Rolling stats (no episode accumulation), CMA-ES every 10 cycles (3 evals),
gc.collect() after CMA, global try/except for crash safety.
100-opponent swarm, 9 assets, 270 scenarios.
Fixed OOM kill by replacing all_episodes list accumulation with:
- Rolling stats (clear every 20 episodes)
- Only PnL history kept for characterization
- Peak/worst tracking without full episode storage
- Periodic stdout reports from rolling aggregates
100-opponent swarm + 9 assets × 30 scenarios = 270 scenarios per cycle.
CMA-ES every 5 cycles (3 evals). 3-hour target.
- 100 diverse opponents (randomized params within each type)
- Risk gate: empty book guard in _post_only_would_cross
- CMA-ES: only every 5 cycles, 3 evals, robust error handling
- Main loop: try/except prevents silent crashes
- Profiling: 11.7 steps/sec with 100 opponents
