feat(lotto-lab): purchase_manager — 구매 결과 자동 체크 + 전략 성과 집계

- backend/app/purchase_manager.py 신규 생성
  - check_purchases_for_draw(): 회차별 미채점 구매 건 자동 채점
  - checker._calc_rank 재사용, RANK_PRIZE 상수 정의
  - 채점 후 strategy_performance 자동 upsert (전략별 집계)
- backend/tests/test_purchase_manager.py에 통합 테스트 2건 추가
  - test_check_purchases_for_draw: 1등/낙첨 결과 검증
  - test_check_purchases_updates_strategy_performance: 성과 테이블 갱신 검증

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/purchase_manager.py

@@ -0,0 +1,90 @@
+"""
+구매 이력 관리 + 결과 체크 모듈.
+
+- check_purchases_for_draw(): 특정 회차 구매 건들의 결과를 자동 체크
+- 체커의 _calc_rank 재사용
+- 결과 체크 후 strategy_performance 자동 갱신
+"""
+import logging
+from .db import (
+    get_draw, get_purchases, update_purchase_results,
+    upsert_strategy_performance,
+)
+from .checker import _calc_rank
+
+logger = logging.getLogger("lotto-backend")
+
+RANK_PRIZE = {1: 0, 2: 0, 3: 1_500_000, 4: 50_000, 5: 5_000}
+
+
+def check_purchases_for_draw(drw_no: int) -> int:
+    """
+    특정 회차 결과로 해당 회차 구매 건들을 채점한다.
+    Returns: 채점한 구매 건 수
+    """
+    win_row = get_draw(drw_no)
+    if not win_row:
+        return 0
+
+    win_nums = [win_row["n1"], win_row["n2"], win_row["n3"],
+                win_row["n4"], win_row["n5"], win_row["n6"]]
+    bonus = win_row["bonus"]
+
+    unchecked = get_purchases(draw_no=drw_no, checked=False)
+
+    strategy_agg = {}
+
+    count = 0
+    for purchase in unchecked:
+        numbers_list = purchase["numbers"]
+        if not numbers_list:
+            continue
+
+        results = []
+        for nums in numbers_list:
+            rank, correct, has_bonus = _calc_rank(nums, win_nums, bonus)
+            prize = RANK_PRIZE.get(rank, 0)
+            results.append({
+                "numbers": nums,
+                "rank": rank,
+                "correct": correct,
+                "has_bonus": has_bonus,
+                "prize": prize,
+            })
+
+        total_prize = sum(r["prize"] for r in results)
+        update_purchase_results(purchase["id"], results, total_prize)
+
+        strat = purchase["source_strategy"]
+        if strat not in strategy_agg:
+            strategy_agg[strat] = {
+                "sets_count": 0,
+                "total_correct": 0,
+                "max_correct": 0,
+                "prize_total": 0,
+                "scores": [],
+            }
+        agg = strategy_agg[strat]
+        for r in results:
+            agg["sets_count"] += 1
+            agg["total_correct"] += r["correct"]
+            agg["max_correct"] = max(agg["max_correct"], r["correct"])
+            agg["prize_total"] += r["prize"]
+            agg["scores"].append(r["correct"] / 6.0)
+
+        count += 1
+
+    for strat, agg in strategy_agg.items():
+        avg_score = sum(agg["scores"]) / len(agg["scores"]) if agg["scores"] else 0.0
+        upsert_strategy_performance(
+            strategy=strat,
+            draw_no=drw_no,
+            sets_count=agg["sets_count"],
+            total_correct=agg["total_correct"],
+            max_correct=agg["max_correct"],
+            prize_total=agg["prize_total"],
+            avg_score=round(avg_score, 4),
+        )
+
+    logger.info(f"[purchase_manager] {drw_no}회차 구매 {count}건 체크 완료")
+    return count