# Portfolio Optimization

Reinforcement-learning portfolio policies can propose allocations but lack a calibrated, replayable way to certify before execution that a proposal stays inside a bounded risk envelope. Gated-CSRC has a language model read timestamped news only into structured semantic risk states, converts those states into factor-shock ellipsoids through rolling conformal calibration, and lets a second-order cone gate correct an allocation only when its budget is breached. In walk-forward tests on nine U.S. sector SPDR ETFs, raw PPO earned 12.7% annual return with a 4.05% semantic violation rate; gating cut violations to zero while retaining 11.7% annual return.

Reinforcement-learning portfolio policies can propose allocations but lack a calibrated, replayable way to certify before execution that a proposal stays inside a bounded risk envelope. Gated-CSRC has a language model read timestamped news only into structured semantic risk states, converts those states into factor-shock ellipsoids through rolling conformal calibration, and lets a second-order cone gate correct an allocation only when its budget is breached. In walk-forward tests on nine U.S. sector SPDR ETFs, raw PPO earned 12.7% annual return with a 4.05% semantic violation rate; gating cut violations to zero while retaining 11.7% annual return.