Generate warning when the matrix is ill-defined and the regularization term is added to fix it.

Author: dppalomar

src/riskparityportfolio/sca.py

@@ -1,4 +1,5 @@
 import numpy as np
+import warnings
 from tqdm import tqdm
 
 try:
@@ -251,12 +252,11 @@ def iterate(self, verbose=True):
             w_hat = quadprog.solve_qp(Q, -q, C=self.CCmat, b=self.bvec, meq=self.meq)[0]
         except ValueError as e:
             if str(e) == "matrix G is not positive definite":
-                # Add a small positive value to the diagonal of Q to make it positive definite
-                if verbose:
-                    print("  Matrix Q is not positive definite: adding regularization term and then calling QP solver again.")
-                    #eigvals = np.linalg.eigvals(Q)
-                    #print("    - before regularization: cond. number = {:,.0f}".format(max(eigvals) / min(eigvals)))
-                    #print("    - after regularization: cond. number = {:,.0f}".format(max(eigvals + np.trace(Q)/1e7) / min(eigvals + np.trace(Q)/1e7)))
+                warnings.warn(
+                    "Matrix Q is not positive definite: adding regularization term and then calling QP solver again.")
+                # eigvals = np.linalg.eigvals(Q)
+                # print("    - before regularization: cond. number = {:,.0f}".format(max(eigvals) / min(eigvals)))
+                # print("    - after regularization: cond. number = {:,.0f}".format(max(eigvals + np.trace(Q)/1e7) / min(eigvals + np.trace(Q)/1e7)))
                 Q += np.eye(Q.shape[0]) * np.trace(Q)/1e7
                 w_hat = quadprog.solve_qp(Q, -q, C=self.CCmat, b=self.bvec, meq=self.meq)[0]
             else:

src/riskparityportfolio/tests/test_modern_rpp.py

@@ -1,5 +1,6 @@
 import numpy as np
 import riskparityportfolio as rpp
+import pytest
 import pdb
 
 def test_on_tricky_example():
@@ -49,7 +50,8 @@ def test_singularity_issues_with_G_matrix():
     Sigma = U @ U.T  # singular covariance matrix
 
     my_portfolio = rpp.RiskParityPortfolio(Sigma, budget=b)
-    my_portfolio.design(verbose=False, tau=1e-10)  # <-- force ill-conditioned matrix
+    with pytest.warns(UserWarning, match="Matrix Q is not positive definite: adding regularization term and then calling QP solver again."):
+        my_portfolio.design(verbose=False, tau=1e-12)  # <-- force ill-conditioned matrix
     w_ref = my_portfolio.weights
 
     my_portfolio = rpp.RiskParityPortfolio(Sigma, budget=b)