merge 2 implementation

alphamind/portfolio/linearbuilder.py

@@ -15,19 +15,18 @@ def linear_build(er: np.ndarray,
                  lbound: Union[np.ndarray, float],
                  ubound: Union[np.ndarray, float],
                  risk_constraints: np.ndarray,
-                 risk_target: Tuple[np.ndarray, np.ndarray]) -> Tuple[str, np.ndarray, np.ndarray]:
+                 risk_target: Tuple[np.ndarray, np.ndarray],
+                 turn_over_target: float = None,
+                 current_position: np.ndarray = None) -> Tuple[str, np.ndarray, np.ndarray]:
     er = er.flatten()
     n, m = risk_constraints.shape
 
     if not risk_target:
-        risk_lbound = -np.inf * np.ones(m)
-        risk_ubound = np.inf * np.ones(m)
-        cons_matrix = np.concatenate((risk_constraints.T, risk_lbound.reshape((-1, 1)), risk_ubound.reshape((-1, 1))),
-                                     axis=1)
+        risk_lbound = -np.inf * np.ones((m, 1))
+        risk_ubound = np.inf * np.ones((m, 1))
     else:
-        cons_matrix = np.concatenate(
-            (risk_constraints.T, risk_target[0].reshape((-1, 1)), risk_target[1].reshape((-1, 1))),
-            axis=1)
+        risk_lbound = risk_target[0].reshape((-1, 1))
+        risk_ubound = risk_target[1].reshape((-1, 1))
 
     if isinstance(lbound, float):
         lbound = np.ones(n) * lbound
@@ -35,6 +34,8 @@ def linear_build(er: np.ndarray,
     if isinstance(ubound, float):
         ubound = np.ones(n) * ubound
 
+    if not turn_over_target:
+        cons_matrix = np.concatenate((risk_constraints.T, risk_lbound, risk_ubound), axis=1)
         opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
 
         status = opt.status()
@@ -43,31 +44,8 @@ def linear_build(er: np.ndarray,
             status = 'optimal'
 
         return status, opt.feval(), opt.x_value()
-
-
-def linear_build_with_to_constraint(er: np.ndarray,
-                                    lbound: Union[np.ndarray, float],
-                                    ubound: Union[np.ndarray, float],
-                                    risk_constraints: np.ndarray,
-                                    risk_target: Tuple[np.ndarray, np.ndarray],
-                                    turn_over_target: float,
-                                    current_position: np.ndarray):
-    er = er.flatten()
-    current_position = current_position.reshape((-1, 1))
-    n, m = risk_constraints.shape
-
-    if not risk_target:
-        risk_lbound = -np.inf * np.ones((m, 1))
-        risk_ubound = np.inf * np.ones((m, 1))
     else:
-        risk_lbound = risk_target[0].reshape((-1, 1))
-        risk_ubound = risk_target[1].reshape((-1, 1))
-
-    if isinstance(lbound, float):
-        lbound = np.ones(n) * lbound
-
-    if isinstance(ubound, float):
-        ubound = np.ones(n) * ubound
+        current_position = current_position.reshape((-1, 1))
 
         # we need to expand bounded condition and constraint matrix to handle L1 bound
         lbound = np.concatenate((lbound, np.zeros(n)), axis=0)
@@ -122,7 +100,7 @@ if __name__ == '__main__':
     risk_lbound = np.ones(1)
     risk_ubound = np.ones(1)
 
-    status, fvalue, x_values = linear_build_with_to_constraint(er,
+    status, fvalue, x_values = linear_build(er,
                                             lb,
                                             ub,
                                             cons,

alphamind/tests/portfolio/test_linearbuild.py

@@ -8,7 +8,6 @@ Created on 2017-5-5
 import unittest
 import numpy as np
 from alphamind.portfolio.linearbuilder import linear_build
-from alphamind.portfolio.linearbuilder import linear_build_with_to_constraint
 
 
 class TestLinearBuild(unittest.TestCase):
@@ -76,7 +75,7 @@ class TestLinearBuild(unittest.TestCase):
         risk_lbound[:-1] = risk_lbound[:-1] - risk_tolerance
         risk_ubound[:-1] = risk_ubound[:-1] + risk_tolerance
 
-        status, _, w = linear_build_with_to_constraint(self.er,
+        status, _, w = linear_build(self.er,
                                     0.,
                                     0.01,
                                     self.risk_exp,