2a8e04fe57
- 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
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""
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Quick cluster status dashboard
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"""
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import sqlite3
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from pathlib import Path
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from datetime import datetime
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CLUSTER_DIR = Path(__file__).parent
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DB_PATH = CLUSTER_DIR / 'strategies.db'
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QUEUE_DIR = CLUSTER_DIR / 'queue'
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def show_status():
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"""Display cluster status"""
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print("="*70)
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print("🎯 OPTIMIZATION CLUSTER STATUS")
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print("="*70)
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print(f"📅 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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# Job queue status
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queued_jobs = list(QUEUE_DIR.glob("*.json")) if QUEUE_DIR.exists() else []
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print(f"📋 Queue: {len(queued_jobs)} jobs waiting")
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# Database status (if exists)
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if DB_PATH.exists():
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conn = sqlite3.connect(DB_PATH)
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c = conn.cursor()
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# Job stats
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c.execute("SELECT status, COUNT(*) FROM jobs GROUP BY status")
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job_stats = dict(c.fetchall())
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print(f" Running: {job_stats.get('running', 0)}")
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print(f" Completed: {job_stats.get('completed', 0)}")
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# Top strategies
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c.execute("""
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SELECT name, pnl_per_1k, trade_count, win_rate, profit_factor
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FROM strategies
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WHERE status = 'completed' AND pnl_per_1k IS NOT NULL
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ORDER BY pnl_per_1k DESC
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LIMIT 5
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""")
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top_strats = c.fetchall()
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if top_strats:
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print("\n🏆 TOP 5 STRATEGIES:")
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print("-" * 70)
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print(f"{'Rank':<6} {'Strategy':<30} {'P&L/1k':<12} {'Trades':<8} {'WR%':<8} {'PF':<6}")
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print("-" * 70)
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for i, (name, pnl, trades, wr, pf) in enumerate(top_strats, 1):
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print(f"{i:<6} {name[:30]:<30} ${pnl:<11.2f} {trades:<8} {wr:<7.1f}% {pf:<6.2f}")
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else:
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print("\n⏳ No completed strategies yet...")
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# Current baseline
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print("\n📊 BASELINE COMPARISON:")
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print(f" v9 baseline: $192.00/1k (current production)")
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if top_strats:
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best_pnl = top_strats[0][1]
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improvement = ((best_pnl - 192) / 192) * 100
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if improvement > 0:
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print(f" Best found: ${best_pnl:.2f}/1k ({improvement:+.1f}% improvement) ✅")
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else:
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print(f" Best found: ${best_pnl:.2f}/1k ({improvement:+.1f}%)")
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conn.close()
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else:
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print("\n⚠️ Database not initialized. Run setup_cluster.sh first.")
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print("\n" + "="*70)
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if __name__ == '__main__':
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try:
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show_status()
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except KeyboardInterrupt:
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print("\n")
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