# Dual v9 Parameter Sweep Package

**Purpose:** Run two INDEPENDENT parameter sweeps to compare which performs better

## What This Tests

**TWO SEPARATE SWEEPS** (not combined):

1. **Raw v9 Sweep**: v9 Money Line indicator WITHOUT any filter  
   - Baseline performance across all parameters
   - File: `scripts/run_backtest_sweep.py`
   - Output: `sweep_v9_raw.csv`

2. **RSI Filtered Sweep**: v9 Money Line indicator WITH RSI divergence filter  
   - Same parameters, but only trades with RSI divergence
   - File: `scripts/run_backtest_sweep_rsi.py`
   - Output: `sweep_v9_rsi_divergence.csv`

**Both test 65,536 parameter combinations independently, then we compare best results.**

## Package Contents
- `data/solusdt_5m.csv` - OHLCV data (Aug 1 - Nov 28, 2024, 34,273 candles)
- `backtester/` - Core backtesting modules
- `scripts/run_backtest_sweep.py` - Vanilla v9 sweep
- `scripts/run_backtest_sweep_rsi.py` - RSI divergence filtered sweep
- `setup_dual_sweep.sh` - Setup script
- `run_sweep_vanilla_epyc.sh` - Launch vanilla sweep
- `run_sweep_rsi_epyc.sh` - Launch RSI sweep

## Quick Start (EPYC Servers)

### EPYC Server 1 - Raw v9 Sweep (No Filter)
```bash
# Extract package
tar -xzf backtest_v9_dual_sweep.tar.gz
cd backtest

# Setup environment
./setup_dual_sweep.sh

# Run raw v9 sweep (65,536 combinations, ~12-13h with 24 workers)
./run_sweep_vanilla_epyc.sh

# Monitor progress
tail -f v9_vanilla_sweep.log
```

### EPYC Server 2 - RSI Filtered v9 Sweep
```bash
# Extract package
tar -xzf backtest_v9_dual_sweep.tar.gz
cd backtest

# Setup environment
./setup_dual_sweep.sh

# Run RSI sweep (65,536 combinations, ~12-13h with 24 workers)
./run_sweep_rsi_epyc.sh

# Monitor progress
tail -f v9_rsi_sweep.log
```

## Parameter Grid
Both sweeps test the same 8 parameters (4 values each = 65,536 combinations):

- **flip_threshold:** 0.4, 0.5, 0.6, 0.7
- **ma_gap:** 0.20, 0.30, 0.40, 0.50
- **momentum_adx:** 18, 21, 24, 27
- **momentum_long_pos:** 60, 65, 70, 75
- **momentum_short_pos:** 20, 25, 30, 35
- **cooldown_bars:** 1, 2, 3, 4
- **momentum_spacing:** 2, 3, 4, 5
- **momentum_cooldown:** 1, 2, 3, 4

## Expected Outputs

### Vanilla Sweep
- **File:** `sweep_v9_vanilla_epyc.csv`
- **Columns:** All 8 parameters + trades, total_pnl, win_rate, avg_pnl, max_drawdown, profit_factor
- **Sorted by:** total_pnl (descending)
- **Baseline Performance (default params):** $405.88, 569 trades, 60.98% WR

### RSI Divergence Sweep
- **File:** `sweep_v9_rsi_divergence.csv`
- **Columns:** Same as vanilla
- **Sorted by:** total_pnl (descending)
- **Filter:** Only trades with RSI divergence (bullish/bearish patterns, 20-bar lookback)
- **Top:** 100 results only (to keep file size manageable)
- **Baseline Performance:** $423.46, 224 trades, 63.39% WR (39% fewer trades but better quality)

## Key Differences

### Vanilla v9
- All signals execute (no post-filter)
- Tests which parameters maximize profit across all market conditions
- Higher trade frequency

### RSI Divergence v9
- Post-simulation filter: only keeps trades with RSI divergence detected
- Tests which parameters work best when combined with divergence confirmation
- Lower trade frequency but potentially higher win rate

## Performance Estimates
- **Hardware:** AMD EPYC 7282 (16-core) or similar
- **Workers:** 24 parallel processes
- **Speed:** ~1.6s per combination
- **Total Time:** ~29 hours for 65,536 combinations
- **Output Size:** ~5-10 MB per CSV (vanilla full results, RSI top 100)

## Comparison Strategy
After both sweeps complete:
1. Find best vanilla result: `head -1 sweep_v9_vanilla_epyc.csv`
2. Find best RSI result: `head -1 sweep_v9_rsi_divergence.csv`
3. Compare total P&L, trade count, win rate
4. Decision: Implement whichever yields highest total profit

## Monitoring Commands
```bash
# Check sweep status
ps aux | grep run_backtest_sweep

# Watch progress (vanilla)
tail -f v9_vanilla_sweep.log

# Watch progress (RSI)
tail -f v9_rsi_sweep.log

# Check completion
ls -lh sweep_v9_*.csv

# Kill sweep if needed
pkill -f run_backtest_sweep
```

## Troubleshooting

### Import Errors
- Ensure .venv is activated: `source .venv/bin/activate`
- Check pandas/numpy installed: `pip list | grep -E 'pandas|numpy'`

### Memory Issues
- Reduce workers: Edit run script, change `--workers 24` to `--workers 16`
- Monitor: `htop` or `free -h`

### Slow Progress
- Check CPU usage: `htop` (should see 24 python processes at 100%)
- Check I/O: `iostat -x 1` (shouldn't be bottleneck with CSV in memory)

## Expected Results Format

### Vanilla CSV Example
```
flip_threshold,ma_gap,momentum_adx,momentum_long_pos,momentum_short_pos,cooldown_bars,momentum_spacing,momentum_cooldown,trades,total_pnl,win_rate,avg_pnl,max_drawdown,profit_factor
0.6,0.35,23,70,25,2,3,2,569,405.88,60.98,0.71,1360.58,1.022
```

### RSI Divergence CSV Example
```
flip_threshold,ma_gap,momentum_adx,momentum_long_pos,momentum_short_pos,cooldown_bars,momentum_spacing,momentum_cooldown,total_pnl,num_trades,win_rate,profit_factor,max_drawdown,avg_win,avg_loss
0.6,0.35,23,70,25,2,3,2,423.46,224,63.39,1.087,1124.33,5.23,-3.42
```

## Package Info
- **Size:** 1.1 MB compressed
- **MD5:** d540906b1a9a3eaa0404bbd800349c59
- **Created:** November 29, 2025