feat(lotto): track_record + build_review_payload 집계

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lotto/app/backtest.py

@@ -202,6 +202,45 @@ def run_forward_purchase(draw_no: int, k: int = 5000, pool_n: int = 20000,
     return {"ok": True, "draw_no": draw_no}
 
 
+def track_record() -> Dict[str, Any]:
+    """전략별 누적 등수 집계 (engine_w는 라벨 합산)."""
+    db = _db()
+    rows = db.get_backtest_runs()
+    agg: Dict[str, Dict[str, int]] = {}
+    for r in rows:
+        a = agg.setdefault(r["strategy"], {
+            "n_tickets": 0, "1st": 0, "2nd": 0, "3rd": 0, "4th": 0, "5th": 0, "draws": 0})
+        p = prize_counts(r)
+        a["n_tickets"] += r["n_tickets"]
+        for tier in ("1st", "2nd", "3rd", "4th", "5th"):
+            a[tier] += p[tier]
+        a["draws"] += 1
+    return {"by_strategy": agg}
+
+
+def build_review_payload(draw_no: int) -> Dict[str, Any]:
+    """일요 회고 브리핑용 조립."""
+    db = _db()
+    cal = db.get_winner_calibration(draw_no)
+    runs = db.get_backtest_runs(draw_no=draw_no)
+    hist = db.get_calibration_history(limit=12)
+    forward = []
+    for r in runs:
+        forward.append({"strategy": r["strategy"], "label": r["weight_label"],
+                        "prizes": prize_counts(r), "best_match": r["best_match"],
+                        "avg_meta_score": r["avg_meta_score"]})
+    return {
+        "draw_no": draw_no,
+        "winner_analysis": cal,                       # score_* + percentile
+        "forward": forward,
+        "track_record": track_record()["by_strategy"],
+        "calibration_trend": [
+            {"draw_no": h["draw_no"], "score_total": h["score_total"],
+             "percentile": h["percentile"]} for h in hist
+        ],
+    }
+
+
 def coverage_tickets(k: int, seed: Optional[int] = None) -> List[List[int]]:
     """greedy 커버리지 — 아직 덜 쓰인 번호를 우선 배치해 번호를 넓게 분산.
     (휠링/보장설계는 향후. 현재는 distinct + 번호 사용 균등화)"""