refactor: backend→lotto 서비스 리네이밍 + lotto.db 레거시 테이블 스키마 제거

- backend/ → lotto/ 디렉토리 이동
- docker-compose: lotto-backend→lotto, lotto-frontend→frontend
- deploy scripts, nginx, agent-office config 네이밍 일괄 반영
- lotto/app/db.py에서 todos·blog_posts CREATE TABLE 제거 (personal로 이관 완료)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

lotto/.dockerignore

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+.git
+__pycache__
+*.pyc
+.env
+.env.*
+*.md

lotto/Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates curl \
+  && rm -rf /var/lib/apt/lists/*
+
+COPY app/requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+COPY app /app/app
+
+ENV PYTHONUNBUFFERED=1
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
+
+ARG APP_VERSION=dev
+ENV APP_VERSION=$APP_VERSION
+LABEL org.opencontainers.image.version=$APP_VERSION

lotto/app/analyzer.py

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+"""
+통계 분석 엔진 - lotto-lab 고도화
+
+[팀 회의 합의 기반 5가지 통계 기법]
+1. 빈도 Z-score 분석: 각 번호의 출현 빈도가 기댓값에서 얼마나 벗어났는지
+2. 조합 지문(Fingerprint): 조합의 합계, 홀짝 비율, 구간 분포가 역대 당첨번호와 유사한지
+3. 갭 분석(Gap): 각 번호의 마지막 출현으로부터 경과 회차 수 기반 점수
+4. 공동 출현 행렬(Co-occurrence): 번호 쌍이 역대에 함께 나온 빈도 기반 점수
+5. 다양성(Diversity): 연속 번호, 범위, 구간 분포 다양성
+
+[통계 근거]
+- 1~45번 각각의 이론적 출현 확률: 6/45 ≈ 13.33% per draw
+- 기댓값 합계: E[sum] = 6 × E[1..45] = 6 × 23 = 138
+- 표준편차 합계: std ≈ sqrt(6 × Var[uniform 1..45]) ≈ 31
+- 홀수 23개 (1,3,...,45), 짝수 22개 (2,4,...,44)
+- 번호 쌍 공동 출현 확률: C(43,4)/C(45,6) ≈ 1.516% per draw
+"""
+
+import math
+from collections import Counter, defaultdict
+from datetime import datetime, timezone
+from typing import List, Tuple, Dict, Any, Optional
+
+# 구간 정의: (시작, 끝) 포함
+ZONE_RANGES: List[Tuple[int, int]] = [
+    (1, 9),
+    (10, 19),
+    (20, 29),
+    (30, 39),
+    (40, 45),
+]
+
+
+def _get_zone(n: int) -> int:
+    """번호가 속하는 구간 인덱스 (0-4)"""
+    for z, (lo, hi) in enumerate(ZONE_RANGES):
+        if lo <= n <= hi:
+            return z
+    return 4
+
+
+def build_analysis_cache(draws: List[Tuple[int, List[int]]]) -> Dict[str, Any]:
+    """
+    역대 당첨번호 데이터 기반 통계 분석 캐시 구성.
+    시뮬레이션 실행 시 한 번만 호출하여 재사용 (성능 최적화).
+
+    Args:
+        draws: [(drw_no, [n1,n2,n3,n4,n5,n6]), ...] 오름차순
+
+    Returns:
+        통계 캐시 딕셔너리
+    """
+    if not draws:
+        return {}
+
+    total_draws = len(draws)
+    all_nums_list = [n for _, nums in draws for n in nums]
+    freq_all = Counter(all_nums_list)
+
+    # ── 1. 빈도 Z-score ──────────────────────────────────────────────────────
+    freq_values = [freq_all.get(n, 0) for n in range(1, 46)]
+    mean_freq = sum(freq_values) / 45.0
+    variance_freq = sum((f - mean_freq) ** 2 for f in freq_values) / 45.0
+    std_freq = math.sqrt(variance_freq)
+
+    z_scores: Dict[int, float] = {}
+    for n in range(1, 46):
+        z_scores[n] = (freq_all.get(n, 0) - mean_freq) / max(std_freq, 0.001)
+
+    # ── 2. 갭 분석: 마지막 출현 이후 경과 회차 ──────────────────────────────
+    # gap = 0: 가장 최근 회차에 출현, gap = k: k회 전에 마지막 출현
+    last_seen_gap: Dict[int, int] = {}
+    for gap_idx, (_, nums) in enumerate(reversed(draws)):
+        for n in nums:
+            if n not in last_seen_gap:
+                last_seen_gap[n] = gap_idx
+    for n in range(1, 46):
+        if n not in last_seen_gap:
+            last_seen_gap[n] = total_draws  # 한 번도 안 나옴 (이론상 거의 불가)
+
+    # ── 3. 공동 출현 행렬 ────────────────────────────────────────────────────
+    # cooccur[(i,j)] = 번호 i와 j가 같은 회차에 함께 출현한 횟수 (i < j)
+    cooccur: Dict[Tuple[int, int], int] = defaultdict(int)
+    for _, nums in draws:
+        s = sorted(nums)
+        for i in range(len(s)):
+            for j in range(i + 1, len(s)):
+                cooccur[(s[i], s[j])] += 1
+
+    # 번호 쌍 공동 출현 기댓값: C(43,4)/C(45,6) × total_draws
+    # C(43,4) = 123,410 / C(45,6) = 8,145,060
+    expected_cooccur = total_draws * 123410.0 / 8145060.0
+
+    # ── 4. 역대 조합 통계 (합계, 홀수 개수) ──────────────────────────────────
+    historical_sums = [sum(nums) for _, nums in draws]
+    mean_sum = sum(historical_sums) / total_draws
+    std_sum = math.sqrt(
+        sum((s - mean_sum) ** 2 for s in historical_sums) / total_draws
+    )
+    std_sum = max(std_sum, 1.0)  # 0 나누기 방지
+
+    historical_odds = [sum(1 for n in nums if n % 2 == 1) for _, nums in draws]
+    odd_dist = Counter(historical_odds)
+    odd_prob: Dict[int, float] = {k: v / total_draws for k, v in odd_dist.items()}
+    max_odd_prob = max(odd_prob.values()) if odd_prob else 1.0
+
+    # ── 5. 구간별 분포 통계 ───────────────────────────────────────────────────
+    # 각 구간에 몇 개 포함되는지의 역대 분포
+    zone_counts = [Counter() for _ in ZONE_RANGES]
+    for _, nums in draws:
+        for z_idx, (lo, hi) in enumerate(ZONE_RANGES):
+            cnt = sum(1 for n in nums if lo <= n <= hi)
+            zone_counts[z_idx][cnt] += 1
+
+    zone_probs: List[Dict[int, float]] = []
+    for zc in zone_counts:
+        total_z = sum(zc.values())
+        zone_probs.append({k: v / total_z for k, v in zc.items()})
+
+    max_zone_probs = [max(zp.values()) if zp else 1.0 for zp in zone_probs]
+
+    # ── 6. 최근 빈도 (후보 생성 가중치용) ────────────────────────────────────
+    recent_100 = draws[-100:] if len(draws) >= 100 else draws
+    freq_recent = Counter(n for _, nums in recent_100 for n in nums)
+
+    return {
+        "total_draws": total_draws,
+        "freq_all": freq_all,
+        "z_scores": z_scores,
+        "last_seen_gap": last_seen_gap,
+        "cooccur": dict(cooccur),
+        "expected_cooccur": expected_cooccur,
+        "mean_sum": mean_sum,
+        "std_sum": std_sum,
+        "odd_prob": odd_prob,
+        "max_odd_prob": max_odd_prob,
+        "zone_probs": zone_probs,
+        "max_zone_probs": max_zone_probs,
+        "freq_recent": freq_recent,
+    }
+
+
+def build_number_weights(cache: Dict[str, Any]) -> Dict[int, float]:
+    """
+    몬테카를로 시뮬레이션의 후보 생성에 사용할 번호별 샘플링 가중치.
+    빈도 + 최근 빈도 + 갭 분석을 반영하여 '좋은' 번호가 더 자주 선택되도록 유도.
+    """
+    freq_all = cache["freq_all"]
+    last_seen_gap = cache["last_seen_gap"]
+    freq_recent = cache["freq_recent"]
+
+    weights: Dict[int, float] = {}
+    for n in range(1, 46):
+        w = freq_all.get(n, 0) + 1.5 * freq_recent.get(n, 0)
+
+        gap = last_seen_gap.get(n, 0)
+        if gap <= 1:
+            gap_factor = 0.50   # 바로 직전 등장 → 패널티
+        elif gap <= 3:
+            gap_factor = 0.75
+        elif gap <= 12:
+            gap_factor = 1.00   # 적정 범위
+        elif gap <= 25:
+            gap_factor = 1.10   # 약간 오래된 번호 → 소폭 보너스
+        else:
+            gap_factor = 1.20   # 오래된 번호 → 보너스
+
+        weights[n] = max(w * gap_factor, 0.5)
+
+    return weights
+
+
+def score_combination(numbers: List[int], cache: Dict[str, Any]) -> Dict[str, float]:
+    """
+    6개 번호 조합의 통계적 품질 점수 계산 (0~1 범위 정규화).
+
+    5가지 기법별 점수:
+      - score_frequency  (25%): 빈도 Z-score
+      - score_fingerprint(30%): 조합의 통계적 지문 (합계, 홀짝, 구간)
+      - score_gap        (20%): 갭 분석
+      - score_cooccur    (15%): 공동 출현 기댓값 대비
+      - score_diversity  (10%): 연속번호, 범위, 구간 다양성
+
+    Returns:
+        {"score_total": ..., "score_frequency": ..., ...}
+    """
+    nums = sorted(numbers)
+
+    # ── 1. 빈도 점수 (Frequency Score) ────────────────────────────────────────
+    z_scores = cache["z_scores"]
+    avg_z = sum(z_scores.get(n, 0.0) for n in nums) / 6.0
+    # Sigmoid 정규화: avg_z > 0이면 0.5 이상
+    score_frequency = 1.0 / (1.0 + math.exp(-avg_z / 1.5))
+
+    # ── 2. 조합 지문 점수 (Fingerprint Score) ─────────────────────────────────
+    # 2a. 합계 정규분포 점수
+    total = sum(nums)
+    mean_sum = cache["mean_sum"]
+    std_sum = cache["std_sum"]
+    z_sum = (total - mean_sum) / std_sum
+    sum_score = math.exp(-0.5 * z_sum ** 2)  # 정규분포 밀도 (peak=1 at mean)
+
+    # 2b. 홀짝 비율 점수
+    odd_count = sum(1 for n in nums if n % 2 == 1)
+    odd_prob = cache["odd_prob"]
+    max_odd_prob = cache["max_odd_prob"]
+    odd_score = odd_prob.get(odd_count, 0.01) / max_odd_prob
+
+    # 2c. 구간 분포 점수
+    zone_probs = cache["zone_probs"]
+    max_zone_probs = cache["max_zone_probs"]
+    zone_score = 0.0
+    for z_idx, (lo, hi) in enumerate(ZONE_RANGES):
+        cnt = sum(1 for n in nums if lo <= n <= hi)
+        zp = zone_probs[z_idx]
+        mzp = max_zone_probs[z_idx]
+        zone_score += zp.get(cnt, 0.01) / mzp
+    zone_score /= len(ZONE_RANGES)
+
+    score_fingerprint = sum_score * 0.50 + odd_score * 0.30 + zone_score * 0.20
+
+    # ── 3. 갭 점수 (Gap Score) ────────────────────────────────────────────────
+    last_seen_gap = cache["last_seen_gap"]
+    gap_scores: List[float] = []
+    for n in nums:
+        gap = last_seen_gap.get(n, 0)
+        if gap <= 1:
+            gs = 0.20    # 직전 등장 번호 - 강한 패널티
+        elif gap <= 3:
+            gs = 0.55
+        elif gap <= 7:
+            gs = 0.85
+        elif gap <= 15:
+            gs = 1.00    # 최적 범위
+        elif gap <= 25:
+            gs = 0.90
+        else:
+            gs = 0.75    # 오래된 번호 - 여전히 양호
+        gap_scores.append(gs)
+    score_gap = sum(gap_scores) / 6.0
+
+    # ── 4. 공동 출현 점수 (Co-occurrence Score) ───────────────────────────────
+    cooccur = cache["cooccur"]
+    expected_cooccur = cache["expected_cooccur"]
+
+    pair_scores: List[float] = []
+    for i in range(len(nums)):
+        for j in range(i + 1, len(nums)):
+            actual = cooccur.get((nums[i], nums[j]), 0)
+            ratio = actual / max(expected_cooccur, 0.001)
+            # Sigmoid: ratio = 1에서 0.5, ratio > 1이면 > 0.5
+            ps = 1.0 / (1.0 + math.exp(-2.0 * (ratio - 1.0)))
+            pair_scores.append(ps)
+    score_cooccur = sum(pair_scores) / max(len(pair_scores), 1)
+
+    # ── 5. 다양성 점수 (Diversity Score) ─────────────────────────────────────
+    # 5a. 연속 번호 포함 여부 (역대 당첨번호 약 52%에 최소 1쌍 포함)
+    has_consecutive = any(nums[i + 1] - nums[i] == 1 for i in range(len(nums) - 1))
+    consecutive_score = 0.65 if has_consecutive else 0.40
+
+    # 5b. 범위 점수 (최소~최대 차이)
+    num_range = nums[-1] - nums[0]
+    if 28 <= num_range <= 43:
+        spread_score = 1.00
+    elif 20 <= num_range < 28:
+        spread_score = 0.85
+    elif 13 <= num_range < 20:
+        spread_score = 0.65
+    elif num_range < 13:
+        spread_score = 0.25
+    else:  # > 43 (최대 44: 1~45)
+        spread_score = 0.95
+
+    # 5c. 구간 커버리지 (몇 개 구간에 걸쳐 있는가)
+    zones_used = set(_get_zone(n) for n in nums)
+    zone_coverage = (len(zones_used) - 1) / 4.0  # 0~1
+
+    score_diversity = (
+        consecutive_score * 0.35
+        + spread_score * 0.35
+        + zone_coverage * 0.30
+    )
+
+    # ── 최종 가중 합산 ────────────────────────────────────────────────────────
+    score_total = (
+        score_frequency   * 0.25
+        + score_fingerprint * 0.30
+        + score_gap         * 0.20
+        + score_cooccur     * 0.15
+        + score_diversity   * 0.10
+    )
+
+    return {
+        "score_total":       round(score_total, 6),
+        "score_frequency":   round(score_frequency, 6),
+        "score_fingerprint": round(score_fingerprint, 6),
+        "score_gap":         round(score_gap, 6),
+        "score_cooccur":     round(score_cooccur, 6),
+        "score_diversity":   round(score_diversity, 6),
+    }
+
+
+def get_statistical_report(draws: List[Tuple[int, List[int]]]) -> Dict[str, Any]:
+    """
+    통계 분석 리포트 생성 (GET /api/lotto/analysis 응답용).
+    각 번호의 빈도, Z-score, 갭, 히트/콜드/오버듀 분류를 반환.
+    """
+    if not draws:
+        return {"error": "데이터 없음"}
+
+    cache = build_analysis_cache(draws)
+    total_draws = cache["total_draws"]
+    freq_all = cache["freq_all"]
+    z_scores = cache["z_scores"]
+    last_seen_gap = cache["last_seen_gap"]
+
+    number_stats = []
+    for n in range(1, 46):
+        freq = freq_all.get(n, 0)
+        expected = total_draws * 6.0 / 45.0
+        number_stats.append({
+            "number": n,
+            "frequency": freq,
+            "expected": round(expected, 1),
+            "frequency_pct": round(freq / (total_draws * 6) * 100, 2),
+            "z_score": round(z_scores.get(n, 0.0), 3),
+            "gap": last_seen_gap.get(n, total_draws),
+            "zone": _get_zone(n),
+        })
+
+    sorted_by_freq = sorted(number_stats, key=lambda x: -x["frequency"])
+    sorted_by_gap = sorted(number_stats, key=lambda x: -x["gap"])
+
+    # 역대 합계 분포 요약
+    hist_sums = [sum(nums) for _, nums in draws]
+    sum_buckets: Dict[str, int] = {}
+    for lo in range(21, 256, 20):
+        hi = lo + 19
+        key = f"{lo}-{hi}"
+        sum_buckets[key] = sum(1 for s in hist_sums if lo <= s <= hi)
+
+    return {
+        "total_draws": total_draws,
+        "mean_sum": round(cache["mean_sum"], 2),
+        "std_sum": round(cache["std_sum"], 2),
+        "odd_distribution": {
+            str(k): round(v * 100, 1)
+            for k, v in sorted(cache["odd_prob"].items())
+        },
+        "number_stats": number_stats,
+        "hot_numbers":     [x["number"] for x in sorted_by_freq[:10]],
+        "cold_numbers":    [x["number"] for x in sorted_by_freq[-10:]],
+        "overdue_numbers": [x["number"] for x in sorted_by_gap[:10]],
+        "sum_distribution": sum_buckets,
+    }
+
+
+def analyze_personal_patterns(
+    all_numbers: List[List[int]],
+    draws: List[Tuple[int, List[int]]],
+) -> Dict[str, Any]:
+    """
+    사용자 추천 이력 기반 개인 패턴 분석.
+    all_numbers: 저장된 모든 추천 번호 리스트 (각 원소는 6개짜리 리스트)
+    draws: 역대 당첨번호 (홀짝/합계 평균 비교용)
+    """
+    if not all_numbers:
+        return {"total_analyzed": 0, "message": "추천 이력이 없습니다"}
+
+    total = len(all_numbers)
+    flat = [n for nums in all_numbers for n in nums]
+    freq = Counter(flat)
+
+    # 번호별 선택 빈도
+    number_frequency = {n: freq.get(n, 0) for n in range(1, 46)}
+    top_picks = sorted(range(1, 46), key=lambda n: -freq.get(n, 0))[:10]
+    least_picks = [n for n in sorted(range(1, 46), key=lambda n: freq.get(n, 0)) if freq.get(n, 0) == 0][:10]
+
+    # 패턴 지표
+    odd_counts = [sum(1 for n in nums if n % 2 == 1) for nums in all_numbers]
+    sums = [sum(nums) for nums in all_numbers]
+    ranges = [max(nums) - min(nums) for nums in all_numbers]
+    consecutive_count = sum(
+        1 for nums in all_numbers
+        if any(sorted(nums)[i + 1] - sorted(nums)[i] == 1 for i in range(5))
+    )
+
+    zone_totals = {k: 0 for k in ["1-9", "10-19", "20-29", "30-39", "40-45"]}
+    zone_ranges = [("1-9", 1, 9), ("10-19", 10, 19), ("20-29", 20, 29), ("30-39", 30, 39), ("40-45", 40, 45)]
+    for nums in all_numbers:
+        for label, lo, hi in zone_ranges:
+            zone_totals[label] += sum(1 for n in nums if lo <= n <= hi)
+    zone_avg = {k: round(v / total, 2) for k, v in zone_totals.items()}
+
+    avg_odd = sum(odd_counts) / total
+    avg_sum = sum(sums) / total
+    avg_range = sum(ranges) / total
+
+    # 역대 당첨번호 평균과 비교
+    if draws:
+        draw_odd_avg = sum(sum(1 for n in nums if n % 2 == 1) for _, nums in draws) / len(draws)
+        draw_sum_avg = sum(sum(nums) for _, nums in draws) / len(draws)
+    else:
+        draw_odd_avg = 3.0
+        draw_sum_avg = 138.0
+
+    return {
+        "total_analyzed": total,
+        "number_frequency": number_frequency,
+        "top_picks": top_picks,
+        "least_picks": least_picks,
+        "pattern": {
+            "avg_odd_count": round(avg_odd, 2),
+            "avg_sum": round(avg_sum, 1),
+            "avg_range": round(avg_range, 1),
+            "consecutive_rate": round(consecutive_count / total, 3),
+            "zone_avg": zone_avg,
+        },
+        "vs_draw_avg": {
+            "odd_diff": round(avg_odd - draw_odd_avg, 2),
+            "sum_diff": round(avg_sum - draw_sum_avg, 1),
+            "odd_tendency": "홀수 선호" if avg_odd > draw_odd_avg + 0.2 else ("짝수 선호" if avg_odd < draw_odd_avg - 0.2 else "균형"),
+            "sum_tendency": "고합계 선호" if avg_sum > draw_sum_avg + 5 else ("저합계 선호" if avg_sum < draw_sum_avg - 5 else "균형"),
+        },
+    }
+
+
+def generate_combined_recommendation(draws: List[Tuple[int, List[int]]]) -> Dict[str, Any]:
+    """
+    5가지 통계 기법을 종합한 추론 번호 추천.
+
+    각 기법이 상위 6개 번호를 추천하고, 기법별 가중치(score_combination 가중치와 동일)로
+    투표를 집계한 뒤 최종 6개 번호를 선정한다.
+
+    가중치: 빈도Z(25%) · 지문(30%) · 갭(20%) · 공동출현(15%) · 다양성(10%)
+    """
+    if not draws:
+        return {"error": "데이터 없음"}
+
+    cache = build_analysis_cache(draws)
+    z = cache["z_scores"]
+    gap = cache["last_seen_gap"]
+    freq = cache["freq_all"]
+    cooccur = cache["cooccur"]
+    zone_probs = cache["zone_probs"]
+
+    # ── Method 1: 빈도 Z-score ────────────────────────────────────────────────
+    # Z-score 내림차순 상위 6 (출현 빈도가 기댓값보다 높은 번호)
+    m_frequency = sorted(range(1, 46), key=lambda n: -z.get(n, 0))[:6]
+
+    # ── Method 2: 갭 분석 ─────────────────────────────────────────────────────
+    # 가장 오래 미출현한 번호 6개 (오버듀)
+    m_gap = sorted(range(1, 46), key=lambda n: -gap.get(n, 0))[:6]
+
+    # ── Method 3: 공동출현 ────────────────────────────────────────────────────
+    # 각 번호의 총 공동출현 합산 점수 내림차순 6개
+    cooccur_total: Dict[int, float] = defaultdict(float)
+    for (a, b), cnt in cooccur.items():
+        cooccur_total[a] += cnt
+        cooccur_total[b] += cnt
+    m_cooccur = sorted(range(1, 46), key=lambda n: -cooccur_total.get(n, 0))[:6]
+
+    # ── Method 4: 조합 지문 ───────────────────────────────────────────────────
+    # 역대 당첨 조합의 구간별 최빈 분포에 맞게 각 구간에서 빈도 상위 번호 선택
+    zone_targets: List[int] = []
+    for zp in zone_probs:
+        zone_targets.append(max(zp, key=zp.get) if zp else 1)
+
+    # 합이 정확히 6이 되도록 조정
+    diff = sum(zone_targets) - 6
+    if diff > 0:
+        idxs = sorted(range(5), key=lambda i: -zone_targets[i])
+        for i in idxs:
+            if diff <= 0:
+                break
+            zone_targets[i] = max(0, zone_targets[i] - 1)
+            diff -= 1
+    elif diff < 0:
+        idxs = sorted(range(5), key=lambda i: zone_targets[i])
+        for i in idxs:
+            if diff >= 0:
+                break
+            zone_targets[i] += 1
+            diff += 1
+
+    m_fingerprint: List[int] = []
+    for (lo, hi), tgt in zip(ZONE_RANGES, zone_targets):
+        zone_nums = sorted(range(lo, hi + 1), key=lambda x: -freq.get(x, 0))
+        m_fingerprint.extend(zone_nums[:tgt])
+    m_fingerprint = sorted(m_fingerprint[:6])
+
+    # ── Method 5: 다양성 ──────────────────────────────────────────────────────
+    # 각 구간에서 갭 가장 큰 번호 1개씩 (5개) + 전체 갭 상위 1개 보충
+    m_diversity: List[int] = []
+    for lo, hi in ZONE_RANGES:
+        zone_nums = sorted(range(lo, hi + 1), key=lambda n: -gap.get(n, 0))
+        if zone_nums:
+            m_diversity.append(zone_nums[0])
+    if len(m_diversity) < 6:
+        rest = sorted(
+            [x for x in range(1, 46) if x not in m_diversity],
+            key=lambda n: -gap.get(n, 0),
+        )
+        m_diversity.extend(rest[: 6 - len(m_diversity)])
+    m_diversity = sorted(m_diversity[:6])
+
+    # ── 가중 투표 집계 ────────────────────────────────────────────────────────
+    # score_combination 가중치와 동일: 빈도25, 지문30, 갭20, 공동출현15, 다양성10
+    method_entries = [
+        (m_frequency,   25, "frequency",   "빈도 Z-score"),
+        (m_fingerprint, 30, "fingerprint", "조합 지문"),
+        (m_gap,         20, "gap",         "갭 분석"),
+        (m_cooccur,     15, "cooccur",     "공동 출현"),
+        (m_diversity,   10, "diversity",   "다양성"),
+    ]
+
+    vote_scores: Dict[int, float] = {n: 0.0 for n in range(1, 46)}
+    for method_nums, weight, _, _ in method_entries:
+        for rank, n in enumerate(method_nums):
+            # rank 0 = 1위: (6-0)×weight = 6w, rank 5 = 6위: (6-5)×weight = w
+            vote_scores[n] += (6 - rank) * weight
+
+    # 상위 6개 — 동점 시 Z-score 타이브레이크
+    final_numbers = sorted(
+        sorted(range(1, 46), key=lambda n: (-vote_scores[n], -z.get(n, 0)))[:6]
+    )
+
+    scores = score_combination(final_numbers, cache)
+
+    # 각 번호가 몇 개 방법에서 채택됐는지
+    vote_counts: Dict[str, int] = {
+        str(n): sum(1 for nums, _, _, _ in method_entries if n in nums)
+        for n in range(1, 46)
+    }
+
+    methods_result: Dict[str, Any] = {}
+    for nums, weight, key, label in method_entries:
+        methods_result[key] = {
+            "label": label,
+            "weight_pct": weight,
+            "numbers": sorted(nums),
+        }
+
+    return {
+        "methods": methods_result,
+        "final_numbers": final_numbers,
+        "scores": scores,
+        "vote_scores": {str(n): round(vote_scores[n], 1) for n in range(1, 46)},
+        "vote_counts": vote_counts,
+        "total_draws": cache["total_draws"],
+    }
+
+
+def generate_weekly_report(draws: List[Tuple[int, List[int]]], target_drw_no: int) -> Dict[str, Any]:
+    """
+    특정 회차 공략 리포트 생성.
+    target_drw_no: 공략 대상 회차 (아직 추첨 안 된 회차)
+    draws: target_drw_no 이전까지의 당첨번호 (오름차순)
+    """
+    if not draws:
+        return {"error": "데이터 없음"}
+
+    cache = build_analysis_cache(draws)
+    total_draws = cache["total_draws"]
+    freq_all = cache["freq_all"]
+    last_seen_gap = cache["last_seen_gap"]
+
+    recent_10 = draws[-10:] if len(draws) >= 10 else draws
+    recent_3 = draws[-3:] if len(draws) >= 3 else draws
+
+    # 과출현: 최근 10회에 2회 이상 출현 번호 (출현 많은 순)
+    r10_nums = [n for _, nums in recent_10 for n in nums]
+    r10_freq = Counter(r10_nums)
+    hot_numbers = [n for n, _ in sorted(r10_freq.items(), key=lambda x: -x[1]) if r10_freq[n] >= 2]
+
+    # 냉각: 역대 출현 빈도 낮은 번호
+    cold_numbers = sorted(range(1, 46), key=lambda n: freq_all.get(n, 0))[:10]
+
+    # 오버듀: 가장 오래 미출현 번호
+    overdue_numbers = sorted(range(1, 46), key=lambda n: -last_seen_gap.get(n, 0))[:10]
+
+    # 최근 3회 연속 출현 (2회 이상)
+    r3_nums = [n for _, nums in recent_3 for n in nums]
+    r3_freq = Counter(r3_nums)
+    triple_appear = sorted(n for n, cnt in r3_freq.items() if cnt >= 2)
+
+    recent_sums = [sum(nums) for _, nums in recent_10]
+    recent_odd = [sum(1 for n in nums if n % 2 == 1) for _, nums in recent_10]
+
+    # 갭 기반 가중치 (오래된 번호일수록 높음)
+    gap_w = {n: last_seen_gap.get(n, 0) for n in range(1, 46)}
+
+    def _pick(exclude=None, prefer=None, n=6):
+        ex = set(exclude or [])
+        chosen = []
+        # prefer에서 최대 3개 우선 선택
+        for p in (prefer or []):
+            if p not in ex and len(chosen) < 3:
+                chosen.append(p)
+        # 구간별 1개씩 (갭 우선)
+        for lo, hi in [(1, 9), (10, 19), (20, 29), (30, 39), (40, 45)]:
+            if len(chosen) >= n:
+                break
+            cands = [x for x in range(lo, hi + 1) if x not in ex and x not in chosen]
+            if cands:
+                chosen.append(max(cands, key=lambda x: gap_w.get(x, 0)))
+        # 부족하면 나머지에서 갭 순
+        rest = sorted(
+            [x for x in range(1, 46) if x not in ex and x not in chosen],
+            key=lambda x: -gap_w.get(x, 0),
+        )
+        while len(chosen) < n and rest:
+            chosen.append(rest.pop(0))
+        return sorted(chosen[:n])
+
+    set1 = _pick(exclude=hot_numbers[:5], prefer=overdue_numbers[:5])
+    set2 = _pick()
+    set3 = _pick(exclude=hot_numbers)
+
+    # 신뢰도 점수
+    data_vol = min(total_draws / 500, 1.0)
+    if len(recent_sums) > 1:
+        avg_s = sum(recent_sums) / len(recent_sums)
+        std_s = (sum((s - avg_s) ** 2 for s in recent_sums) / len(recent_sums)) ** 0.5
+        pattern = max(0.0, 1.0 - std_s / 60.0)
+    else:
+        pattern = 0.5
+    trend = max(0.0, 1.0 - len(hot_numbers) / max(len(r10_nums), 1))
+    confidence = round((data_vol * 0.4 + pattern * 0.35 + trend * 0.25) * 100)
+
+    return {
+        "target_drw_no": target_drw_no,
+        "based_on_draw": draws[-1][0],
+        "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
+        "hot_numbers": hot_numbers[:8],
+        "cold_numbers": cold_numbers,
+        "overdue_numbers": overdue_numbers,
+        "recent_pattern": {
+            "last3_numbers": sorted(set(r3_nums)),
+            "triple_appear": triple_appear,
+            "recent_sum_avg": round(sum(recent_sums) / len(recent_sums), 1) if recent_sums else 0,
+            "recent_odd_avg": round(sum(recent_odd) / len(recent_odd), 1) if recent_odd else 0,
+        },
+        "recommended_sets": [
+            {"strategy": "냉각번호 중심", "numbers": set1, "description": "오랫동안 미출현 번호 위주 + 과출현 제외"},
+            {"strategy": "균형형", "numbers": set2, "description": "구간 균형 + 갭 최적화"},
+            {"strategy": "과출현 피하기", "numbers": set3, "description": "최근 자주 나온 번호 완전 제외"},
+        ],
+        "confidence_score": confidence,
+        "confidence_factors": {
+            "data_volume": round(data_vol * 100),
+            "pattern_consistency": round(pattern * 100),
+            "recent_trend": round(trend * 100),
+        },
+    }

lotto/app/checker.py

@@ -0,0 +1,73 @@
+import json
+from .db import (
+    _conn, get_draw, update_recommendation_result
+)
+
+def _calc_rank(my_nums: list[int], win_nums: list[int], bonus: int) -> tuple[int, int, bool]:
+    """
+    (rank, correct_cnt, has_bonus) 반환
+    rank: 1~5 (1등~5등), 0 (낙첨)
+    """
+    matched = set(my_nums) & set(win_nums)
+    cnt = len(matched)
+    has_bonus = bonus in my_nums
+    
+    if cnt == 6:
+        return 1, cnt, has_bonus
+    if cnt == 5 and has_bonus:
+        return 2, cnt, has_bonus
+    if cnt == 5:
+        return 3, cnt, has_bonus
+    if cnt == 4:
+        return 4, cnt, has_bonus
+    if cnt == 3:
+        return 5, cnt, has_bonus
+        
+    return 0, cnt, has_bonus
+
+def check_results_for_draw(drw_no: int) -> int:
+    """
+    특정 회차(drw_no) 결과가 나왔을 때,
+    해당 회차를 타겟으로 했던(based_on_draw == drw_no - 1) 추천들을 채점한다.
+    반환값: 채점한 개수
+    """
+    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"]
+    
+    # based_on_draw가 (이번회차 - 1)인 것들 조회
+    # 즉, 1000회차 추첨 결과로는, 999회차 데이터를 바탕으로 1000회차를 예측한 것들을 채점
+    target_based_on = drw_no - 1
+    
+    with _conn() as conn:
+        rows = conn.execute(
+            """
+            SELECT id, numbers 
+            FROM recommendations 
+            WHERE based_on_draw = ? AND checked = 0
+            """,
+            (target_based_on,)
+        ).fetchall()
+        
+    count = 0
+    for r in rows:
+        my_nums = json.loads(r["numbers"])
+        rank, correct, has_bonus = _calc_rank(my_nums, win_nums, bonus)
+        
+        update_recommendation_result(r["id"], rank, correct, has_bonus)
+        count += 1
+
+    # ── 구매 이력 체크 연동 ──────────────────────────────────────
+    try:
+        from .purchase_manager import check_purchases_for_draw as _check_purchases
+        _check_purchases(drw_no)  # 내부에서 evolve_after_check → recalculate_weights 호출
+    except ImportError:
+        pass  # purchase_manager 미설치 시 무시 (하위호환)
+
+    return count

lotto/app/collector.py

@@ -0,0 +1,85 @@
+import requests
+from typing import Dict, Any
+
+from .db import get_draw, upsert_draw, upsert_many_draws, get_latest_draw, count_draws
+
+def _normalize_item(item: dict) -> dict:
+    # smok95 all.json / latest.json 구조
+    # - draw_no: int
+    # - numbers: [n1..n6]
+    # - bonus_no: int
+    # - date: "YYYY-MM-DD ..."
+    numbers = item["numbers"]
+    return {
+        "drw_no": int(item["draw_no"]),
+        "drw_date": (item.get("date") or "")[:10],
+        "n1": int(numbers[0]),
+        "n2": int(numbers[1]),
+        "n3": int(numbers[2]),
+        "n4": int(numbers[3]),
+        "n5": int(numbers[4]),
+        "n6": int(numbers[5]),
+        "bonus": int(item["bonus_no"]),
+    }
+
+def sync_all_from_json(all_url: str) -> Dict[str, Any]:
+    r = requests.get(all_url, timeout=60)
+    r.raise_for_status()
+    data = r.json()  # list[dict]
+
+    # 정규화
+    rows = [_normalize_item(item) for item in data]
+    
+    # Bulk Insert (성능 향상)
+    upsert_many_draws(rows)
+
+    return {"mode": "all_json", "url": all_url, "total": len(rows)}
+
+def sync_latest(latest_url: str) -> Dict[str, Any]:
+    r = requests.get(latest_url, timeout=30)
+    r.raise_for_status()
+    item = r.json()
+
+    row = _normalize_item(item)
+    before = get_draw(row["drw_no"])
+    upsert_draw(row)
+
+    return {"mode": "latest_json", "url": latest_url, "was_new": (before is None), "drawNo": row["drw_no"]}
+
+def sync_ensure_all(latest_url: str, all_url: str) -> Dict[str, Any]:
+    """
+    1회부터 최신 회차까지 빠짐없이 있는지 확인하고, 없으면 전체 동기화 수행.
+    반환값: {"synced": bool, "reason": str, ...}
+    """
+    # 1. 원격 최신 회차 확인
+    try:
+        r = requests.get(latest_url, timeout=10)
+        r.raise_for_status()
+        remote_item = r.json()
+        remote_no = int(remote_item["draw_no"])
+    except Exception as e:
+        # 외부 통신 실패 시, 그냥 로컬 데이터로 진행하도록 에러 억제 (혹은 에러 발생)
+        # 여기서는 통계 기능 작동이 우선이므로 로그만 남기고 pass하고 싶지만,
+        # 확실한 동기화를 위해 에러를 던지거나 False 리턴
+        return {"synced": False, "error": str(e)}
+
+    # 2. 로컬 상태 확인
+    local_latest_row = get_latest_draw()
+    local_no = local_latest_row["drw_no"] if local_latest_row else 0
+    local_cnt = count_draws()
+
+    # 3. 동기화 필요 여부 판단
+    # - 전체 개수가 최신 회차 번호보다 적으면 중간에 빈 것 (1회부터 시작한다고 가정)
+    # - 혹은 DB 최신 번호가 원격보다 낮으면 업데이트 필요
+    need_sync = (local_no < remote_no) or (local_cnt < local_no)
+    
+    if not need_sync:
+        return {"synced": True, "updated": False, "local_no": local_no}
+
+    # 4. 전체 동기화 실행
+    # (단순 latest sync로는 중간 구멍을 못 채우므로, 구멍이 있거나 차이가 크면 all_sync 수행)
+    # 만약 차이가 1회차 뿐이고 구멍이 없다면 sync_latest만 해도 되지만, 
+    # 로직 단순화를 위해 missing 감지 시 그냥 all_sync (Bulk Insert라 빠름)
+    res = sync_all_from_json(all_url)
+    return {"synced": True, "updated": True, "detail": res}
+

lotto/app/curator_helpers.py

@@ -0,0 +1,151 @@
+"""큐레이터용 후보 가공 — 여러 엔진 결과를 하나로 병합, 중복 제거, 피처 계산."""
+from typing import Dict, List, Any, Set
+from . import db
+from .recommender import recommend_numbers, recommend_with_heatmap
+from .analyzer import get_statistical_report
+from .strategy_evolver import generate_smart_recommendation
+
+
+LOW_HIGH_CUT = 22
+
+
+def compute_features(numbers: List[int], hot: Set[int], cold: Set[int]) -> Dict[str, Any]:
+    nums = sorted(numbers)
+    odd = sum(1 for n in nums if n % 2 == 1)
+    low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
+    buckets = [0, 0, 0, 0, 0]
+    for n in nums:
+        if n <= 10: buckets[0] += 1
+        elif n <= 20: buckets[1] += 1
+        elif n <= 30: buckets[2] += 1
+        elif n <= 40: buckets[3] += 1
+        else: buckets[4] += 1
+    consecutive = any(nums[i+1] - nums[i] == 1 for i in range(len(nums) - 1))
+    return {
+        "odd_count": odd,
+        "even_count": 6 - odd,
+        "low_count": low,
+        "high_count": 6 - low,
+        "range_distribution": buckets,
+        "has_consecutive": consecutive,
+        "hot_number_count": len(set(nums) & hot),
+        "cold_number_count": len(set(nums) & cold),
+        "sum": sum(nums),
+    }
+
+
+def _key(numbers: List[int]) -> str:
+    return ",".join(str(n) for n in sorted(numbers))
+
+
+def collect_candidates(n: int, hot: Set[int], cold: Set[int]) -> List[Dict[str, Any]]:
+    """우선순위: simulation best_picks → meta → heatmap → statistics. 중복 제거 후 최대 n세트."""
+    seen: Dict[str, Dict[str, Any]] = {}
+    order: List[str] = []
+
+    def _add(numbers: List[int], source: str) -> None:
+        if not numbers:
+            return
+        k = _key(numbers)
+        if k in seen:
+            return
+        seen[k] = {"numbers": sorted(numbers), "source": source}
+        order.append(k)
+
+    # 1. simulation best_picks
+    try:
+        for row in db.get_best_picks(limit=n):
+            _add(row.get("numbers") or [], "simulation")
+    except Exception:
+        pass
+
+    # draws는 한 번만 로드
+    draws = []
+    try:
+        draws = db.get_all_draw_numbers()
+    except Exception:
+        pass
+
+    # 2. meta-strategy (smart)
+    try:
+        meta = generate_smart_recommendation(sets=n)
+        for s in meta.get("sets", []):
+            _add(s.get("numbers") or [], "meta")
+    except Exception:
+        pass
+
+    # 3. heatmap (n번 호출, 중복 회피)
+    if draws:
+        try:
+            for _ in range(n * 2):
+                if len(order) >= n * 2:
+                    break
+                r = recommend_with_heatmap(draws, [])
+                _add(r.get("numbers") or [], "heatmap")
+        except Exception:
+            pass
+
+    # 4. statistics
+    if draws:
+        try:
+            for _ in range(n * 2):
+                if len(order) >= n * 2:
+                    break
+                r = recommend_numbers(draws)
+                _add(r.get("numbers") or [], "statistics")
+        except Exception:
+            pass
+
+    out = []
+    for k in order[:n]:
+        item = seen[k]
+        item["features"] = compute_features(item["numbers"], hot, cold)
+        out.append(item)
+    return out
+
+
+def build_context(hot_limit: int = 10, cold_limit: int = 10) -> Dict[str, Any]:
+    """주간 맥락 패키지 — get_statistical_report가 이미 hot/cold를 제공."""
+    hot: List[int] = []
+    cold: List[int] = []
+    last_summary = ""
+
+    try:
+        draws = db.get_all_draw_numbers()
+    except Exception:
+        draws = []
+
+    if draws:
+        try:
+            report = get_statistical_report(draws)
+            hot = list(report.get("hot_numbers", []))[:hot_limit]
+            cold = list(report.get("cold_numbers", []))[:cold_limit]
+        except Exception:
+            pass
+
+    try:
+        latest = db.get_latest_draw()
+    except Exception:
+        latest = None
+
+    if latest:
+        nums = [latest.get(f"n{i}") for i in range(1, 7)]
+        nums = [n for n in nums if n is not None]
+        if nums:
+            odd = sum(1 for n in nums if n % 2 == 1)
+            low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
+            last_summary = f"{latest.get('drw_no')}회: {', '.join(str(n) for n in nums)} (홀{odd}짝{6-odd}, 저{low}고{6-low})"
+
+    my_perf: List[Dict[str, Any]] = []
+    try:
+        from .purchase_manager import get_recent_performance
+        my_perf = get_recent_performance(limit=3)
+    except Exception:
+        my_perf = []
+
+    return {
+        "hot_numbers": hot,
+        "cold_numbers": cold,
+        "last_draw_summary": last_summary,
+        "my_recent_performance": my_perf,
+    }

lotto/app/generator.py

@@ -0,0 +1,135 @@
+"""
+시뮬레이션 엔진 - lotto-lab 고도화
+
+[몬테카를로 시뮬레이션 흐름]
+1. 역대 당첨번호 기반 통계 캐시 구성 (build_analysis_cache)
+2. 통계 가중치로 N개 후보 조합 생성 (weighted sampling)
+3. 5가지 기법으로 각 후보 스코어링 (score_combination)
+4. 상위 top_k개 선별하여 DB 저장 (simulation_candidates, best_picks 교체)
+
+[시뮬레이션 파라미터]
+- n_candidates: 1회 시뮬레이션당 생성 후보 수 (기본 20,000)
+- top_k: 선별 및 저장할 상위 개수 (기본 100)
+- best_n: best_picks에 올릴 최상위 개수 (기본 20)
+"""
+
+import random
+from typing import Dict, Any, List, Optional
+
+from .db import (
+    get_latest_draw,
+    get_all_draw_numbers,
+    save_simulation_run,
+    save_simulation_candidates_bulk,
+    replace_best_picks,
+)
+from .analyzer import build_analysis_cache, build_number_weights, score_combination
+from .utils import weighted_sample_6
+
+
+def run_simulation(
+    n_candidates: int = 20000,
+    top_k: int = 100,
+    best_n: int = 20,
+) -> Dict[str, Any]:
+    """
+    몬테카를로 시뮬레이션 실행 메인 함수.
+
+    Args:
+        n_candidates: 생성할 후보 조합 수 (기본 20,000)
+        top_k: DB에 저장할 상위 후보 수 (기본 100)
+        best_n: best_picks에 올릴 최상위 수 (기본 20)
+
+    Returns:
+        {run_id, total_generated, top_k_selected, avg_score, best_score, based_on_draw}
+        또는 {"error": ...}
+    """
+    draws = get_all_draw_numbers()
+    if not draws:
+        return {"error": "당첨번호 데이터가 없습니다. 먼저 동기화를 실행하세요."}
+
+    latest = get_latest_draw()
+    based_on_draw = latest["drw_no"] if latest else None
+
+    # ── 1. 통계 캐시 및 가중치 구성 (시뮬레이션 전체에서 재사용) ────────────
+    cache = build_analysis_cache(draws)
+    weights = build_number_weights(cache)
+
+    # ── 2. 후보 생성 및 스코어링 ──────────────────────────────────────────────
+    candidates: List[Dict[str, Any]] = []
+    seen_keys: set = set()
+    max_attempts = n_candidates * 3  # 중복 제거 여유분
+
+    attempts = 0
+    while len(candidates) < n_candidates and attempts < max_attempts:
+        attempts += 1
+        nums = weighted_sample_6(weights)
+        key = tuple(sorted(nums))
+        if key in seen_keys:
+            continue
+        seen_keys.add(key)
+
+        scores = score_combination(nums, cache)
+        candidates.append({
+            "numbers": sorted(nums),
+            **scores,
+        })
+
+    # ── 3. 점수 내림차순 정렬 및 상위 선별 ──────────────────────────────────
+    candidates.sort(key=lambda x: -x["score_total"])
+    top_candidates = candidates[:top_k]
+
+    # is_best 플래그 표시
+    best_keys = {tuple(c["numbers"]) for c in top_candidates[:best_n]}
+    for c in top_candidates:
+        c["is_best"] = tuple(c["numbers"]) in best_keys
+
+    avg_score = (
+        sum(c["score_total"] for c in top_candidates) / len(top_candidates)
+        if top_candidates else 0.0
+    )
+    best_score = top_candidates[0]["score_total"] if top_candidates else 0.0
+
+    # ── 4. DB 저장 ────────────────────────────────────────────────────────────
+    run_id = save_simulation_run(
+        strategy="monte_carlo",
+        total_generated=len(candidates),
+        top_k_selected=len(top_candidates),
+        avg_score=avg_score,
+        notes=f"based_on_draw={based_on_draw}, history={len(draws)}회",
+    )
+
+    # 상위 top_k개만 DB에 저장 (전체 20,000개는 메모리에서만 처리)
+    save_simulation_candidates_bulk(run_id, top_candidates, based_on_draw)
+
+    # best_picks 교체 (상위 best_n개)
+    best_picks_data = [
+        {
+            "numbers": c["numbers"],
+            "score_total": c["score_total"],
+            "rank_in_run": i + 1,
+        }
+        for i, c in enumerate(top_candidates[:best_n])
+    ]
+    replace_best_picks(best_picks_data, run_id, based_on_draw)
+
+    return {
+        "run_id": run_id,
+        "total_generated": len(candidates),
+        "top_k_selected": len(top_candidates),
+        "best_n_saved": len(best_picks_data),
+        "avg_score": round(avg_score, 6),
+        "best_score": round(best_score, 6),
+        "based_on_draw": based_on_draw,
+    }
+
+
+def generate_smart_recommendations(count: int = 10) -> int:
+    """
+    하위 호환성 유지용 래퍼.
+    내부적으로 run_simulation을 호출하며, 기존 /api/admin/auto_gen 등에서 계속 사용 가능.
+    """
+    result = run_simulation(n_candidates=5000, top_k=count, best_n=count)
+    if "error" in result:
+        return 0
+    return result.get("best_n_saved", 0)

lotto/app/main.py

@@ -0,0 +1,837 @@
+import os
+import time
+import logging
+from typing import Optional, List, Dict, Any, Tuple
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from apscheduler.schedulers.background import BackgroundScheduler
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(levelname)s %(message)s")
+logger = logging.getLogger("lotto-backend")
+
+from .db import (
+    init_db, get_draw, get_latest_draw, get_all_draw_numbers,
+    save_recommendation_dedup, list_recommendations_ex, delete_recommendation,
+    update_recommendation,
+    # 시뮬레이션 관련
+    get_best_picks, get_simulation_runs, get_simulation_candidates,
+    # 성과 통계
+    get_recommendation_performance,
+    # Phase 2: 구매 이력
+    add_purchase, get_purchases, update_purchase, delete_purchase, get_purchase_stats,
+    # Phase 2: 주간 리포트 캐시
+    save_weekly_report, get_weekly_report_list, get_weekly_report,
+    # Phase 2: 개인 패턴 분석
+    get_all_recommendation_numbers,
+    # Phase 3: 전략 관련
+    get_strategy_performance as db_get_strategy_performance,
+)
+from .recommender import recommend_numbers, recommend_with_heatmap
+from .collector import sync_latest, sync_ensure_all
+from .generator import run_simulation, generate_smart_recommendations
+from .checker import check_results_for_draw
+from .utils import calc_metrics, calc_recent_overlap
+from .analyzer import get_statistical_report, generate_weekly_report, analyze_personal_patterns, generate_combined_recommendation
+from .purchase_manager import check_purchases_for_draw
+from .strategy_evolver import (
+    get_weights_with_trend, recalculate_weights,
+    generate_smart_recommendation,
+)
+from .routers import curator as curator_router
+from .routers import briefing as briefing_router
+
+app = FastAPI()
+app.include_router(curator_router.router)
+app.include_router(briefing_router.router)
+scheduler = BackgroundScheduler(timezone=os.getenv("TZ", "Asia/Seoul"))
+
+ALL_URL = os.getenv("LOTTO_ALL_URL", "https://smok95.github.io/lotto/results/all.json")
+LATEST_URL = os.getenv("LOTTO_LATEST_URL", "https://smok95.github.io/lotto/results/latest.json")
+
+# ── 성과 통계 인메모리 캐시 ───────────────────────────────────────────────────
+# 채점 데이터는 하루 2번 스케줄러 실행 시에만 갱신되므로 인메모리 캐시로 충분
+_PERF_CACHE: Dict[str, Any] = {"data": None, "at": 0.0}
+_PERF_CACHE_TTL = 3600  # 1시간 (스케줄러 미실행 상황 대비 폴백)
+
+
+def _refresh_perf_cache() -> None:
+    _PERF_CACHE["data"] = get_recommendation_performance()
+    _PERF_CACHE["at"] = time.time()
+    logger.info("성과 통계 캐시 갱신")
+
+
+@app.on_event("startup")
+def on_startup():
+    init_db()
+
+    # 1. 로또 당첨번호 동기화 (매일 9시, 21시 10분)
+    #    동기화 후 새로운 회차가 있으면 채점(check)까지 수행
+    def _sync_and_check():
+        res = sync_latest(LATEST_URL)
+        if res["was_new"]:
+            check_results_for_draw(res["drawNo"])
+            _refresh_perf_cache()  # 새 채점 결과 반영 → 즉시 갱신
+
+    scheduler.add_job(_sync_and_check, "cron", hour="9,21", minute=10)
+
+    # 2. 몬테카를로 시뮬레이션 (하루 6회: 0, 4, 8, 12, 16, 20시)
+    #    20,000개 후보 생성 → 스코어링 → 상위 100개 저장 → best_picks 교체
+    def _run_simulation_job():
+        run_simulation(n_candidates=20000, top_k=100, best_n=20)
+
+    scheduler.add_job(_run_simulation_job, "cron", hour="0,4,8,12,16,20", minute=5)
+
+    # 3. 토요일 오전 9시 — 다음 회차 공략 리포트 자동 캐싱
+    def _save_weekly_report_job():
+        import json as _json
+        draws = get_all_draw_numbers()
+        latest = get_latest_draw()
+        if not draws or not latest:
+            return
+        target = latest["drw_no"] + 1
+        report = generate_weekly_report(draws, target)
+        save_weekly_report(target, _json.dumps(report, ensure_ascii=False))
+        logger.info(f"{target}회차 리포트 저장 완료")
+
+    scheduler.add_job(_save_weekly_report_job, "cron", day_of_week="sat", hour=9, minute=0)
+
+    scheduler.start()
+
+
+@app.get("/health")
+def health():
+    return {"ok": True}
+
+
+@app.get("/api/lotto/latest")
+def api_latest():
+    row = get_latest_draw()
+    if not row:
+        raise HTTPException(status_code=404, detail="No data yet")
+    return {
+        "drawNo": row["drw_no"],
+        "date": row["drw_date"],
+        "numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
+        "bonus": row["bonus"],
+        "metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
+    }
+
+
+@app.get("/api/lotto/{drw_no:int}")
+def api_draw(drw_no: int):
+    row = get_draw(drw_no)
+    if not row:
+        raise HTTPException(status_code=404, detail="Not found")
+    return {
+        "drwNo": row["drw_no"],
+        "date": row["drw_date"],
+        "numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
+        "bonus": row["bonus"],
+        "metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
+    }
+
+
+@app.post("/api/admin/sync_latest")
+def admin_sync_latest():
+    res = sync_latest(LATEST_URL)
+    if res["was_new"]:
+        check_results_for_draw(res["drawNo"])
+    return res
+
+
+@app.post("/api/admin/auto_gen")
+def admin_auto_gen(count: int = 10):
+    """기존 호환 유지: 소규모 시뮬레이션 수동 트리거"""
+    n = generate_smart_recommendations(count)
+    return {"generated": n}
+
+
+@app.post("/api/admin/simulate")
+def admin_simulate(n_candidates: int = 20000, top_k: int = 100, best_n: int = 20):
+    """
+    몬테카를로 시뮬레이션 수동 트리거.
+    백그라운드 스케줄과 동일한 동작을 즉시 실행.
+    """
+    result = run_simulation(
+        n_candidates=max(1000, min(n_candidates, 50000)),
+        top_k=max(10, min(top_k, 500)),
+        best_n=max(10, min(best_n, 50)),
+    )
+    if "error" in result:
+        raise HTTPException(status_code=500, detail=result["error"])
+    return result
+
+
+@app.get("/api/lotto/stats")
+def api_stats():
+    sync_ensure_all(LATEST_URL, ALL_URL)
+
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+
+    frequency = {n: 0 for n in range(1, 46)}
+    total_draws = len(draws)
+
+    for _, nums in draws:
+        for n in nums:
+            frequency[n] += 1
+
+    stats = [
+        {"number": n, "count": frequency[n]}
+        for n in range(1, 46)
+    ]
+
+    return {
+        "total_draws": total_draws,
+        "frequency": stats,
+    }
+
+
+# ── 추천 성과 통계 (Phase 1, 인메모리 캐시) ──────────────────────────────────
+@app.get("/api/lotto/stats/performance")
+def api_performance_stats():
+    """
+    채점된 추천 이력 기반 성과 통계 (캐시 반환).
+    캐시 갱신 시점: 새 회차 채점 직후 | TTL 1시간 만료 시
+    """
+    if _PERF_CACHE["data"] is None or time.time() - _PERF_CACHE["at"] > _PERF_CACHE_TTL:
+        _refresh_perf_cache()
+    return _PERF_CACHE["data"]
+
+
+# ── 회차 공략 리포트 (Phase 1) ────────────────────────────────────────────────
+@app.get("/api/lotto/report/latest")
+def api_report_latest():
+    """
+    다음 회차 공략 리포트 (최신 회차 기준으로 자동 계산).
+    - 과출현/냉각/오버듀 번호 분석
+    - 최근 3회 패턴
+    - 3가지 전략별 추천 번호
+    - AI 신뢰도 점수
+    """
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+    latest = get_latest_draw()
+    target = latest["drw_no"] + 1
+    return generate_weekly_report(draws, target)
+
+
+@app.get("/api/lotto/report/history")
+def api_report_history(limit: int = 10):
+    """저장된 주간 리포트 목록 (자동 저장된 것만)"""
+    return {"reports": get_weekly_report_list(limit=min(limit, 52))}
+
+
+@app.get("/api/lotto/report/{drw_no}")
+def api_report_by_draw(drw_no: int):
+    """
+    특정 회차 공략 리포트 (캐시 우선, 없으면 실시간 생성).
+    """
+    cached = get_weekly_report(drw_no)
+    if cached:
+        return {**cached, "cached": True}
+
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+    base_draws = [(no, nums) for no, nums in draws if no < drw_no]
+    if not base_draws:
+        raise HTTPException(status_code=400, detail=f"{drw_no}회차 이전 데이터가 없습니다")
+    return {**generate_weekly_report(base_draws, drw_no), "cached": False}
+
+
+# ── 개인 패턴 분석 (Phase 2) ─────────────────────────────────────────────────
+@app.get("/api/lotto/analysis/personal")
+def api_personal_analysis():
+    """
+    저장된 추천 이력 기반 개인 패턴 분석.
+    - 자주 선택한 번호 TOP 10 / 한 번도 선택 안 한 번호
+    - 홀짝 비율, 합계, 범위, 연속번호 포함률
+    - 구간별 분포, 역대 당첨번호 평균과 비교
+    """
+    all_numbers = get_all_recommendation_numbers()
+    draws = get_all_draw_numbers()
+    return analyze_personal_patterns(all_numbers, draws)
+
+
+# ── 구매 이력 API (Phase 2) ───────────────────────────────────────────────────
+
+class PurchaseCreate(BaseModel):
+    draw_no: int
+    amount: int
+    sets: int = 1
+    prize: int = 0
+    note: str = ""
+    numbers: List[List[int]] = []
+    is_real: bool = True
+    source_strategy: str = "manual"
+    source_detail: dict = {}
+
+
+class PurchaseUpdate(BaseModel):
+    draw_no: Optional[int] = None
+    amount: Optional[int] = None
+    sets: Optional[int] = None
+    prize: Optional[int] = None
+    note: Optional[str] = None
+    numbers: Optional[List[List[int]]] = None
+    is_real: Optional[bool] = None
+    source_strategy: Optional[str] = None
+
+
+@app.get("/api/lotto/purchase/stats")
+def api_purchase_stats():
+    """투자 수익률 통계 (총 투자금, 총 당첨금, 수익률 등)"""
+    return get_purchase_stats()
+
+
+@app.get("/api/lotto/purchase")
+def api_purchase_list(draw_no: Optional[int] = None, days: Optional[int] = None,
+                      is_real: Optional[bool] = None, strategy: Optional[str] = None):
+    """구매 이력 조회 (필터: draw_no, days, is_real, strategy)"""
+    return {"records": get_purchases(draw_no=draw_no, days=days, is_real=is_real, strategy=strategy)}
+
+
+@app.post("/api/lotto/purchase", status_code=201)
+def api_purchase_create(body: PurchaseCreate):
+    """구매 이력 추가 (실제/가상)"""
+    sets = body.sets if body.sets > 0 else max(len(body.numbers), 1)
+    amount = body.amount if body.amount > 0 else sets * 1000
+    return add_purchase(
+        draw_no=body.draw_no,
+        amount=amount,
+        sets=sets,
+        prize=body.prize,
+        note=body.note,
+        numbers=body.numbers,
+        is_real=body.is_real,
+        source_strategy=body.source_strategy,
+        source_detail=body.source_detail,
+    )
+
+
+@app.put("/api/lotto/purchase/{purchase_id}")
+def api_purchase_update(purchase_id: int, body: PurchaseUpdate):
+    """구매 이력 수정 (당첨금 업데이트 등)"""
+    updated = update_purchase(purchase_id, body.model_dump(exclude_none=True))
+    if updated is None:
+        raise HTTPException(status_code=404, detail="Purchase not found")
+    return updated
+
+
+@app.delete("/api/lotto/purchase/{purchase_id}")
+def api_purchase_delete(purchase_id: int):
+    """구매 이력 삭제"""
+    if not delete_purchase(purchase_id):
+        raise HTTPException(status_code=404, detail="Purchase not found")
+    return {"ok": True}
+
+
+# ── 전략 진화 API ──────────────────────────────────────────────────────────
+
+@app.get("/api/lotto/strategy/weights")
+def api_strategy_weights():
+    """현재 전략별 가중치 + 성과 요약 + trend"""
+    return get_weights_with_trend()
+
+
+@app.get("/api/lotto/strategy/performance")
+def api_strategy_performance(strategy: Optional[str] = None, days: Optional[int] = None):
+    """전략별 회차 성과 이력 (차트용)"""
+    rows = db_get_strategy_performance(strategy=strategy, days=days)
+    return {"records": rows}
+
+
+@app.post("/api/lotto/strategy/evolve")
+def api_strategy_evolve():
+    """수동 가중치 재계산 트리거"""
+    new_weights = recalculate_weights()
+    return {"ok": True, "weights": new_weights}
+
+
+# ── 스마트 추천 API ────────────────────────────────────────────────────────
+
+@app.get("/api/lotto/recommend/smart")
+def api_recommend_smart(sets: int = 5):
+    """전략 가중치 기반 메타 전략 추천"""
+    sets = max(1, min(sets, 10))
+    result = generate_smart_recommendation(sets=sets)
+    if "error" in result:
+        raise HTTPException(status_code=500, detail=result["error"])
+    return result
+
+
+# ── 통계 분석 리포트 ────────────────────────────────────────────────────────
+@app.get("/api/lotto/analysis")
+def api_analysis():
+    """
+    5가지 통계 기법 기반 분석 리포트.
+    - 번호별 빈도, Z-score, 갭
+    - 핫/콜드/오버듀 번호
+    - 역대 합계 분포, 홀짝 분포
+    """
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+    return get_statistical_report(draws)
+
+
+# ── 시뮬레이션 best_picks (메인 추천 엔드포인트) ────────────────────────────
+@app.get("/api/lotto/best")
+def api_best_picks(limit: int = 20):
+    """
+    시뮬레이션을 통해 선별된 최적 번호 조합 반환 (기본 20쌍).
+    하루 6회 시뮬레이션 후 자동 갱신됨.
+    각 조합에 점수 및 메트릭 포함.
+    """
+    limit = max(1, min(limit, 50))
+    picks = get_best_picks(limit=limit)
+    if not picks:
+        raise HTTPException(
+            status_code=404,
+            detail="시뮬레이션 결과가 없습니다. /api/admin/simulate로 먼저 실행하세요.",
+        )
+
+    draws = get_all_draw_numbers()
+
+    result = []
+    for p in picks:
+        nums = p["numbers"]
+        result.append({
+            "rank": p["rank_in_run"],
+            "numbers": nums,
+            "score_total": p["score_total"],
+            "based_on_draw": p["based_on_draw"],
+            "simulation_run_id": p["source_run_id"],
+            "created_at": p["created_at"],
+            "metrics": calc_metrics(nums),
+        })
+
+    latest = get_latest_draw()
+    return {
+        "based_on_draw": latest["drw_no"] if latest else None,
+        "count": len(result),
+        "items": result,
+    }
+
+
+# ── 시뮬레이션 전체 결과 조회 (상세 API) ────────────────────────────────────
+@app.get("/api/lotto/simulation")
+def api_simulation(run_id: Optional[int] = None, runs_limit: int = 5):
+    """
+    시뮬레이션 실행 기록 및 상위 후보 상세 조회.
+    run_id 미지정 시: 최근 runs_limit개 실행 기록 + 가장 최근 run의 후보 반환.
+    run_id 지정 시: 해당 run의 후보만 반환.
+    """
+    runs = get_simulation_runs(limit=runs_limit)
+    if not runs:
+        raise HTTPException(status_code=404, detail="시뮬레이션 기록이 없습니다.")
+
+    target_run_id = run_id if run_id is not None else runs[0]["id"]
+    candidates = get_simulation_candidates(target_run_id, limit=100)
+
+    # 후보에 메트릭 추가
+    enriched = []
+    for c in candidates:
+        enriched.append({
+            **c,
+            "metrics": calc_metrics(c["numbers"]),
+        })
+
+    return {
+        "runs": runs,
+        "selected_run_id": target_run_id,
+        "candidates_count": len(enriched),
+        "candidates": enriched,
+    }
+
+
+# ── 종합 추론 추천 ───────────────────────────────────────────────────────────
+
+@app.get("/api/lotto/recommend/combined")
+def api_recommend_combined():
+    """5가지 통계 기법 종합 추론 추천 — 결과를 이력에 저장한다."""
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data")
+
+    latest = get_latest_draw()
+    result = generate_combined_recommendation(draws)
+    if "error" in result:
+        raise HTTPException(status_code=500, detail=result["error"])
+
+    # 추천 이력 저장 (태그: 종합추론)
+    params = {"method": "combined"}
+    saved = save_recommendation_dedup(
+        latest["drw_no"] if latest else None,
+        result["final_numbers"],
+        params,
+    )
+    if saved["saved"]:
+        update_recommendation(saved["id"], tags=["종합추론"])
+
+    return {
+        **result,
+        "id": saved["id"],
+        "saved": saved["saved"],
+        "deduped": saved["deduped"],
+        "based_on_latest_draw": latest["drw_no"] if latest else None,
+    }
+
+
+@app.get("/api/lotto/recommend/combined/history")
+def api_combined_history(limit: int = 30):
+    """종합추론 추천 이력 조회."""
+    items = list_recommendations_ex(limit=limit, tag="종합추론", sort="id_desc")
+    return {"items": items, "total": len(items)}
+
+
+# ── 기존 수동 추천 API (하위 호환 유지) ─────────────────────────────────────
+@app.get("/api/lotto/recommend")
+def api_recommend(
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+    sum_min: Optional[int] = None,
+    sum_max: Optional[int] = None,
+    odd_min: Optional[int] = None,
+    odd_max: Optional[int] = None,
+    range_min: Optional[int] = None,
+    range_max: Optional[int] = None,
+    max_overlap_latest: Optional[int] = None,
+    max_try: int = 200,
+):
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+
+    latest = get_latest_draw()
+
+    params = {
+        "recent_window": recent_window,
+        "recent_weight": float(recent_weight),
+        "avoid_recent_k": avoid_recent_k,
+        "sum_min": sum_min,
+        "sum_max": sum_max,
+        "odd_min": odd_min,
+        "odd_max": odd_max,
+        "range_min": range_min,
+        "range_max": range_max,
+        "max_overlap_latest": max_overlap_latest,
+        "max_try": int(max_try),
+    }
+
+    def _accept(nums: List[int]) -> bool:
+        m = calc_metrics(nums)
+        if sum_min is not None and m["sum"] < sum_min:
+            return False
+        if sum_max is not None and m["sum"] > sum_max:
+            return False
+        if odd_min is not None and m["odd"] < odd_min:
+            return False
+        if odd_max is not None and m["odd"] > odd_max:
+            return False
+        if range_min is not None and m["range"] < range_min:
+            return False
+        if range_max is not None and m["range"] > range_max:
+            return False
+        if max_overlap_latest is not None:
+            ov = calc_recent_overlap(nums, draws, last_k=avoid_recent_k)
+            if ov["repeats"] > max_overlap_latest:
+                return False
+        return True
+
+    chosen = None
+    explain = None
+
+    tries = 0
+    while tries < max_try:
+        tries += 1
+        result = recommend_numbers(
+            draws,
+            recent_window=recent_window,
+            recent_weight=recent_weight,
+            avoid_recent_k=avoid_recent_k,
+        )
+        nums = result["numbers"]
+        if _accept(nums):
+            chosen = nums
+            explain = result["explain"]
+            break
+
+    if chosen is None:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Constraints too strict. No valid set found in max_try={max_try}.",
+        )
+
+    saved = save_recommendation_dedup(
+        latest["drw_no"] if latest else None,
+        chosen,
+        params,
+    )
+
+    metrics = calc_metrics(chosen)
+    overlap = calc_recent_overlap(chosen, draws, last_k=avoid_recent_k)
+
+    return {
+        "id": saved["id"],
+        "saved": saved["saved"],
+        "deduped": saved["deduped"],
+        "based_on_latest_draw": latest["drw_no"] if latest else None,
+        "numbers": chosen,
+        "explain": explain,
+        "params": params,
+        "metrics": metrics,
+        "recent_overlap": overlap,
+        "tries": tries,
+    }
+
+
+# ── 히트맵 기반 추천 (하위 호환 유지) ────────────────────────────────────────
+@app.get("/api/lotto/recommend/heatmap")
+def api_recommend_heatmap(
+    heatmap_window: int = 20,
+    heatmap_weight: float = 1.5,
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+    sum_min: Optional[int] = None,
+    sum_max: Optional[int] = None,
+    odd_min: Optional[int] = None,
+    odd_max: Optional[int] = None,
+    range_min: Optional[int] = None,
+    range_max: Optional[int] = None,
+    max_overlap_latest: Optional[int] = None,
+    max_try: int = 200,
+):
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+
+    past_recs = list_recommendations_ex(limit=100, sort="id_desc")
+    latest = get_latest_draw()
+
+    params = {
+        "heatmap_window": heatmap_window,
+        "heatmap_weight": float(heatmap_weight),
+        "recent_window": recent_window,
+        "recent_weight": float(recent_weight),
+        "avoid_recent_k": avoid_recent_k,
+        "sum_min": sum_min,
+        "sum_max": sum_max,
+        "odd_min": odd_min,
+        "odd_max": odd_max,
+        "range_min": range_min,
+        "range_max": range_max,
+        "max_overlap_latest": max_overlap_latest,
+        "max_try": int(max_try),
+    }
+
+    def _accept(nums: List[int]) -> bool:
+        m = calc_metrics(nums)
+        if sum_min is not None and m["sum"] < sum_min:
+            return False
+        if sum_max is not None and m["sum"] > sum_max:
+            return False
+        if odd_min is not None and m["odd"] < odd_min:
+            return False
+        if odd_max is not None and m["odd"] > odd_max:
+            return False
+        if range_min is not None and m["range"] < range_min:
+            return False
+        if range_max is not None and m["range"] > range_max:
+            return False
+        if max_overlap_latest is not None:
+            ov = calc_recent_overlap(nums, draws, last_k=avoid_recent_k)
+            if ov["repeats"] > max_overlap_latest:
+                return False
+        return True
+
+    chosen = None
+    explain = None
+
+    tries = 0
+    while tries < max_try:
+        tries += 1
+        result = recommend_with_heatmap(
+            draws,
+            past_recs,
+            heatmap_window=heatmap_window,
+            heatmap_weight=heatmap_weight,
+            recent_window=recent_window,
+            recent_weight=recent_weight,
+            avoid_recent_k=avoid_recent_k,
+        )
+        nums = result["numbers"]
+        if _accept(nums):
+            chosen = nums
+            explain = result["explain"]
+            break
+
+    if chosen is None:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Constraints too strict. No valid set found in max_try={max_try}.",
+        )
+
+    saved = save_recommendation_dedup(
+        latest["drw_no"] if latest else None,
+        chosen,
+        params,
+    )
+
+    metrics = calc_metrics(chosen)
+    overlap = calc_recent_overlap(chosen, draws, last_k=avoid_recent_k)
+
+    return {
+        "id": saved["id"],
+        "saved": saved["saved"],
+        "deduped": saved["deduped"],
+        "based_on_latest_draw": latest["drw_no"] if latest else None,
+        "numbers": chosen,
+        "explain": explain,
+        "params": params,
+        "metrics": metrics,
+        "recent_overlap": overlap,
+        "tries": tries,
+    }
+
+
+# ── 추천 이력 ────────────────────────────────────────────────────────────────
+@app.get("/api/history")
+def api_history(
+    limit: int = 30,
+    offset: int = 0,
+    favorite: Optional[bool] = None,
+    tag: Optional[str] = None,
+    q: Optional[str] = None,
+    sort: str = "id_desc",
+):
+    items = list_recommendations_ex(
+        limit=limit,
+        offset=offset,
+        favorite=favorite,
+        tag=tag,
+        q=q,
+        sort=sort,
+    )
+
+    draws = get_all_draw_numbers()
+
+    out = []
+    for it in items:
+        nums = it["numbers"]
+        out.append({
+            **it,
+            "metrics": calc_metrics(nums),
+            "recent_overlap": calc_recent_overlap(
+                nums, draws, last_k=int(it["params"].get("avoid_recent_k", 0) or 0)
+            ),
+        })
+
+    return {
+        "items": out,
+        "limit": limit,
+        "offset": offset,
+        "filters": {"favorite": favorite, "tag": tag, "q": q, "sort": sort},
+    }
+
+
+@app.delete("/api/history/{rec_id:int}")
+def api_history_delete(rec_id: int):
+    ok = delete_recommendation(rec_id)
+    if not ok:
+        raise HTTPException(status_code=404, detail="Not found")
+    return {"deleted": True, "id": rec_id}
+
+
+class HistoryUpdate(BaseModel):
+    favorite: Optional[bool] = None
+    note: Optional[str] = None
+    tags: Optional[List[str]] = None
+
+
+@app.patch("/api/history/{rec_id:int}")
+def api_history_patch(rec_id: int, body: HistoryUpdate):
+    ok = update_recommendation(rec_id, favorite=body.favorite, note=body.note, tags=body.tags)
+    if not ok:
+        raise HTTPException(status_code=404, detail="Not found or no changes")
+    return {"updated": True, "id": rec_id}
+
+
+# ── 배치 추천 (하위 호환 유지) ───────────────────────────────────────────────
+def _batch_unique(draws, count: int, recent_window: int, recent_weight: float, avoid_recent_k: int, max_try: int = 200):
+    items = []
+    seen = set()
+    tries = 0
+    while len(items) < count and tries < max_try:
+        tries += 1
+        r = recommend_numbers(draws, recent_window=recent_window, recent_weight=recent_weight, avoid_recent_k=avoid_recent_k)
+        key = tuple(sorted(r["numbers"]))
+        if key in seen:
+            continue
+        seen.add(key)
+        items.append(r)
+    return items
+
+
+@app.get("/api/lotto/recommend/batch")
+def api_recommend_batch(
+    count: int = 5,
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+):
+    count = max(1, min(count, 20))
+    draws = get_all_draw_numbers()
+    if not draws:
+        raise HTTPException(status_code=404, detail="No data yet")
+
+    latest = get_latest_draw()
+    params = {
+        "recent_window": recent_window,
+        "recent_weight": float(recent_weight),
+        "avoid_recent_k": avoid_recent_k,
+        "count": count,
+    }
+
+    items = _batch_unique(draws, count, recent_window, float(recent_weight), avoid_recent_k)
+
+    return {
+        "based_on_latest_draw": latest["drw_no"] if latest else None,
+        "count": count,
+        "items": [{
+            "numbers": it["numbers"],
+            "explain": it["explain"],
+            "metrics": calc_metrics(it["numbers"]),
+        } for it in items],
+        "params": params,
+    }
+
+
+class BatchSave(BaseModel):
+    items: List[List[int]]
+    params: dict
+
+
+@app.post("/api/lotto/recommend/batch")
+def api_recommend_batch_save(body: BatchSave):
+    latest = get_latest_draw()
+    based = latest["drw_no"] if latest else None
+
+    created, deduped = [], []
+    for nums in body.items:
+        saved = save_recommendation_dedup(based, nums, body.params)
+        (created if saved["saved"] else deduped).append(saved["id"])
+
+    return {"saved": True, "created_ids": created, "deduped_ids": deduped}
+
+
+@app.get("/api/version")
+def version():
+    return {"version": os.getenv("APP_VERSION", "dev")}
+
+

lotto/app/purchase_manager.py

@@ -0,0 +1,116 @@
+"""
+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": [],
+                "_results": [],
+            }
+        agg = strategy_agg[strat]
+        agg["_results"].extend(results)
+        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),
+        )
+
+        # EMA 피드백 루프: 전략 가중치 진화
+        try:
+            from .strategy_evolver import evolve_after_check
+            evolve_after_check(strat, drw_no, agg["_results"])
+        except Exception:
+            logger.debug(f"[purchase_manager] evolve_after_check 건너뜀: {strat}")
+
+    logger.info(f"[purchase_manager] {drw_no}회차 구매 {count}건 체크 완료")
+    return count
+
+
+def get_recent_performance(limit: int = 3) -> list:
+    """최근 N회차 내 구매 성과 요약. 없으면 빈 리스트."""
+    from . import db
+    purchases = db.get_purchases() or []
+    by_draw: dict = {}
+    for p in purchases:
+        d = p.get("draw_no")
+        if not d:
+            continue
+        results = p.get("results") or []
+        max_correct = max((int(r.get("correct") or 0) for r in results), default=0)
+        slot = by_draw.setdefault(d, {"draw_no": d, "purchased_sets": 0, "best_match": 0})
+        slot["purchased_sets"] += int(p.get("sets") or 1)
+        slot["best_match"] = max(slot["best_match"], max_correct)
+    return sorted(by_draw.values(), key=lambda x: -x["draw_no"])[:limit]

lotto/app/recommender.py

@@ -0,0 +1,139 @@
+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]]],
+    *,
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+    seed: int | None = None,
+) -> Dict[str, Any]:
+    """
+    가벼운 통계 기반 추천:
+    - 전체 빈도 + 최근(recent_window) 빈도에 가중치를 더한 가중 샘플링
+    - 최근 avoid_recent_k 회차에 나온 번호는 확률을 낮춤(완전 제외는 아님)
+    """
+    if seed is not None:
+        random.seed(seed)
+
+    # 전체 빈도
+    all_nums = [n for _, nums in draws for n in nums]
+    freq_all = Counter(all_nums)
+
+    # 최근 빈도
+    recent = draws[-recent_window:] if len(draws) >= recent_window else draws
+    recent_nums = [n for _, nums in recent for n in nums]
+    freq_recent = Counter(recent_nums)
+
+    # 최근 k회차 번호(패널티)
+    last_k = draws[-avoid_recent_k:] if len(draws) >= avoid_recent_k else draws
+    last_k_nums = set(n for _, nums in last_k for n in nums)
+
+    # 가중치 구성
+    weights = {}
+    for n in range(1, 46):
+        w = freq_all[n] + recent_weight * freq_recent[n]
+        if n in last_k_nums:
+            w *= 0.6  # 최근에 너무 방금 나온 건 살짝 덜 뽑히게
+        weights[n] = max(w, 0.1)
+
+    # 중복 없이 6개 뽑기(가중 샘플링)
+    chosen_sorted = sorted(weighted_sample_6(weights))
+
+    explain = {
+        "recent_window": recent_window,
+        "recent_weight": recent_weight,
+        "avoid_recent_k": avoid_recent_k,
+        "top_all": [n for n, _ in freq_all.most_common(10)],
+        "top_recent": [n for n, _ in freq_recent.most_common(10)],
+        "last_k_draws": [d for d, _ in last_k],
+    }
+
+    return {"numbers": chosen_sorted, "explain": explain}
+
+
+def recommend_with_heatmap(
+    draws: List[Tuple[int, List[int]]],
+    past_recommendations: List[Dict[str, Any]],
+    *,
+    heatmap_window: int = 10,
+    heatmap_weight: float = 1.5,
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+    seed: int | None = None,
+) -> Dict[str, Any]:
+    """
+    히트맵 기반 가중치 추천:
+    - 과거 추천 번호들의 적중률을 분석하여 잘 맞춘 번호에 가중치 부여
+    - 기존 통계 기반 추천과 결합
+    
+    Args:
+        draws: 실제 당첨 번호 리스트 [(회차, [번호들]), ...]
+        past_recommendations: 과거 추천 데이터 [{"numbers": [...], "correct_count": N, "based_on_draw": M}, ...]
+        heatmap_window: 히트맵 분석할 최근 추천 개수
+        heatmap_weight: 히트맵 가중치 (높을수록 과거 적중 번호 선호)
+    """
+    if seed is not None:
+        random.seed(seed)
+    
+    # 1. 기존 통계 기반 가중치 계산
+    all_nums = [n for _, nums in draws for n in nums]
+    freq_all = Counter(all_nums)
+    
+    recent = draws[-recent_window:] if len(draws) >= recent_window else draws
+    recent_nums = [n for _, nums in recent for n in nums]
+    freq_recent = Counter(recent_nums)
+    
+    last_k = draws[-avoid_recent_k:] if len(draws) >= avoid_recent_k else draws
+    last_k_nums = set(n for _, nums in last_k for n in nums)
+    
+    # 2. 히트맵 생성: 과거 추천에서 적중한 번호들의 빈도
+    heatmap = Counter()
+    recent_recs = past_recommendations[-heatmap_window:] if len(past_recommendations) >= heatmap_window else past_recommendations
+    
+    for rec in recent_recs:
+        if rec.get("correct_count", 0) > 0:  # 적중한 추천만
+            # 적중 개수에 비례해서 가중치 부여 (많이 맞춘 추천일수록 높은 가중)
+            weight = rec["correct_count"] ** 1.5  # 제곱으로 강조
+            for num in rec["numbers"]:
+                heatmap[num] += weight
+    
+    # 3. 최종 가중치 = 기존 통계 + 히트맵
+    weights = {}
+    for n in range(1, 46):
+        w = freq_all[n] + recent_weight * freq_recent[n]
+        
+        # 히트맵 가중치 추가
+        if n in heatmap:
+            w += heatmap_weight * heatmap[n]
+        
+        # 최근 출현 번호 패널티
+        if n in last_k_nums:
+            w *= 0.6
+        
+        weights[n] = max(w, 0.1)
+    
+    # 4. 가중 샘플링으로 6개 선택
+    chosen_sorted = sorted(weighted_sample_6(weights))
+    
+    # 5. 설명 데이터
+    explain = {
+        "recent_window": recent_window,
+        "recent_weight": recent_weight,
+        "avoid_recent_k": avoid_recent_k,
+        "heatmap_window": heatmap_window,
+        "heatmap_weight": heatmap_weight,
+        "top_all": [n for n, _ in freq_all.most_common(10)],
+        "top_recent": [n for n, _ in freq_recent.most_common(10)],
+        "top_heatmap": [n for n, _ in heatmap.most_common(10)],
+        "last_k_draws": [d for d, _ in last_k],
+        "analyzed_recommendations": len(recent_recs),
+    }
+    
+    return {"numbers": chosen_sorted, "explain": explain}
+

lotto/app/requirements.txt

@@ -0,0 +1,5 @@
+fastapi==0.115.6
+uvicorn[standard]==0.30.6
+requests==2.32.3
+beautifulsoup4==4.12.3
+APScheduler==3.10.4

lotto/app/routers/briefing.py

@@ -0,0 +1,53 @@
+"""브리핑 저장/조회 + 큐레이터 사용량 엔드포인트."""
+from typing import Any, Dict, List
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, Field
+from .. import db
+
+router = APIRouter(prefix="/api/lotto")
+
+
+class BriefingRequest(BaseModel):
+    draw_no: int
+    picks: List[Dict[str, Any]]
+    narrative: Dict[str, Any]
+    confidence: int = Field(ge=0, le=100)
+    model: str
+    tokens_input: int = 0
+    tokens_output: int = 0
+    cache_read: int = 0
+    cache_write: int = 0
+    latency_ms: int = 0
+    source: str = "auto"
+
+
+@router.post("/briefing", status_code=201)
+def save_briefing(body: BriefingRequest):
+    bid = db.save_briefing(body.model_dump())
+    return {"ok": True, "id": bid}
+
+
+@router.get("/briefing/latest")
+def latest():
+    b = db.get_latest_briefing()
+    if not b:
+        raise HTTPException(404, "no briefing yet")
+    return b
+
+
+@router.get("/briefing/{draw_no}")
+def get_one(draw_no: int):
+    b = db.get_briefing(draw_no)
+    if not b:
+        raise HTTPException(404, f"no briefing for draw {draw_no}")
+    return b
+
+
+@router.get("/briefing")
+def history(limit: int = 10):
+    return {"briefings": db.list_briefings(limit)}
+
+
+@router.get("/curator/usage")
+def usage(days: int = 30):
+    return db.get_curator_usage(days)

lotto/app/routers/curator.py

@@ -0,0 +1,24 @@
+"""큐레이터 입력 엔드포인트 — agent-office에서만 호출."""
+from fastapi import APIRouter
+from ..curator_helpers import collect_candidates, build_context
+from .. import db
+
+router = APIRouter(prefix="/api/lotto/curator")
+
+
+@router.get("/candidates")
+def candidates(n: int = 20):
+    ctx = build_context()
+    hot = set(ctx["hot_numbers"])
+    cold = set(ctx["cold_numbers"])
+    latest = db.get_latest_draw()
+    draw_no = (latest["drw_no"] + 1) if latest else 0
+    items = collect_candidates(n, hot, cold)
+    return {"draw_no": draw_no, "candidates": items}
+
+
+@router.get("/context")
+def context():
+    latest = db.get_latest_draw()
+    draw_no = (latest["drw_no"] + 1) if latest else 0
+    return {"draw_no": draw_no, **build_context()}

lotto/app/strategy_evolver.py

@@ -0,0 +1,277 @@
+"""
+전략 진화 엔진 — EMA + Softmax 기반 적응형 가중치 관리.
+"""
+import math
+import json
+import logging
+from typing import Dict, List, Any
+
+logger = logging.getLogger("lotto-backend")
+
+# ── Constants (importable without DB) ─────────────────────────────────────────
+ALPHA = 0.3           # EMA 감쇠율
+TEMPERATURE = 2.0     # Softmax 온도
+MIN_WEIGHT = 0.05     # 최소 가중치
+INITIAL_EMA = 0.15    # 콜드스타트 초기값
+MIN_DATA_DRAWS = 10   # 학습 최소 회차
+
+STRATEGIES = ["combined", "simulation", "heatmap", "manual", "custom"]
+RANK_BONUS = {5: 0.1, 4: 0.3, 3: 0.6, 2: 0.8, 1: 1.0}
+
+
+# ── Pure functions (no DB dependency) ─────────────────────────────────────────
+
+def calc_draw_score(results: List[Dict]) -> float:
+    """구매 결과 리스트 → 평균 성과 점수"""
+    if not results:
+        return 0.0
+    scores = []
+    for r in results:
+        s = r.get("correct", 0) / 6.0
+        s += RANK_BONUS.get(r.get("rank", 0), 0)
+        scores.append(s)
+    return sum(scores) / len(scores)
+
+
+def _softmax_weights(ema_scores: Dict[str, float]) -> Dict[str, float]:
+    """EMA 점수 → Softmax → 최소 가중치 보장 → 정규화"""
+    raw = {s: math.exp(ema / TEMPERATURE) for s, ema in ema_scores.items()}
+    total = sum(raw.values())
+    weights = {s: v / total for s, v in raw.items()}
+
+    clamped = {}
+    surplus = 0.0
+    unclamped = []
+    for s, w in weights.items():
+        if w < MIN_WEIGHT:
+            clamped[s] = MIN_WEIGHT
+            surplus += MIN_WEIGHT - w
+        else:
+            unclamped.append(s)
+            clamped[s] = w
+
+    if surplus > 0 and unclamped:
+        unclamped_total = sum(clamped[s] for s in unclamped)
+        for s in unclamped:
+            clamped[s] -= surplus * (clamped[s] / unclamped_total)
+
+    final_total = sum(clamped.values())
+    return {s: round(v / final_total, 4) for s, v in clamped.items()}
+
+
+# ── DB-dependent functions (use lazy imports) ─────────────────────────────────
+
+def _db():
+    """Lazy import to avoid circular/relative import issues in tests"""
+    from . import db as _db_mod
+    return _db_mod
+
+
+def _recommender():
+    from . import recommender as _rec_mod
+    return _rec_mod
+
+
+def _analyzer():
+    from . import analyzer as _ana_mod
+    return _ana_mod
+
+
+def update_ema_for_strategy(strategy: str, draw_score: float) -> float:
+    db = _db()
+    weights = db.get_strategy_weights()
+    current = next((w for w in weights if w["strategy"] == strategy), None)
+    old_ema = current["ema_score"] if current else INITIAL_EMA
+    new_ema = ALPHA * draw_score + (1 - ALPHA) * old_ema
+    return new_ema
+
+
+def recalculate_weights() -> Dict[str, float]:
+    db = _db()
+    weights_rows = db.get_strategy_weights()
+    ema_scores = {w["strategy"]: w["ema_score"] for w in weights_rows}
+
+    for s in STRATEGIES:
+        if s not in ema_scores:
+            ema_scores[s] = INITIAL_EMA
+
+    new_weights = _softmax_weights(ema_scores)
+
+    for s, w in new_weights.items():
+        row = next((r for r in weights_rows if r["strategy"] == s), None)
+        db.update_strategy_weight(
+            strategy=s,
+            weight=w,
+            ema_score=ema_scores[s],
+            total_sets=row["total_sets"] if row else 0,
+            total_hits_3plus=row["total_hits_3plus"] if row else 0,
+        )
+
+    logger.info(f"[strategy_evolver] 가중치 재계산: {new_weights}")
+    return new_weights
+
+
+def evolve_after_check(strategy: str, draw_no: int, results: List[Dict]) -> None:
+    db = _db()
+    draw_score = calc_draw_score(results)
+    new_ema = update_ema_for_strategy(strategy, draw_score)
+
+    weights_rows = db.get_strategy_weights()
+    current = next((w for w in weights_rows if w["strategy"] == strategy), None)
+    hits_3plus = sum(1 for r in results if r.get("correct", 0) >= 3)
+
+    db.update_strategy_weight(
+        strategy=strategy,
+        weight=current["weight"] if current else 0.2,
+        ema_score=new_ema,
+        total_sets=(current["total_sets"] if current else 0) + len(results),
+        total_hits_3plus=(current["total_hits_3plus"] if current else 0) + hits_3plus,
+    )
+
+    recalculate_weights()
+
+
+def get_weights_with_trend() -> Dict[str, Any]:
+    db = _db()
+    weights = db.get_strategy_weights()
+    perfs = db.get_strategy_performance()
+
+    strat_perfs = {}
+    for p in perfs:
+        s = p["strategy"]
+        if s not in strat_perfs:
+            strat_perfs[s] = []
+        strat_perfs[s].append(p)
+
+    result = []
+    for w in weights:
+        sp = strat_perfs.get(w["strategy"], [])
+        if len(sp) >= 5:
+            recent_avg = sum(p["avg_score"] for p in sp[-3:]) / 3
+            older_avg = sum(p["avg_score"] for p in sp[-5:-2]) / 3
+            delta = recent_avg - older_avg
+            trend = "up" if delta > 0.02 else ("down" if delta < -0.02 else "stable")
+        else:
+            trend = "stable"
+
+        result.append({
+            "strategy": w["strategy"],
+            "weight": w["weight"],
+            "ema_score": w["ema_score"],
+            "total_sets": w["total_sets"],
+            "hits_3plus": w["total_hits_3plus"],
+            "trend": trend,
+        })
+
+    all_draws = set()
+    for p in perfs:
+        all_draws.add(p["draw_no"])
+
+    return {
+        "weights": result,
+        "last_evolved": weights[0]["updated_at"] if weights else None,
+        "min_data_draws": MIN_DATA_DRAWS,
+        "current_data_draws": len(all_draws),
+        "status": "active" if len(all_draws) >= MIN_DATA_DRAWS else "learning",
+    }
+
+
+def generate_smart_recommendation(sets: int = 5) -> Dict[str, Any]:
+    db = _db()
+    rec = _recommender()
+    ana = _analyzer()
+
+    weights_data = db.get_strategy_weights()
+    weight_map = {w["strategy"]: w["weight"] for w in weights_data}
+    draws = db.get_all_draw_numbers()
+    if not draws:
+        return {"error": "No draw data"}
+
+    latest = db.get_latest_draw()
+    cache = ana.build_analysis_cache(draws)
+    past_recs = db.list_recommendations_ex(limit=100, sort="id_desc")
+
+    candidates = []
+    seen_keys = set()
+
+    def _add_candidate(nums: list, strategy: str, raw_score: float = None):
+        key = tuple(sorted(nums))
+        if key in seen_keys:
+            return
+        seen_keys.add(key)
+        if raw_score is None:
+            sc = ana.score_combination(nums, cache)
+            raw_score = sc["score_total"]
+        meta = raw_score * weight_map.get(strategy, 0.1)
+        candidates.append({
+            "numbers": sorted(nums),
+            "raw_score": round(raw_score, 4),
+            "strategy": strategy,
+            "meta_score": round(meta, 4),
+        })
+
+    # combined: 10세트
+    for _ in range(10):
+        try:
+            r = ana.generate_combined_recommendation(draws)
+            if "final_numbers" in r:
+                _add_candidate(r["final_numbers"], "combined")
+        except Exception:
+            pass
+
+    # simulation: best_picks 상위 10개
+    best = db.get_best_picks(limit=10)
+    for b in best:
+        nums = json.loads(b["numbers"]) if isinstance(b["numbers"], str) else b["numbers"]
+        _add_candidate(nums, "simulation", b.get("score_total"))
+
+    # heatmap: 10세트
+    for _ in range(10):
+        try:
+            r = rec.recommend_with_heatmap(draws, past_recs)
+            _add_candidate(r["numbers"], "heatmap")
+        except Exception:
+            pass
+
+    # manual: 10세트
+    for _ in range(10):
+        try:
+            r = rec.recommend_numbers(draws)
+            _add_candidate(r["numbers"], "manual")
+        except Exception:
+            pass
+
+    candidates.sort(key=lambda c: -c["meta_score"])
+    top = candidates[:sets]
+
+    result_sets = []
+    for c in top:
+        sc = ana.score_combination(c["numbers"], cache)
+        contributions = {}
+        for strat in STRATEGIES:
+            contributions[strat] = round(weight_map.get(strat, 0) * sc["score_total"], 4)
+        contrib_total = sum(contributions.values()) or 1
+        contributions = {s: round(v / contrib_total, 3) for s, v in contributions.items()}
+
+        result_sets.append({
+            "numbers": c["numbers"],
+            "meta_score": c["meta_score"],
+            "source_strategy": c["strategy"],
+            "contribution": contributions,
+            "individual_scores": {k: round(v, 4) for k, v in sc.items()},
+        })
+
+    perfs = db.get_strategy_performance()
+    data_draws = len(set(p["draw_no"] for p in perfs))
+    status = "active" if data_draws >= MIN_DATA_DRAWS else "learning"
+
+    return {
+        "sets": result_sets,
+        "strategy_weights_used": weight_map,
+        "learning_status": {
+            "draws_learned": data_draws,
+            "status": status,
+            "message": "" if status == "active" else f"{MIN_DATA_DRAWS}회차 이상 데이터 필요 (현재 {data_draws}회차)",
+        },
+        "based_on_latest_draw": latest["drw_no"] if latest else None,
+    }

lotto/app/utils.py

@@ -0,0 +1,80 @@
+import random
+from typing import List, Dict, Any, Tuple
+
+
+def weighted_sample_6(weights: Dict[int, float]) -> List[int]:
+    """
+    가중 확률 샘플링으로 중복 없이 6개 번호 추출.
+    weights: {1: w1, 2: w2, ..., 45: w45}
+    """
+    pool = list(range(1, 46))
+    chosen: List[int] = []
+    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
+    return chosen
+
+def calc_metrics(numbers: List[int]) -> Dict[str, Any]:
+    nums = sorted(numbers)
+    s = sum(nums)
+    odd = sum(1 for x in nums if x % 2 == 1)
+    even = len(nums) - odd
+    mn, mx = nums[0], nums[-1]
+    rng = mx - mn
+
+    # 1-10, 11-20, 21-30, 31-40, 41-45
+    buckets = {
+        "1-10": 0,
+        "11-20": 0,
+        "21-30": 0,
+        "31-40": 0,
+        "41-45": 0,
+    }
+    for x in nums:
+        if 1 <= x <= 10:
+            buckets["1-10"] += 1
+        elif 11 <= x <= 20:
+            buckets["11-20"] += 1
+        elif 21 <= x <= 30:
+            buckets["21-30"] += 1
+        elif 31 <= x <= 40:
+            buckets["31-40"] += 1
+        else:
+            buckets["41-45"] += 1
+
+    return {
+        "sum": s,
+        "odd": odd,
+        "even": even,
+        "min": mn,
+        "max": mx,
+        "range": rng,
+        "buckets": buckets,
+    }
+
+def calc_recent_overlap(numbers: List[int], draws: List[Tuple[int, List[int]]], last_k: int) -> Dict[str, Any]:
+    """
+    draws: [(drw_no, [n1..n6]), ...] 오름차순
+    last_k: 최근 k회 기준 중복
+    """
+    if last_k <= 0:
+        return {"last_k": 0, "repeats": 0, "repeated_numbers": []}
+
+    recent = draws[-last_k:] if len(draws) >= last_k else draws
+    recent_set = set()
+    for _, nums in recent:
+        recent_set.update(nums)
+
+    repeated = sorted(set(numbers) & recent_set)
+    return {
+        "last_k": len(recent),
+        "repeats": len(repeated),
+        "repeated_numbers": repeated,
+    }

lotto/tests/test_integration.py

@@ -0,0 +1,61 @@
+# backend/tests/test_integration.py
+"""checker.py → purchase_manager 연동 통합 테스트"""
+import sys, os
+import sqlite3
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
+
+import pytest
+from unittest.mock import patch
+
+
+def _make_mem_conn():
+    conn = sqlite3.connect(":memory:")
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def test_check_results_triggers_purchase_check():
+    """check_results_for_draw가 purchase 체크도 트리거하는지 검증"""
+    import db
+    import backend.app.purchase_manager as pm
+
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+
+        # 당첨번호 삽입
+        mem.execute(
+            "INSERT INTO draws (drw_no, drw_date, n1, n2, n3, n4, n5, n6, bonus) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)",
+            (1124, "2026-03-28", 1, 2, 3, 4, 5, 6, 7)
+        )
+        mem.execute(
+            "INSERT INTO draws (drw_no, drw_date, n1, n2, n3, n4, n5, n6, bonus) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)",
+            (1125, "2026-04-04", 10, 20, 30, 35, 40, 44, 15)
+        )
+        mem.commit()
+
+        # 1125회차 대상 구매 등록
+        db.add_purchase(
+            draw_no=1125, amount=1000, sets=1,
+            numbers=[[10, 20, 30, 1, 2, 3]],
+            is_real=True, source_strategy="combined",
+        )
+
+    # purchase_manager의 check_purchases_for_draw<61><77><EFBFBD> 직접 호출하여 연동 검증
+    with patch("db._conn", return_value=mem), \
+         patch("backend.app.purchase_manager.get_draw", side_effect=lambda drw: db.get_draw(drw)), \
+         patch("backend.app.purchase_manager.get_purchases", side_effect=lambda **kw: db.get_purchases(**kw)), \
+         patch("backend.app.purchase_manager.update_purchase_results", side_effect=lambda *a, **kw: db.update_purchase_results(*a, **kw)), \
+         patch("backend.app.purchase_manager.upsert_strategy_performance", side_effect=lambda **kw: db.upsert_strategy_performance(**kw)):
+        purchase_count = pm.check_purchases_for_draw(1125)
+
+    assert purchase_count == 1
+
+    # purchase가 체크되었는지 확인
+    with patch("db._conn", return_value=mem):
+        purchases = db.get_purchases(draw_no=1125)
+    assert purchases[0]["checked"] == 1
+    assert purchases[0]["results"][0]["correct"] == 3  # 10, 20, 30 맞음
+
+    mem.close()

lotto/tests/test_purchase_manager.py

@@ -0,0 +1,309 @@
+# backend/tests/test_purchase_manager.py
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
+# Also insert the backend root so that "backend.app" package is importable
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
+
+import sqlite3
+import pytest
+from unittest.mock import patch, MagicMock
+
+# ":memory:" 공유 커넥션 — 각 테스트에서 독립적으로 생성
+def _make_mem_conn():
+    conn = sqlite3.connect(":memory:")
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def test_purchase_history_has_new_columns():
+    """purchase_history 테이블에 신규 컬럼이 존재하는지 검증"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+
+    cols = {r["name"] for r in mem.execute("PRAGMA table_info(purchase_history)").fetchall()}
+    assert "numbers" in cols
+    assert "is_real" in cols
+    assert "source_strategy" in cols
+    assert "source_detail" in cols
+    assert "checked" in cols
+    assert "results" in cols
+    assert "total_prize" in cols
+    # 기존 컬럼도 유지
+    assert "draw_no" in cols
+    assert "amount" in cols
+    assert "sets" in cols
+    assert "prize" in cols
+    assert "note" in cols
+    mem.close()
+
+
+def test_strategy_performance_table_exists():
+    """strategy_performance 테이블이 생성되는지 검증"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+
+    cols = {r["name"] for r in mem.execute("PRAGMA table_info(strategy_performance)").fetchall()}
+    assert "strategy" in cols
+    assert "draw_no" in cols
+    assert "sets_count" in cols
+    assert "total_correct" in cols
+    assert "avg_score" in cols
+    mem.close()
+
+
+def test_strategy_weights_table_exists():
+    """strategy_weights 테이블이 생성되고 초기값이 있는지 검증"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+
+    rows = mem.execute("SELECT * FROM strategy_weights ORDER BY strategy").fetchall()
+    strategies = {r["strategy"] for r in rows}
+    assert strategies == {"combined", "simulation", "heatmap", "manual", "custom"}
+    # 가중치 합이 1.0
+    total_weight = sum(r["weight"] for r in rows)
+    assert abs(total_weight - 1.0) < 0.01
+    mem.close()
+
+
+def test_add_purchase_with_numbers():
+    """번호 포함 구매 등록"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        result = db.add_purchase(
+            draw_no=1150,
+            amount=5000,
+            sets=5,
+            numbers=[[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]],
+            is_real=False,
+            source_strategy="simulation",
+            source_detail={"run_id": 42},
+        )
+    assert result["draw_no"] == 1150
+    assert result["amount"] == 5000
+    assert result["is_real"] == 0
+    assert result["source_strategy"] == "simulation"
+    assert result["numbers"] == [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
+    assert result["source_detail"] == {"run_id": 42}
+    mem.close()
+
+
+def test_get_purchases_filter_is_real():
+    """is_real 필터 동작"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        db.add_purchase(draw_no=1150, amount=5000, sets=5, is_real=True)
+        db.add_purchase(draw_no=1150, amount=1000, sets=1, is_real=False)
+        real_only = db.get_purchases(is_real=True)
+        virtual_only = db.get_purchases(is_real=False)
+    assert len(real_only) == 1
+    assert real_only[0]["is_real"] == 1
+    assert len(virtual_only) == 1
+    assert virtual_only[0]["is_real"] == 0
+    mem.close()
+
+
+def test_get_purchase_stats_by_type():
+    """실제/가상 분리 통계"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        db.add_purchase(draw_no=1150, amount=5000, sets=5, is_real=True, source_strategy="manual")
+        db.add_purchase(draw_no=1150, amount=1000, sets=1, is_real=False, source_strategy="simulation")
+        stats = db.get_purchase_stats()
+    assert "total" in stats
+    assert "real" in stats
+    assert "virtual" in stats
+    assert "by_strategy" in stats
+    assert stats["total"]["sets"] == 6
+    assert stats["real"]["sets"] == 5
+    assert stats["virtual"]["sets"] == 1
+    assert "manual" in stats["by_strategy"]
+    assert "simulation" in stats["by_strategy"]
+    # 하위호환 필드
+    assert "total_records" in stats
+    assert stats["total_records"] == 2
+    mem.close()
+
+
+def test_upsert_strategy_performance():
+    """전략 성과 upsert"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        # 최초 insert
+        db.upsert_strategy_performance(
+            strategy="simulation",
+            draw_no=1150,
+            sets_count=10,
+            total_correct=30,
+            max_correct=5,
+            prize_total=5000,
+            avg_score=3.0,
+        )
+        rows = db.get_strategy_performance(strategy="simulation")
+        assert len(rows) == 1
+        assert rows[0]["sets_count"] == 10
+        assert rows[0]["avg_score"] == 3.0
+        # upsert (동일 strategy+draw_no)
+        db.upsert_strategy_performance(
+            strategy="simulation",
+            draw_no=1150,
+            sets_count=20,
+            total_correct=60,
+            max_correct=6,
+            prize_total=10000,
+            avg_score=4.5,
+        )
+        rows = db.get_strategy_performance(strategy="simulation")
+        assert len(rows) == 1  # 중복 없이 1개
+        assert rows[0]["sets_count"] == 20
+        assert rows[0]["avg_score"] == 4.5
+    mem.close()
+
+
+def test_update_strategy_weight():
+    """전략 가중치 업데이트"""
+    import db
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        # 초기값 확인
+        weights_before = db.get_strategy_weights()
+        combined_before = next(w for w in weights_before if w["strategy"] == "combined")
+        original_weight = combined_before["weight"]
+        # 업데이트
+        db.update_strategy_weight(
+            strategy="combined",
+            weight=0.5,
+            ema_score=0.75,
+            total_sets=100,
+            total_hits_3plus=20,
+        )
+        weights_after = db.get_strategy_weights()
+        combined_after = next(w for w in weights_after if w["strategy"] == "combined")
+        assert combined_after["weight"] == 0.5
+        assert combined_after["ema_score"] == 0.75
+        assert combined_after["total_sets"] == 100
+        assert combined_after["total_hits_3plus"] == 20
+    mem.close()
+
+
+# ── purchase_manager 테스트 ───────────────────────────────────────────────────
+
+def _import_purchase_manager_with_mem(mem_conn):
+    """purchase_manager를 메모리 DB에 연결된 상태로 임포트."""
+    import db
+    import importlib
+    # backend.app 패키지로 로드해 상대 임포트가 동작하게 함
+    import backend.app.purchase_manager as pm
+    return pm
+
+
+def test_check_purchases_for_draw():
+    """특정 회차 구매 건들의 결과 체크"""
+    import db
+    import backend.app.purchase_manager as pm
+
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        # 당첨번호 삽입: 1125회 [3,12,23,34,38,45] bonus=7
+        mem.execute(
+            """INSERT INTO draws (drw_no, drw_date, n1, n2, n3, n4, n5, n6, bonus)
+               VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+            (1125, "2024-12-01", 3, 12, 23, 34, 38, 45, 7),
+        )
+        mem.commit()
+
+        # 구매 등록: 1등 번호 세트 + 낙첨 세트
+        purchase = db.add_purchase(
+            draw_no=1125,
+            amount=2000,
+            sets=2,
+            numbers=[[3, 12, 23, 34, 38, 45], [1, 2, 3, 4, 5, 6]],
+            is_real=False,
+            source_strategy="simulation",
+        )
+
+    with patch("db._conn", return_value=mem), \
+         patch("backend.app.purchase_manager.get_draw", side_effect=lambda drw: db.get_draw(drw)), \
+         patch("backend.app.purchase_manager.get_purchases", side_effect=lambda **kw: db.get_purchases(**kw)), \
+         patch("backend.app.purchase_manager.update_purchase_results", side_effect=lambda *a, **kw: db.update_purchase_results(*a, **kw)), \
+         patch("backend.app.purchase_manager.upsert_strategy_performance", side_effect=lambda **kw: db.upsert_strategy_performance(**kw)):
+        count = pm.check_purchases_for_draw(1125)
+
+    assert count == 1
+
+    # 결과 확인
+    with patch("db._conn", return_value=mem):
+        checked = db.get_purchases(draw_no=1125, checked=True)
+    assert len(checked) == 1
+    results = checked[0]["results"]
+    assert results is not None
+    assert len(results) == 2
+    # 첫 번째 세트: 6개 일치 → 1등
+    assert results[0]["rank"] == 1
+    assert results[0]["correct"] == 6
+    # 두 번째 세트: 3 하나만 일치 → 낙첨(correct=1)
+    assert results[1]["rank"] == 0
+    assert results[1]["correct"] == 1
+
+    mem.close()
+
+
+def test_check_purchases_updates_strategy_performance():
+    """결과 체크 후 strategy_performance가 갱신되는지 검증"""
+    import db
+    import backend.app.purchase_manager as pm
+
+    mem = _make_mem_conn()
+    with patch("db._conn", return_value=mem):
+        db.init_db()
+        # 당첨번호 삽입: 1126회
+        mem.execute(
+            """INSERT INTO draws (drw_no, drw_date, n1, n2, n3, n4, n5, n6, bonus)
+               VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+            (1126, "2024-12-08", 1, 2, 3, 4, 5, 6, 7),
+        )
+        mem.commit()
+
+        db.add_purchase(
+            draw_no=1126,
+            amount=5000,
+            sets=5,
+            numbers=[[1, 2, 3, 4, 5, 6], [10, 20, 30, 40, 41, 42]],
+            is_real=False,
+            source_strategy="simulation",
+        )
+
+    with patch("db._conn", return_value=mem), \
+         patch("backend.app.purchase_manager.get_draw", side_effect=lambda drw: db.get_draw(drw)), \
+         patch("backend.app.purchase_manager.get_purchases", side_effect=lambda **kw: db.get_purchases(**kw)), \
+         patch("backend.app.purchase_manager.update_purchase_results", side_effect=lambda *a, **kw: db.update_purchase_results(*a, **kw)), \
+         patch("backend.app.purchase_manager.upsert_strategy_performance", side_effect=lambda **kw: db.upsert_strategy_performance(**kw)):
+        count = pm.check_purchases_for_draw(1126)
+
+    assert count == 1
+
+    with patch("db._conn", return_value=mem):
+        perf = db.get_strategy_performance(strategy="simulation")
+
+    assert len(perf) >= 1
+    entry = next((p for p in perf if p["draw_no"] == 1126), None)
+    assert entry is not None, "draw_no=1126 에 대한 strategy_performance 없음"
+    assert entry["strategy"] == "simulation"
+    assert entry["sets_count"] == 2  # 2개 세트
+
+    mem.close()

lotto/tests/test_strategy_evolver.py

@@ -0,0 +1,72 @@
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
+
+import math
+import pytest
+
+
+def test_calc_draw_score_basic():
+    """세트별 결과 → draw_score 계산"""
+    from strategy_evolver import calc_draw_score
+
+    results = [
+        {"correct": 3, "rank": 5},  # 3/6 + 0.1 = 0.6
+        {"correct": 1, "rank": 0},  # 1/6 + 0 = 0.167
+    ]
+    score = calc_draw_score(results)
+    expected = ((3/6 + 0.1) + (1/6)) / 2
+    assert abs(score - expected) < 0.01
+
+
+def test_calc_draw_score_empty():
+    """빈 결과 → 0"""
+    from strategy_evolver import calc_draw_score
+    assert calc_draw_score([]) == 0.0
+
+
+def test_recalculate_weights_softmax():
+    """EMA → Softmax 가중치 변환"""
+    from strategy_evolver import _softmax_weights
+
+    ema_scores = {
+        "combined": 0.30,
+        "simulation": 0.25,
+        "heatmap": 0.15,
+        "manual": 0.10,
+        "custom": 0.05,
+    }
+    weights = _softmax_weights(ema_scores)
+
+    assert abs(sum(weights.values()) - 1.0) < 0.001
+    assert weights["combined"] > weights["simulation"]
+    assert weights["simulation"] > weights["heatmap"]
+    assert all(w >= 0.049 for w in weights.values())
+
+
+def test_recalculate_weights_min_weight():
+    """한 전략의 EMA가 매우 낮아도 최소 5% 보장"""
+    from strategy_evolver import _softmax_weights
+
+    ema_scores = {
+        "combined": 0.50,
+        "simulation": 0.01,
+        "heatmap": 0.01,
+        "manual": 0.01,
+        "custom": 0.01,
+    }
+    weights = _softmax_weights(ema_scores)
+
+    assert weights["simulation"] >= 0.049
+    assert weights["custom"] >= 0.049
+    assert abs(sum(weights.values()) - 1.0) < 0.001
+
+
+def test_update_ema():
+    """EMA 갱신 공식 검증"""
+    from strategy_evolver import ALPHA
+
+    old_ema = 0.15
+    draw_score = 0.40
+    new_ema = ALPHA * draw_score + (1 - ALPHA) * old_ema
+    expected = 0.3 * 0.40 + 0.7 * 0.15  # = 0.225
+    assert abs(new_ema - expected) < 0.001