feat: implement evolution engine for self-improving strategies

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>

src/evolution/performance_tracker.py

@@ -0,0 +1,303 @@
+"""Performance tracking system for strategy monitoring.
+
+Tracks win rates, monitors improvement over time,
+and provides performance metrics dashboard.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+from dataclasses import asdict, dataclass
+from datetime import UTC, datetime, timedelta
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class StrategyMetrics:
+    """Performance metrics for a strategy over a time period."""
+
+    strategy_name: str
+    period_start: str
+    period_end: str
+    total_trades: int
+    wins: int
+    losses: int
+    holds: int
+    win_rate: float
+    avg_pnl: float
+    total_pnl: float
+    best_trade: float
+    worst_trade: float
+    avg_confidence: float
+
+
+@dataclass
+class PerformanceDashboard:
+    """Comprehensive performance dashboard."""
+
+    generated_at: str
+    overall_metrics: StrategyMetrics
+    daily_metrics: list[StrategyMetrics]
+    weekly_metrics: list[StrategyMetrics]
+    improvement_trend: dict[str, Any]
+
+
+class PerformanceTracker:
+    """Tracks and monitors strategy performance over time."""
+
+    def __init__(self, db_path: str) -> None:
+        """Initialize performance tracker.
+
+        Args:
+            db_path: Path to the trade logs database
+        """
+        self._db_path = db_path
+
+    def get_strategy_metrics(
+        self,
+        strategy_name: str | None = None,
+        start_date: str | None = None,
+        end_date: str | None = None,
+    ) -> StrategyMetrics:
+        """Get performance metrics for a strategy over a time period.
+
+        Args:
+            strategy_name: Name of the strategy (None = all strategies)
+            start_date: Start date in ISO format (None = beginning of time)
+            end_date: End date in ISO format (None = now)
+
+        Returns:
+            StrategyMetrics object with performance data
+        """
+        conn = sqlite3.connect(self._db_path)
+        conn.row_factory = sqlite3.Row
+
+        try:
+            # Build query with optional filters
+            query = """
+                SELECT
+                    COUNT(*) as total_trades,
+                    SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) as wins,
+                    SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) as losses,
+                    SUM(CASE WHEN action = 'HOLD' THEN 1 ELSE 0 END) as holds,
+                    COALESCE(AVG(CASE WHEN pnl IS NOT NULL THEN pnl END), 0) as avg_pnl,
+                    COALESCE(SUM(CASE WHEN pnl IS NOT NULL THEN pnl ELSE 0 END), 0) as total_pnl,
+                    COALESCE(MAX(pnl), 0) as best_trade,
+                    COALESCE(MIN(pnl), 0) as worst_trade,
+                    COALESCE(AVG(confidence), 0) as avg_confidence,
+                    MIN(timestamp) as period_start,
+                    MAX(timestamp) as period_end
+                FROM trades
+                WHERE 1=1
+            """
+            params: list[Any] = []
+
+            if start_date:
+                query += " AND timestamp >= ?"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND timestamp <= ?"
+                params.append(end_date)
+
+            # Note: Currently trades table doesn't have strategy_name column
+            # This is a placeholder for future extension
+
+            row = conn.execute(query, params).fetchone()
+
+            total_trades = row["total_trades"] or 0
+            wins = row["wins"] or 0
+            win_rate = (wins / total_trades * 100) if total_trades > 0 else 0.0
+
+            return StrategyMetrics(
+                strategy_name=strategy_name or "default",
+                period_start=row["period_start"] or "",
+                period_end=row["period_end"] or "",
+                total_trades=total_trades,
+                wins=wins,
+                losses=row["losses"] or 0,
+                holds=row["holds"] or 0,
+                win_rate=round(win_rate, 2),
+                avg_pnl=round(row["avg_pnl"], 2),
+                total_pnl=round(row["total_pnl"], 2),
+                best_trade=round(row["best_trade"], 2),
+                worst_trade=round(row["worst_trade"], 2),
+                avg_confidence=round(row["avg_confidence"], 2),
+            )
+        finally:
+            conn.close()
+
+    def get_daily_metrics(
+        self, days: int = 7, strategy_name: str | None = None
+    ) -> list[StrategyMetrics]:
+        """Get daily performance metrics for the last N days.
+
+        Args:
+            days: Number of days to retrieve (default 7)
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            List of StrategyMetrics, one per day
+        """
+        metrics = []
+        end_date = datetime.now(UTC)
+
+        for i in range(days):
+            day_end = end_date - timedelta(days=i)
+            day_start = day_end - timedelta(days=1)
+
+            day_metrics = self.get_strategy_metrics(
+                strategy_name=strategy_name,
+                start_date=day_start.isoformat(),
+                end_date=day_end.isoformat(),
+            )
+            metrics.append(day_metrics)
+
+        return metrics
+
+    def get_weekly_metrics(
+        self, weeks: int = 4, strategy_name: str | None = None
+    ) -> list[StrategyMetrics]:
+        """Get weekly performance metrics for the last N weeks.
+
+        Args:
+            weeks: Number of weeks to retrieve (default 4)
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            List of StrategyMetrics, one per week
+        """
+        metrics = []
+        end_date = datetime.now(UTC)
+
+        for i in range(weeks):
+            week_end = end_date - timedelta(weeks=i)
+            week_start = week_end - timedelta(weeks=1)
+
+            week_metrics = self.get_strategy_metrics(
+                strategy_name=strategy_name,
+                start_date=week_start.isoformat(),
+                end_date=week_end.isoformat(),
+            )
+            metrics.append(week_metrics)
+
+        return metrics
+
+    def calculate_improvement_trend(
+        self, metrics_history: list[StrategyMetrics]
+    ) -> dict[str, Any]:
+        """Calculate improvement trend from historical metrics.
+
+        Args:
+            metrics_history: List of StrategyMetrics ordered from oldest to newest
+
+        Returns:
+            Dictionary with trend analysis
+        """
+        if len(metrics_history) < 2:
+            return {
+                "trend": "insufficient_data",
+                "win_rate_change": 0.0,
+                "pnl_change": 0.0,
+                "confidence_change": 0.0,
+            }
+
+        oldest = metrics_history[0]
+        newest = metrics_history[-1]
+
+        win_rate_change = newest.win_rate - oldest.win_rate
+        pnl_change = newest.avg_pnl - oldest.avg_pnl
+        confidence_change = newest.avg_confidence - oldest.avg_confidence
+
+        # Determine overall trend
+        if win_rate_change > 5.0 and pnl_change > 0:
+            trend = "improving"
+        elif win_rate_change < -5.0 or pnl_change < 0:
+            trend = "declining"
+        else:
+            trend = "stable"
+
+        return {
+            "trend": trend,
+            "win_rate_change": round(win_rate_change, 2),
+            "pnl_change": round(pnl_change, 2),
+            "confidence_change": round(confidence_change, 2),
+            "period_count": len(metrics_history),
+        }
+
+    def generate_dashboard(
+        self, strategy_name: str | None = None
+    ) -> PerformanceDashboard:
+        """Generate a comprehensive performance dashboard.
+
+        Args:
+            strategy_name: Name of the strategy (None = all strategies)
+
+        Returns:
+            PerformanceDashboard with all metrics
+        """
+        # Get overall metrics
+        overall_metrics = self.get_strategy_metrics(strategy_name=strategy_name)
+
+        # Get daily metrics (last 7 days)
+        daily_metrics = self.get_daily_metrics(days=7, strategy_name=strategy_name)
+
+        # Get weekly metrics (last 4 weeks)
+        weekly_metrics = self.get_weekly_metrics(weeks=4, strategy_name=strategy_name)
+
+        # Calculate improvement trend
+        improvement_trend = self.calculate_improvement_trend(weekly_metrics[::-1])
+
+        return PerformanceDashboard(
+            generated_at=datetime.now(UTC).isoformat(),
+            overall_metrics=overall_metrics,
+            daily_metrics=daily_metrics,
+            weekly_metrics=weekly_metrics,
+            improvement_trend=improvement_trend,
+        )
+
+    def export_dashboard_json(
+        self, dashboard: PerformanceDashboard
+    ) -> str:
+        """Export dashboard as JSON string.
+
+        Args:
+            dashboard: PerformanceDashboard object
+
+        Returns:
+            JSON string representation
+        """
+        data = {
+            "generated_at": dashboard.generated_at,
+            "overall_metrics": asdict(dashboard.overall_metrics),
+            "daily_metrics": [asdict(m) for m in dashboard.daily_metrics],
+            "weekly_metrics": [asdict(m) for m in dashboard.weekly_metrics],
+            "improvement_trend": dashboard.improvement_trend,
+        }
+        return json.dumps(data, indent=2)
+
+    def log_dashboard(self, dashboard: PerformanceDashboard) -> None:
+        """Log dashboard summary to logger.
+
+        Args:
+            dashboard: PerformanceDashboard object
+        """
+        logger.info("=" * 60)
+        logger.info("PERFORMANCE DASHBOARD")
+        logger.info("=" * 60)
+        logger.info("Generated: %s", dashboard.generated_at)
+        logger.info("")
+        logger.info("Overall Performance:")
+        logger.info("  Total Trades: %d", dashboard.overall_metrics.total_trades)
+        logger.info("  Win Rate: %.2f%%", dashboard.overall_metrics.win_rate)
+        logger.info("  Average P&L: %.2f", dashboard.overall_metrics.avg_pnl)
+        logger.info("  Total P&L: %.2f", dashboard.overall_metrics.total_pnl)
+        logger.info("")
+        logger.info("Improvement Trend (%s):", dashboard.improvement_trend["trend"])
+        logger.info("  Win Rate Change: %+.2f%%", dashboard.improvement_trend["win_rate_change"])
+        logger.info("  P&L Change: %+.2f", dashboard.improvement_trend["pnl_change"])
+        logger.info("=" * 60)