,
                           bm: np.ndarray,
                           lbound: Union[np.ndarray, float],
                           ubound: Union[np.ndarray, float],
-                          risk_exposure: np.ndarray,
-                          risk_target: Tuple[np.ndarray, np.ndarray],
+                          risk_exposure: Optional[np.ndarray],
+                          risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
                           lam: float=1.) -> Tuple[str, float, np.ndarray]:
-
-    lbound = lbound - bm
-    ubound = ubound - bm
-
-    bm_risk = risk_exposure.T @ bm
-
-    clbound = risk_target[0] - bm_risk
-    cubound = risk_target[1] - bm_risk
+    lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
 
     optimizer = QPOptimizer(er,
                             cov,
                             lbound,
                             ubound,
-                            risk_exposure.T,
+                            cons_mat,
                             clbound,
                             cubound,
                             lam)
 
-    if optimizer.status() == 0 or optimizer.status() == 1:
-        status = 'optimal'
-    else:
-        status = optimizer.status()
+    return _create_result(optimizer, bm)
 
-    return status, optimizer.feval(), optimizer.x_value() + 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.):
+    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)
 
 
 

alphamind/tests/cython/test_optimizers.py

@@ -77,10 +77,6 @@ class TestOptimizers(unittest.TestCase):
                         [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,

alphamind/tests/portfolio/test_meanvariancebuild.py

@@ -8,6 +8,7 @@ Created on 2017-6-27
 import unittest
 import numpy as np
 from alphamind.portfolio.meanvariancebuilder import mean_variance_builder
+from alphamind.portfolio.meanvariancebuilder import target_vol_builder
 
 
 class TestMeanVarianceBuild(unittest.TestCase):
@@ -62,4 +63,25 @@ 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, [0.2950, 0.3000, 0.4050])
\ No newline at end of file
+        np.testing.assert_array_almost_equal(x, [0.2950, 0.3000, 0.4050])
+
+    def test_target_vol_builder(self):
+        er = 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., 0., 0.])
+        ubound = np.array([0.8, 0.8, 0.8])
+
+        bm = np.array([0.3, 0.3, 0.3])
+
+        risk_exposure = np.array([[1., 1., 1.]]).T
+        risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))
+        status, _, x = target_vol_builder(er, cov, bm, lbound, ubound, risk_exposure, risk_target, 0.1, 0.1)
+        self.assertTrue(status == 'optimal')
+        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, [-0.3, -0.10919033, 0.40919033] + bm)
\ No newline at end of file