PROBLEM:
- Trades with quality score 35 and 45 were executed (threshold: 60)
- Position opened without risk management after signal flips
- "Parse Signal" node didn't extract ATR/ADX/RSI/volumeRatio/pricePosition
- "Check Risk" node only sent symbol+direction, skipped quality validation
- "Execute Trade" node didn't forward metrics to backend
ROOT CAUSE:
n8n workflow had TWO paths:
1. NEW: Parse Signal Enhanced → Check Risk1 → Execute Trade1 (working)
2. OLD: Parse Signal → Check Risk → Execute Trade (broken)
Old path bypassed quality check because check-risk endpoint saw
hasContextMetrics=false and allowed trade without validation.
FIX:
1. Changed "Parse Signal" from 'set' to 'code' node with metric extraction
2. Updated "Check Risk" to send atr/adx/rsi/volumeRatio/pricePosition
3. Updated "Execute Trade" to forward all metrics to backend
IMPACT:
- All trades now validated against quality score threshold (60)
- Low-quality signals properly blocked before execution
- Prevents positions opening without proper risk management
Evidence from database showed 3 trades in 2 hours with scores <60:
- 10:00:31 SOL LONG - Score 35 (phantom detected)
- 09:55:30 SOL SHORT - Score 35 (executed)
- 09:35:14 SOL LONG - Score 45 (executed)
All three should have been blocked. Fix prevents future bypasses.
- Detect position size mismatches (>50% variance) after opening
- Save phantom trades to database with expectedSizeUSD, actualSizeUSD, phantomReason
- Return error from execute endpoint to prevent Position Manager tracking
- Add comprehensive documentation of phantom trade issue and solution
- Enable data collection for pattern analysis and future optimization
Fixes oracle price lag issue during volatile markets where transactions
confirm but positions don't actually open at expected size.
- Remove trade from Position Manager BEFORE closing Drift position (prevents race condition)
- Explicitly save closure to database with proper P&L calculation
- Mark flipped positions as 'manual' exit reason
- Increase delay from 1s to 2s for better on-chain confirmation
- Preserve MAE/MFE data in closure records
Fixes issue where SHORT signal would close LONG but not properly track the new SHORT position.
Database now correctly records both old position closure and new position opening.
- Add SymbolSettings interface with enabled/positionSize/leverage fields
- Implement per-symbol ENV variables (SOLANA_*, ETHEREUM_*)
- Add SOL and ETH sections to settings UI with enable/disable toggles
- Add symbol-specific test buttons (SOL LONG/SHORT, ETH LONG/SHORT)
- Update execute and test endpoints to check symbol enabled status
- Add real-time risk/reward calculator per symbol
- Rename 'Position Sizing' to 'Global Fallback' for clarity
- Fix position manager P&L calculation for externally closed positions
- Fix zero P&L bug affecting 12 historical trades
- Add SQL scripts for recalculating historical P&L data
- Move archive TypeScript files to .archive to fix build
Defaults:
- SOL: 10 base × 10x leverage = 100 notional (profit trading)
- ETH: base × 1x leverage = notional (data collection)
- Global: 10 × 10x for BTC and other symbols
Configuration priority: Per-symbol ENV > Market config > Global ENV > Defaults
- Extended MarketConfig with optional positionSize and leverage fields
- Configured ETH-PERP at @ 1x leverage for minimal-risk data collection
- Created getPositionSizeForSymbol() helper function in config/trading.ts
- Integrated symbol-specific sizing into execute endpoint
- Added comprehensive guide in docs/guides/SYMBOL_SPECIFIC_SIZING.md
Purpose: Enable ETH trading for faster signal quality data collection
while preserving SOL's profit-generation sizing (0 @ 10x)
Next: Create ETH alert in TradingView and restart bot
Comprehensive guide covering:
- How ATR is captured and stored (entry value frozen)
- Static ATR approach (Phases 1-3): Use entry ATR for entire trade
- Dynamic ATR approach (Phase 5+): Real-time updates via TradingView or bot calculation
- Use cases: Dynamic TP/SL, trailing stops, scaling in/out decisions
- Implementation path: Start simple with entry ATR, add real-time later if data supports
- Code examples for all approaches
- Troubleshooting common ATR issues
- Database schema considerations
Explains why waiting for data is critical before implementing advanced ATR features.
