- Created /api/analytics/tp-sl-optimization endpoint
- Analyzes historical trades using MAE/MFE data
- Calculates optimal TP1/TP2/SL levels based on percentiles
- Provides win rate, profit factor, and hit rate analysis
- Shows money left on table (MFE - realized P&L)
- Projects impact of optimal levels on future performance
Analytics calculated:
- MAE analysis: avg, median, percentiles, worst
- MFE analysis: avg, median, percentiles, best
- Current level performance: TP1/TP2/SL hit rates
- Optimal recommendations: TP1=50% of avg MFE, TP2=80%, SL=70% of avg MAE
- Projected improvements: win rate change, profit factor, total P&L
Requires 10+ closed trades with MAE/MFE data to generate recommendations
Test script: scripts/test-analytics.sh
Next: Phase 4 (visual dashboard) or wait for trades with MAE/MFE data
- 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
