최초 프로젝트 업로드 (Script Auto Commit)

analyze_backtest_log.py

@@ -0,0 +1,132 @@
+# analyze_backtest_log.py
+# 백테스트 로그 분석 스크립트: exit_reason별 성과, 승률, 평균 손익 계산
+
+import re
+from typing import Dict, List, Tuple
+from collections import defaultdict
+
+
+def parse_sell_trades(log_path: str) -> List[Dict[str, any]]:
+    """로그 파일에서 SELL 거래 파싱"""
+    trades = []
+    line_count = 0
+    match_count = 0
+    
+    try:
+        with open(log_path, 'r', encoding='utf-16-le', errors='ignore') as f:
+            for line in f:
+                line_count += 1
+                # SELL 패턴: [날짜] SELL (exit_reason): 종목명(티커) @ 가격 / 수량주 (수익률%)
+                # 예: [2022-01-10] SELL (STOP_LOSS_TECH): Delta Air Lines(DAL) @ 40 / 247,260주 (-2.12%)
+                match = re.search(
+                    r'\[(\d{4}-\d{2}-\d{2})\]\s+SELL\s+\(([^)]+)\):\s+(.+?)\s+@\s+[\d,]+\s+/\s+[\d,]+주\s+\(([+-]?\d+\.\d+)%\)',
+                    line
+                )
+                if match:
+                    match_count += 1
+                    date, exit_reason, ticker, return_pct = match.groups()
+                    trades.append({
+                        'date': date,
+                        'exit_reason': exit_reason,
+                        'ticker': ticker.strip(),
+                        'return_pct': float(return_pct)
+                    })
+    except Exception as e:
+        print(f"Error reading file: {e}")
+    
+    print(f"Debug: Scanned {line_count} lines, found {match_count} SELL trades")
+    return trades
+
+
+def analyze_by_exit_reason(trades: List[Dict[str, any]]) -> None:
+    """exit_reason별 통계 분석"""
+    stats: Dict[str, List[float]] = defaultdict(list)
+    
+    for trade in trades:
+        stats[trade['exit_reason']].append(trade['return_pct'])
+    
+    print("\n" + "="*70)
+    print("📊 EXIT REASON별 성과 분석")
+    print("="*70)
+    print(f"{'Exit Reason':<30} {'횟수':>6} {'승률':>7} {'평균':>8} {'누적':>8}")
+    print("-"*70)
+    
+    total_trades = len(trades)
+    total_profit = sum(t['return_pct'] for t in trades)
+    total_wins = sum(1 for t in trades if t['return_pct'] > 0)
+    
+    for reason in sorted(stats.keys()):
+        returns = stats[reason]
+        count = len(returns)
+        win_rate = sum(1 for r in returns if r > 0) / count * 100
+        avg_return = sum(returns) / count
+        total_return = sum(returns)
+        
+        print(f"{reason:<30} {count:>6} {win_rate:>6.1f}% {avg_return:>7.2f}% {total_return:>7.2f}%")
+    
+    print("-"*70)
+    if total_trades > 0:
+        print(f"{'TOTAL':<30} {total_trades:>6} {total_wins/total_trades*100:>6.1f}% "
+              f"{total_profit/total_trades:>7.2f}% {total_profit:>7.2f}%")
+    print("="*70)
+
+
+def analyze_losers(trades: List[Dict[str, any]], top_n: int = 10) -> None:
+    """손실 거래 TOP N 분석"""
+    losers = sorted([t for t in trades if t['return_pct'] < 0], 
+                    key=lambda x: x['return_pct'])
+    
+    print(f"\n❌ 손실 TOP {top_n} 거래")
+    print("-"*70)
+    for i, trade in enumerate(losers[:top_n], 1):
+        print(f"{i:2}. [{trade['date']}] {trade['ticker']:<30} "
+              f"{trade['exit_reason']:<25} {trade['return_pct']:>7.2f}%")
+
+
+def analyze_winners(trades: List[Dict[str, any]], top_n: int = 10) -> None:
+    """수익 거래 TOP N 분석"""
+    winners = sorted([t for t in trades if t['return_pct'] > 0], 
+                     key=lambda x: x['return_pct'], reverse=True)
+    
+    print(f"\n✅ 수익 TOP {top_n} 거래")
+    print("-"*70)
+    for i, trade in enumerate(winners[:top_n], 1):
+        print(f"{i:2}. [{trade['date']}] {trade['ticker']:<30} "
+              f"{trade['exit_reason']:<25} {trade['return_pct']:>7.2f}%")
+
+
+def main() -> None:
+    log_path = 'latest_backtest.log'
+    
+    print("백테스트 로그 분석 시작...")
+    trades = parse_sell_trades(log_path)
+    print(f"총 {len(trades)}개 거래 파싱 완료")
+    
+    analyze_by_exit_reason(trades)
+    analyze_losers(trades, top_n=15)
+    analyze_winners(trades, top_n=15)
+    
+    # 추가 인사이트
+    print("\n" + "="*70)
+    print("💡 최적화 힌트")
+    print("="*70)
+    
+    stop_losses = [t for t in trades if 'STOP_LOSS' in t['exit_reason']]
+    trailing_stops = [t for t in trades if 'TRAILING_STOP' in t['exit_reason']]
+    profit_takes = [t for t in trades if 'PROFIT_TAKE' in t['exit_reason']]
+    
+    if stop_losses:
+        avg_sl = sum(t['return_pct'] for t in stop_losses) / len(stop_losses)
+        print(f"• STOP_LOSS 평균 손실: {avg_sl:.2f}% → 너무 타이트하면 완화 고려")
+    
+    if trailing_stops:
+        ts_win_rate = sum(1 for t in trailing_stops if t['return_pct'] > 0) / len(trailing_stops) * 100
+        print(f"• TRAILING_STOP 승률: {ts_win_rate:.1f}% → 50% 이하면 파라미터 조정 필요")
+    
+    if profit_takes:
+        avg_pt = sum(t['return_pct'] for t in profit_takes) / len(profit_takes)
+        print(f"• PROFIT_TAKE 평균 수익: {avg_pt:.2f}% → 10% 이상이면 목표가 상향 고려")
+
+
+if __name__ == '__main__':
+    main()