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Implement Evolution Engine - Self-improvement feedback loop (Pillar 4) #19

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opened 2026-02-04 16:11:52 +09:00 by agentson · 0 comments
agentson commented 2026-02-04 16:11:52 +09:00
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Goal

Complete Pillar 4: 목표 중심의 끊임없는 진화

Implement self-improvement system that learns from mistakes and evolves strategies automatically.

Background

  • ✅ Decision logging system exists (issue #17)
  • ✅ Context tree implemented (issue #15)
  • ⬜ Need to close the feedback loop for continuous improvement

Tasks

1. Analyze Losing Decisions

  • Query high-confidence losing trades from decision_logs
  • Identify patterns (time of day, market conditions, stock characteristics)
  • Generate failure reports

2. Strategy Generation

  • Use Gemini to analyze failure patterns
  • Generate improved strategy variants
  • Create new BaseStrategy subclasses

3. A/B Testing Framework

  • Implement strategy comparison system
  • Test new strategies in paper mode
  • Track performance metrics per strategy

4. Auto-deployment

  • Gradual rollout of winning strategies
  • Performance-based strategy selection
  • Safety checks before live deployment

5. Performance Dashboard

  • Track strategy win rates
  • Monitor improvement over time
  • Generate evolution reports

Implementation Files

  • src/evolution/optimizer.py - Complete existing implementation
  • src/evolution/ab_test.py - New A/B testing framework
  • src/evolution/performance_tracker.py - New performance tracking
  • tests/test_evolution.py - Comprehensive tests

Acceptance Criteria

  • Losing decisions automatically analyzed weekly
  • New strategies generated and tested
  • A/B test framework with statistical significance
  • Auto-deploy strategies with >60% win rate
  • Performance dashboard accessible
  • Tests achieve ≥80% coverage

Priority

HIGH - Core pillar for long-term success

Related

Part of The 4 Pillars: Pillar 4 (Evolution)
Depends on: #17 (Decision Logging)

## Goal Complete Pillar 4: 목표 중심의 끊임없는 진화 Implement self-improvement system that learns from mistakes and evolves strategies automatically. ## Background - ✅ Decision logging system exists (issue #17) - ✅ Context tree implemented (issue #15) - ⬜ Need to close the feedback loop for continuous improvement ## Tasks ### 1. Analyze Losing Decisions - Query high-confidence losing trades from `decision_logs` - Identify patterns (time of day, market conditions, stock characteristics) - Generate failure reports ### 2. Strategy Generation - Use Gemini to analyze failure patterns - Generate improved strategy variants - Create new `BaseStrategy` subclasses ### 3. A/B Testing Framework - Implement strategy comparison system - Test new strategies in paper mode - Track performance metrics per strategy ### 4. Auto-deployment - Gradual rollout of winning strategies - Performance-based strategy selection - Safety checks before live deployment ### 5. Performance Dashboard - Track strategy win rates - Monitor improvement over time - Generate evolution reports ## Implementation Files - `src/evolution/optimizer.py` - Complete existing implementation - `src/evolution/ab_test.py` - New A/B testing framework - `src/evolution/performance_tracker.py` - New performance tracking - `tests/test_evolution.py` - Comprehensive tests ## Acceptance Criteria - [ ] Losing decisions automatically analyzed weekly - [ ] New strategies generated and tested - [ ] A/B test framework with statistical significance - [ ] Auto-deploy strategies with >60% win rate - [ ] Performance dashboard accessible - [ ] Tests achieve ≥80% coverage ## Priority **HIGH** - Core pillar for long-term success ## Related Part of The 4 Pillars: Pillar 4 (Evolution) Depends on: #17 (Decision Logging)
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Reference: jihoson/The-Ouroboros#19
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