# 🚀 Continuous Optimization Cluster - Deployment Guide

## ⚡ Quick Deploy (5 minutes)

```bash
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

- [x] **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)

- [x] **Python 3.7+** installed on all servers

- [x] **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

```bash
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:**
```json
{
  "pnl": 192.50,
  "trades": 569,
  "win_rate": 60.98,
  "profit_factor": 1.022,
  "max_drawdown": 1360.58
}
```

### Step 2: Deploy to EPYC Servers

```bash
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

```bash
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:**
```bash
cd /home/icke/traderv4/cluster
python3 master.py
```

**Terminal 2 - Status dashboard:**
```bash
watch -n 10 'python3 status.py'
```

**Terminal 3 - Queue size:**
```bash
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:**
```bash
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:**
```bash
ssh root@10.10.254.106 'tail -f /root/optimization-cluster/logs/worker.log'
```

**Restart worker manually:**
```bash
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):**
```bash
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:**
```bash
pkill -f 'python3 master.py'
```

**Remove lock file:**
```bash
rm -f cluster/strategies.db-journal
```

**Restart master:**
```bash
python3 master.py
```

### Problem: Out of disk space

**Archive old results:**
```bash
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:**
```bash
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:**
```bash
vim backtester/volume_profile.py
```

2. **Add job generation in master.py:**
```python
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:**
```python
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:**
```bash
./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

```bash
# 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?**

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

Let the machines find better strategies while you sleep! 🚀