Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice

We develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent OL-BPTT adjoint after deployment and solves the constrained update. Shifted-adjoint cancellation controls the adjoint--HJB Hamiltonian-gradient discrepancy by the policy-improvement residual. For CRRA portfolios, exact HJB policy iteration identifies the optimal reduced value factor, while population OL-BPTT iteration converges globally when the adjoint update is directionally improving and approximate stationarity is asymptotically HJB-compatible. A theorem-matched occupation audit yields maximal 95% upper endpoints of 0.066 for the primitive directional ratio and 0.074 for a stronger norm-relative ratio, both against the half-step threshold 0.75. In a three-factor, fifty-asset design, current-policy re-evaluation outperforms matched pooled refinement under both evaluation laws.