CRITICAL FIX (Nov 30, 2025):
- Dashboard showed 'idle' despite 22+ worker processes running
- Root cause: SSH-based worker detection timing out
- Solution: Check database for running chunks FIRST
Changes:
1. app/api/cluster/status/route.ts:
- Query exploration database before SSH detection
- If running chunks exist, mark workers 'active' even if SSH fails
- Override worker status: 'offline' → 'active' when chunks running
- Log: '✅ Cluster status: ACTIVE (database shows running chunks)'
- Database is source of truth, SSH only for supplementary metrics
2. app/cluster/page.tsx:
- Stop button ALREADY EXISTS (conditionally shown)
- Shows Start when status='idle', Stop when status='active'
- No code changes needed - fixed by status detection
Result:
- Dashboard now shows 'ACTIVE' with 2 workers (correct)
- Workers show 'active' status (was 'offline')
- Stop button automatically visible when cluster active
- System resilient to SSH timeouts/network issues
Verified:
- Container restarted: Nov 30 21:18 UTC
- API tested: Returns status='active', activeWorkers=2
- Logs confirm: Database-first logic working
- Workers confirmed running: 22+ processes on worker1, workers on worker2
3.9 KiB
3.9 KiB
V9 Diagnostic Results Summary
Date: November 29, 2025
Data: 95,617 5-minute bars (SOLUSDT, Jan-Nov 2024)
🚨 CRITICAL FINDINGS
1. BASELINE IS LOSING MONEY
- Baseline PnL: -$1,532.30 (1,663 trades)
- Win Rate: 0.6% (essentially all losses!)
- This explains the "parameter insensitivity" - when strategy loses on EVERY trade, parameters don't matter much
2. momentum_min_adx Parameter BROKEN
momentum_min_adx=18.0: 1663 trades, $-1532.30 PnL
momentum_min_adx=21.0: 1663 trades, $-1532.30 PnL ← IDENTICAL
momentum_min_adx=24.0: 1663 trades, $-1532.30 PnL ← IDENTICAL
momentum_min_adx=27.0: 1663 trades, $-1532.30 PnL ← IDENTICAL
Status: 🔴 NO EFFECT - Parameter is NOT being applied or is overridden
3. Other Parameters Show Minimal Effect
- flip_threshold: 1662-1663 trades (0.1% variation), PnL: -$1,185 to -$1,532
- cooldown_bars: 1660-1664 trades (0.2% variation), PnL: -$1,408 to -$1,859
- ma_gap_threshold: 1662-1663 trades (0.1% variation), PnL: -$1,185 to -$1,532
Signal counts barely change - most parameters have almost zero effect on trade generation.
📊 Comparison to Sweep Results
Exhaustive Sweep (EPYC):
- Best Result: $498.12 PnL, 568 trades, 61.09% WR
- Configuration: Different from baseline
Diagnostic Test (Local):
- Baseline: -$1,532.30 PnL, 1,663 trades, 0.6% WR
- Best: -$1,514.75 PnL, 1,663 trades, 0.6% WR
🤔 Why The Discrepancy?
Hypothesis 1: Data Mismatch
- EPYC used: Aug 1 - Nov 28, 2024 (34,273 candles - mentioned in DUAL_SWEEP_README.md)
- Local used: Jan 1 - Nov 28, 2024 (95,617 candles - full year)
- Impact: Different time periods = different market conditions = different results
Hypothesis 2: Configuration Mismatch
- EPYC sweep might be using different TradeConfig settings
- Position size, max bars per trade, or other simulator settings might differ
Hypothesis 3: Strategy Implementation Difference
- Backtester
simulate_money_line()might not match live v9 indicator - Parameters might not map correctly between TradingView and Python
🎯 Action Items
IMMEDIATE (Before Any Optimization):
-
✅ VERIFY DATA ALIGNMENT
# Download exact same date range as EPYC python3 scripts/export_binance_ohlcv.py \ --symbol SOLUSDT --interval 5m \ --start 2024-08-01 --end 2024-11-28 \ --output backtester/data/solusdt_5m_aug_nov.csv # Re-run diagnostics on matched dataset ./run_comprehensive_diagnostics.sh backtester/data/solusdt_5m_aug_nov.csv -
VERIFY SIMULATOR SETTINGS
- Check if EPYC sweep uses different position_size or max_bars_per_trade
- Compare TradeConfig between sweep script and diagnostic scripts
-
FIX momentum_min_adx BUG
- Investigate money_line_signals() to find why ADX parameter is ignored
- This is likely why all sweep configs produced similar results
-
FIX EXTREME BUGS
- Fix load_csv() call in test_extreme_configs() (missing symbol/timeframe)
- Fix SimulatedTrade.pnl attribute access in trade_analysis.py
AFTER VERIFICATION:
-
If Data Mismatch Confirmed:
- Use Aug-Nov 2024 dataset for all future analysis
- Understand why Q1-Q3 2024 was so terrible (bear market?)
-
If Simulator Bug Confirmed:
- Fix Python backtester to match TradingView v9 exactly
- Validate against known live trades
-
Parameter Optimization:
- Only optimize AFTER baseline is profitable on test data
- No point optimizing if strategy loses money fundamentally
💡 Key Insight
You can't optimize a fundamentally losing strategy.
If v9 baseline loses $1,532 on full-year data but makes $498 on Aug-Nov subset, either:
- A) Aug-Nov was a favorable period (cherry-picked results)
- B) Jan-Jul market was unfavorable for momentum strategies (bear market)
- C) Backtester doesn't match production v9 indicator
Must resolve this before any parameter tuning!