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
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mindesbunister
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cluster/master.py
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371
cluster/master.py
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#!/usr/bin/env python3
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"""
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Continuous Optimization Cluster - MASTER Controller
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Manages job queue, coordinates workers, aggregates results
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"""
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import json
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import time
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import os
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import subprocess
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from datetime import datetime
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from pathlib import Path
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import sqlite3
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# Configuration
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WORKERS = {
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'worker1': {
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'host': 'root@10.10.254.106',
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'cores': 22, # 70% of 32
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'workspace': '/root/optimization-cluster',
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},
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'worker2': {
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'host': 'root@10.10.254.106', # Connect through monitor01
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'ssh_hop': 'root@10.20.254.100', # Actual target
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'cores': 22, # 70% of 32
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'workspace': '/root/optimization-cluster',
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}
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}
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CLUSTER_DIR = Path(__file__).parent
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QUEUE_DIR = CLUSTER_DIR / 'queue'
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RESULTS_DIR = CLUSTER_DIR / 'results'
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DB_PATH = CLUSTER_DIR / 'strategies.db'
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# Job priorities
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PRIORITY_HIGH = 1 # Known good strategies (v9 refinements)
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PRIORITY_MEDIUM = 2 # New concepts (volume profiles, orderflow)
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PRIORITY_LOW = 3 # Experimental (ML, neural networks)
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class StrategyDatabase:
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"""SQLite database for strategy performance tracking"""
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def __init__(self, db_path):
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self.db_path = db_path
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self.init_db()
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def init_db(self):
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"""Create tables if not exist"""
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conn = sqlite3.connect(self.db_path)
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c = conn.cursor()
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c.execute('''
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CREATE TABLE IF NOT EXISTS strategies (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT UNIQUE NOT NULL,
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indicator_type TEXT NOT NULL,
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params JSON NOT NULL,
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pnl_per_1k REAL,
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trade_count INTEGER,
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win_rate REAL,
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profit_factor REAL,
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max_drawdown REAL,
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sharpe_ratio REAL,
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tested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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status TEXT DEFAULT 'pending',
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notes TEXT
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)
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''')
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c.execute('''
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CREATE TABLE IF NOT EXISTS backtest_results (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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strategy_id INTEGER,
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config JSON NOT NULL,
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pnl REAL,
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trades INTEGER,
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win_rate REAL,
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completed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (strategy_id) REFERENCES strategies (id)
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)
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''')
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c.execute('''
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CREATE TABLE IF NOT EXISTS jobs (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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job_file TEXT UNIQUE NOT NULL,
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priority INTEGER DEFAULT 2,
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worker_id TEXT,
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status TEXT DEFAULT 'queued',
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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started_at TIMESTAMP,
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completed_at TIMESTAMP
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)
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''')
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conn.commit()
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conn.close()
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def add_strategy(self, name, indicator_type, params):
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"""Add new strategy to test"""
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conn = sqlite3.connect(self.db_path)
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c = conn.cursor()
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try:
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c.execute('''
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INSERT INTO strategies (name, indicator_type, params)
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VALUES (?, ?, ?)
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''', (name, indicator_type, json.dumps(params)))
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conn.commit()
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return c.lastrowid
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except sqlite3.IntegrityError:
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# Strategy already exists
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return None
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finally:
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conn.close()
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def get_top_strategies(self, limit=10):
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"""Get top performing strategies"""
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conn = sqlite3.connect(self.db_path)
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c = conn.cursor()
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c.execute('''
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SELECT name, indicator_type, pnl_per_1k, trade_count,
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win_rate, profit_factor, max_drawdown
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FROM strategies
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WHERE status = 'completed'
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ORDER BY pnl_per_1k DESC
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LIMIT ?
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''', (limit,))
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results = c.fetchall()
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conn.close()
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return results
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class JobQueue:
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"""Manages job queue and worker assignments"""
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def __init__(self, queue_dir, db):
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self.queue_dir = Path(queue_dir)
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self.queue_dir.mkdir(exist_ok=True)
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self.db = db
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def create_job(self, indicator, params, priority=PRIORITY_MEDIUM):
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"""Create a new backtest job"""
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job_id = f"{indicator}_{int(time.time() * 1000)}"
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job_file = self.queue_dir / f"{job_id}.json"
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job = {
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'id': job_id,
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'indicator': indicator,
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'params': params,
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'priority': priority,
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'created_at': datetime.now().isoformat(),
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'status': 'queued'
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}
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with open(job_file, 'w') as f:
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json.dump(job, f, indent=2)
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# Log to database
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conn = sqlite3.connect(self.db.db_path)
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c = conn.cursor()
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c.execute('''
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INSERT INTO jobs (job_file, priority, status)
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VALUES (?, ?, 'queued')
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''', (job_file.name, priority))
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conn.commit()
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conn.close()
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print(f"✅ Created job: {job_id} (priority {priority})")
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return job_id
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def get_next_job(self):
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"""Get highest priority job from queue"""
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jobs = sorted(
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self.queue_dir.glob("*.json"),
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key=lambda f: json.load(open(f)).get('priority', 2)
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)
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if jobs:
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return jobs[0]
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return None
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def mark_running(self, job_file, worker_id):
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"""Mark job as running"""
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with open(job_file, 'r') as f:
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job = json.load(f)
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job['status'] = 'running'
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job['worker_id'] = worker_id
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job['started_at'] = datetime.now().isoformat()
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with open(job_file, 'w') as f:
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json.dump(job, f, indent=2)
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# Update database
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conn = sqlite3.connect(self.db.db_path)
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c = conn.cursor()
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c.execute('''
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UPDATE jobs
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SET status = 'running', worker_id = ?, started_at = CURRENT_TIMESTAMP
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WHERE job_file = ?
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''', (worker_id, job_file.name))
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conn.commit()
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conn.close()
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class WorkerManager:
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"""Manages worker coordination"""
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def __init__(self, workers):
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self.workers = workers
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def check_worker_status(self, worker_id):
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"""Check if worker is idle"""
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worker = self.workers[worker_id]
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# Check via SSH if worker has running jobs
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cmd = f"ssh {worker['host']}"
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if 'ssh_hop' in worker:
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cmd = f"ssh {worker['host']} ssh {worker['ssh_hop']}"
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cmd += " 'pgrep -f backtester || echo IDLE'"
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try:
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result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=5)
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return 'IDLE' in result.stdout
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except:
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return False
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def assign_job(self, worker_id, job_file):
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"""Assign job to worker"""
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worker = self.workers[worker_id]
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print(f"📤 Assigning {job_file.name} to {worker_id}...")
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# Copy job to worker
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cmd = f"scp {job_file} {worker['host']}:{worker['workspace']}/jobs/"
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subprocess.run(cmd, shell=True)
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# Trigger worker execution
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cmd = f"ssh {worker['host']}"
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if 'ssh_hop' in worker:
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cmd = f"ssh {worker['host']} ssh {worker['ssh_hop']}"
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cmd += f" 'cd {worker['workspace']} && python3 worker.py {job_file.name} > logs/worker.log 2>&1 &'"
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subprocess.run(cmd, shell=True)
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print(f"✅ Job started on {worker_id}")
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class ClusterMaster:
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"""Main cluster controller"""
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def __init__(self):
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self.db = StrategyDatabase(DB_PATH)
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self.queue = JobQueue(QUEUE_DIR, self.db)
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self.workers = WorkerManager(WORKERS)
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self.results_dir = Path(RESULTS_DIR)
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self.results_dir.mkdir(exist_ok=True)
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def generate_v9_jobs(self):
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"""Generate v9 parameter sweep jobs"""
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print("🔧 Generating v9 parameter sweep jobs...")
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# Refined parameter grid based on baseline results
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flip_thresholds = [0.5, 0.6, 0.7]
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ma_gaps = [0.30, 0.35, 0.40]
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momentum_adx = [21, 23, 25]
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job_count = 0
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for flip in flip_thresholds:
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for ma_gap in ma_gaps:
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for adx in momentum_adx:
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params = {
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'flip_threshold': flip,
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'ma_gap': ma_gap,
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'momentum_adx': adx,
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'long_pos': 70,
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'short_pos': 25,
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'cooldown_bars': 2
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}
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self.queue.create_job('v9_moneyline', params, PRIORITY_HIGH)
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job_count += 1
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print(f"✅ Created {job_count} v9 refinement jobs")
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def run_forever(self):
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"""Main control loop - runs 24/7"""
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print("🚀 Cluster Master starting...")
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print(f"📊 Workers: {len(WORKERS)}")
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print(f"💾 Database: {DB_PATH}")
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print(f"📁 Queue: {QUEUE_DIR}")
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# Generate initial jobs if queue empty
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if not list(QUEUE_DIR.glob("*.json")):
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self.generate_v9_jobs()
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iteration = 0
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while True:
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iteration += 1
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print(f"\n{'='*60}")
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print(f"🔄 Iteration {iteration} - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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# Check for completed results
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self.collect_results()
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# Assign jobs to idle workers
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for worker_id in WORKERS:
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if self.workers.check_worker_status(worker_id):
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job = self.queue.get_next_job()
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if job:
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self.queue.mark_running(job, worker_id)
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self.workers.assign_job(worker_id, job)
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# Show status
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self.show_status()
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# Sleep before next iteration
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time.sleep(60) # Check every minute
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def collect_results(self):
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"""Collect completed results from workers"""
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for result_file in self.results_dir.glob("*.json"):
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try:
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with open(result_file, 'r') as f:
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result = json.load(f)
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# Store in database
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self.store_result(result)
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# Archive result
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archive_dir = self.results_dir / 'archive'
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archive_dir.mkdir(exist_ok=True)
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result_file.rename(archive_dir / result_file.name)
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print(f"✅ Processed result: {result['job_id']}")
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except Exception as e:
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print(f"❌ Error processing {result_file}: {e}")
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def store_result(self, result):
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"""Store backtest result in database"""
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# Implementation here
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pass
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def show_status(self):
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"""Show current cluster status"""
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queued = len(list(QUEUE_DIR.glob("*.json")))
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conn = sqlite3.connect(self.db.db_path)
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c = conn.cursor()
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c.execute("SELECT COUNT(*) FROM jobs WHERE status = 'running'")
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running = c.fetchone()[0]
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c.execute("SELECT COUNT(*) FROM jobs WHERE status = 'completed'")
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completed = c.fetchone()[0]
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conn.close()
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print(f"📊 Status: {queued} queued | {running} running | {completed} completed")
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# Show top strategies
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top = self.db.get_top_strategies(3)
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if top:
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print("\n🏆 Top 3 Strategies:")
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for i, strat in enumerate(top, 1):
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print(f" {i}. {strat[0]}: ${strat[2]:.2f}/1k ({strat[3]} trades, {strat[4]:.1f}% WR)")
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if __name__ == '__main__':
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master = ClusterMaster()
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master.run_forever()
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