4c36fa2bc3
**Documentation Structure:** - Created docs/ subdirectory organization (analysis/, architecture/, bugs/, cluster/, deployments/, roadmaps/, setup/, archived/) - Moved 68 root markdown files to appropriate categories - Root directory now clean (only README.md remains) - Total: 83 markdown files now organized by purpose **New Content:** - Added comprehensive Environment Variable Reference to copilot-instructions.md - 100+ ENV variables documented with types, defaults, purpose, notes - Organized by category: Required (Drift/RPC/Pyth), Trading Config (quality/ leverage/sizing), ATR System, Runner System, Risk Limits, Notifications, etc. - Includes usage examples (correct vs wrong patterns) **File Distribution:** - docs/analysis/ - Performance analyses, blocked signals, profit projections - docs/architecture/ - Adaptive leverage, ATR trailing, indicator tracking - docs/bugs/ - CRITICAL_*.md, FIXES_*.md bug reports (7 files) - docs/cluster/ - EPYC setup, distributed computing docs (3 files) - docs/deployments/ - *_COMPLETE.md, DEPLOYMENT_*.md status (12 files) - docs/roadmaps/ - All *ROADMAP*.md strategic planning files (7 files) - docs/setup/ - TradingView guides, signal quality, n8n setup (8 files) - docs/archived/2025_pre_nov/ - Obsolete verification checklist (1 file) **Key Improvements:** - ENV variable reference: Single source of truth for all configuration - Common Pitfalls #68-71: Already complete, verified during audit - Better findability: Category-based navigation vs 68 files in root - Preserves history: All files git mv (rename), not copy/delete - Zero broken functionality: Only documentation moved, no code changes **Verification:** - 83 markdown files now in docs/ subdirectories - Root directory cleaned: 68 files → 0 files (except README.md) - Git history preserved for all moved files - Container running: trading-bot-v4 (no restart needed) **Next Steps:** - Create README.md files in each docs subdirectory - Add navigation index - Update main README.md with new structure - Consolidate duplicate deployment docs - Archive truly obsolete files (old SQL backups) See: docs/analysis/CLEANUP_PLAN.md for complete reorganization strategy
150 lines
3.0 KiB
Markdown
150 lines
3.0 KiB
Markdown
# Running Comprehensive Sweep on EPYC Server
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## Transfer Package to EPYC
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```bash
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# From your local machine
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scp comprehensive_sweep_package.tar.gz root@72.62.39.24:/root/
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```
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## Setup on EPYC
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```bash
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# SSH to EPYC
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ssh root@72.62.39.24
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# Extract package
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cd /root
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tar -xzf comprehensive_sweep_package.tar.gz
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cd comprehensive_sweep
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# Setup Python environment
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python3 -m venv .venv
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source .venv/bin/activate
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pip install pandas numpy
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# Create logs directory
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mkdir -p backtester/logs
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# Make scripts executable
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chmod +x run_comprehensive_sweep.sh
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chmod +x backtester/scripts/comprehensive_sweep.py
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```
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## Run the Sweep
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```bash
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# Start the sweep in background
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./run_comprehensive_sweep.sh
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# Or manually with more control:
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cd /root/comprehensive_sweep
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source .venv/bin/activate
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nohup python3 backtester/scripts/comprehensive_sweep.py > sweep.log 2>&1 &
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# Get the PID
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echo $! > sweep.pid
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```
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## Monitor Progress
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```bash
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# Watch live progress (updates every 100 configs)
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tail -f backtester/logs/sweep_comprehensive_*.log
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# Or if using manual method:
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tail -f sweep.log
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# See current best result
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grep 'Best so far' backtester/logs/sweep_comprehensive_*.log | tail -5
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# Check if still running
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ps aux | grep comprehensive_sweep
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# Check CPU usage
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htop
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```
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## Stop if Needed
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```bash
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# Using PID file:
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kill $(cat sweep.pid)
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# Or by name:
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pkill -f comprehensive_sweep
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```
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## EPYC Performance Estimate
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- **Your EPYC:** 16 cores/32 threads
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- **Local Server:** 6 cores
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- **Speedup:** ~5-6× faster on EPYC
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**Total combinations:** 14,929,920
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**Estimated times:**
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- Local (6 cores): ~30-40 hours
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- EPYC (16 cores): ~6-8 hours 🚀
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## Retrieve Results
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```bash
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# After completion, download results
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scp root@72.62.39.24:/root/comprehensive_sweep/sweep_comprehensive.csv .
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# Check top results on server first:
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head -21 /root/comprehensive_sweep/sweep_comprehensive.csv
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```
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## Results Format
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CSV columns:
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- rank
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- trades
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- win_rate
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- total_pnl
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- pnl_per_1k (most important - profitability per $1000)
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- flip_threshold
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- ma_gap
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- adx_min
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- long_pos_max
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- short_pos_min
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- cooldown
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- position_size
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- tp1_mult
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- tp2_mult
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- sl_mult
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- tp1_close_pct
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- trailing_mult
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- vol_min
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- max_bars
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## Quick Test
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Before running full sweep, test that everything works:
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```bash
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cd /root/comprehensive_sweep
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source .venv/bin/activate
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# Quick test with just 10 combinations
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python3 -c "
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from pathlib import Path
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from backtester.data_loader import load_csv
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from backtester.simulator import simulate_money_line, TradeConfig
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from backtester.indicators.money_line import MoneyLineInputs
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data_slice = load_csv(Path('backtester/data/solusdt_5m_aug_nov.csv'), 'SOL-PERP', '5m')
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print(f'Loaded {len(data_slice.data)} candles')
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inputs = MoneyLineInputs(flip_threshold_percent=0.6)
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config = TradeConfig(position_size=210.0)
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results = simulate_money_line(data_slice.data, 'SOL-PERP', inputs, config)
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print(f'Test: {len(results.trades)} trades, {results.win_rate*100:.1f}% WR, \${results.total_pnl:.2f} P&L')
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print('✅ Everything working!')
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"
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```
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If test passes, run the full sweep!
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