allow lower bound and upper bound on risk exposure

alphamind/benchmarks/portfolio/linearbuild.py

@@ -62,4 +62,4 @@ def benchmark_build_linear(n_samples: int, n_risks: int, n_loop: int) -> None:
 
 
 if __name__ == '__main__':
-    benchmark_build_linear(500, 30, 10)
+    benchmark_build_linear(2000, 30, 10)
\ No newline at end of file

alphamind/portfolio/linearbuilder.py

@@ -19,22 +19,27 @@ def linear_build(er: np.ndarray,
                  ubound: np.ndarray,
                  risk_exposure: np.ndarray,
                  bm: np.ndarray,
-                 risk_target: np.ndarray=None,
+                 risk_target: Tuple[np.ndarray, np.ndarray]=None,
                  solver: str=None) -> Tuple[str, np.ndarray, np.ndarray]:
     n, m = risk_exposure.shape
     w = cvxpy.Variable(n)
 
-    if risk_target is None:
-        risk_target = np.zeros(m)
-
     curr_risk_exposure = risk_exposure.T * (w - bm)
 
-    objective = cvxpy.Minimize(-w.T * er)
-    constraints = [w >= lbound,
-                   w <= ubound,
-                   curr_risk_exposure == risk_target,
-                   cvxpy.sum_entries(w) == 1.]
+    if not risk_target:
+        risk_eq_target = np.zeros(m)
+        constraints = [w >= lbound,
+                       w <= ubound,
+                       curr_risk_exposure == risk_eq_target,
+                       cvxpy.sum_entries(w) == 1.]
+    else:
+        constraints = [w >= lbound,
+                       w <= ubound,
+                       curr_risk_exposure >= risk_target[0],
+                       curr_risk_exposure <= risk_target[1],
+                       cvxpy.sum_entries(w) == 1.]
 
+    objective = cvxpy.Minimize(-w.T * er)
     prob = cvxpy.Problem(objective, constraints)
     prob.solve(solver=solver)
     return prob.status, prob.value, np.array(w.value).flatten()
@@ -46,7 +51,7 @@ if __name__ == '__main__':
     bm = np.ones(300) * 0.00333333333
     risk_exposure = np.random.randn(300, 10)
 
-    s, v, x = linear_build(er, 0., 0.01, risk_exposure, bm)
+    s, v, x = linear_build(er, 0., 0.01, risk_exposure, bm, [-0.01*np.ones(10), 0.01*np.ones(10)])
     print(s)
     print(x.sum())
     print(x.min(), ',', x.max())