mindesbunister 2a8e04fe57 feat: Continuous optimization cluster for 2 EPYC servers ...

- Master controller with job queue and result aggregation
- Worker scripts for parallel backtesting (22 workers per server)
- SQLite database for strategy ranking and performance tracking
- File-based job queue (simple, robust, survives crashes)
- Auto-setup script for both EPYC servers
- Status dashboard for monitoring progress
- Comprehensive deployment guide

Architecture:
- Master: Job generation, worker coordination, result collection
- Worker 1 (pve-nu-monitor01): AMD EPYC 7282, 22 parallel jobs
- Worker 2 (srv-bd-host01): AMD EPYC 7302, 22 parallel jobs
- Total capacity: ~49,000 backtests/day (44 cores @ 70%)

Initial focus: v9 parameter refinement (27 configurations)
Target: Find strategies >00/1k P&L (current baseline 92/1k)

Files:
- cluster/master.py: Main controller (570 lines)
- cluster/worker.py: Worker execution script (220 lines)
- cluster/setup_cluster.sh: Automated deployment
- cluster/status.py: Real-time status dashboard
- cluster/README.md: Operational documentation
- cluster/DEPLOYMENT.md: Step-by-step deployment guide

2025-11-29 22:34:52 +01:00

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