feat(lotto): calibrate_winner_compute 당첨조합 역분석+percentile

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

lotto/app/backtest.py

@@ -91,6 +91,20 @@ def point_in_time_draws(draws: List[Tuple[int, List[int]]],
     return [(d, nums) for d, nums in draws if d < target_draw_no]
 
 
+def calibrate_winner_compute(draws, target_draw_no, winning6,
+                             sample_m: int = 2000, seed: Optional[int] = None) -> Dict[str, Any]:
+    """순수 연산: point-in-time 캐시로 당첨조합 채점 + 무작위 M표본 percentile."""
+    pit = point_in_time_draws(draws, target_draw_no)
+    cache = build_analysis_cache(pit)
+    scores = score_combination(sorted(winning6), cache)
+    win_total = scores["score_total"]
+    samples = random_null_tickets(sample_m, seed=seed)
+    le = sum(1 for t in samples
+             if score_combination(t, cache)["score_total"] <= win_total)
+    percentile = le / max(len(samples), 1)
+    return {"scores": scores, "percentile": percentile, "cache_draws": len(pit)}
+
+
 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)"""

lotto/tests/test_backtest.py

@@ -68,6 +68,17 @@ def test_point_in_time_excludes_target_draw():
     assert len(pit) == 29
 
 
+def test_calibrate_winner_scores_and_percentile():
+    draws = _toy_draws(60)
+    winning6 = [3, 11, 19, 27, 35, 44]
+    res = bt.calibrate_winner_compute(draws, target_draw_no=60,
+                                      winning6=winning6, sample_m=500, seed=9)
+    assert set(res["scores"].keys()) >= {"score_total", "score_frequency",
+        "score_fingerprint", "score_gap", "score_cooccur", "score_diversity"}
+    assert 0.0 <= res["percentile"] <= 1.0
+    assert res["cache_draws"] == 59      # 1..59
+
+
 def test_generate_pool_partial_fill(monkeypatch):
     """weighted_sample_6이 항상 같은 조합만 반환하도록 패치 → cap에 먼저 걸려 len < n — 예외 없이 반환."""
     import random as _r