trading_bot_v4/cluster/DEPLOYMENT.md

🚀 Continuous Optimization Cluster - Deployment Guide

⚡ Quick Deploy (5 minutes)

cd /home/icke/traderv4/cluster

# 1. Setup both EPYC servers
./setup_cluster.sh

# 2. Start master controller
python3 master.py

# 3. Monitor status (separate terminal)
watch -n 10 'python3 status.py'

---

📋 Prerequisites Checklist

• SSH Access: Keys configured for both EPYC servers

  • root@10.10.254.106 (pve-nu-monitor01)
  • root@10.20.254.100 (srv-bd-host01 via monitor01)

• Python 3.7+ installed on all servers

• OHLCV Data: backtester/data/solusdt_5m.csv (139,678 rows)

• Backtester Code: In /home/icke/traderv4/backtester/

  • backtester_core.py
  • v9_moneyline_ma_gap.py
  • moneyline_core.py

---

🏗️ Step-by-Step Setup

Step 1: Verify Backtester Works Locally

cd /home/icke/traderv4/backtester

# Test v9 backtest
python3 backtester_core.py \
  --data data/solusdt_5m.csv \
  --indicator v9 \
  --flip-threshold 0.6 \
  --ma-gap 0.35 \
  --momentum-adx 23 \
  --output json

Expected output:

{
  "pnl": 192.50,
  "trades": 569,
  "win_rate": 60.98,
  "profit_factor": 1.022,
  "max_drawdown": 1360.58
}

Step 2: Deploy to EPYC Servers

cd /home/icke/traderv4/cluster
./setup_cluster.sh

This will:

1. Create /root/optimization-cluster on both servers
2. Install Python venv + pandas/numpy
3. Copy backtester code
4. Copy worker.py script
5. Copy OHLCV data
6. Verify installation

Expected output:

🚀 Setting up optimization cluster...

Setting up Worker 1 (root@10.10.254.106)...
  📦 Installing Python packages...
  📁 Copying backtester modules...
  📄 Installing worker script...
  📊 Copying OHLCV data...
  ✅ Verifying installation...
✅ Worker 1 (pve-nu-monitor01) setup complete

Setting up Worker 2 (root@10.20.254.100)...
  📦 Installing Python packages...
  📁 Copying backtester modules...
  📄 Installing worker script...
  📊 Copying OHLCV data...
  ✅ Verifying installation...
✅ Worker 2 (srv-bd-host01) setup complete

🎉 Cluster setup complete!

Step 3: Start Master Controller

python3 master.py

Master will:

1. Initialize SQLite database (strategies.db)
2. Generate initial v9 parameter sweep (27 jobs)
3. Start monitoring loop (60-second intervals)
4. Assign jobs to idle workers
5. Collect and rank results

Expected output:

🚀 Cluster Master starting...
📊 Workers: 2
💾 Database: /home/icke/traderv4/cluster/strategies.db
📁 Queue: /home/icke/traderv4/cluster/queue

🔧 Generating v9 parameter sweep jobs...
✅ Created job: v9_moneyline_1701234567890 (priority 1)
✅ Created job: v9_moneyline_1701234567891 (priority 1)
...
✅ Created 27 v9 refinement jobs

============================================================
🔄 Iteration 1 - 2025-11-29 15:30:00

📤 Assigning v9_moneyline_1701234567890.json to worker1...
✅ Job started on worker1

📤 Assigning v9_moneyline_1701234567891.json to worker2...
✅ Job started on worker2

📊 Status: 25 queued | 2 running | 0 completed

Step 4: Monitor Progress

Terminal 1 - Master logs:

cd /home/icke/traderv4/cluster
python3 master.py

Terminal 2 - Status dashboard:

watch -n 10 'python3 status.py'

Terminal 3 - Queue size:

watch -n 5 'ls -1 cluster/queue/*.json 2>/dev/null | wc -l'

---

📊 Understanding Results

Status Dashboard Output

======================================================================
🎯 OPTIMIZATION CLUSTER STATUS
======================================================================
📅 2025-11-29 15:45:00

📋 Queue: 15 jobs waiting
   Running: 2
   Completed: 10

🏆 TOP 5 STRATEGIES:
----------------------------------------------------------------------
Rank   Strategy                       P&L/1k       Trades   WR%      PF    
----------------------------------------------------------------------
1      v9_flip0.7_ma0.40_adx25       $215.80      587      62.3%    1.18  
2      v9_flip0.6_ma0.35_adx23       $208.40      569      61.5%    1.12  
3      v9_flip0.7_ma0.35_adx25       $205.20      601      60.8%    1.09  
4      v9_flip0.6_ma0.40_adx21       $198.70      553      61.2%    1.07  
5      v9_flip0.5_ma0.35_adx23       $192.50      569      60.9%    1.02  

📊 BASELINE COMPARISON:
   v9 baseline: $192.00/1k (current production)
   Best found: $215.80/1k (+12.4% improvement) ✅

======================================================================

Strategy Naming Convention

Format: {indicator}_{param1}_{param2}_{param3}...

Example: v9_flip0.7_ma0.40_adx25

• v9: Money Line indicator
• flip0.7: flip_threshold = 0.7 (70% EMA flip confirmation)
• ma0.40: ma_gap = 0.40 (MA50-MA200 gap bonus threshold)
• adx25: momentum_adx = 25 (ADX requirement for momentum filter)

---

🔧 Troubleshooting

Problem: Workers not picking up jobs

Check worker health:

ssh root@10.10.254.106 'pgrep -f backtester || echo IDLE'
ssh root@10.10.254.106 'ssh root@10.20.254.100 "pgrep -f backtester || echo IDLE"'

View worker logs:

ssh root@10.10.254.106 'tail -f /root/optimization-cluster/logs/worker.log'

Restart worker manually:

ssh root@10.10.254.106 'cd /root/optimization-cluster && python3 worker.py jobs/v9_moneyline_*.json'

Problem: Jobs stuck in "running" status

Reset stale jobs (>30 minutes):

sqlite3 cluster/strategies.db <<EOF
UPDATE jobs 
SET status = 'queued', worker_id = NULL, started_at = NULL
WHERE status = 'running' 
  AND started_at < datetime('now', '-30 minutes');
EOF

Problem: Database locked

Kill master process:

pkill -f 'python3 master.py'

Remove lock file:

rm -f cluster/strategies.db-journal

Restart master:

python3 master.py

Problem: Out of disk space

Archive old results:

cd cluster/results
tar -czf archive_$(date +%Y%m%d).tar.gz archive/
mv archive_$(date +%Y%m%d).tar.gz ~/backups/
rm -rf archive/*

Clean worker temp files:

ssh root@10.10.254.106 'rm -rf /root/optimization-cluster/results/archive/*'
ssh root@10.10.254.106 'ssh root@10.20.254.100 "rm -rf /root/optimization-cluster/results/archive/*"'

---

🎯 Adding Custom Strategies

Example: Volume Profile Indicator

1. Implement backtester module:

vim backtester/volume_profile.py

2. Add job generation in master.py:

def generate_volume_jobs(self):
    """Generate volume profile jobs"""
    for window in [20, 50, 100]:
        for threshold in [0.6, 0.7, 0.8]:
            params = {
                'profile_window': window,
                'entry_threshold': threshold,
                'stop_loss_atr': 3.0
            }
            
            self.queue.create_job(
                'volume_profile',
                params,
                PRIORITY_MEDIUM
            )

3. Update worker.py to handle new indicator:

def execute(self):
    if self.job['indicator'] == 'v9_moneyline':
        result = self.run_v9_backtest()
    elif self.job['indicator'] == 'volume_profile':
        result = self.run_volume_backtest()
    # ...

4. Deploy updates:

./setup_cluster.sh  # Redeploy with new code

---

📈 Performance Expectations

Cluster Capacity

Hardware:

• 64 total cores (32 per server)
• 44 cores @ 70% utilization
• ~22 parallel backtests

Throughput:

• ~1.6s per v9 backtest (EPYC 7282/7302)
• ~49,000 backtests per day
• ~1.47 million backtests per month

Optimization Timeline

Week 1 (v9 refinement):

• 27 initial jobs (flip × ma_gap × adx)
• Expand to 81 jobs (add long_pos, short_pos variations)
• Target: Find >$200/1k P&L configuration

Week 2-3 (New indicators):

• Volume profile: 27 configurations
• Order flow: 27 configurations
• Market structure: 27 configurations
• Target: Find >$250/1k P&L strategy

Week 4+ (Advanced):

• Multi-timeframe: 81 configurations
• ML-based scoring: 100+ hyperparameter combinations
• Target: Find >$300/1k P&L strategy

---

🔒 Safety & Deployment

Validation Gates

Before deploying strategy to production:

1. Trade Count: ≥700 trades (statistical significance)
2. Win Rate: 63-68% realistic range
3. Profit Factor: ≥1.5 solid edge
4. Max Drawdown: <20% manageable
5. Sharpe Ratio: ≥1.0 risk-adjusted
6. Consistency: Top 3 for 7 days straight

Manual Deployment Process

# 1. Review top strategy
sqlite3 cluster/strategies.db "SELECT * FROM strategies WHERE name = 'v9_flip0.7_ma0.40_adx25'"

# 2. Extract parameters
# flip_threshold: 0.7
# ma_gap: 0.40
# momentum_adx: 25

# 3. Update TradingView indicator
# Edit moneyline_v9_ma_gap.pinescript
# Change parameters to winning values

# 4. Update TradingView alerts
# Verify alerts fire with new parameters

# 5. Monitor first 10 trades
# Ensure behavior matches backtest

# 6. Full deployment after validation

Auto-Deployment (Future)

Once confident in system:

1. Master marks top strategy as status = 'staging'
2. User reviews via web dashboard
3. User clicks "Deploy" button
4. System auto-updates TradingView via API
5. Alerts regenerated with new parameters
6. First 10 trades monitored closely

---

📞 Support

• Main docs: /home/icke/traderv4/.github/copilot-instructions.md
• Cluster README: /home/icke/traderv4/cluster/README.md
• Backtester docs: /home/icke/traderv4/backtester/README.md (if exists)

---

Ready to deploy?

cd /home/icke/traderv4/cluster
./setup_cluster.sh

Let the machines find better strategies while you sleep! 🚀