merged long-only and unconstrained

cvxgrp · Oct 23, 2023 · b917d94 · b917d94
1 parent abfba19
commit b917d94
Showing 1 changed file with 30 additions and 18 deletions.
diff --git a/experiments/taming.py b/experiments/taming.py
@@ -7,21 +7,28 @@
 import matplotlib.pyplot as plt
 
 
-def unconstrained_markowitz(inputs: OptimizationInput) -> np.ndarray:
+def unconstrained_markowitz(
+    inputs: OptimizationInput, long_only: bool = False
+) -> np.ndarray:
     """Compute the unconstrained Markowitz portfolio weights."""
     n_assets = inputs.prices.shape[1]
 
     mu, Sigma = inputs.mean.values, inputs.covariance.values
 
-    w = cp.Variable(n_assets)
-    c = cp.Variable()
+    if long_only:
+        w = cp.Variable(n_assets, nonneg=True)
+        c = cp.Variable(nonneg=True)
+    else:
+        w = cp.Variable(n_assets)
+        c = cp.Variable()
     objective = mu @ w
 
     chol = np.linalg.cholesky(Sigma)
     constraints = [
         cp.sum(w) + c == 1,
         cp.norm2(chol.T @ w) <= inputs.risk_target,
     ]
+
     problem = cp.Problem(cp.Maximize(objective), constraints)
     problem.solve(get_solver())
     assert problem.status in {cp.OPTIMAL, cp.OPTIMAL_INACCURATE}
@@ -30,21 +37,26 @@ def unconstrained_markowitz(inputs: OptimizationInput) -> np.ndarray:
 
 def long_only_markowitz(inputs: OptimizationInput) -> np.ndarray:
     """Compute the long-only Markowitz portfolio weights."""
-    n_assets = inputs.prices.shape[1]
-
-    mu, Sigma = inputs.mean.values, inputs.covariance.values
-
-    w = cp.Variable(n_assets, nonneg=True)
-    c = cp.Variable(nonneg=True)
-    objective = mu @ w
-    constraints = [
-        cp.sum(w) + c == 1,
-        cp.quad_form(w, Sigma, assume_PSD=True) <= inputs.risk_target**2,
-    ]
-    problem = cp.Problem(cp.Maximize(objective), constraints)
-    problem.solve(get_solver())
-    assert problem.status in {cp.OPTIMAL, cp.OPTIMAL_INACCURATE}
-    return w.value, c.value
+    return unconstrained_markowitz(inputs, long_only=True)
+
+
+# def long_only_markowitz(inputs: OptimizationInput) -> np.ndarray:
+#     """Compute the long-only Markowitz portfolio weights."""
+#     n_assets = inputs.prices.shape[1]
+
+#     mu, Sigma = inputs.mean.values, inputs.covariance.values
+
+#     w = cp.Variable(n_assets, nonneg=True)
+#     c = cp.Variable(nonneg=True)
+#     objective = mu @ w
+#     constraints = [
+#         cp.sum(w) + c == 1,
+#         cp.quad_form(w, Sigma, assume_PSD=True) <= inputs.risk_target**2,
+#     ]
+#     problem = cp.Problem(cp.Maximize(objective), constraints)
+#     problem.solve(get_solver())
+#     assert problem.status in {cp.OPTIMAL, cp.OPTIMAL_INACCURATE}
+#     return w.value, c.value
 
 
 def equal_weights(inputs: OptimizationInput) -> np.ndarray: