refactor: 전체 코드베이스 감사 기반 리팩토링 — 버그 수정, 데드코드 제거, 보안 강화

P0 버그 수정:
- stock-lab: trade 엔드포인트 NameError 수정 (resp 미정의)
- deployer: 동시 배포 시 HTTP 200 → 503 반환

P1 데드코드 제거:
- stock-lab: fetch_overseas_news(), get_broker_cash() 제거
- blog-lab: 미사용 urlparse import 제거
- lotto-lab: 중복 inline import json 7곳 제거

P2 성능/효율 개선:
- lotto-lab: 가중 샘플링 3중 복사 → utils.weighted_sample_6() 통합
- lotto-lab: DB 인덱스 3개 추가 (recommendations, purchase_history)
- stock-lab: Pydantic .dict() → .model_dump() 호환
- blog-lab: 페이지네이션 상한(le=100) 추가

P3 보안/인프라:
- nginx: X-Frame-Options, X-Content-Type-Options, Referrer-Policy 헤더 추가
- docker-compose: travel-proxy CORS 와일드카드 → localhost 전용
- Dockerfile: music-lab, blog-lab, realestate-lab에 PYTHONUNBUFFERED 추가

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

backend/app/recommender.py

@@ -2,6 +2,8 @@ import random
 from collections import Counter
 from typing import Dict, Any, List, Tuple
 
+from .utils import weighted_sample_6
+
 def recommend_numbers(
     draws: List[Tuple[int, List[int]]],
     *,
@@ -40,20 +42,7 @@ def recommend_numbers(
         weights[n] = max(w, 0.1)
 
     # 중복 없이 6개 뽑기(가중 샘플링)
-    chosen = []
-    pool = list(range(1, 46))
-    for _ in range(6):
-        total = sum(weights[n] for n in pool)
-        r = random.random() * total
-        acc = 0.0
-        for n in pool:
-            acc += weights[n]
-            if acc >= r:
-                chosen.append(n)
-                pool.remove(n)
-                break
-
-    chosen_sorted = sorted(chosen)
+    chosen_sorted = sorted(weighted_sample_6(weights))
 
     explain = {
         "recent_window": recent_window,
@@ -130,20 +119,7 @@ def recommend_with_heatmap(
         weights[n] = max(w, 0.1)
     
     # 4. 가중 샘플링으로 6개 선택
-    chosen = []
-    pool = list(range(1, 46))
-    for _ in range(6):
-        total = sum(weights[n] for n in pool)
-        r = random.random() * total
-        acc = 0.0
-        for n in pool:
-            acc += weights[n]
-            if acc >= r:
-                chosen.append(n)
-                pool.remove(n)
-                break
-    
-    chosen_sorted = sorted(chosen)
+    chosen_sorted = sorted(weighted_sample_6(weights))
     
     # 5. 설명 데이터
     explain = {