- 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
5.8 KiB
5.8 KiB