Bug: Multiple monitoring loops detect ghost simultaneously
- Loop 1: has(tradeId) → true → proceeds
- Loop 2: has(tradeId) → true → ALSO proceeds (race condition)
- Both send Telegram notifications with compounding P&L
Real incident (Dec 2, 2025):
- Manual SHORT at $138.84
- 23 duplicate notifications
- P&L compounded: -$47.96 → -$1,129.24 (23× accumulation)
- Database shows single trade with final compounded value
Fix: Map.delete() returns true if key existed, false if already removed
- Call delete() FIRST
- Check return value
proceeds
- All other loops get false → skip immediately
- Atomic operation prevents race condition
Pattern: This is variant of Common Pitfalls #48, #49, #59, #60, #61
- All had "check then delete" pattern
- All vulnerable to async timing issues
- Solution: "delete then check" pattern
- Map.delete() is synchronous and atomic
Files changed:
- lib/trading/position-manager.ts lines 390-410
Related: DUPLICATE PREVENTED message was working but too late
Problem Discovered (Nov 22, 2025):
- User observed: Green dots (Money Line signals) blocked but "shot up" - would have been winners
- Current system: Only tracks DATA_COLLECTION_ONLY signals (multi-timeframe)
- Blindspot: QUALITY_SCORE_TOO_LOW signals (70-90 range) have NO price tracking
- Impact: Can't validate if quality 91 threshold is filtering winners or losers
Real Data from Signal 1 (Nov 21 16:50):
- LONG quality 80, ADX 16.6 (blocked: weak trend)
- Entry: $126.20
- Peak: $126.86 within 1 minute
- **+0.52% profit** (TP1 target: +1.51%, would NOT have hit but still profit)
- User was RIGHT: Signal moved favorably immediately
Changes:
- lib/analysis/blocked-signal-tracker.ts: Changed blockReason filter
* BEFORE: Only 'DATA_COLLECTION_ONLY'
* AFTER: Both 'DATA_COLLECTION_ONLY' AND 'QUALITY_SCORE_TOO_LOW'
- Now tracking ALL blocked signals for data-driven threshold optimization
Expected Data Collection:
- Track quality 70-90 blocked signals over 2-4 weeks
- Compare: Would-be winners vs actual blocks
- Decision point: Does quality 91 filter too many profitable setups?
- Options: Lower threshold (85?), adjust ADX/RSI weights, or keep 91
Next Steps:
- Wait for 20-30 quality-blocked signals with price data
- SQL analysis: Win rate of blocked signals vs executed trades
- Data-driven decision: Keep 91, lower to 85, or adjust scoring
Deployment: Container rebuilt and restarted, tracker confirmed running
- Changed from getPythPriceMonitor() to initializeDriftService()
- Uses getOraclePrice() with Drift market index
- Skips signals with entryPrice = 0
- Initialize Drift service in trackPrices() before processing
- Price tracking now working: priceAfter1Min/5Min/15Min/30Min fields populate
- analysisComplete transitions to true after 30 minutes
- wouldHitTP1/TP2/SL detection working (based on ATR targets)
Bug: Pyth price cache didn't have SOL-PERP prices, tracker skipped all signals
Fix: Drift oracle prices always available, tracker now functional
Impact: Multi-timeframe data collection now operational for Phase 1 analysis
Implemented comprehensive price tracking for multi-timeframe signal analysis.
**Components Added:**
- lib/analysis/blocked-signal-tracker.ts - Background job tracking prices
- app/api/analytics/signal-tracking/route.ts - Status/metrics endpoint
**Features:**
- Automatic price tracking at 1min, 5min, 15min, 30min intervals
- TP1/TP2/SL hit detection using ATR-based targets
- Max favorable/adverse excursion tracking (MFE/MAE)
- Analysis completion after 30 minutes
- Background job runs every 5 minutes
- Entry price captured from signal time
**Database Changes:**
- Added entryPrice field to BlockedSignal (for price tracking baseline)
- Added maxFavorablePrice, maxAdversePrice fields
- Added maxFavorableExcursion, maxAdverseExcursion fields
**Integration:**
- Auto-starts on container startup
- Tracks all DATA_COLLECTION_ONLY signals
- Uses same TP/SL calculation as live trades (ATR-based)
- Calculates profit % based on direction (long vs short)
**API Endpoints:**
- GET /api/analytics/signal-tracking - View tracking status and metrics
- POST /api/analytics/signal-tracking - Manually trigger update (auth required)
**Purpose:**
Enables data-driven multi-timeframe comparison. After 50+ signals per
timeframe, can analyze which timeframe (5min vs 15min vs 1H vs 4H vs Daily)
has best win rate, profit potential, and signal quality.
**What It Tracks:**
- Price at 1min, 5min, 15min, 30min after signal
- Would TP1/TP2/SL have been hit?
- Maximum profit/loss during 30min window
- Complete analysis of signal profitability
**How It Works:**
1. Signal comes in (15min, 1H, 4H, Daily) → saved to BlockedSignal
2. Background job runs every 5min
3. Queries current price from Pyth
4. Calculates profit % from entry
5. Checks if TP/SL thresholds crossed
6. Updates MFE/MAE if new highs/lows
7. After 30min, marks analysisComplete=true
**Future Analysis:**
After 50+ signals per timeframe:
- Compare TP1 hit rates across timeframes
- Identify which timeframe has highest win rate
- Determine optimal signal frequency vs quality trade-off
- Switch production to best-performing timeframe
User requested: "i want all the bells and whistles. lets make the
powerhouse more powerfull. i cant see any reason why we shouldnt"
