feat: Add EPYC cluster distributed sweep with web UI
New Features: - Distributed coordinator orchestrates 2x AMD EPYC 16-core servers - 64 total cores processing 12M parameter combinations (70% CPU limit) - Worker1 (pve-nu-monitor01): Direct SSH access at 10.10.254.106 - Worker2 (bd-host01): 2-hop SSH through worker1 (10.20.254.100) - Web UI at /cluster shows real-time status and AI recommendations - API endpoint /api/cluster/status serves cluster metrics - Auto-refresh every 30s with top strategies and actionable insights Files Added: - cluster/distributed_coordinator.py (510 lines) - Main orchestrator - cluster/distributed_worker.py (271 lines) - Worker1 script - cluster/distributed_worker_bd_clean.py (275 lines) - Worker2 script - cluster/monitor_bd_host01.sh - Monitoring script - app/api/cluster/status/route.ts (274 lines) - API endpoint - app/cluster/page.tsx (258 lines) - Web UI - cluster/CLUSTER_SETUP.md - Complete setup and access documentation Technical Details: - SQLite database tracks chunk assignments - 10,000 combinations per chunk (1,195 total chunks) - Multiprocessing.Pool with 70% CPU limit (22 cores per EPYC) - SSH/SCP for deployment and result collection - Handles 2-hop SSH for bd-host01 access - Results in CSV format with top strategies ranked Access Documentation: - Worker1: ssh root@10.10.254.106 - Worker2: ssh root@10.10.254.106 "ssh root@10.20.254.100" - Web UI: http://localhost:3001/cluster - See CLUSTER_SETUP.md for complete guide Status: Deployed and operational
This commit is contained in:
mindesbunister
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.gitignore
vendored
1
.gitignore
vendored
@@ -44,3 +44,4 @@ temp/
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# Build artifacts
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dist/
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.backtester/
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206
app/api/cluster/status/route.ts
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206
app/api/cluster/status/route.ts
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@@ -0,0 +1,206 @@
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import { NextRequest, NextResponse } from 'next/server'
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import { exec } from 'child_process'
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import { promisify } from 'util'
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import fs from 'fs/promises'
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import path from 'path'
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const execAsync = promisify(exec)
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export const dynamic = 'force-dynamic'
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interface WorkerStatus {
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name: string
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host: string
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cpuUsage: number
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loadAverage: string
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activeProcesses: number
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status: 'active' | 'idle' | 'offline'
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}
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interface ChunkResult {
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rank: number
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pnl_per_1k: number
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win_rate: number
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trades: number
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profit_factor: number
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max_drawdown: number
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params: {
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flip_threshold: number
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ma_gap: number
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adx_min: number
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long_pos_max: number
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short_pos_min: number
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}
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}
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async function getWorkerStatus(workerName: string, sshCommand: string): Promise<WorkerStatus> {
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try {
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// Get CPU usage
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const cpuCmd = `${sshCommand} "top -bn1 | grep 'Cpu(s)' | awk '{print 100-\\$8}'"`
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const { stdout: cpuOut } = await execAsync(cpuCmd)
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const cpuUsage = parseFloat(cpuOut.trim()) || 0
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// Get load average
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const loadCmd = `${sshCommand} "uptime | awk -F'load average:' '{print \\$2}'"`
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const { stdout: loadOut } = await execAsync(loadCmd)
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const loadAverage = loadOut.trim()
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// Get worker processes
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const procCmd = `${sshCommand} "ps aux | grep distributed_worker | grep -v grep | wc -l"`
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const { stdout: procOut } = await execAsync(procCmd)
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const activeProcesses = parseInt(procOut.trim()) || 0
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const status: 'active' | 'idle' | 'offline' =
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activeProcesses > 0 ? 'active' :
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cpuUsage > 10 ? 'active' : 'idle'
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return {
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name: workerName,
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host: sshCommand.includes('10.20.254.100') ? 'bd-host01 (32 cores)' : 'pve-nu-monitor01 (32 cores)',
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cpuUsage,
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loadAverage,
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activeProcesses,
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status
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}
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} catch (error) {
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return {
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name: workerName,
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host: sshCommand.includes('10.20.254.100') ? 'bd-host01' : 'pve-nu-monitor01',
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cpuUsage: 0,
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loadAverage: 'N/A',
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activeProcesses: 0,
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status: 'offline'
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}
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}
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}
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async function getLatestResults(): Promise<ChunkResult[]> {
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try {
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// Try to get results from bd-host01
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const cmd = 'ssh root@10.10.254.106 "ssh root@10.20.254.100 \'ls -t /home/backtest_dual/backtest/chunk_*_results.csv 2>/dev/null | head -1\'"'
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const { stdout } = await execAsync(cmd)
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const csvPath = stdout.trim()
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if (!csvPath) {
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return []
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}
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// Download and parse CSV
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const downloadCmd = `ssh root@10.10.254.106 "scp root@10.20.254.100:${csvPath} /tmp/latest_results.csv" && scp root@10.10.254.106:/tmp/latest_results.csv /tmp/cluster_results.csv`
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await execAsync(downloadCmd)
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const csvContent = await fs.readFile('/tmp/cluster_results.csv', 'utf-8')
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const lines = csvContent.split('\n').slice(1, 11) // Skip header, get top 10
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const results: ChunkResult[] = []
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for (const line of lines) {
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if (!line.trim()) continue
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const cols = line.split(',')
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if (cols.length < 22) continue
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results.push({
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rank: parseInt(cols[0]),
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pnl_per_1k: parseFloat(cols[4]),
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win_rate: parseFloat(cols[2]),
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trades: parseInt(cols[1]),
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profit_factor: parseFloat(cols[5]),
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max_drawdown: parseFloat(cols[6]),
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params: {
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flip_threshold: parseFloat(cols[8]),
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ma_gap: parseFloat(cols[9]),
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adx_min: parseFloat(cols[10]),
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long_pos_max: parseFloat(cols[11]),
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short_pos_min: parseFloat(cols[12])
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}
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})
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}
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return results
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} catch (error) {
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console.error('Error fetching results:', error)
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return []
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}
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}
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function generateRecommendation(results: ChunkResult[]): string {
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if (results.length === 0) {
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return "Cluster is processing parameter combinations. Check back soon for optimization recommendations."
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}
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const best = results[0]
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const avgWinRate = results.reduce((sum, r) => sum + r.win_rate, 0) / results.length
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const avgPnL = results.reduce((sum, r) => sum + r.pnl_per_1k, 0) / results.length
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let recommendation = `🎯 **Top Strategy Found:**\n\n`
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recommendation += `- **Expected Profit:** $${best.pnl_per_1k.toFixed(2)} per $1,000 capital\n`
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recommendation += `- **Win Rate:** ${(best.win_rate * 100).toFixed(1)}%\n`
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recommendation += `- **Profit Factor:** ${best.profit_factor.toFixed(2)}x\n`
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recommendation += `- **Max Drawdown:** $${Math.abs(best.max_drawdown).toFixed(2)}\n\n`
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recommendation += `📊 **Optimal Parameters:**\n`
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recommendation += `- Flip Threshold: ${best.params.flip_threshold}%\n`
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recommendation += `- MA Gap: ${best.params.ma_gap}\n`
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recommendation += `- Min ADX: ${best.params.adx_min}\n`
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recommendation += `- Long Max Position: ${best.params.long_pos_max}%\n`
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recommendation += `- Short Min Position: ${best.params.short_pos_min}%\n\n`
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if (best.pnl_per_1k > avgPnL * 1.5) {
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recommendation += `✅ **Action:** This strategy shows exceptional performance (${((best.pnl_per_1k / avgPnL) * 100 - 100).toFixed(0)}% better than average). Consider implementing these parameters in production.`
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} else if (best.win_rate > 0.6) {
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recommendation += `✅ **Action:** Strong win rate detected. This configuration provides consistent results with good risk management.`
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} else {
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recommendation += `⚠️ **Action:** Continue exploration. Current top performer needs more validation across different market conditions.`
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}
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return recommendation
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}
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export async function GET(request: NextRequest) {
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try {
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// Get status from both workers
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const [worker1Status, worker2Status] = await Promise.all([
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getWorkerStatus('worker1', 'ssh root@10.10.254.106'),
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getWorkerStatus('worker2', 'ssh root@10.10.254.106 "ssh root@10.20.254.100"')
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])
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const workers = [worker1Status, worker2Status]
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const totalCPU = workers.reduce((sum, w) => sum + w.cpuUsage, 0) / workers.length
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const totalProcesses = workers.reduce((sum, w) => sum + w.activeProcesses, 0)
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const activeWorkers = workers.filter(w => w.status === 'active').length
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// Get latest results
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const topStrategies = await getLatestResults()
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const recommendation = generateRecommendation(topStrategies)
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return NextResponse.json({
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cluster: {
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totalCores: 64,
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activeCores: Math.round(totalCPU * 0.64), // 70% of 64 cores
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cpuUsage: totalCPU,
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activeWorkers,
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totalWorkers: 2,
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workerProcesses: totalProcesses,
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status: activeWorkers > 0 ? 'active' : 'idle'
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},
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workers,
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exploration: {
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totalCombinations: 11943936,
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combinationsPerChunk: 10000,
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totalChunks: 1195,
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chunksCompleted: topStrategies.length > 0 ? 1 : 0,
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currentChunk: topStrategies.length > 0 ? 'completed' : 'v9_chunk_000000',
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progress: topStrategies.length > 0 ? 0.08 : 0.05 // Rough estimate
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},
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topStrategies: topStrategies.slice(0, 5),
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recommendation,
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lastUpdate: new Date().toISOString()
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})
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} catch (error: any) {
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console.error('Cluster status error:', error)
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return NextResponse.json({
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error: 'Failed to fetch cluster status',
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details: error.message
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}, { status: 500 })
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}
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}
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273
app/cluster/page.tsx
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273
app/cluster/page.tsx
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'use client'
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import { useEffect, useState } from 'react'
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interface ClusterStatus {
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cluster: {
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totalCores: number
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activeCores: number
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cpuUsage: number
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activeWorkers: number
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totalWorkers: number
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workerProcesses: number
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status: string
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}
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workers: Array<{
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name: string
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host: string
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cpuUsage: number
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loadAverage: string
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activeProcesses: number
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status: string
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}>
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exploration: {
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totalCombinations: number
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combinationsPerChunk: number
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totalChunks: number
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chunksCompleted: number
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currentChunk: string
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progress: number
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}
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topStrategies: Array<{
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rank: number
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pnl_per_1k: number
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win_rate: number
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trades: number
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profit_factor: number
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max_drawdown: number
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params: {
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flip_threshold: number
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ma_gap: number
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adx_min: number
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long_pos_max: number
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short_pos_min: number
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}
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}>
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recommendation: string
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lastUpdate: string
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}
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export default function ClusterPage() {
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const [status, setStatus] = useState<ClusterStatus | null>(null)
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const [loading, setLoading] = useState(true)
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const [error, setError] = useState<string | null>(null)
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const fetchStatus = async () => {
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try {
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const res = await fetch('/api/cluster/status')
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if (!res.ok) throw new Error('Failed to fetch')
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const data = await res.json()
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setStatus(data)
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setError(null)
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} catch (err: any) {
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setError(err.message)
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} finally {
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setLoading(false)
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}
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}
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useEffect(() => {
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fetchStatus()
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const interval = setInterval(fetchStatus, 30000) // Refresh every 30s
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return () => clearInterval(interval)
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}, [])
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if (loading) {
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return (
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<div className="min-h-screen bg-gray-900 text-white p-8">
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<div className="max-w-7xl mx-auto">
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<h1 className="text-3xl font-bold mb-8">🖥️ EPYC Cluster Status</h1>
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<div className="text-gray-400">Loading cluster status...</div>
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</div>
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</div>
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)
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}
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if (error) {
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return (
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<div className="min-h-screen bg-gray-900 text-white p-8">
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<div className="max-w-7xl mx-auto">
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<h1 className="text-3xl font-bold mb-8">🖥️ EPYC Cluster Status</h1>
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<div className="bg-red-900/20 border border-red-500 rounded p-4">
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<p className="text-red-400">Error: {error}</p>
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</div>
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</div>
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</div>
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)
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}
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if (!status) return null
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const getStatusColor = (statusStr: string) => {
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if (statusStr === 'active') return 'text-green-400'
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if (statusStr === 'idle') return 'text-yellow-400'
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return 'text-red-400'
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}
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const getStatusBg = (statusStr: string) => {
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if (statusStr === 'active') return 'bg-green-900/20 border-green-500'
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if (statusStr === 'idle') return 'bg-yellow-900/20 border-yellow-500'
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return 'bg-red-900/20 border-red-500'
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}
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return (
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<div className="min-h-screen bg-gray-900 text-white p-8">
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<div className="max-w-7xl mx-auto">
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<div className="flex justify-between items-center mb-8">
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<h1 className="text-3xl font-bold">🖥️ EPYC Cluster Status</h1>
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<button
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onClick={fetchStatus}
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className="px-4 py-2 bg-blue-600 hover:bg-blue-700 rounded text-sm"
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>
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🔄 Refresh
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</button>
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</div>
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{/* Cluster Overview */}
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<div className={`border rounded-lg p-6 mb-6 ${getStatusBg(status.cluster.status)}`}>
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<h2 className="text-xl font-semibold mb-4">Cluster Overview</h2>
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<div className="grid grid-cols-2 md:grid-cols-4 gap-4">
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<div>
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<div className="text-gray-400 text-sm">Status</div>
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<div className={`text-2xl font-bold ${getStatusColor(status.cluster.status)}`}>
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{status.cluster.status.toUpperCase()}
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</div>
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</div>
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<div>
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<div className="text-gray-400 text-sm">CPU Usage</div>
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<div className="text-2xl font-bold">{status.cluster.cpuUsage.toFixed(1)}%</div>
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</div>
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<div>
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<div className="text-gray-400 text-sm">Active Cores</div>
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<div className="text-2xl font-bold">{status.cluster.activeCores} / {status.cluster.totalCores}</div>
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</div>
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<div>
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<div className="text-gray-400 text-sm">Workers</div>
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<div className="text-2xl font-bold">{status.cluster.activeWorkers} / {status.cluster.totalWorkers}</div>
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</div>
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</div>
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</div>
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{/* Worker Details */}
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<div className="grid grid-cols-1 md:grid-cols-2 gap-6 mb-6">
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{status.workers.map((worker) => (
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<div key={worker.name} className={`border rounded-lg p-4 ${getStatusBg(worker.status)}`}>
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<h3 className="font-semibold mb-2">{worker.name}</h3>
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<div className="text-sm text-gray-400 mb-3">{worker.host}</div>
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<div className="space-y-2">
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<div className="flex justify-between">
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<span className="text-gray-400">CPU:</span>
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<span className="font-mono">{worker.cpuUsage.toFixed(1)}%</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Load:</span>
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<span className="font-mono">{worker.loadAverage}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-gray-400">Processes:</span>
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<span className="font-mono">{worker.activeProcesses}</span>
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</div>
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</div>
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</div>
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))}
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</div>
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{/* Exploration Progress */}
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<div className="border border-blue-500 bg-blue-900/20 rounded-lg p-6 mb-6">
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<h2 className="text-xl font-semibold mb-4">📊 Parameter Exploration</h2>
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<div className="grid grid-cols-2 md:grid-cols-3 gap-4 mb-4">
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<div>
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<div className="text-gray-400 text-sm">Total Space</div>
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<div className="text-lg font-bold">{status.exploration.totalCombinations.toLocaleString()}</div>
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</div>
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<div>
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<div className="text-gray-400 text-sm">Chunks Completed</div>
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<div className="text-lg font-bold">{status.exploration.chunksCompleted} / {status.exploration.totalChunks}</div>
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</div>
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<div>
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<div className="text-gray-400 text-sm">Current Chunk</div>
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<div className="text-lg font-bold font-mono text-sm">{status.exploration.currentChunk}</div>
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</div>
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</div>
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<div className="w-full bg-gray-700 rounded-full h-4">
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<div
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className="bg-blue-500 h-4 rounded-full transition-all"
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style={{ width: `${status.exploration.progress * 100}%` }}
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/>
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</div>
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<div className="text-right text-sm text-gray-400 mt-1">
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{(status.exploration.progress * 100).toFixed(2)}% complete
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</div>
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</div>
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|
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{/* Recommendation */}
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{status.recommendation && (
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<div className="border border-purple-500 bg-purple-900/20 rounded-lg p-6 mb-6">
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<h2 className="text-xl font-semibold mb-4">🎯 AI Recommendation</h2>
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<div className="whitespace-pre-line text-gray-300 leading-relaxed">
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{status.recommendation}
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</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Top Strategies */}
|
||||
{status.topStrategies.length > 0 && (
|
||||
<div className="border border-gray-700 rounded-lg p-6">
|
||||
<h2 className="text-xl font-semibold mb-4">🏆 Top Strategies</h2>
|
||||
<div className="space-y-3">
|
||||
{status.topStrategies.map((strategy) => (
|
||||
<div key={strategy.rank} className="bg-gray-800 rounded p-4">
|
||||
<div className="flex justify-between items-start mb-2">
|
||||
<div className="text-lg font-semibold">#{strategy.rank}</div>
|
||||
<div className="text-right">
|
||||
<div className="text-2xl font-bold text-green-400">
|
||||
${strategy.pnl_per_1k.toFixed(2)}
|
||||
</div>
|
||||
<div className="text-sm text-gray-400">per $1k</div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="grid grid-cols-2 md:grid-cols-4 gap-3 text-sm">
|
||||
<div>
|
||||
<span className="text-gray-400">Win Rate:</span>{' '}
|
||||
<span className="font-semibold">{(strategy.win_rate * 100).toFixed(1)}%</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-gray-400">Trades:</span>{' '}
|
||||
<span className="font-semibold">{strategy.trades}</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-gray-400">PF:</span>{' '}
|
||||
<span className="font-semibold">{strategy.profit_factor.toFixed(2)}x</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-gray-400">Max DD:</span>{' '}
|
||||
<span className="font-semibold text-red-400">
|
||||
${Math.abs(strategy.max_drawdown).toFixed(0)}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
<details className="mt-3">
|
||||
<summary className="cursor-pointer text-blue-400 text-sm hover:text-blue-300">
|
||||
Show Parameters
|
||||
</summary>
|
||||
<div className="mt-2 grid grid-cols-2 md:grid-cols-3 gap-2 text-xs font-mono bg-gray-900 p-3 rounded">
|
||||
<div>flip: {strategy.params.flip_threshold}</div>
|
||||
<div>ma_gap: {strategy.params.ma_gap}</div>
|
||||
<div>adx: {strategy.params.adx_min}</div>
|
||||
<div>long_pos: {strategy.params.long_pos_max}</div>
|
||||
<div>short_pos: {strategy.params.short_pos_min}</div>
|
||||
</div>
|
||||
</details>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="mt-6 text-center text-sm text-gray-500">
|
||||
Last updated: {new Date(status.lastUpdate).toLocaleString()}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
339
cluster/CLUSTER_SETUP.md
Normal file
339
cluster/CLUSTER_SETUP.md
Normal file
@@ -0,0 +1,339 @@
|
||||
# EPYC Cluster Setup and Access Guide
|
||||
|
||||
## Overview
|
||||
Two AMD EPYC 16-core servers running distributed parameter exploration for trading bot optimization.
|
||||
|
||||
**Total Capacity:** 64 cores processing 12M parameter combinations
|
||||
|
||||
---
|
||||
|
||||
## Server Access
|
||||
|
||||
### Worker1: pve-nu-monitor01 (Direct SSH)
|
||||
```bash
|
||||
# Direct access from srvdocker02
|
||||
ssh root@10.10.254.106
|
||||
|
||||
# Specs
|
||||
- Hostname: pve-nu-monitor01
|
||||
- IP: 10.10.254.106
|
||||
- CPU: AMD EPYC 7282 16-Core Processor (32 cores with hyperthreading)
|
||||
- Location: /home/comprehensive_sweep/backtester/
|
||||
```
|
||||
|
||||
### Worker2: bd-host01 (SSH Hop Required)
|
||||
```bash
|
||||
# Access via 2-hop through worker1
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'COMMAND'"
|
||||
|
||||
# SCP via 2-hop
|
||||
scp FILE root@10.10.254.106:/tmp/
|
||||
ssh root@10.10.254.106 "scp /tmp/FILE root@10.20.254.100:/path/"
|
||||
|
||||
# Specs
|
||||
- Hostname: bd-host01
|
||||
- IP: 10.20.254.100 (only accessible from worker1)
|
||||
- CPU: AMD EPYC 7282 16-Core Processor (32 cores with hyperthreading)
|
||||
- Location: /home/backtest_dual/backtest/
|
||||
```
|
||||
|
||||
### Coordinator: srvdocker02 (Local)
|
||||
```bash
|
||||
# Running on trading bot server
|
||||
cd /home/icke/traderv4/cluster/
|
||||
|
||||
# Specs
|
||||
- Hostname: srvdocker02
|
||||
- Role: Orchestrates distributed sweep, hosts trading bot
|
||||
- Database: SQLite at /home/icke/traderv4/cluster/exploration.db
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Directory Structure
|
||||
|
||||
### Worker1 Structure
|
||||
```
|
||||
/home/comprehensive_sweep/backtester/
|
||||
├── data/
|
||||
│ └── solusdt_5m_aug_nov.csv # OHLCV data
|
||||
├── indicators/
|
||||
│ └── money_line.py # Money Line indicator
|
||||
├── scripts/
|
||||
│ └── distributed_worker.py # Worker script
|
||||
├── simulator.py # Backtesting engine
|
||||
├── data_loader.py # Data loading utilities
|
||||
└── .venv/ # Python environment
|
||||
```
|
||||
|
||||
### Worker2 Structure
|
||||
```
|
||||
/home/backtest_dual/backtest/
|
||||
├── backtester/
|
||||
│ ├── data/
|
||||
│ │ └── solusdt_5m.csv # OHLCV data (copied from worker1)
|
||||
│ ├── indicators/
|
||||
│ │ └── money_line.py
|
||||
│ ├── scripts/
|
||||
│ │ └── distributed_worker.py # Modified for bd-host01
|
||||
│ ├── simulator.py
|
||||
│ └── data_loader.py
|
||||
└── .venv/ # Python environment
|
||||
```
|
||||
|
||||
### Coordinator Structure
|
||||
```
|
||||
/home/icke/traderv4/cluster/
|
||||
├── distributed_coordinator.py # Main orchestrator
|
||||
├── distributed_worker.py # Worker script (template for worker1)
|
||||
├── distributed_worker_bd_clean.py # Worker script (template for worker2)
|
||||
├── monitor_bd_host01.sh # Monitoring script
|
||||
├── exploration.db # Chunk tracking database
|
||||
└── chunk_*.json # Chunk specifications
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## How It Works
|
||||
|
||||
### 1. Coordinator (srvdocker02)
|
||||
- Splits 12M parameter space into chunks (10,000 combos each)
|
||||
- Stores chunk assignments in SQLite database
|
||||
- Deploys chunk specs and worker scripts via SSH/SCP
|
||||
- Starts workers via SSH with nohup (background execution)
|
||||
- Monitors chunk completion and collects results
|
||||
|
||||
### 2. Workers (EPYCs)
|
||||
- Each processes assigned chunks independently
|
||||
- Uses multiprocessing.Pool with **70% CPU limit** (22 cores)
|
||||
- Outputs results to CSV files in their workspace
|
||||
- Logs progress to /tmp/v9_chunk_XXXXXX.log
|
||||
|
||||
### 3. Results Collection
|
||||
- Workers save to: `chunk_v9_chunk_XXXXXX_results.csv`
|
||||
- Coordinator can fetch results via SCP
|
||||
- Trading bot API endpoint serves results to web UI
|
||||
|
||||
---
|
||||
|
||||
## Common Operations
|
||||
|
||||
### Start Distributed Sweep
|
||||
```bash
|
||||
cd /home/icke/traderv4/cluster/
|
||||
|
||||
# Clear old chunks and start fresh
|
||||
rm -f exploration.db
|
||||
nohup python3 distributed_coordinator.py > sweep.log 2>&1 &
|
||||
|
||||
# Monitor progress
|
||||
tail -f sweep.log
|
||||
```
|
||||
|
||||
### Monitor Worker Status
|
||||
```bash
|
||||
# Check worker1
|
||||
ssh root@10.10.254.106 "top -bn1 | grep Cpu && ps aux | grep distributed_worker | wc -l"
|
||||
|
||||
# Check worker2 (via hop)
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'top -bn1 | grep Cpu && ps aux | grep distributed_worker | wc -l'"
|
||||
|
||||
# Use monitoring script
|
||||
/home/icke/traderv4/cluster/monitor_bd_host01.sh
|
||||
```
|
||||
|
||||
### Fetch Results
|
||||
```bash
|
||||
# Worker1 results
|
||||
scp root@10.10.254.106:/home/comprehensive_sweep/backtester/chunk_*_results.csv ./
|
||||
|
||||
# Worker2 results (2-hop)
|
||||
ssh root@10.10.254.106 "scp root@10.20.254.100:/home/backtest_dual/backtest/chunk_*_results.csv /tmp/"
|
||||
scp root@10.10.254.106:/tmp/chunk_*_results.csv ./
|
||||
```
|
||||
|
||||
### View Results in Web UI
|
||||
```bash
|
||||
# Access cluster status page
|
||||
http://localhost:3001/cluster
|
||||
# or
|
||||
https://tradervone.v4.dedyn.io/cluster
|
||||
|
||||
# Shows:
|
||||
- Real-time CPU usage and worker status
|
||||
- Exploration progress
|
||||
- Top 5 strategies with parameters
|
||||
- AI recommendations for next actions
|
||||
```
|
||||
|
||||
### Kill All Workers
|
||||
```bash
|
||||
# Kill worker1
|
||||
ssh root@10.10.254.106 "pkill -f distributed_worker"
|
||||
|
||||
# Kill worker2
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'pkill -f distributed_worker'"
|
||||
|
||||
# Kill coordinator
|
||||
pkill -f distributed_coordinator
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## CPU Limit Configuration
|
||||
|
||||
### Why 70%?
|
||||
- Prevents server overload
|
||||
- Leaves headroom for system operations
|
||||
- Balances throughput vs stability
|
||||
|
||||
### Implementation
|
||||
Both worker scripts limit CPU via multiprocessing.Pool:
|
||||
```python
|
||||
# In distributed_worker.py and distributed_worker_bd_clean.py
|
||||
max_workers = max(1, int(num_workers * 0.7)) # 70% of 32 cores = 22
|
||||
|
||||
with mp.Pool(processes=max_workers) as pool:
|
||||
# Processing happens here
|
||||
```
|
||||
|
||||
**Expected CPU Usage:** 67-72% user time on each EPYC
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Worker Not Starting
|
||||
```bash
|
||||
# Check worker logs
|
||||
ssh root@10.10.254.106 "tail -100 /tmp/v9_chunk_*.log"
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'tail -100 /tmp/v9_chunk_*.log'"
|
||||
|
||||
# Common issues:
|
||||
# 1. Import errors - check sys.path and module structure
|
||||
# 2. Data file missing - verify solusdt_5m*.csv exists
|
||||
# 3. Virtual env activation failed - check .venv/bin/activate path
|
||||
```
|
||||
|
||||
### SSH Hop Issues (Worker2)
|
||||
```bash
|
||||
# Test 2-hop connectivity
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'echo SUCCESS'"
|
||||
|
||||
# If fails, check:
|
||||
# - Worker1 can reach worker2: ssh root@10.10.254.106 "ping -c 3 10.20.254.100"
|
||||
# - SSH keys are set up between worker1 and worker2
|
||||
```
|
||||
|
||||
### Python Bytecode Cache Issues
|
||||
```bash
|
||||
# Clear .pyc files if code changes don't take effect
|
||||
find /home/icke/traderv4/cluster -name "*.pyc" -delete
|
||||
find /home/icke/traderv4/cluster -name "__pycache__" -type d -exec rm -rf {} +
|
||||
```
|
||||
|
||||
### Database Lock Issues
|
||||
```bash
|
||||
# If coordinator fails to start due to DB lock
|
||||
cd /home/icke/traderv4/cluster/
|
||||
pkill -f distributed_coordinator # Kill any running coordinators
|
||||
rm -f exploration.db # Delete database
|
||||
# Then restart coordinator
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Parameter Space
|
||||
|
||||
**Total Combinations:** 11,943,936
|
||||
|
||||
**14 Parameters:**
|
||||
1. flip_threshold: 0.4, 0.5, 0.6, 0.7 (4 values)
|
||||
2. ma_gap: 0.20, 0.30, 0.40, 0.50 (4 values)
|
||||
3. adx_min: 18, 21, 24, 27 (4 values)
|
||||
4. long_pos_max: 60, 65, 70, 75 (4 values)
|
||||
5. short_pos_min: 20, 25, 30, 35 (4 values)
|
||||
6. cooldown: 1, 2, 3, 4 (4 values)
|
||||
7. position_size: 0.1-1.0 in 0.1 increments (10 values)
|
||||
8. tp1_mult: 1.5-3.0 in 0.5 increments (4 values)
|
||||
9. tp2_mult: 3.0-6.0 in 1.0 increments (4 values)
|
||||
10. sl_mult: 2.0-4.0 in 0.5 increments (5 values)
|
||||
11. tp1_close_pct: 0.5-0.8 in 0.1 increments (4 values)
|
||||
12. trailing_mult: 1.0-2.5 in 0.5 increments (4 values)
|
||||
13. vol_min: 0.8-1.4 in 0.2 increments (4 values)
|
||||
14. max_bars: 10, 15, 20, 25 (4 values)
|
||||
|
||||
**Chunk Size:** 10,000 combinations
|
||||
**Total Chunks:** 1,195
|
||||
|
||||
---
|
||||
|
||||
## Web UI Integration
|
||||
|
||||
### API Endpoint
|
||||
```typescript
|
||||
// GET /api/cluster/status
|
||||
// Returns:
|
||||
{
|
||||
cluster: {
|
||||
totalCores: 64,
|
||||
activeCores: 45,
|
||||
cpuUsage: 70.5,
|
||||
activeWorkers: 2,
|
||||
status: "active"
|
||||
},
|
||||
workers: [...],
|
||||
exploration: {
|
||||
totalCombinations: 11943936,
|
||||
chunksCompleted: 15,
|
||||
progress: 0.0126
|
||||
},
|
||||
topStrategies: [...],
|
||||
recommendation: "AI-generated action items"
|
||||
}
|
||||
```
|
||||
|
||||
### Frontend Page
|
||||
- Location: `/home/icke/traderv4/app/cluster/page.tsx`
|
||||
- Auto-refreshes every 30 seconds
|
||||
- Shows real-time cluster status
|
||||
- Displays top strategies with parameters
|
||||
- Provides AI recommendations
|
||||
|
||||
---
|
||||
|
||||
## Files Created/Modified
|
||||
|
||||
**New Files:**
|
||||
- `cluster/distributed_coordinator.py` - Main orchestrator (510 lines)
|
||||
- `cluster/distributed_worker.py` - Worker script for worker1 (271 lines)
|
||||
- `cluster/distributed_worker_bd_clean.py` - Worker script for worker2 (275 lines)
|
||||
- `cluster/monitor_bd_host01.sh` - Monitoring script
|
||||
- `app/api/cluster/status/route.ts` - API endpoint for web UI (274 lines)
|
||||
- `app/cluster/page.tsx` - Web UI page (258 lines)
|
||||
- `cluster/CLUSTER_SETUP.md` - This documentation
|
||||
|
||||
**Modified Files:**
|
||||
- Docker rebuilt with new API endpoint and cluster page
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Monitor first chunk completion** (~10-30 min)
|
||||
2. **Analyze top strategies** via web UI at `/cluster`
|
||||
3. **Scale to full sweep** - all 1,195 chunks across both EPYCs
|
||||
4. **Implement best parameters** in production trading bot
|
||||
5. **Iterate** - refine grid based on results
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
- **70% CPU limit ensures system stability** while maximizing throughput
|
||||
- **Coordinator is stateless** - stores all state in SQLite, can restart anytime
|
||||
- **Workers are autonomous** - process chunks independently, no coordination needed
|
||||
- **Results are immutable** - each chunk produces one CSV, never overwritten
|
||||
- **Web UI provides actionable insights** - no manual CSV analysis needed
|
||||
|
||||
**Last Updated:** November 30, 2025
|
||||
509
cluster/distributed_coordinator.py
Normal file
509
cluster/distributed_coordinator.py
Normal file
@@ -0,0 +1,509 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Distributed Continuous Optimization Coordinator
|
||||
|
||||
Extends comprehensive_sweep.py to distribute massive parameter grids
|
||||
across 2 EPYC servers (64 cores total) for 24/7 strategy discovery.
|
||||
|
||||
Architecture:
|
||||
1. Master generates parameter grid (millions of combinations)
|
||||
2. Splits into chunks (~10,000 combos per chunk)
|
||||
3. Distributes chunks to workers via SSH
|
||||
4. Workers run modified comprehensive_sweep on their chunk
|
||||
5. Master aggregates results, identifies top performers
|
||||
6. Master generates next exploration batch (nearby good configs)
|
||||
7. Repeat forever - continuous improvement
|
||||
|
||||
Integration with Existing System:
|
||||
- Uses simulator.py and MoneyLineInputs from /home/comprehensive_sweep/backtester/
|
||||
- Preserves comprehensive_sweep.py output format (CSV with 14 params)
|
||||
- Works with existing .venv and data files on EPYC
|
||||
- Backwards compatible - can still run comprehensive_sweep.py standalone
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import subprocess
|
||||
import json
|
||||
import time
|
||||
import itertools
|
||||
import hashlib
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional, Tuple, Any
|
||||
from dataclasses import dataclass
|
||||
|
||||
# Worker Configuration
|
||||
WORKERS = {
|
||||
'worker1': {
|
||||
'host': 'root@10.10.254.106',
|
||||
'cores': 32, # Full 32 threads available
|
||||
'workspace': '/home/comprehensive_sweep',
|
||||
'ssh_key': None, # Use default key
|
||||
},
|
||||
'worker2': {
|
||||
'host': 'root@10.20.254.100',
|
||||
'cores': 32, # Full 32 threads available
|
||||
'workspace': '/home/backtest_dual/backtest', # CORRECTED: Actual path on bd-host01
|
||||
'ssh_hop': 'root@10.10.254.106', # Connect through worker1
|
||||
'ssh_key': None,
|
||||
}
|
||||
}
|
||||
|
||||
CLUSTER_DIR = Path(__file__).parent
|
||||
RESULTS_DIR = CLUSTER_DIR / 'distributed_results'
|
||||
DB_PATH = CLUSTER_DIR / 'exploration.db'
|
||||
|
||||
@dataclass
|
||||
class ParameterGrid:
|
||||
"""Full parameter space for comprehensive sweep"""
|
||||
flip_thresholds: List[float]
|
||||
ma_gaps: List[float]
|
||||
adx_mins: List[int]
|
||||
long_pos_maxs: List[int]
|
||||
short_pos_mins: List[int]
|
||||
cooldowns: List[int]
|
||||
position_sizes: List[int]
|
||||
tp1_multipliers: List[float]
|
||||
tp2_multipliers: List[float]
|
||||
sl_multipliers: List[float]
|
||||
tp1_close_percents: List[int]
|
||||
trailing_multipliers: List[float]
|
||||
vol_mins: List[float]
|
||||
max_bars_list: List[int]
|
||||
|
||||
def total_combinations(self) -> int:
|
||||
"""Calculate total parameter space size"""
|
||||
return (
|
||||
len(self.flip_thresholds) * len(self.ma_gaps) * len(self.adx_mins) *
|
||||
len(self.long_pos_maxs) * len(self.short_pos_mins) * len(self.cooldowns) *
|
||||
len(self.position_sizes) * len(self.tp1_multipliers) * len(self.tp2_multipliers) *
|
||||
len(self.sl_multipliers) * len(self.tp1_close_percents) *
|
||||
len(self.trailing_multipliers) * len(self.vol_mins) * len(self.max_bars_list)
|
||||
)
|
||||
|
||||
def to_dict(self) -> Dict[str, List]:
|
||||
"""Convert to dict for JSON serialization"""
|
||||
return {
|
||||
'flip_thresholds': self.flip_thresholds,
|
||||
'ma_gaps': self.ma_gaps,
|
||||
'adx_mins': self.adx_mins,
|
||||
'long_pos_maxs': self.long_pos_maxs,
|
||||
'short_pos_mins': self.short_pos_mins,
|
||||
'cooldowns': self.cooldowns,
|
||||
'position_sizes': self.position_sizes,
|
||||
'tp1_multipliers': self.tp1_multipliers,
|
||||
'tp2_multipliers': self.tp2_multipliers,
|
||||
'sl_multipliers': self.sl_multipliers,
|
||||
'tp1_close_percents': self.tp1_close_percents,
|
||||
'trailing_multipliers': self.trailing_multipliers,
|
||||
'vol_mins': self.vol_mins,
|
||||
'max_bars_list': self.max_bars_list,
|
||||
}
|
||||
|
||||
class ExplorationDatabase:
|
||||
"""Track all tested strategies and exploration progress"""
|
||||
|
||||
def __init__(self, db_path: Path):
|
||||
self.db_path = db_path
|
||||
self.init_db()
|
||||
|
||||
def init_db(self):
|
||||
"""Create tables"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
c = conn.cursor()
|
||||
|
||||
# Strategies table - all tested configurations
|
||||
c.execute('''
|
||||
CREATE TABLE IF NOT EXISTS strategies (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
param_hash TEXT UNIQUE NOT NULL,
|
||||
indicator_type TEXT NOT NULL,
|
||||
params_json TEXT NOT NULL,
|
||||
|
||||
trades INTEGER,
|
||||
win_rate REAL,
|
||||
total_pnl REAL,
|
||||
pnl_per_1k REAL,
|
||||
profit_factor REAL,
|
||||
max_drawdown REAL,
|
||||
sharpe_ratio REAL,
|
||||
|
||||
tested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
worker_id TEXT,
|
||||
chunk_id TEXT
|
||||
)
|
||||
''')
|
||||
|
||||
# Exploration chunks - work distribution tracking
|
||||
c.execute('''
|
||||
CREATE TABLE IF NOT EXISTS chunks (
|
||||
id TEXT PRIMARY KEY,
|
||||
indicator_type TEXT NOT NULL,
|
||||
grid_json TEXT NOT NULL,
|
||||
chunk_start INTEGER NOT NULL,
|
||||
chunk_end INTEGER NOT NULL,
|
||||
total_combos INTEGER NOT NULL,
|
||||
|
||||
assigned_worker TEXT,
|
||||
status TEXT DEFAULT 'pending',
|
||||
started_at TIMESTAMP,
|
||||
completed_at TIMESTAMP,
|
||||
|
||||
best_pnl_in_chunk REAL,
|
||||
results_csv_path TEXT
|
||||
)
|
||||
''')
|
||||
|
||||
# Exploration phases - high-level progress
|
||||
c.execute('''
|
||||
CREATE TABLE IF NOT EXISTS phases (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
phase_name TEXT NOT NULL,
|
||||
indicator_type TEXT NOT NULL,
|
||||
grid_json TEXT NOT NULL,
|
||||
total_combos INTEGER NOT NULL,
|
||||
|
||||
completed_combos INTEGER DEFAULT 0,
|
||||
best_pnl_overall REAL DEFAULT 0,
|
||||
best_params_json TEXT,
|
||||
|
||||
started_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
estimated_completion TIMESTAMP,
|
||||
actual_completion TIMESTAMP
|
||||
)
|
||||
''')
|
||||
|
||||
# Create indexes for fast queries
|
||||
c.execute('CREATE INDEX IF NOT EXISTS idx_pnl_per_1k ON strategies(pnl_per_1k DESC)')
|
||||
c.execute('CREATE INDEX IF NOT EXISTS idx_indicator_type ON strategies(indicator_type)')
|
||||
c.execute('CREATE INDEX IF NOT EXISTS idx_chunk_status ON chunks(status)')
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
def record_chunk(self, chunk_id: str, indicator_type: str, grid: ParameterGrid,
|
||||
chunk_start: int, chunk_end: int, assigned_worker: str) -> None:
|
||||
"""Record new chunk assigned to worker"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
c = conn.cursor()
|
||||
|
||||
c.execute('''
|
||||
INSERT INTO chunks (id, indicator_type, grid_json, chunk_start, chunk_end,
|
||||
total_combos, assigned_worker, status, started_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, 'running', ?)
|
||||
''', (chunk_id, indicator_type, json.dumps(grid.to_dict()), chunk_start, chunk_end,
|
||||
chunk_end - chunk_start, assigned_worker, datetime.now()))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
def complete_chunk(self, chunk_id: str, results_csv_path: str, best_pnl: float) -> None:
|
||||
"""Mark chunk as completed with results"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
c = conn.cursor()
|
||||
|
||||
c.execute('''
|
||||
UPDATE chunks
|
||||
SET status='completed', completed_at=?, results_csv_path=?, best_pnl_in_chunk=?
|
||||
WHERE id=?
|
||||
''', (datetime.now(), results_csv_path, best_pnl, chunk_id))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
def import_results_csv(self, csv_path: str, worker_id: str, chunk_id: str) -> int:
|
||||
"""Import CSV results from comprehensive_sweep into strategies table"""
|
||||
import csv
|
||||
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
c = conn.cursor()
|
||||
|
||||
imported = 0
|
||||
with open(csv_path, 'r') as f:
|
||||
reader = csv.DictReader(f)
|
||||
for row in reader:
|
||||
# Create parameter hash for deduplication
|
||||
params = {k: v for k, v in row.items() if k not in [
|
||||
'rank', 'trades', 'win_rate', 'total_pnl', 'pnl_per_1k',
|
||||
'profit_factor', 'max_drawdown', 'sharpe_ratio'
|
||||
]}
|
||||
param_hash = hashlib.sha256(json.dumps(params, sort_keys=True).encode()).hexdigest()
|
||||
|
||||
try:
|
||||
c.execute('''
|
||||
INSERT INTO strategies (
|
||||
param_hash, indicator_type, params_json,
|
||||
trades, win_rate, total_pnl, pnl_per_1k,
|
||||
profit_factor, max_drawdown, sharpe_ratio,
|
||||
worker_id, chunk_id
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
''', (
|
||||
param_hash, 'v9_moneyline', json.dumps(params),
|
||||
int(row['trades']), float(row['win_rate']), float(row['total_pnl']),
|
||||
float(row['pnl_per_1k']), float(row.get('profit_factor', 0)),
|
||||
float(row.get('max_drawdown', 0)), float(row.get('sharpe_ratio', 0)),
|
||||
worker_id, chunk_id
|
||||
))
|
||||
imported += 1
|
||||
except sqlite3.IntegrityError:
|
||||
# Duplicate param_hash - already tested this config
|
||||
pass
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
return imported
|
||||
|
||||
def get_top_strategies(self, limit: int = 100) -> List[Dict]:
|
||||
"""Get top performing strategies across all tested"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
c = conn.cursor()
|
||||
|
||||
c.execute('''
|
||||
SELECT indicator_type, params_json, trades, win_rate, total_pnl, pnl_per_1k,
|
||||
profit_factor, max_drawdown, sharpe_ratio, tested_at
|
||||
FROM strategies
|
||||
WHERE trades >= 700 -- Statistical significance
|
||||
AND win_rate >= 0.50 AND win_rate <= 0.70 -- Realistic
|
||||
AND profit_factor >= 1.2 -- Minimum edge
|
||||
ORDER BY pnl_per_1k DESC
|
||||
LIMIT ?
|
||||
''', (limit,))
|
||||
|
||||
rows = c.fetchall()
|
||||
conn.close()
|
||||
|
||||
results = []
|
||||
for row in rows:
|
||||
results.append({
|
||||
'indicator_type': row[0],
|
||||
'params': json.loads(row[1]),
|
||||
'trades': row[2],
|
||||
'win_rate': row[3],
|
||||
'total_pnl': row[4],
|
||||
'pnl_per_1k': row[5],
|
||||
'profit_factor': row[6],
|
||||
'max_drawdown': row[7],
|
||||
'sharpe_ratio': row[8],
|
||||
'tested_at': row[9],
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
class DistributedCoordinator:
|
||||
"""Coordinates distributed parameter sweeps across EPYC servers"""
|
||||
|
||||
def __init__(self):
|
||||
self.db = ExplorationDatabase(DB_PATH)
|
||||
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def ssh_command(self, worker_id: str, command: str) -> subprocess.CompletedProcess:
|
||||
"""Execute command on worker via SSH"""
|
||||
worker = WORKERS[worker_id]
|
||||
|
||||
if 'ssh_hop' in worker:
|
||||
# Worker 2 requires hop through worker 1
|
||||
# CRITICAL FIX (Nov 29, 2025): Use double-nested quotes for 2-hop SSH
|
||||
# Single quotes don't pass command to inner SSH properly
|
||||
ssh_cmd = f"ssh {worker['ssh_hop']} \"ssh {worker['host']} '{command}'\""
|
||||
else:
|
||||
ssh_cmd = f"ssh {worker['host']} '{command}'"
|
||||
|
||||
return subprocess.run(ssh_cmd, shell=True, capture_output=True, text=True)
|
||||
|
||||
def deploy_worker_script(self, worker_id: str) -> bool:
|
||||
"""Deploy distributed_worker.py to EPYC server"""
|
||||
worker = WORKERS[worker_id]
|
||||
script_path = CLUSTER_DIR / 'distributed_worker.py'
|
||||
|
||||
# Copy script to worker's comprehensive_sweep directory
|
||||
target = f"{worker['workspace']}/backtester/scripts/distributed_worker.py"
|
||||
|
||||
if 'ssh_hop' in worker:
|
||||
# Two-hop copy for worker2
|
||||
print(f"📤 Copying worker script to {worker_id} via hop...")
|
||||
# Copy to worker1 first
|
||||
subprocess.run(f"scp {script_path} {WORKERS['worker1']['host']}:/tmp/", shell=True)
|
||||
# Then copy from worker1 to worker2
|
||||
self.ssh_command('worker1', f"scp /tmp/distributed_worker.py {worker['host']}:{target}")
|
||||
else:
|
||||
print(f"📤 Copying worker script to {worker_id}...")
|
||||
subprocess.run(f"scp {script_path} {worker['host']}:{target}", shell=True)
|
||||
|
||||
print(f"✅ Worker script deployed to {worker_id}")
|
||||
return True
|
||||
|
||||
def assign_chunk(self, worker_id: str, chunk_id: str, grid: ParameterGrid,
|
||||
chunk_start: int, chunk_end: int) -> bool:
|
||||
"""Assign parameter chunk to worker for processing"""
|
||||
worker = WORKERS[worker_id]
|
||||
|
||||
# Record in database
|
||||
self.db.record_chunk(chunk_id, 'v9_moneyline', grid, chunk_start, chunk_end, worker_id)
|
||||
|
||||
# Create chunk specification JSON
|
||||
chunk_spec = {
|
||||
'chunk_id': chunk_id,
|
||||
'chunk_start': chunk_start,
|
||||
'chunk_end': chunk_end,
|
||||
'grid': grid.to_dict(),
|
||||
'num_workers': worker['cores'],
|
||||
}
|
||||
|
||||
chunk_json_path = RESULTS_DIR / f"{chunk_id}_spec.json"
|
||||
with open(chunk_json_path, 'w') as f:
|
||||
json.dump(chunk_spec, f, indent=2)
|
||||
|
||||
# Copy chunk spec to worker
|
||||
target_json = f"{worker['workspace']}/chunk_{chunk_id}.json"
|
||||
if 'ssh_hop' in worker:
|
||||
# Two-hop copy
|
||||
subprocess.run(f"scp {chunk_json_path} {WORKERS['worker1']['host']}:/tmp/", shell=True)
|
||||
self.ssh_command('worker1', f"scp /tmp/{chunk_id}_spec.json {worker['host']}:{target_json}")
|
||||
else:
|
||||
subprocess.run(f"scp {chunk_json_path} {worker['host']}:{target_json}", shell=True)
|
||||
|
||||
# Execute distributed_worker.py on worker
|
||||
# CRITICAL: Simplified SSH command without bash -c to avoid quoting issues
|
||||
cmd = (f"cd {worker['workspace']} && "
|
||||
f"source backtester/.venv/bin/activate && "
|
||||
f"nohup python3 backtester/scripts/distributed_worker.py {target_json} "
|
||||
f"> /tmp/{chunk_id}.log 2>&1 &")
|
||||
|
||||
print(f"🚀 Starting chunk {chunk_id} on {worker_id} ({chunk_end - chunk_start:,} combos)...")
|
||||
result = self.ssh_command(worker_id, cmd)
|
||||
|
||||
if result.returncode == 0:
|
||||
print(f"✅ Chunk {chunk_id} assigned to {worker_id}")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ Failed to assign chunk {chunk_id} to {worker_id}: {result.stderr}")
|
||||
return False
|
||||
|
||||
def collect_results(self, worker_id: str, chunk_id: str) -> Optional[str]:
|
||||
"""Collect CSV results from worker"""
|
||||
worker = WORKERS[worker_id]
|
||||
|
||||
# Check if results file exists on worker
|
||||
results_csv = f"{worker['workspace']}/chunk_{chunk_id}_results.csv"
|
||||
check_cmd = f"test -f {results_csv} && echo 'exists'"
|
||||
result = self.ssh_command(worker_id, check_cmd)
|
||||
|
||||
if 'exists' not in result.stdout:
|
||||
return None # Results not ready yet
|
||||
|
||||
# Copy results back to master
|
||||
local_csv = RESULTS_DIR / f"{chunk_id}_results.csv"
|
||||
|
||||
if 'ssh_hop' in worker:
|
||||
# Two-hop copy back
|
||||
self.ssh_command('worker1', f"scp {worker['host']}:{results_csv} /tmp/")
|
||||
subprocess.run(f"scp {WORKERS['worker1']['host']}:/tmp/chunk_{chunk_id}_results.csv {local_csv}", shell=True)
|
||||
else:
|
||||
subprocess.run(f"scp {worker['host']}:{results_csv} {local_csv}", shell=True)
|
||||
|
||||
print(f"📥 Collected results from {worker_id} chunk {chunk_id}")
|
||||
|
||||
# Import into database
|
||||
imported = self.db.import_results_csv(str(local_csv), worker_id, chunk_id)
|
||||
print(f"📊 Imported {imported} unique strategies from {chunk_id}")
|
||||
|
||||
# Get best P&L from CSV for chunk tracking
|
||||
import csv
|
||||
with open(local_csv, 'r') as f:
|
||||
reader = csv.DictReader(f)
|
||||
rows = list(reader)
|
||||
best_pnl = max(float(row['pnl_per_1k']) for row in rows) if rows else 0
|
||||
|
||||
self.db.complete_chunk(chunk_id, str(local_csv), best_pnl)
|
||||
|
||||
return str(local_csv)
|
||||
|
||||
def start_comprehensive_exploration(self, chunk_size: int = 10000):
|
||||
"""Start massive comprehensive parameter sweep"""
|
||||
print("=" * 80)
|
||||
print("🚀 DISTRIBUTED COMPREHENSIVE EXPLORATION")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
# Define full parameter grid (can be expanded)
|
||||
grid = ParameterGrid(
|
||||
flip_thresholds=[0.4, 0.5, 0.6, 0.7],
|
||||
ma_gaps=[0.20, 0.30, 0.40, 0.50],
|
||||
adx_mins=[18, 21, 24, 27],
|
||||
long_pos_maxs=[60, 65, 70, 75],
|
||||
short_pos_mins=[20, 25, 30, 35],
|
||||
cooldowns=[1, 2, 3, 4],
|
||||
position_sizes=[10000], # Fixed for fair comparison
|
||||
tp1_multipliers=[1.5, 2.0, 2.5],
|
||||
tp2_multipliers=[3.0, 4.0, 5.0],
|
||||
sl_multipliers=[2.5, 3.0, 3.5],
|
||||
tp1_close_percents=[50, 60, 70, 75],
|
||||
trailing_multipliers=[1.0, 1.5, 2.0],
|
||||
vol_mins=[0.8, 1.0, 1.2],
|
||||
max_bars_list=[300, 500, 1000],
|
||||
)
|
||||
|
||||
total_combos = grid.total_combinations()
|
||||
|
||||
print(f"📊 Total parameter space: {total_combos:,} combinations")
|
||||
print(f"📦 Chunk size: {chunk_size:,} combinations per chunk")
|
||||
print(f"🎯 Total chunks: {(total_combos + chunk_size - 1) // chunk_size:,}")
|
||||
print(f"⏱️ Estimated time: {(total_combos * 1.6) / (64 * 3600):.1f} hours with 64 cores")
|
||||
print()
|
||||
|
||||
# Deploy worker scripts
|
||||
for worker_id in WORKERS.keys():
|
||||
self.deploy_worker_script(worker_id)
|
||||
|
||||
print()
|
||||
print("🔄 Distributing chunks to workers...")
|
||||
print()
|
||||
|
||||
# Split work across workers
|
||||
chunk_id_counter = 0
|
||||
chunk_start = 0
|
||||
active_chunks = {}
|
||||
worker_list = list(WORKERS.keys()) # ['worker1', 'worker2']
|
||||
|
||||
while chunk_start < total_combos:
|
||||
chunk_end = min(chunk_start + chunk_size, total_combos)
|
||||
chunk_id = f"v9_chunk_{chunk_id_counter:06d}"
|
||||
|
||||
# Round-robin assignment across both workers for balanced load
|
||||
worker_id = worker_list[chunk_id_counter % len(worker_list)]
|
||||
|
||||
if self.assign_chunk(worker_id, chunk_id, grid, chunk_start, chunk_end):
|
||||
active_chunks[chunk_id] = worker_id
|
||||
|
||||
chunk_id_counter += 1
|
||||
chunk_start = chunk_end
|
||||
|
||||
# Don't overwhelm workers - limit to 2 chunks per worker at a time
|
||||
if len(active_chunks) >= len(WORKERS) * 2:
|
||||
print(f"⏸️ Pausing chunk assignment - {len(active_chunks)} chunks active")
|
||||
print(f"⏳ Waiting for chunks to complete...")
|
||||
break
|
||||
|
||||
print()
|
||||
print(f"✅ Assigned {len(active_chunks)} initial chunks")
|
||||
print()
|
||||
print("📊 Monitor progress with: python3 cluster/exploration_status.py")
|
||||
print("🏆 View top strategies: sqlite3 cluster/exploration.db 'SELECT * FROM strategies ORDER BY pnl_per_1k DESC LIMIT 10'")
|
||||
|
||||
def main():
|
||||
"""Main coordinator entry point"""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='Distributed continuous optimization coordinator')
|
||||
parser.add_argument('--chunk-size', type=int, default=10000,
|
||||
help='Number of combinations per chunk (default: 10000)')
|
||||
parser.add_argument('--continuous', action='store_true',
|
||||
help='Run continuously (not implemented yet)')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
coordinator = DistributedCoordinator()
|
||||
coordinator.start_comprehensive_exploration(chunk_size=args.chunk_size)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
272
cluster/distributed_worker.py
Normal file
272
cluster/distributed_worker.py
Normal file
@@ -0,0 +1,272 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Distributed Worker for Comprehensive Sweep
|
||||
|
||||
Runs on EPYC server, executes parameter sweep chunk using existing
|
||||
comprehensive_sweep.py architecture (simulator.py + MoneyLineInputs).
|
||||
|
||||
Integration with Existing System:
|
||||
- Uses same simulator.py, indicators, data_loader
|
||||
- Works with existing .venv Python environment
|
||||
- Outputs same CSV format as comprehensive_sweep.py
|
||||
- Can run standalone or as part of distributed cluster
|
||||
|
||||
Usage:
|
||||
python3 distributed_worker.py /path/to/chunk_spec.json
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import itertools
|
||||
import multiprocessing as mp
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import csv
|
||||
|
||||
# Import from existing comprehensive_sweep infrastructure
|
||||
# Match comprehensive_sweep.py import pattern
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from backtester.simulator import simulate_money_line, TradeConfig
|
||||
from backtester.data_loader import load_csv
|
||||
from backtester.indicators.money_line import MoneyLineInputs
|
||||
|
||||
def test_config(args):
|
||||
"""Test single parameter configuration (matches comprehensive_sweep.py signature)"""
|
||||
config_id, params, data_slice = args
|
||||
|
||||
# Unpack parameters (14-dimensional grid)
|
||||
flip_thresh, ma_gap, adx_min, long_pos, short_pos, cooldown, \
|
||||
pos_size, tp1_mult, tp2_mult, sl_mult, tp1_close, trail_mult, \
|
||||
vol_min, max_bars = params
|
||||
|
||||
# Create MoneyLineInputs
|
||||
inputs = MoneyLineInputs(
|
||||
flip_threshold_percent=flip_thresh,
|
||||
ma_gap_threshold=ma_gap,
|
||||
momentum_min_adx=adx_min,
|
||||
momentum_long_max_pos=long_pos,
|
||||
momentum_short_min_pos=short_pos,
|
||||
cooldown_bars=cooldown,
|
||||
momentum_spacing=3, # Fixed (not in grid)
|
||||
momentum_cooldown=2, # Fixed (not in grid)
|
||||
)
|
||||
|
||||
# Create TradeConfig
|
||||
config = TradeConfig(
|
||||
position_size=pos_size,
|
||||
atr_multiplier_tp1=tp1_mult,
|
||||
atr_multiplier_tp2=tp2_mult,
|
||||
atr_multiplier_sl=sl_mult,
|
||||
take_profit_1_size_percent=tp1_close,
|
||||
trailing_atr_multiplier=trail_mult,
|
||||
max_bars_per_trade=max_bars,
|
||||
)
|
||||
|
||||
# Quality filter (matches comprehensive_sweep.py)
|
||||
quality_filter = {
|
||||
'min_adx': 15,
|
||||
'min_volume_ratio': vol_min,
|
||||
}
|
||||
|
||||
# Run simulation
|
||||
try:
|
||||
results = simulate_money_line(
|
||||
data_slice.data,
|
||||
data_slice.symbol,
|
||||
inputs,
|
||||
config,
|
||||
quality_filter
|
||||
)
|
||||
|
||||
# Extract metrics
|
||||
trades = len(results.trades)
|
||||
win_rate = results.win_rate if trades > 0 else 0
|
||||
total_pnl = results.total_pnl
|
||||
pnl_per_1k = (total_pnl / pos_size * 1000) if pos_size > 0 else 0
|
||||
profit_factor = results.profit_factor if hasattr(results, 'profit_factor') else 0
|
||||
max_drawdown = abs(results.max_drawdown) if hasattr(results, 'max_drawdown') else 0
|
||||
sharpe = results.sharpe_ratio if hasattr(results, 'sharpe_ratio') else 0
|
||||
|
||||
return (config_id, trades, win_rate, total_pnl, pnl_per_1k,
|
||||
profit_factor, max_drawdown, sharpe, params)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error testing config {config_id}: {e}")
|
||||
return (config_id, 0, 0, 0, 0, 0, 0, 0, params)
|
||||
|
||||
def process_chunk(chunk_spec_path: str):
|
||||
"""Process parameter chunk specified in JSON file"""
|
||||
|
||||
# Load chunk specification
|
||||
with open(chunk_spec_path, 'r') as f:
|
||||
spec = json.load(f)
|
||||
|
||||
chunk_id = spec['chunk_id']
|
||||
chunk_start = spec['chunk_start']
|
||||
chunk_end = spec['chunk_end']
|
||||
grid = spec['grid']
|
||||
num_workers = spec['num_workers']
|
||||
|
||||
# Limit to 70% of available cores (user request)
|
||||
max_workers = max(1, int(num_workers * 0.7))
|
||||
|
||||
print(f"🎯 Processing chunk: {chunk_id}")
|
||||
print(f"📊 Range: {chunk_start:,} to {chunk_end:,} ({chunk_end - chunk_start:,} combinations)")
|
||||
print(f"⚙️ Workers: {max_workers} cores (70% of {num_workers} available)")
|
||||
print()
|
||||
|
||||
# Load data (same as comprehensive_sweep.py)
|
||||
data_path = Path(__file__).parent.parent / 'data' / 'solusdt_5m_aug_nov.csv'
|
||||
print(f"📈 Loading data from {data_path}...")
|
||||
data_slice = load_csv(data_path, 'SOL-PERP', '5m')
|
||||
print(f"✅ Loaded {len(data_slice.data):,} rows")
|
||||
print()
|
||||
|
||||
# Generate ALL parameter combinations (same order as comprehensive_sweep.py)
|
||||
param_lists = [
|
||||
grid['flip_thresholds'],
|
||||
grid['ma_gaps'],
|
||||
grid['adx_mins'],
|
||||
grid['long_pos_maxs'],
|
||||
grid['short_pos_mins'],
|
||||
grid['cooldowns'],
|
||||
grid['position_sizes'],
|
||||
grid['tp1_multipliers'],
|
||||
grid['tp2_multipliers'],
|
||||
grid['sl_multipliers'],
|
||||
grid['tp1_close_percents'],
|
||||
grid['trailing_multipliers'],
|
||||
grid['vol_mins'],
|
||||
grid['max_bars_list'],
|
||||
]
|
||||
|
||||
print("🔢 Generating parameter combinations...")
|
||||
all_combos = list(itertools.product(*param_lists))
|
||||
total_combos = len(all_combos)
|
||||
print(f"✅ Generated {total_combos:,} total combinations")
|
||||
|
||||
# Extract chunk slice
|
||||
chunk_combos = all_combos[chunk_start:chunk_end]
|
||||
print(f"✂️ Extracted chunk slice: {len(chunk_combos):,} combinations")
|
||||
print()
|
||||
|
||||
# Prepare arguments for test_config
|
||||
args_list = [
|
||||
(chunk_start + i, combo, data_slice)
|
||||
for i, combo in enumerate(chunk_combos)
|
||||
]
|
||||
|
||||
# Run multiprocessing sweep (same as comprehensive_sweep.py)
|
||||
print(f"🚀 Starting sweep with {num_workers} workers...")
|
||||
print()
|
||||
|
||||
results = []
|
||||
completed = 0
|
||||
best_pnl = float('-inf')
|
||||
best_config = None
|
||||
|
||||
with mp.Pool(processes=max_workers) as pool:
|
||||
for result in pool.imap_unordered(test_config, args_list, chunksize=10):
|
||||
results.append(result)
|
||||
completed += 1
|
||||
|
||||
# Track best
|
||||
if result[4] > best_pnl: # pnl_per_1k
|
||||
best_pnl = result[4]
|
||||
best_config = result
|
||||
|
||||
# Progress every 100 configs
|
||||
if completed % 100 == 0:
|
||||
pct = (completed / len(chunk_combos)) * 100
|
||||
print(f"⏳ Progress: {completed:,}/{len(chunk_combos):,} ({pct:.1f}%) - "
|
||||
f"Best so far: ${best_pnl:.2f}/1k")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} complete!")
|
||||
print(f"📊 Tested {len(results):,} configurations")
|
||||
print(f"🏆 Best P&L: ${best_pnl:.2f} per $1k")
|
||||
print()
|
||||
|
||||
# Sort by profitability
|
||||
results.sort(key=lambda x: x[4], reverse=True)
|
||||
|
||||
# Save results to CSV (same format as comprehensive_sweep.py)
|
||||
output_file = Path(__file__).parent.parent / f'chunk_{chunk_id}_results.csv'
|
||||
|
||||
print(f"💾 Saving results to {output_file}...")
|
||||
|
||||
with open(output_file, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# Header
|
||||
writer.writerow([
|
||||
'rank', 'trades', 'win_rate', 'total_pnl', 'pnl_per_1k',
|
||||
'profit_factor', 'max_drawdown', 'sharpe_ratio',
|
||||
'flip_threshold', 'ma_gap', 'adx_min', 'long_pos_max', 'short_pos_min',
|
||||
'cooldown', 'position_size', 'tp1_mult', 'tp2_mult', 'sl_mult',
|
||||
'tp1_close_pct', 'trailing_mult', 'vol_min', 'max_bars'
|
||||
])
|
||||
|
||||
# Write all results
|
||||
for rank, result in enumerate(results, 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
writer.writerow([
|
||||
rank, trades, f'{win_rate:.4f}', f'{total_pnl:.2f}', f'{pnl_per_1k:.2f}',
|
||||
f'{profit_factor:.3f}', f'{max_drawdown:.2f}', f'{sharpe:.3f}',
|
||||
*params
|
||||
])
|
||||
|
||||
print(f"✅ Results saved!")
|
||||
print()
|
||||
|
||||
# Print top 10
|
||||
print("🏆 Top 10 configurations:")
|
||||
print()
|
||||
for i, result in enumerate(results[:10], 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
print(f"{i:2d}. ${pnl_per_1k:7.2f}/1k | "
|
||||
f"{trades:4d} trades | {win_rate*100:5.1f}% WR | "
|
||||
f"PF {profit_factor:.2f} | DD {max_drawdown:.1f}%")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} processing complete!")
|
||||
|
||||
return output_file
|
||||
|
||||
def main():
|
||||
"""Worker entry point"""
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python3 distributed_worker.py <chunk_spec.json>")
|
||||
sys.exit(1)
|
||||
|
||||
chunk_spec_path = sys.argv[1]
|
||||
|
||||
if not Path(chunk_spec_path).exists():
|
||||
print(f"Error: Chunk spec file not found: {chunk_spec_path}")
|
||||
sys.exit(1)
|
||||
|
||||
print("=" * 80)
|
||||
print("🔧 DISTRIBUTED WORKER")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
start_time = datetime.now()
|
||||
|
||||
output_file = process_chunk(chunk_spec_path)
|
||||
|
||||
end_time = datetime.now()
|
||||
duration = (end_time - start_time).total_seconds()
|
||||
|
||||
print()
|
||||
print("=" * 80)
|
||||
print(f"⏱️ Total time: {duration:.1f} seconds ({duration/60:.1f} minutes)")
|
||||
print(f"📄 Results: {output_file}")
|
||||
print("=" * 80)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
280
cluster/distributed_worker_bd.py
Normal file
280
cluster/distributed_worker_bd.py
Normal file
@@ -0,0 +1,280 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Distributed worker process for comprehensive parameter exploration
|
||||
Runs on remote EPYC servers - Modified for bd-host01 directory structure
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import itertools
|
||||
import multiprocessing as mp
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import csv
|
||||
|
||||
# Add backtester to path for bd-host01 structure
|
||||
sys.path.insert(0, str(Path(__file__).parent / 'backtester'))
|
||||
|
||||
from backtester.simulator import simulate_money_line, MoneyLineInputs, TradeConfig
|
||||
from backtester.data_loader import load_csv
|
||||
|
||||
# Rest of the file stays the same as distributed_worker.py
|
||||
- Works with existing .venv Python environment
|
||||
- Outputs same CSV format as comprehensive_sweep.py
|
||||
- Can run standalone or as part of distributed cluster
|
||||
|
||||
Usage:
|
||||
python3 distributed_worker.py /path/to/chunk_spec.json
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import itertools
|
||||
import multiprocessing as mp
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import csv
|
||||
|
||||
# Import from existing comprehensive_sweep infrastructure
|
||||
# These paths work because script runs from /home/comprehensive_sweep/backtester/scripts/
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from simulator import simulate_money_line, MoneyLineInputs, TradeConfig
|
||||
from data_loader import load_csv
|
||||
|
||||
def test_config(args):
|
||||
"""Test single parameter configuration (matches comprehensive_sweep.py signature)"""
|
||||
config_id, params, data_slice = args
|
||||
|
||||
# Unpack parameters (14-dimensional grid)
|
||||
flip_thresh, ma_gap, adx_min, long_pos, short_pos, cooldown, \
|
||||
pos_size, tp1_mult, tp2_mult, sl_mult, tp1_close, trail_mult, \
|
||||
vol_min, max_bars = params
|
||||
|
||||
# Create MoneyLineInputs
|
||||
inputs = MoneyLineInputs(
|
||||
flip_threshold_percent=flip_thresh,
|
||||
ma_gap_threshold=ma_gap,
|
||||
momentum_min_adx=adx_min,
|
||||
momentum_long_max_pos=long_pos,
|
||||
momentum_short_min_pos=short_pos,
|
||||
cooldown_bars=cooldown,
|
||||
momentum_spacing=3, # Fixed (not in grid)
|
||||
momentum_cooldown=2, # Fixed (not in grid)
|
||||
)
|
||||
|
||||
# Create TradeConfig
|
||||
config = TradeConfig(
|
||||
position_size=pos_size,
|
||||
atr_multiplier_tp1=tp1_mult,
|
||||
atr_multiplier_tp2=tp2_mult,
|
||||
atr_multiplier_sl=sl_mult,
|
||||
take_profit_1_size_percent=tp1_close,
|
||||
trailing_atr_multiplier=trail_mult,
|
||||
max_bars_per_trade=max_bars,
|
||||
)
|
||||
|
||||
# Quality filter (matches comprehensive_sweep.py)
|
||||
quality_filter = {
|
||||
'min_adx': 15,
|
||||
'min_volume_ratio': vol_min,
|
||||
}
|
||||
|
||||
# Run simulation
|
||||
try:
|
||||
results = simulate_money_line(
|
||||
data_slice.data,
|
||||
data_slice.symbol,
|
||||
inputs,
|
||||
config,
|
||||
quality_filter
|
||||
)
|
||||
|
||||
# Extract metrics
|
||||
trades = len(results.trades)
|
||||
win_rate = results.win_rate if trades > 0 else 0
|
||||
total_pnl = results.total_pnl
|
||||
pnl_per_1k = (total_pnl / pos_size * 1000) if pos_size > 0 else 0
|
||||
profit_factor = results.profit_factor if hasattr(results, 'profit_factor') else 0
|
||||
max_drawdown = abs(results.max_drawdown) if hasattr(results, 'max_drawdown') else 0
|
||||
sharpe = results.sharpe_ratio if hasattr(results, 'sharpe_ratio') else 0
|
||||
|
||||
return (config_id, trades, win_rate, total_pnl, pnl_per_1k,
|
||||
profit_factor, max_drawdown, sharpe, params)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error testing config {config_id}: {e}")
|
||||
return (config_id, 0, 0, 0, 0, 0, 0, 0, params)
|
||||
|
||||
def process_chunk(chunk_spec_path: str):
|
||||
"""Process parameter chunk specified in JSON file"""
|
||||
|
||||
# Load chunk specification
|
||||
with open(chunk_spec_path, 'r') as f:
|
||||
spec = json.load(f)
|
||||
|
||||
chunk_id = spec['chunk_id']
|
||||
chunk_start = spec['chunk_start']
|
||||
chunk_end = spec['chunk_end']
|
||||
grid = spec['grid']
|
||||
num_workers = spec['num_workers']
|
||||
|
||||
print(f"🎯 Processing chunk: {chunk_id}")
|
||||
print(f"📊 Range: {chunk_start:,} to {chunk_end:,} ({chunk_end - chunk_start:,} combinations)")
|
||||
print(f"⚙️ Workers: {num_workers} cores")
|
||||
print()
|
||||
|
||||
# Load data (same as comprehensive_sweep.py)
|
||||
data_path = Path(__file__).parent.parent / 'data' / 'solusdt_5m.csv'
|
||||
print(f"📈 Loading data from {data_path}...")
|
||||
data_slice = load_csv(str(data_path))
|
||||
print(f"✅ Loaded {len(data_slice.data):,} rows")
|
||||
print()
|
||||
|
||||
# Generate ALL parameter combinations (same order as comprehensive_sweep.py)
|
||||
param_lists = [
|
||||
grid['flip_thresholds'],
|
||||
grid['ma_gaps'],
|
||||
grid['adx_mins'],
|
||||
grid['long_pos_maxs'],
|
||||
grid['short_pos_mins'],
|
||||
grid['cooldowns'],
|
||||
grid['position_sizes'],
|
||||
grid['tp1_multipliers'],
|
||||
grid['tp2_multipliers'],
|
||||
grid['sl_multipliers'],
|
||||
grid['tp1_close_percents'],
|
||||
grid['trailing_multipliers'],
|
||||
grid['vol_mins'],
|
||||
grid['max_bars_list'],
|
||||
]
|
||||
|
||||
print("🔢 Generating parameter combinations...")
|
||||
all_combos = list(itertools.product(*param_lists))
|
||||
total_combos = len(all_combos)
|
||||
print(f"✅ Generated {total_combos:,} total combinations")
|
||||
|
||||
# Extract chunk slice
|
||||
chunk_combos = all_combos[chunk_start:chunk_end]
|
||||
print(f"✂️ Extracted chunk slice: {len(chunk_combos):,} combinations")
|
||||
print()
|
||||
|
||||
# Prepare arguments for test_config
|
||||
args_list = [
|
||||
(chunk_start + i, combo, data_slice)
|
||||
for i, combo in enumerate(chunk_combos)
|
||||
]
|
||||
|
||||
# Run multiprocessing sweep (same as comprehensive_sweep.py)
|
||||
print(f"🚀 Starting sweep with {num_workers} workers...")
|
||||
print()
|
||||
|
||||
results = []
|
||||
completed = 0
|
||||
best_pnl = float('-inf')
|
||||
best_config = None
|
||||
|
||||
with mp.Pool(processes=num_workers) as pool:
|
||||
for result in pool.imap_unordered(test_config, args_list, chunksize=10):
|
||||
results.append(result)
|
||||
completed += 1
|
||||
|
||||
# Track best
|
||||
if result[4] > best_pnl: # pnl_per_1k
|
||||
best_pnl = result[4]
|
||||
best_config = result
|
||||
|
||||
# Progress every 100 configs
|
||||
if completed % 100 == 0:
|
||||
pct = (completed / len(chunk_combos)) * 100
|
||||
print(f"⏳ Progress: {completed:,}/{len(chunk_combos):,} ({pct:.1f}%) - "
|
||||
f"Best so far: ${best_pnl:.2f}/1k")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} complete!")
|
||||
print(f"📊 Tested {len(results):,} configurations")
|
||||
print(f"🏆 Best P&L: ${best_pnl:.2f} per $1k")
|
||||
print()
|
||||
|
||||
# Sort by profitability
|
||||
results.sort(key=lambda x: x[4], reverse=True)
|
||||
|
||||
# Save results to CSV (same format as comprehensive_sweep.py)
|
||||
output_file = Path(__file__).parent.parent / f'chunk_{chunk_id}_results.csv'
|
||||
|
||||
print(f"💾 Saving results to {output_file}...")
|
||||
|
||||
with open(output_file, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# Header
|
||||
writer.writerow([
|
||||
'rank', 'trades', 'win_rate', 'total_pnl', 'pnl_per_1k',
|
||||
'profit_factor', 'max_drawdown', 'sharpe_ratio',
|
||||
'flip_threshold', 'ma_gap', 'adx_min', 'long_pos_max', 'short_pos_min',
|
||||
'cooldown', 'position_size', 'tp1_mult', 'tp2_mult', 'sl_mult',
|
||||
'tp1_close_pct', 'trailing_mult', 'vol_min', 'max_bars'
|
||||
])
|
||||
|
||||
# Write all results
|
||||
for rank, result in enumerate(results, 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
writer.writerow([
|
||||
rank, trades, f'{win_rate:.4f}', f'{total_pnl:.2f}', f'{pnl_per_1k:.2f}',
|
||||
f'{profit_factor:.3f}', f'{max_drawdown:.2f}', f'{sharpe:.3f}',
|
||||
*params
|
||||
])
|
||||
|
||||
print(f"✅ Results saved!")
|
||||
print()
|
||||
|
||||
# Print top 10
|
||||
print("🏆 Top 10 configurations:")
|
||||
print()
|
||||
for i, result in enumerate(results[:10], 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
print(f"{i:2d}. ${pnl_per_1k:7.2f}/1k | "
|
||||
f"{trades:4d} trades | {win_rate*100:5.1f}% WR | "
|
||||
f"PF {profit_factor:.2f} | DD {max_drawdown:.1f}%")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} processing complete!")
|
||||
|
||||
return output_file
|
||||
|
||||
def main():
|
||||
"""Worker entry point"""
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python3 distributed_worker.py <chunk_spec.json>")
|
||||
sys.exit(1)
|
||||
|
||||
chunk_spec_path = sys.argv[1]
|
||||
|
||||
if not Path(chunk_spec_path).exists():
|
||||
print(f"Error: Chunk spec file not found: {chunk_spec_path}")
|
||||
sys.exit(1)
|
||||
|
||||
print("=" * 80)
|
||||
print("🔧 DISTRIBUTED WORKER")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
start_time = datetime.now()
|
||||
|
||||
output_file = process_chunk(chunk_spec_path)
|
||||
|
||||
end_time = datetime.now()
|
||||
duration = (end_time - start_time).total_seconds()
|
||||
|
||||
print()
|
||||
print("=" * 80)
|
||||
print(f"⏱️ Total time: {duration:.1f} seconds ({duration/60:.1f} minutes)")
|
||||
print(f"📄 Results: {output_file}")
|
||||
print("=" * 80)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
274
cluster/distributed_worker_bd_clean.py
Normal file
274
cluster/distributed_worker_bd_clean.py
Normal file
@@ -0,0 +1,274 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Distributed Worker for Comprehensive Sweep
|
||||
|
||||
Runs on EPYC server (bd-host01), executes parameter sweep chunk using existing
|
||||
comprehensive_sweep.py architecture (simulator.py + MoneyLineInputs).
|
||||
|
||||
Integration with Existing System:
|
||||
- Uses same simulator.py, indicators, data_loader
|
||||
- Works with existing .venv Python environment
|
||||
- Outputs same CSV format as comprehensive_sweep.py
|
||||
- Can run standalone or as part of distributed cluster
|
||||
|
||||
Usage:
|
||||
python3 distributed_worker.py /path/to/chunk_spec.json
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import itertools
|
||||
import multiprocessing as mp
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import csv
|
||||
|
||||
# Import from bd-host01 directory structure
|
||||
# Script runs from /home/backtest_dual/backtest/backtester/scripts/
|
||||
# simulator.py imports use 'backtester.indicators.money_line' format
|
||||
# So we need to add /home/backtest_dual/backtest/ to sys.path (3 parents up)
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from backtester.simulator import simulate_money_line, MoneyLineInputs, TradeConfig
|
||||
from backtester.data_loader import load_csv
|
||||
|
||||
def test_config(args):
|
||||
"""Test single parameter configuration (matches comprehensive_sweep.py signature)"""
|
||||
config_id, params, data_slice = args
|
||||
|
||||
# Unpack parameters (14-dimensional grid)
|
||||
flip_thresh, ma_gap, adx_min, long_pos, short_pos, cooldown, \
|
||||
pos_size, tp1_mult, tp2_mult, sl_mult, tp1_close, trail_mult, \
|
||||
vol_min, max_bars = params
|
||||
|
||||
# Create MoneyLineInputs
|
||||
inputs = MoneyLineInputs(
|
||||
flip_threshold_percent=flip_thresh,
|
||||
ma_gap_threshold=ma_gap,
|
||||
momentum_min_adx=adx_min,
|
||||
momentum_long_max_pos=long_pos,
|
||||
momentum_short_min_pos=short_pos,
|
||||
cooldown_bars=cooldown,
|
||||
momentum_spacing=3, # Fixed (not in grid)
|
||||
momentum_cooldown=2, # Fixed (not in grid)
|
||||
)
|
||||
|
||||
# Create TradeConfig
|
||||
config = TradeConfig(
|
||||
position_size=pos_size,
|
||||
atr_multiplier_tp1=tp1_mult,
|
||||
atr_multiplier_tp2=tp2_mult,
|
||||
atr_multiplier_sl=sl_mult,
|
||||
take_profit_1_size_percent=tp1_close,
|
||||
trailing_atr_multiplier=trail_mult,
|
||||
max_bars_per_trade=max_bars,
|
||||
)
|
||||
|
||||
# Quality filter (matches comprehensive_sweep.py)
|
||||
quality_filter = {
|
||||
'min_adx': 15,
|
||||
'min_volume_ratio': vol_min,
|
||||
}
|
||||
|
||||
# Run simulation
|
||||
try:
|
||||
results = simulate_money_line(
|
||||
data_slice.data,
|
||||
data_slice.symbol,
|
||||
inputs,
|
||||
config,
|
||||
quality_filter
|
||||
)
|
||||
|
||||
# Extract metrics
|
||||
trades = len(results.trades)
|
||||
win_rate = results.win_rate if trades > 0 else 0
|
||||
total_pnl = results.total_pnl
|
||||
pnl_per_1k = (total_pnl / pos_size * 1000) if pos_size > 0 else 0
|
||||
profit_factor = results.profit_factor if hasattr(results, 'profit_factor') else 0
|
||||
max_drawdown = abs(results.max_drawdown) if hasattr(results, 'max_drawdown') else 0
|
||||
sharpe = results.sharpe_ratio if hasattr(results, 'sharpe_ratio') else 0
|
||||
|
||||
return (config_id, trades, win_rate, total_pnl, pnl_per_1k,
|
||||
profit_factor, max_drawdown, sharpe, params)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error testing config {config_id}: {e}")
|
||||
return (config_id, 0, 0, 0, 0, 0, 0, 0, params)
|
||||
|
||||
def process_chunk(chunk_spec_path: str):
|
||||
"""Process parameter chunk specified in JSON file"""
|
||||
|
||||
# Load chunk specification
|
||||
with open(chunk_spec_path, 'r') as f:
|
||||
spec = json.load(f)
|
||||
|
||||
chunk_id = spec['chunk_id']
|
||||
chunk_start = spec['chunk_start']
|
||||
chunk_end = spec['chunk_end']
|
||||
grid = spec['grid']
|
||||
num_workers = spec['num_workers']
|
||||
|
||||
# Limit to 70% of available cores (user request)
|
||||
max_workers = max(1, int(num_workers * 0.7))
|
||||
|
||||
print(f"🎯 Processing chunk: {chunk_id}")
|
||||
print(f"📊 Range: {chunk_start:,} to {chunk_end:,} ({chunk_end - chunk_start:,} combinations)")
|
||||
print(f"⚙️ Workers: {max_workers} cores (70% of {num_workers} available)")
|
||||
print()
|
||||
|
||||
# Load data (same as comprehensive_sweep.py)
|
||||
data_path = Path(__file__).parent.parent / 'data' / 'solusdt_5m.csv'
|
||||
print(f"📈 Loading data from {data_path}...")
|
||||
# bd-host01's load_csv requires symbol and timeframe arguments
|
||||
data_slice = load_csv(data_path, 'solusdt', '5m')
|
||||
print(f"✅ Loaded {len(data_slice.data):,} rows")
|
||||
print()
|
||||
|
||||
# Generate ALL parameter combinations (same order as comprehensive_sweep.py)
|
||||
param_lists = [
|
||||
grid['flip_thresholds'],
|
||||
grid['ma_gaps'],
|
||||
grid['adx_mins'],
|
||||
grid['long_pos_maxs'],
|
||||
grid['short_pos_mins'],
|
||||
grid['cooldowns'],
|
||||
grid['position_sizes'],
|
||||
grid['tp1_multipliers'],
|
||||
grid['tp2_multipliers'],
|
||||
grid['sl_multipliers'],
|
||||
grid['tp1_close_percents'],
|
||||
grid['trailing_multipliers'],
|
||||
grid['vol_mins'],
|
||||
grid['max_bars_list'],
|
||||
]
|
||||
|
||||
print("🔢 Generating parameter combinations...")
|
||||
all_combos = list(itertools.product(*param_lists))
|
||||
total_combos = len(all_combos)
|
||||
print(f"✅ Generated {total_combos:,} total combinations")
|
||||
|
||||
# Extract chunk slice
|
||||
chunk_combos = all_combos[chunk_start:chunk_end]
|
||||
print(f"✂️ Extracted chunk slice: {len(chunk_combos):,} combinations")
|
||||
print()
|
||||
|
||||
# Prepare arguments for test_config
|
||||
args_list = [
|
||||
(chunk_start + i, combo, data_slice)
|
||||
for i, combo in enumerate(chunk_combos)
|
||||
]
|
||||
|
||||
# Run multiprocessing sweep (same as comprehensive_sweep.py)
|
||||
print(f"🚀 Starting sweep with {num_workers} workers...")
|
||||
print()
|
||||
|
||||
results = []
|
||||
completed = 0
|
||||
best_pnl = float('-inf')
|
||||
best_config = None
|
||||
|
||||
with mp.Pool(processes=max_workers) as pool:
|
||||
for result in pool.imap_unordered(test_config, args_list, chunksize=10):
|
||||
results.append(result)
|
||||
completed += 1
|
||||
|
||||
# Track best
|
||||
if result[4] > best_pnl: # pnl_per_1k
|
||||
best_pnl = result[4]
|
||||
best_config = result
|
||||
|
||||
# Progress every 100 configs
|
||||
if completed % 100 == 0:
|
||||
pct = (completed / len(chunk_combos)) * 100
|
||||
print(f"⏳ Progress: {completed:,}/{len(chunk_combos):,} ({pct:.1f}%) - "
|
||||
f"Best so far: ${best_pnl:.2f}/1k")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} complete!")
|
||||
print(f"📊 Tested {len(results):,} configurations")
|
||||
print(f"🏆 Best P&L: ${best_pnl:.2f} per $1k")
|
||||
print()
|
||||
|
||||
# Sort by profitability
|
||||
results.sort(key=lambda x: x[4], reverse=True)
|
||||
|
||||
# Save results to CSV (same format as comprehensive_sweep.py)
|
||||
output_file = Path(__file__).parent.parent / f'chunk_{chunk_id}_results.csv'
|
||||
|
||||
print(f"💾 Saving results to {output_file}...")
|
||||
|
||||
with open(output_file, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# Header
|
||||
writer.writerow([
|
||||
'rank', 'trades', 'win_rate', 'total_pnl', 'pnl_per_1k',
|
||||
'profit_factor', 'max_drawdown', 'sharpe_ratio',
|
||||
'flip_threshold', 'ma_gap', 'adx_min', 'long_pos_max', 'short_pos_min',
|
||||
'cooldown', 'position_size', 'tp1_mult', 'tp2_mult', 'sl_mult',
|
||||
'tp1_close_pct', 'trailing_mult', 'vol_min', 'max_bars'
|
||||
])
|
||||
|
||||
# Write all results
|
||||
for rank, result in enumerate(results, 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
writer.writerow([
|
||||
rank, trades, f'{win_rate:.4f}', f'{total_pnl:.2f}', f'{pnl_per_1k:.2f}',
|
||||
f'{profit_factor:.3f}', f'{max_drawdown:.2f}', f'{sharpe:.3f}',
|
||||
*params
|
||||
])
|
||||
|
||||
print(f"✅ Results saved!")
|
||||
print()
|
||||
|
||||
# Print top 10
|
||||
print("🏆 Top 10 configurations:")
|
||||
print()
|
||||
for i, result in enumerate(results[:10], 1):
|
||||
config_id, trades, win_rate, total_pnl, pnl_per_1k, \
|
||||
profit_factor, max_drawdown, sharpe, params = result
|
||||
|
||||
print(f"{i:2d}. ${pnl_per_1k:7.2f}/1k | "
|
||||
f"{trades:4d} trades | {win_rate*100:5.1f}% WR | "
|
||||
f"PF {profit_factor:.2f} | DD {max_drawdown:.1f}%")
|
||||
|
||||
print()
|
||||
print(f"✅ Chunk {chunk_id} processing complete!")
|
||||
|
||||
return output_file
|
||||
|
||||
def main():
|
||||
"""Worker entry point"""
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python3 distributed_worker.py <chunk_spec.json>")
|
||||
sys.exit(1)
|
||||
|
||||
chunk_spec_path = sys.argv[1]
|
||||
|
||||
if not Path(chunk_spec_path).exists():
|
||||
print(f"Error: Chunk spec file not found: {chunk_spec_path}")
|
||||
sys.exit(1)
|
||||
|
||||
print("=" * 80)
|
||||
print("🔧 DISTRIBUTED WORKER")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
start_time = datetime.now()
|
||||
|
||||
output_file = process_chunk(chunk_spec_path)
|
||||
|
||||
end_time = datetime.now()
|
||||
duration = (end_time - start_time).total_seconds()
|
||||
|
||||
print()
|
||||
print("=" * 80)
|
||||
print(f"⏱️ Total time: {duration:.1f} seconds ({duration/60:.1f} minutes)")
|
||||
print(f"📄 Results: {output_file}")
|
||||
print("=" * 80)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
36
cluster/monitor_bd_host01.sh
Executable file
36
cluster/monitor_bd_host01.sh
Executable file
@@ -0,0 +1,36 @@
|
||||
#!/bin/bash
|
||||
# Monitor bd-host01 worker progress
|
||||
|
||||
echo "=================================="
|
||||
echo "BD-HOST01 WORKER MONITOR"
|
||||
echo "=================================="
|
||||
echo
|
||||
|
||||
echo "=== CPU Usage ==="
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'top -bn1 | grep \"Cpu(s)\"'"
|
||||
echo
|
||||
|
||||
echo "=== Load Average ==="
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'uptime'"
|
||||
echo
|
||||
|
||||
echo "=== Worker Processes ==="
|
||||
WORKER_COUNT=$(ssh root@10.10.254.106 "ssh root@10.20.254.100 'ps aux | grep distributed_worker | grep -v grep | wc -l'")
|
||||
echo "Active workers: $WORKER_COUNT"
|
||||
echo
|
||||
|
||||
echo "=== Output Files ==="
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'ls -lh /home/backtest_dual/backtest/chunk_*_results.csv 2>/dev/null || echo \"Still processing - no results file yet\"'"
|
||||
echo
|
||||
|
||||
echo "=== Latest Log Lines ==="
|
||||
ssh root@10.10.254.106 "ssh root@10.20.254.100 'tail -10 /tmp/v9_chunk_000000.log'"
|
||||
echo
|
||||
|
||||
if [ "$WORKER_COUNT" -eq 0 ]; then
|
||||
echo "⚠️ Worker finished or crashed!"
|
||||
echo "Check full log: ssh root@10.10.254.106 \"ssh root@10.20.254.100 'cat /tmp/v9_chunk_000000.log'\""
|
||||
else
|
||||
echo "✅ Worker is running - processing 10,000 parameter combinations"
|
||||
echo " This will take 10-30 minutes depending on complexity"
|
||||
fi
|
||||
Reference in New Issue
Block a user