agentson
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ae7195c829
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feat: implement evolution engine for self-improving strategies
CI / test (pull_request) Has been cancelled
Complete Pillar 4 implementation with comprehensive testing and analysis.
Components:
- EvolutionOptimizer: Analyzes losing decisions from DecisionLogger,
identifies failure patterns (time, market, action), and uses Gemini
to generate improved strategies with auto-deployment capability
- ABTester: A/B testing framework with statistical significance testing
(two-sample t-test), performance comparison, and deployment criteria
(>60% win rate, >20 trades minimum)
- PerformanceTracker: Tracks strategy win rates, monitors improvement
trends over time, generates comprehensive dashboards with daily/weekly
metrics and trend analysis
Key Features:
- Uses DecisionLogger.get_losing_decisions() for failure identification
- Pattern analysis: market distribution, action types, time-of-day patterns
- Gemini integration for AI-powered strategy generation
- Statistical validation using scipy.stats.ttest_ind
- Sharpe ratio calculation for risk-adjusted returns
- Auto-deploy strategies meeting 60% win rate threshold
- Performance dashboard with JSON export capability
Testing:
- 24 comprehensive tests covering all evolution components
- 90% coverage of evolution module (304 lines, 31 missed)
- Integration tests for full evolution pipeline
- All 105 project tests passing with 72% overall coverage
Dependencies:
- Added scipy>=1.11,<2 for statistical analysis
Closes #19
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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2026-02-04 16:34:10 +09:00 |
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