- Remove userId filtering to match ai-analytics behavior
- Now shows correct 911 learning records (was 602)
- Shows correct 69 trades (was 67)
- Displays real 64% win rate instead of subset data
- AI Learning Status panel now shows actual trading performance
- Fixed ai-analytics API: Created missing endpoint and corrected model names
- Fixed ai-learning-status.ts: Updated to use ai_learning_data and trades models
- Fixed batch-analysis route: Corrected ai_learning_data model references
- Fixed analysis-details route: Updated automation_sessions and trades models
- Fixed test scripts: Updated model names in check-learning-data.js and others
- Disabled conflicting route files to prevent Next.js confusion
All APIs now use correct snake_case model names matching Prisma schema:
- ai_learning_data (not aILearningData)
- automation_sessions (not automationSession)
- trades (not trade)
This resolves 'Unable to load REAL AI analytics' frontend errors.
- Fixed analysis-details API to use stored profit field as fallback when exit prices missing
- Updated UI to use Status API data instead of calculating from limited recent trades
- Modified AI Learning Status to use real database trade data instead of demo numbers
- Enhanced price monitor with automatic trade closing logic for TP/SL hits
- Modified automation service to create trades with OPEN status for proper monitoring
- Added test scripts for creating OPEN trades and validating monitoring system
Key changes:
- Status section now shows accurate 50% win rate from complete database
- AI Learning Status shows consistent metrics based on real trading performance
- Both sections display same correct P&L (8.62) from actual trade results
- Real-time price monitor properly detects and tracks OPEN status trades
- Fixed trade lifecycle: OPEN → monitoring → COMPLETED when TP/SL hit
All trading performance metrics now display consistent, accurate data from the same source.
- Created comprehensive AI learning system documentation (AI_LEARNING_SYSTEM.md)
- Implemented real-time AI learning status tracking service (lib/ai-learning-status.ts)
- Added AI learning status API endpoint (/api/ai-learning-status)
- Enhanced dashboard with AI learning status indicators
- Added detailed AI learning status section to automation page
- Learning phase tracking (INITIAL → PATTERN_RECOGNITION → ADVANCED → EXPERT)
- Real-time performance metrics (accuracy, win rate, confidence level)
- Progress tracking with milestones and recommendations
- Strengths and improvement areas identification
- Realistic progression based on actual trading data
- Dashboard overview: AI learning status card with key metrics
- Automation page: Comprehensive learning breakdown with phase indicators
- Real-time updates every 30 seconds
- Color-coded phase indicators and performance metrics
- Next milestone tracking and AI recommendations
- TypeScript service for learning status calculation
- RESTful API endpoint for programmatic access
- Integration with existing database schema
- Realistic progression algorithms based on analysis count
- Accurate trade counting matching UI display (fixed from 1 to 4 trades)
Features:
Complete learning phase progression system
Real-time performance tracking and metrics
Intelligent recommendations based on AI performance
Transparent learning process with clear milestones
Enhanced user confidence through progress visibility
Accurate trade count matching actual UI display (4 trades)
Realistic win rate calculation (66.7% from demo data)
Progressive accuracy and confidence improvements
