ae7195c829
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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>
20 lines
498 B
Python
20 lines
498 B
Python
"""Evolution engine for self-improving trading strategies."""
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from src.evolution.ab_test import ABTester, ABTestResult, StrategyPerformance
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from src.evolution.optimizer import EvolutionOptimizer
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from src.evolution.performance_tracker import (
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PerformanceDashboard,
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PerformanceTracker,
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StrategyMetrics,
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)
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__all__ = [
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"EvolutionOptimizer",
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"ABTester",
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"ABTestResult",
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"StrategyPerformance",
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"PerformanceTracker",
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"PerformanceDashboard",
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"StrategyMetrics",
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]
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