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"
Database changes:
- Added indicatorVersion field to Trade table
- Added indicatorVersion field to BlockedSignal table
- Tracks which Pine Script version (v5, v6, etc.) generated each signal
Pine Script changes:
- v6 now includes '| IND:v6' in alert messages
- Enables differentiation between v5 and v6 signals in database
Documentation:
- Created INDICATOR_VERSION_TRACKING.md with full implementation guide
- Includes n8n workflow update instructions
- Includes SQL analysis queries for v5 vs v6 comparison
- Includes rollback plan if needed
Next steps (manual):
1. Update n8n workflow Parse Signal Enhanced node to extract IND field
2. Update n8n HTTP requests to pass indicatorVersion
3. Update API endpoints to accept and save indicatorVersion
4. Rebuild Docker container
Benefits:
- Compare v5 vs v6 Pine Script effectiveness
- Track which version generated winning/losing trades
- Validate that v6 price position filter reduces blocked signals
- Data-driven decisions on Pine Script improvements
- Add BlockedSignal table with 25 fields for comprehensive signal analysis
- Track all blocked signals with metrics (ATR, ADX, RSI, volume, price position)
- Store quality scores, block reasons, and detailed breakdowns
- Include future fields for automated price analysis (priceAfter1/5/15/30Min)
- Restore signalQualityVersion field to Trade table
Database changes:
- New table: BlockedSignal with indexes on symbol, createdAt, score, blockReason
- Fixed schema drift from manual changes
API changes:
- Modified check-risk endpoint to save blocked signals automatically
- Fixed hasContextMetrics variable scope (moved to line 209)
- Save blocks for: quality score too low, cooldown period, hourly limit
- Use config.minSignalQualityScore instead of hardcoded 60
Database helpers:
- Added createBlockedSignal() function with try/catch safety
- Added getRecentBlockedSignals(limit) for queries
- Added getBlockedSignalsForAnalysis(olderThanMinutes) for automation
Documentation:
- Created BLOCKED_SIGNALS_TRACKING.md with SQL queries and analysis workflow
- Created SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md with 5-phase plan
- Documented data-first approach: collect 10-20 signals before optimization
Rationale:
Only 2 historical trades scored 60-64 (insufficient sample size for threshold decision).
Building data collection infrastructure before making premature optimizations.
Phase 1 (current): Collect blocked signals for 1-2 weeks
Phase 2 (next): Analyze patterns and make data-driven threshold decision
Phase 3-5 (future): Automation and ML optimization
- Add market data cache service (5min expiry) for storing TradingView metrics
- Create /api/trading/market-data webhook endpoint for continuous data updates
- Add /api/analytics/reentry-check endpoint for validating manual trades
- Update execute endpoint to auto-cache metrics from incoming signals
- Enhance Telegram bot with pre-execution analytics validation
- Support --force flag to override analytics blocks
- Use fresh ADX/ATR/RSI data when available, fallback to historical
- Apply performance modifiers: -20 for losing streaks, +10 for winning
- Minimum re-entry score 55 (vs 60 for new signals)
- Fail-open design: proceeds if analytics unavailable
- Show data freshness and source in Telegram responses
- Add comprehensive setup guide in docs/guides/REENTRY_ANALYTICS_QUICKSTART.md
Phase 1 implementation for smart manual trade validation.
- Added signalQualityVersion field to Trade model
- Tracks which scoring logic version was used for each trade
- v1: Original logic (price position < 5% threshold)
- v2: Added volume compensation for low ADX
- v3: CURRENT - Stricter logic requiring ADX > 18 for extreme positions (< 15%)
This enables future analysis to:
- Compare performance between logic versions
- Filter trades by scoring algorithm
- Data-driven improvements based on clean datasets
All new trades will be marked as v3. Old trades remain null/v1 for comparison.
- 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.
- Add signalQualityScore field to Trade model (0-100)
- Calculate quality score in execute endpoint using same logic as check-risk
- Save score with every trade for correlation analysis
- Create database migration for new field
- Enables future analysis: score vs win rate, P&L, etc.
This allows data-driven decisions on dynamic position sizing
- Fixed Prisma client not being available in Docker container
- Added isTestTrade flag to exclude test trades from analytics
- Created analytics views for net positions (matches Drift UI netting)
- Added API endpoints: /api/analytics/positions and /api/analytics/stats
- Added test trade endpoint: /api/trading/test-db
- Updated Dockerfile to properly copy Prisma client from builder stage
- Database now successfully stores all trades with full details
- Supports position netting calculations to match Drift perpetuals behavior
- Add PostgreSQL database with Prisma ORM
- Trade model: tracks entry/exit, P&L, order signatures, config snapshots
- PriceUpdate model: tracks price movements for drawdown analysis
- SystemEvent model: logs errors and system events
- DailyStats model: aggregated performance metrics
- Implement dual stop loss system (enabled by default)
- Soft stop (TRIGGER_LIMIT) at -1.5% to avoid wicks
- Hard stop (TRIGGER_MARKET) at -2.5% to guarantee exit
- Configurable via USE_DUAL_STOPS, SOFT_STOP_PERCENT, HARD_STOP_PERCENT
- Backward compatible with single stop modes
- Add database service layer (lib/database/trades.ts)
- createTrade(): save new trades with all details
- updateTradeExit(): close trades with P&L calculations
- addPriceUpdate(): track price movements during trade
- getTradeStats(): calculate win rate, profit factor, avg win/loss
- logSystemEvent(): log errors and system events
- Update execute endpoint to use dual stops and save to database
- Calculate dual stop prices when enabled
- Pass dual stop parameters to placeExitOrders
- Save complete trade record to database after execution
- Add test trade button to settings page
- New /api/trading/test endpoint for executing test trades
- Displays detailed results including dual stop prices
- Confirmation dialog before execution
- Shows entry price, position size, stops, and TX signature
- Generate Prisma client in Docker build
- Update DATABASE_URL for container networking
