Integrate three standalone PPO modules into the hyperopt training loop:
- PPORewardShaper: hold penalty + rolling Sharpe + diversity bonus per step
- CompositeReward: risk-adjusted reward (downside dev + differential return)
- TrajectoryReplayBuffer: ExO-PPO M=4 rollouts with IS-weighted replay
Also fixes GAE hardcoded gamma=0.99/lambda=0.95 — now uses hyperopt params.
2726 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
4b15108895