AI-powered DCA manager with sophisticated reversal detection
Multi-factor analysis: price movements, RSI, support/resistance, 24h trends
Real example: SOL position analysis shows 5.2:1 risk/reward improvement
lib/ai-dca-manager.ts - Complete DCA analysis engine with risk management
Intelligent scaling: adds to positions when AI detects 50%+ reversal confidence
Account-aware: uses up to 50% available balance with conservative 3x leverage
Dynamic SL/TP: adjusts stop loss and take profit for new average position
lib/automation-service-simple.ts - DCA monitoring in main trading cycle
prisma/schema.prisma - DCARecord model for comprehensive tracking
Checks DCA opportunities before new trade analysis (priority system)
test-ai-dca-simple.js - Real SOL position test from screenshot data
Entry: 85.98, Current: 83.87 (-1.13% underwater)
AI recommendation: 1.08 SOL DCA → 4.91 profit potential
Risk level: LOW with 407% liquidation safety margin
LOGIC
Price movement analysis: 1-10% against position optimal for DCA
Market sentiment: 24h trends must align with DCA direction
Technical indicators: RSI oversold (<35) for longs, overbought (>65) for shorts
Support/resistance: proximity to key levels increases confidence
Risk management: respects leverage limits and liquidation distances
Complete error handling and fallback mechanisms
Database persistence for DCA tracking and performance analysis
Seamless integration with existing AI leverage calculator
Real-time market data integration for accurate decision making
- AI risk management vs manual controls - never mix approaches
- Balance calculation rules using official Drift SDK methods
- Timeframe handling best practices for TradingView integration
- System integration debugging patterns and data flow validation
- Analysis timer implementation with database persistence
- Comprehensive testing and validation patterns for complex systems
- Trading integration validation against actual platform values
- AI analysis output validation for realistic trading parameters
Based on hard-learned lessons from debugging automation system issues.
- Add direct container editing workflow for immediate testing
- Document robust cleanup system architecture and implementation
- Include comprehensive troubleshooting section with common issues
- Add git commit patterns for progress tracking and persistence
- Update testing procedures with process monitoring
- Enhance API documentation with cleanup integration
- Add successful implementation workflow with validation steps
- Updated README.md with automation features and Docker troubleshooting
- Enhanced copilot-instructions.md with multi-timeframe patterns and Docker workflows
- Created DEVELOPMENT_GUIDE.md with comprehensive implementation patterns
- Added troubleshooting section for volume mount issues
- Documented fresh implementation approach vs file editing
- Included performance optimization tips and future roadmap
- Added testing strategies and common pitfall solutions
Key knowledge preserved:
- Multi-timeframe UI patterns and state management
- Docker Compose v2 syntax and volume mount troubleshooting
- Fresh file creation approach for problematic edits
- Complete automation page implementation examples
- Add TECHNICAL_ANALYSIS_BASICS.md with complete indicator explanations
- Add TA_QUICK_REFERENCE.md for quick lookup
- Enhance AI analysis prompts with TA principles integration
- Improve JSON response structure with dedicated analysis sections
- Add cross-layout consensus analysis for higher confidence signals
- Include timeframe-specific risk assessment and position sizing
- Add educational content for RSI, MACD, EMAs, Stochastic RSI, VWAP, OBV
- Implement layout-specific analysis (AI vs DIY layouts)
- Add momentum, trend, and volume analysis separation
- Update README with TA documentation references
- Create implementation summary and test files
