refactor: MCP 공통 함수/httpx 싱글톤, CORS 수정, ML 이벤트루프 블로킹 해소 (#4~#7)

backend/app/api/endpoints/mcp.py

@@ -1,12 +1,117 @@
+"""
+MCP 프록시 엔드포인트 + 공통 리스크 평가 유틸리티
+
+이슈 4 - request_mcp_risk_assessment():
+    auto/day 스케줄러 양쪽에서 재사용 가능한 공통 RISK_ASSESSMENT 함수.
+    호출부는 trade_plan / portfolio / signals 세 dict를 넘긴다.
+
+이슈 6 - httpx.AsyncClient 싱글톤:
+    요청마다 클라이언트를 생성/소멸하지 않는다.
+    lifespan에서 init_http_client() → 사용 → close_http_client() 순서로 호출.
+"""
 from fastapi import APIRouter, HTTPException
-from typing import Dict, Any
+from typing import Dict, Any, Optional, Tuple
 from pydantic import BaseModel
 import httpx
+import logging
+
 from app.core.config import settings
 
+logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
+# ─── httpx 싱글톤 클라이언트 ──────────────────────────────────────────
+_http_client: Optional[httpx.AsyncClient] = None
+
+
+async def init_http_client() -> None:
+    """lifespan 시작 시 한 번만 호출. keepalive 연결 풀 설정."""
+    global _http_client
+    _http_client = httpx.AsyncClient(
+        timeout=httpx.Timeout(connect=5.0, read=60.0, write=10.0, pool=5.0),
+        limits=httpx.Limits(max_keepalive_connections=5, max_connections=10),
+    )
+    logger.info("✅ MCP httpx.AsyncClient initialized")
+
+
+async def close_http_client() -> None:
+    """lifespan 종료 시 한 번만 호출."""
+    global _http_client
+    if _http_client is not None:
+        await _http_client.aclose()
+        _http_client = None
+        logger.info("🔒 MCP httpx.AsyncClient closed")
+
+
+def get_http_client() -> httpx.AsyncClient:
+    """공유 클라이언트 반환. 초기화 전 접근 시 RuntimeError."""
+    if _http_client is None:
+        raise RuntimeError(
+            "http_client not initialized — call init_http_client() in lifespan"
+        )
+    return _http_client
+
+
+# ─── 공통 리스크 평가 함수 ────────────────────────────────────────────
+async def request_mcp_risk_assessment(
+    trade_plan: Dict[str, Any],
+    portfolio: Dict[str, Any],
+    signals: Dict[str, Any],
+) -> Tuple[bool, str]:
+    """MCP RISK_ASSESSMENT 호출 — auto/day 스케줄러 공통 진입점.
+
+    Args:
+        trade_plan:  주문 계획 (action, strategy, stock_code, quantity, price 등)
+        portfolio:   현재 포트폴리오 요약 (보유 종목 목록, 총 평가금액 등)
+        signals:     진입 신호 정보 (strategy, rules, indicators 등)
+
+    Returns:
+        (approve: bool, reason: str)
+        - approve=True  → 주문 실행
+        - approve=False → 주문 스킵
+        - MCP 장애 / 타임아웃 → fail-open (approve=True, 매매 중단 방지)
+    """
+    try:
+        client = get_http_client()
+    except RuntimeError:
+        logger.warning("⚠️ http_client 미초기화 — fail-open")
+        return True, "client_not_initialized_fail_open"
+
+    try:
+        resp = await client.post(
+            f"{settings.MCP_SERVER_URL}/analyze",
+            json={
+                "analysis_type": "RISK_ASSESSMENT",
+                "data": {
+                    "trade_plan": trade_plan,
+                    "portfolio": portfolio,
+                    "signals": signals,
+                },
+            },
+            timeout=15.0,
+        )
+        if resp.status_code == 200:
+            result = resp.json()
+            # 응답: {"result": {"decision": "approve"|"deny", "reason": "..."}}
+            payload = result.get("result", result)
+            decision = str(payload.get("decision", "approve")).lower()
+            reason = str(payload.get("reason", ""))
+            if "deny" in decision or "reject" in decision:
+                return False, reason
+            return True, reason
+        else:
+            logger.warning(f"⚠️ MCP RISK_ASSESSMENT HTTP {resp.status_code} — fail-open")
+            return True, f"mcp_http_{resp.status_code}_fail_open"
+    except httpx.TimeoutException:
+        logger.warning("⚠️ MCP RISK_ASSESSMENT 타임아웃 — fail-open")
+        return True, "mcp_timeout_fail_open"
+    except Exception as e:
+        logger.warning(f"⚠️ MCP RISK_ASSESSMENT 오류: {e} — fail-open")
+        return True, f"mcp_error_fail_open: {e}"
+
+
+# ─── 모델 ──────────────────────────────────────────────────────────────
 class MCPToolRequest(BaseModel):
     tool_name: str
     parameters: Dict[str, Any]
@@ -17,13 +122,16 @@ class MCPAnalysisRequest(BaseModel):
     data: Dict[str, Any]
 
 
+# ─── 엔드포인트 ────────────────────────────────────────────────────────
 @router.get("/status")
 async def get_mcp_status():
     """MCP 서버 상태 확인"""
     try:
-        async with httpx.AsyncClient() as client:
+        client = get_http_client()
+    except RuntimeError:
+        return {"status": "disconnected", "message": "http_client not initialized"}
+    try:
         response = await client.get(f"{settings.MCP_SERVER_URL}/health", timeout=2.0)
-            
         if response.status_code == 200:
             health_data = response.json()
             return {
@@ -31,45 +139,36 @@ async def get_mcp_status():
                 "mcp_server_url": settings.MCP_SERVER_URL,
                 "claude_configured": health_data.get("claude_configured", False),
                 "ai_provider": health_data.get("ai_provider", "unknown"),
-                    "details": health_data
+                "details": health_data,
             }
-            else:
-                return {
-                    "status": "error",
-                    "message": "MCP server responded with error"
-                }
-    
+        return {"status": "error", "message": "MCP server responded with error"}
     except Exception as e:
-        return {
-            "status": "disconnected",
-            "message": str(e)
-        }
+        return {"status": "disconnected", "message": str(e)}
 
 
 @router.post("/tools/call")
 async def call_mcp_tool(request: MCPToolRequest):
     """MCP 도구 호출"""
     try:
-        async with httpx.AsyncClient() as client:
+        client = get_http_client()
+    except RuntimeError as e:
+        raise HTTPException(status_code=503, detail=str(e))
+    try:
         response = await client.post(
             f"{settings.MCP_SERVER_URL}/tools/call",
-                json={
-                    "tool_name": request.tool_name,
-                    "parameters": request.parameters
-                },
-                timeout=30.0
+            json={"tool_name": request.tool_name, "parameters": request.parameters},
+            timeout=30.0,
         )
-            
         if response.status_code == 200:
             return response.json()
-            else:
         raise HTTPException(
             status_code=response.status_code,
-                    detail=f"MCP tool call failed: {response.text}"
+            detail=f"MCP tool call failed: {response.text}",
         )
-    
     except httpx.TimeoutException:
         raise HTTPException(status_code=504, detail="MCP server timeout")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to call MCP tool: {str(e)}")
 
@@ -78,26 +177,25 @@ async def call_mcp_tool(request: MCPToolRequest):
 async def analyze_with_claude(request: MCPAnalysisRequest):
     """Claude AI를 통한 분석 요청"""
     try:
-        async with httpx.AsyncClient() as client:
+        client = get_http_client()
+    except RuntimeError as e:
+        raise HTTPException(status_code=503, detail=str(e))
+    try:
         response = await client.post(
             f"{settings.MCP_SERVER_URL}/analyze",
-                json={
-                    "analysis_type": request.analysis_type,
-                    "data": request.data
-                },
-                timeout=60.0
+            json={"analysis_type": request.analysis_type, "data": request.data},
+            timeout=60.0,
         )
-            
         if response.status_code == 200:
             return response.json()
-            else:
         raise HTTPException(
             status_code=response.status_code,
-                    detail=f"Claude analysis failed: {response.text}"
+            detail=f"Claude analysis failed: {response.text}",
         )
-    
     except httpx.TimeoutException:
         raise HTTPException(status_code=504, detail="Analysis timeout")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to analyze: {str(e)}")
 
@@ -106,16 +204,17 @@ async def analyze_with_claude(request: MCPAnalysisRequest):
 async def list_mcp_tools():
     """사용 가능한 MCP 도구 목록"""
     try:
-        async with httpx.AsyncClient() as client:
+        client = get_http_client()
+    except RuntimeError as e:
+        raise HTTPException(status_code=503, detail=str(e))
+    try:
         response = await client.get(f"{settings.MCP_SERVER_URL}/tools", timeout=10.0)
-            
         if response.status_code == 200:
             return response.json()
-            else:
         raise HTTPException(
-                    status_code=response.status_code,
-                    detail="Failed to fetch tools list"
+            status_code=response.status_code, detail="Failed to fetch tools list"
         )
-    
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to list tools: {str(e)}")

backend/app/main.py

@@ -76,100 +76,76 @@ async def batch_analysis_scheduler():
         raise
 
 
-async def _call_mcp_risk_assessment(rec: dict) -> tuple[bool, str]:
-    """MCP RISK_ASSESSMENT 호출.
-    반환: (approve: bool, reason: str)
-    MCP 서버 장애 / 타임아웃 시 기본 approve=True (fail-open)로 처리.
-    """
-    import httpx
-
-    # 추천 요약 데이터 구성
-    recommendations = rec.get("recommendations_by_rule", {})
-    summary = {
-        "recommendation_count": sum(len(v) for v in recommendations.values()),
-        "rules": list(recommendations.keys()),
-        "top_stocks": [
-            item.get("stock_code") or item.get("code", "")
-            for rule_items in recommendations.values()
-            for item in (rule_items[:3] if isinstance(rule_items, list) else [])
-        ][:10],
-    }
-
-    try:
-        async with httpx.AsyncClient() as client:
-            resp = await client.post(
-                f"{settings.MCP_SERVER_URL}/analyze",
-                json={
-                    "analysis_type": "RISK_ASSESSMENT",
-                    "data": summary,
-                },
-                timeout=15.0,
-            )
-        if resp.status_code == 200:
-            result = resp.json()
-            # MCP 서버 응답 형식: {"decision": "approve"|"deny", "reason": "..."}
-            # 또는 {"result": {"decision": ..., "reason": ...}}
-            payload = result.get("result", result)
-            decision = str(payload.get("decision", "approve")).lower()
-            reason = str(payload.get("reason", ""))
-            if "deny" in decision or "reject" in decision:
-                return False, reason
-            return True, reason
-        else:
-            logger.warning(f"⚠️ MCP RISK_ASSESSMENT HTTP {resp.status_code} — fail-open")
-            return True, "mcp_http_error_fail_open"
-    except httpx.TimeoutException:
-        logger.warning("⚠️ MCP RISK_ASSESSMENT 타임아웃 — fail-open")
-        return True, "mcp_timeout_fail_open"
-    except Exception as e:
-        logger.warning(f"⚠️ MCP RISK_ASSESSMENT 오류: {e} — fail-open")
-        return True, f"mcp_error_fail_open: {e}"
-
-
 async def auto_trade_scheduler():
     """1분 간격 자동매매 스케줄러.
     DB 캐시에서 추천 종목을 읽어 손절/익절 및 신규 매수를 실행한다.
+    주문 직전에 MCP RISK_ASSESSMENT를 호출해 승인된 경우에만 주문한다.
     실제 KIS API 분석은 batch_analysis_scheduler(10분)가 담당한다.
     """
-    # 지연 시작으로 앱 부팅 안정화
     await asyncio.sleep(2)
     from app.api.endpoints.recommendations import (
         get_rule_based_recommendations_from_db,
         execute_auto_trade_background,
     )
     from app.api.endpoints.portfolio import check_and_execute_exit_trades
+    # 공통 MCP 리스크 평가 함수 (이슈 4)
+    from app.api.endpoints.mcp import request_mcp_risk_assessment
 
     logger.info("⏱️ Auto-trade scheduler started (every 60s, DB mode)")
     try:
         while True:
             try:
-                # 장 운영 시간 체크
                 if not is_market_open():
-                    logger.info("💤 Market is closed. Skipping auto-trade cycle. Next check in 30 minutes.")
+                    logger.info("💤 Market is closed. Next check in 30 minutes.")
                     await asyncio.sleep(1800)
                     continue
 
                 async with AsyncSessionLocal() as session:
-                    # 1. 보유 종목 손절/익절 체크 (먼저 실행)
+                    # 1. 보유 종목 손절/익절 체크
                     logger.info("💰 Checking portfolio for stop-loss/take-profit...")
                     try:
                         await check_and_execute_exit_trades(session)
                     except Exception as e:
                         logger.error(f"Exit trade check error: {e}", exc_info=True)
 
-                    # 2. 자동매매: DB 캐시에서 추천 조회 (빠름 - API 호출 없음)
+                    # 2. 자동매매: DB 캐시에서 추천 조회
                     if settings.AUTO_TRADE_ENABLED:
                         logger.info("🔄 Checking DB recommendations for auto-trade...")
                         rec = await get_rule_based_recommendations_from_db(session)
 
                         if rec and rec.get("recommendations_by_rule"):
-                            # ── MCP RISK_ASSESSMENT 승인 체크 ──────────────────────
-                            approve, reason = await _call_mcp_risk_assessment(rec)
+                            # ── MCP RISK_ASSESSMENT (공통 함수, 이슈 4) ──────────
+                            recommendations = rec.get("recommendations_by_rule", {})
+                            trade_plan = {
+                                "action": "BUY",
+                                "strategy": "rule_based_batch",
+                                "recommendation_count": sum(
+                                    len(v) for v in recommendations.values()
+                                ),
+                                "rules": list(recommendations.keys()),
+                                "top_stocks": [
+                                    item.get("stock_code") or item.get("code", "")
+                                    for rule_items in recommendations.values()
+                                    for item in (
+                                        rule_items[:3]
+                                        if isinstance(rule_items, list)
+                                        else []
+                                    )
+                                ][:10],
+                            }
+                            portfolio: dict = {}  # 확장 시 실제 포트폴리오 요약 전달
+                            signals = {
+                                "source": "db_recommendations",
+                                "rules_triggered": list(recommendations.keys()),
+                            }
+                            approve, reason = await request_mcp_risk_assessment(
+                                trade_plan, portfolio, signals
+                            )
+                            # ─────────────────────────────────────────────────────
                             if not approve:
                                 logger.warning(
                                     f"🛑 MCP RISK_ASSESSMENT DENY → 주문 스킵. 사유: {reason}"
                                 )
-                                # deny 사유 로그 저장 (선택적)
                                 try:
                                     from app.services.db_worker import enqueue_db_write
                                     await enqueue_db_write({
@@ -180,10 +156,11 @@ async def auto_trade_scheduler():
                                     pass
                             else:
                                 if reason:
-                                    logger.info(f"✅ MCP RISK_ASSESSMENT APPROVE. 참고: {reason}")
-                                logger.info(f"📊 Found recommendations, executing auto-trade")
+                                    logger.info(
+                                        f"✅ MCP RISK_ASSESSMENT APPROVE. 참고: {reason}"
+                                    )
+                                logger.info("📊 Found recommendations, executing auto-trade")
                                 await execute_auto_trade_background(rec, session)
-                            # ────────────────────────────────────────────────────────
                         else:
                             logger.info("⏭️ Auto-trade skipped: no recommendations in DB")
                     else:
@@ -205,7 +182,7 @@ async def day_trade_scheduler():
     - EOD 강제 청산 (15:20~15:30)
     - 장 외 시간: 30분 슬립
     """
-    await asyncio.sleep(5)  # 부팅 안정화 대기
+    await asyncio.sleep(5)
     from app.api.endpoints.day_trading import run_day_trading_cycle
 
     logger.info("📈 Day-trade scheduler started (every 30s)")
@@ -238,7 +215,6 @@ async def day_trade_scheduler():
 async def lifespan(app: FastAPI):
     """애플리케이션 시작/종료 시 실행될 코드"""
     logger.info("Starting Auto Stock Trading System...")
-    kis_mode = settings.KIS_MODE.upper()
     logger.info(f"KIS API Mode: {'모의투자 (VIRTUAL)' if settings.is_virtual_trading else '실전투자 (REAL)'}")
     logger.info(f"KIS Base URL: {settings.KOREA_INVESTMENT_API_BASE_URL}")
     logger.info(f"KIS Account: {settings.KOREA_INVESTMENT_ACCOUNT_NUMBER}")
@@ -248,7 +224,6 @@ async def lifespan(app: FastAPI):
         logger.info(f"Connecting to database: {settings.DATABASE_URL.split('@')[1] if '@' in settings.DATABASE_URL else 'unknown'}")
         async with engine.begin() as conn:
             await conn.run_sync(Base.metadata.create_all)
-            # Ensure trade_history columns exist (self-heal without external migration)
             await conn.execute(text("""
             ALTER TABLE trade_history
             ADD COLUMN IF NOT EXISTS total_amount NUMERIC(15, 2),
@@ -264,9 +239,10 @@ async def lifespan(app: FastAPI):
             await conn.execute(text("""
             UPDATE trade_history
             SET total_amount = executed_price * executed_quantity
-            WHERE total_amount IS NULL AND executed_price IS NOT NULL AND executed_quantity IS NOT NULL;
+            WHERE total_amount IS NULL
+              AND executed_price IS NOT NULL
+              AND executed_quantity IS NOT NULL;
             """))
-            # stock_analysis에 ATR / ADX / ML 컬럼 추가 (CLAUDE.md 섹션 6.2)
             await conn.execute(text("""
             ALTER TABLE stock_analysis
             ADD COLUMN IF NOT EXISTS atr_14 NUMERIC(15, 2),
@@ -275,7 +251,6 @@ async def lifespan(app: FastAPI):
             ADD COLUMN IF NOT EXISTS ml_confidence NUMERIC(5, 4),
             ADD COLUMN IF NOT EXISTS ml_signal VARCHAR(10);
             """))
-            # UPSERT를 위한 unique constraint — init-db.sql에 없을 경우 자동 생성
             await conn.execute(text("""
             DO $$ BEGIN
                 IF NOT EXISTS (
@@ -289,14 +264,12 @@ async def lifespan(app: FastAPI):
                 END IF;
             END $$;
             """))
-            # active_trades에 분할 청산 컬럼 추가 (CLAUDE.md 섹션 7.3)
             await conn.execute(text("""
             ALTER TABLE active_trades
             ADD COLUMN IF NOT EXISTS remaining_pct NUMERIC(5, 2) DEFAULT 1.0,
             ADD COLUMN IF NOT EXISTS highest_since_entry NUMERIC(15, 2),
             ADD COLUMN IF NOT EXISTS strategy_name VARCHAR(100);
             """))
-            # Risk Management 테이블 생성
             await conn.execute(text("""
             CREATE TABLE IF NOT EXISTS trading_sessions (
                 id SERIAL PRIMARY KEY,
@@ -309,7 +282,6 @@ async def lifespan(app: FastAPI):
                 updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
             );
             """))
-            # 종목별 연속 손실 ban 영속화 테이블 (CLAUDE.md 섹션 7.4)
             await conn.execute(text("""
             CREATE TABLE IF NOT EXISTS stock_bans (
                 stock_code VARCHAR(20) PRIMARY KEY,
@@ -320,7 +292,6 @@ async def lifespan(app: FastAPI):
             """))
         logger.info("✅ Database tables created successfully (and columns verified)")
 
-        # 연속 손실 ban 상태 복원 (재시작 후에도 유지 — CLAUDE.md 섹션 7.4)
         try:
             from app.services.loss_tracker import loss_tracker
             async with AsyncSessionLocal() as session:
@@ -328,16 +299,14 @@ async def lifespan(app: FastAPI):
         except Exception as e:
             logger.warning(f"⚠️ loss_tracker 초기화 실패 (non-critical): {e}")
 
-        # 테스트 데이터 및 오래된 데이터 자동 정리
         try:
             from app.models.models import StockAnalysis, TradeHistory, Portfolio
             from sqlalchemy import delete
-            from datetime import datetime, date, timedelta
+            from datetime import datetime, timedelta
 
             async with AsyncSessionLocal() as session:
                 _kst = pytz.timezone("Asia/Seoul")
                 _today_kst = datetime.now(_kst).date()
-                # 오래된 stock_analysis 삭제 (7일 이전)
                 cutoff_date = _today_kst - timedelta(days=7)
                 result = await session.execute(
                     delete(StockAnalysis).where(StockAnalysis.analysis_date < cutoff_date)
@@ -345,18 +314,16 @@ async def lifespan(app: FastAPI):
                 if result.rowcount > 0:
                     logger.info(f"🗑️ Deleted {result.rowcount} old stock_analysis records")
 
-                # PENDING/FAILED 오래된 거래 삭제 (1일 이전)
                 cutoff_datetime = _today_kst - timedelta(days=1)
                 result = await session.execute(
                     delete(TradeHistory).where(
                         TradeHistory.status.in_(['PENDING', 'FAILED']),
-                        TradeHistory.created_at < cutoff_datetime
+                        TradeHistory.created_at < cutoff_datetime,
                     )
                 )
                 if result.rowcount > 0:
                     logger.info(f"🗑️ Deleted {result.rowcount} old PENDING/FAILED trades")
 
-                # 수량 0인 포트폴리오 삭제
                 result = await session.execute(
                     delete(Portfolio).where(Portfolio.quantity <= 0)
                 )
@@ -370,7 +337,6 @@ async def lifespan(app: FastAPI):
 
     except ConnectionRefusedError as e:
         logger.error(f"❌ Database connection refused: {e}")
-        logger.error("Please check if PostgreSQL server is running and accessible")
         raise
     except Exception as e:
         logger.error(f"❌ Failed to create database tables: {e}")
@@ -378,17 +344,18 @@ async def lifespan(app: FastAPI):
         logger.error(traceback.format_exc())
         raise
 
-    # 데이트레이딩 포지션 복구 (재시작 시 인메모리 유실 방지)
     try:
         from app.api.endpoints.day_trading import restore_positions_from_db
         await restore_positions_from_db()
     except Exception as e:
         logger.warning(f"⚠️ 데이트레이딩 포지션 복구 실패 (non-critical): {e}")
 
-    # 워커 시작 (스케줄러 및 API 요청 처리 전 큐 준비)
-    app.state.db_worker_task = asyncio.create_task(db_writer_worker())
+    # ── httpx 싱글톤 클라이언트 초기화 (이슈 6) ─────────────────────────
+    from app.api.endpoints.mcp import init_http_client, close_http_client
+    await init_http_client()
 
-    # 스케줄러 시작
+    # 워커 + 스케줄러 시작
+    app.state.db_worker_task = asyncio.create_task(db_writer_worker())
     app.state.auto_trade_task = asyncio.create_task(auto_trade_scheduler())
     app.state.batch_analysis_task = asyncio.create_task(batch_analysis_scheduler())
     app.state.day_trade_task = asyncio.create_task(day_trade_scheduler())
@@ -400,13 +367,13 @@ async def lifespan(app: FastAPI):
 
     yield
 
-    # 종료 시 정리 작업
+    # 태스크 정리
     for task_attr in (
         "auto_trade_task",
         "batch_analysis_task",
         "day_trade_task",
         "db_worker_task",
-        "kis_stream_task",   # ← 추가
+        "kis_stream_task",
     ):
         try:
             task = getattr(app.state, task_attr, None)
@@ -417,6 +384,12 @@ async def lifespan(app: FastAPI):
         except Exception as e:
             logger.warning(f"Failed to stop {task_attr} gracefully: {e}")
 
+    # ── httpx 클라이언트 종료 (이슈 6) ──────────────────────────────────
+    try:
+        await close_http_client()
+    except Exception as e:
+        logger.warning(f"Failed to close http_client: {e}")
+
     logger.info("Shutting down Auto Stock Trading System...")
     await engine.dispose()
 
@@ -429,8 +402,7 @@ app = FastAPI(
     debug=settings.DEBUG,
 )
 
-# CORS 설정 (반드시 라우터 등록 전에)
-# allow_origins=["*"] + allow_credentials=True 조합은 CORS 스펙 위반으로 브라우저 차단됨
+# CORS 설정
 _cors_origins = getattr(settings, "CORS_ORIGINS", None)
 if _cors_origins:
     _allowed_origins = [o.strip() for o in _cors_origins.split(",") if o.strip()]
@@ -449,33 +421,28 @@ app.add_middleware(
     allow_headers=["*"],
     expose_headers=["*"],
 )
-
 logger.info("CORS middleware configured")
 
-# 요청 로깅 미들웨어
+
 @app.middleware("http")
 async def log_requests(request: Request, call_next):
     start_time = time.time()
     logger.info(f"🔵 Request: {request.method} {request.url}")
-    
     response = await call_next(request)
-    
     process_time = time.time() - start_time
-    logger.info(f"🟢 Response: {request.method} {request.url} - Status: {response.status_code} - Time: {process_time:.3f}s")
-    
+    logger.info(
+        f"🟢 Response: {request.method} {request.url} "
+        f"- Status: {response.status_code} - Time: {process_time:.3f}s"
+    )
     return response
 
-# API 라우터 등록
+
 app.include_router(api_router, prefix="/api/v1")
 
 
 @app.get("/")
 async def root():
-    return {
-        "message": "Auto Stock Trading System API",
-        "version": "1.0.0",
-        "status": "running"
-    }
+    return {"message": "Auto Stock Trading System API", "version": "1.0.0", "status": "running"}
 
 
 @app.get("/health")
@@ -488,22 +455,11 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
     """WebSocket 연결 엔드포인트.
 
     클라이언트→서버 지원 프로토콜:
-      {"type": "ping"}
-        → {"type": "pong"}
-
-      {"type": "subscribe", "stock_codes": ["005930", ...]}
-        → {"type": "ack", "action": "subscribe",
-           "stock_codes": [...], "subscriptions": [현재 전체 목록]}
-
-      {"type": "unsubscribe", "stock_codes": ["005930", ...]}
-        → {"type": "ack", "action": "unsubscribe",
-           "stock_codes": [...], "subscriptions": [현재 전체 목록]}
-
-      {"type": "set_subscriptions", "stock_codes": ["005930", ...]}
-        → {"type": "ack", "action": "set_subscriptions",
-           "stock_codes": [...], "subscriptions": [현재 전체 목록]}
-
-      그 외 메시지 → echo 응답
+      {"type": "ping"}                                       → {"type": "pong"}
+      {"type": "subscribe", "stock_codes": [...]}           → ACK
+      {"type": "unsubscribe", "stock_codes": [...]}         → ACK
+      {"type": "set_subscriptions", "stock_codes": [...]}   → ACK
+      그 외 메시지                                           → echo
     """
     await websocket_manager.connect(websocket, client_id)
     try:
@@ -512,28 +468,23 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
             try:
                 msg = json.loads(data)
             except (json.JSONDecodeError, AttributeError):
-                await websocket_manager.send_personal_message(
-                    f"Echo: {data}", client_id
-                )
+                await websocket_manager.send_personal_message(f"Echo: {data}", client_id)
                 continue
 
             msg_type = msg.get("type", "")
 
-            # ── ping / pong ──────────────────────────────────────────────
             if msg_type == "ping":
                 await websocket_manager.send_personal_message(
                     json.dumps({"type": "pong"}), client_id
                 )
                 continue
 
-            # ── subscribe ───────────────────────────────────────────────
             if msg_type == "subscribe":
                 codes = [str(c) for c in msg.get("stock_codes", [])]
                 websocket_manager.subscribe(client_id, codes)
                 await websocket_manager.send_personal_message(
                     json.dumps({
-                        "type": "ack",
-                        "action": "subscribe",
+                        "type": "ack", "action": "subscribe",
                         "stock_codes": codes,
                         "subscriptions": websocket_manager.get_subscriptions(client_id),
                     }),
@@ -541,14 +492,12 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
                 )
                 continue
 
-            # ── unsubscribe ──────────────────────────────────────────────
             if msg_type == "unsubscribe":
                 codes = [str(c) for c in msg.get("stock_codes", [])]
                 websocket_manager.unsubscribe(client_id, codes)
                 await websocket_manager.send_personal_message(
                     json.dumps({
-                        "type": "ack",
-                        "action": "unsubscribe",
+                        "type": "ack", "action": "unsubscribe",
                         "stock_codes": codes,
                         "subscriptions": websocket_manager.get_subscriptions(client_id),
                     }),
@@ -556,14 +505,12 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
                 )
                 continue
 
-            # ── set_subscriptions ────────────────────────────────────────
             if msg_type == "set_subscriptions":
                 codes = [str(c) for c in msg.get("stock_codes", [])]
                 websocket_manager.set_subscriptions(client_id, codes)
                 await websocket_manager.send_personal_message(
                     json.dumps({
-                        "type": "ack",
-                        "action": "set_subscriptions",
+                        "type": "ack", "action": "set_subscriptions",
                         "stock_codes": codes,
                         "subscriptions": websocket_manager.get_subscriptions(client_id),
                     }),
@@ -571,10 +518,7 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
                 )
                 continue
 
-            # ── 기타 메시지 → echo ───────────────────────────────────────
-            await websocket_manager.send_personal_message(
-                f"Echo: {data}", client_id
-            )
+            await websocket_manager.send_personal_message(f"Echo: {data}", client_id)
 
     except WebSocketDisconnect:
         websocket_manager.disconnect(client_id)

backend/app/services/ml_ensemble.py

@@ -9,6 +9,7 @@ ML 앙상블 모델 서비스
 앙상블 스코어: 0~100 (높을수록 매수 신호 강함)
 """
 
+import asyncio
 import logging
 import os
 from pathlib import Path
@@ -275,7 +276,6 @@ class MLEnsemble:
             metrics["lgbm_accuracy"] = float(lgbm_acc)
             logger.info(f"LightGBM 검증 정확도: {lgbm_acc:.4f}")
 
-            # 피처 중요도
             importance = self.lgbm_model.feature_importances_
             top_features = sorted(
                 zip(FEATURE_COLUMNS, importance),
@@ -302,7 +302,6 @@ class MLEnsemble:
                 X_seq_train, y_train,
                 epochs=50, lr=0.001, batch_size=32,
             )
-            # 검증
             lstm_val_probs = self.lstm_model.predict_proba(X_seq_val)
             lstm_val_pred = np.argmax(lstm_val_probs, axis=1)
             lstm_acc = np.mean(lstm_val_pred == y_val)
@@ -342,7 +341,6 @@ class MLEnsemble:
             metrics["ensemble_accuracy"] = float(ensemble_acc)
             logger.info(f"앙상블 검증 정확도: {ensemble_acc:.4f}")
 
-        # 모델 저장
         self._save_models()
         self.is_trained = True
         self.training_metrics = metrics
@@ -355,7 +353,6 @@ class MLEnsemble:
         probs_list = []
         weights_list = []
 
-        # LightGBM
         if self.lgbm_model is not None:
             try:
                 lgbm_probs = self.lgbm_model.predict_proba(X_tabular)
@@ -364,7 +361,6 @@ class MLEnsemble:
             except Exception:
                 pass
 
-        # LSTM
         if self.lstm_model is not None and self.lstm_model.model is not None:
             try:
                 lstm_probs = self.lstm_model.predict_proba(X_seq)
@@ -373,7 +369,6 @@ class MLEnsemble:
             except Exception:
                 pass
 
-        # RF
         if self.rf_model is not None:
             try:
                 rf_probs = self.rf_model.predict_proba(X_tabular)
@@ -385,7 +380,6 @@ class MLEnsemble:
         if not probs_list:
             return None
 
-        # 가중 평균
         total_weight = sum(weights_list)
         weighted_probs = sum(
             p * (w / total_weight) for p, w in zip(probs_list, weights_list)
@@ -393,19 +387,15 @@ class MLEnsemble:
         return weighted_probs
 
     def predict(self, chart_data: list) -> Dict[str, Any]:
-        """단일 종목 예측.
+        """단일 종목 예측 (동기).
+
+        이벤트 루프 내 async 핸들러에서는 predict_async() 를 사용하세요.
 
         Args:
             chart_data: KIS API 차트 데이터 (최신순)
 
         Returns:
-            {
-                "ensemble_score": int (0~100),
-                "confidence": float (0~1),
-                "signal": str ("BUY"/"HOLD"/"SELL"),
-                "model_scores": {"lgbm": ..., "lstm": ..., "rf": ...},
-                "probabilities": {"up": ..., "hold": ..., "down": ...}
-            }
+            {ensemble_score, confidence, signal, model_scores, probabilities}
         """
         if not self.is_trained:
             return {"error": "모델이 학습되지 않았습니다", "ensemble_score": 50, "confidence": 0.0, "signal": "HOLD"}
@@ -414,25 +404,18 @@ class MLEnsemble:
         if tabular is None or sequence is None:
             return {"error": "피처 추출 실패 (데이터 부족)", "ensemble_score": 50, "confidence": 0.0, "signal": "HOLD"}
 
-        # 스케일링
         tabular_scaled = self.scaler.transform(tabular.reshape(1, -1))
         sequence_batch = sequence.reshape(1, 60, len(LSTM_SEQUENCE_FEATURES))
 
-        # 앙상블 예측
         probs = self._ensemble_predict_proba(tabular_scaled, sequence_batch)
         if probs is None:
             return {"error": "모든 모델 예측 실패", "ensemble_score": 50, "confidence": 0.0, "signal": "HOLD"}
 
         prob_down, prob_hold, prob_up = probs[0]
-
-        # 앙상블 스코어: UP 확률 기반 (0~100)
         ensemble_score = int(prob_up * 100)
-
-        # 신뢰도: 최고 확률과 나머지의 차이
         max_prob = max(prob_down, prob_hold, prob_up)
         confidence = float(max_prob)
 
-        # 신호 결정
         if prob_up > prob_down and prob_up > prob_hold and prob_up > 0.4:
             signal = "BUY"
         elif prob_down > prob_up and prob_down > prob_hold and prob_down > 0.4:
@@ -440,7 +423,6 @@ class MLEnsemble:
         else:
             signal = "HOLD"
 
-        # 개별 모델 스코어
         model_scores = {}
         if self.lgbm_model is not None:
             try:
@@ -473,6 +455,22 @@ class MLEnsemble:
             },
         }
 
+    # ─── 비동기 래퍼 (이슈 7: 이벤트 루프 블로킹 방지) ──────────────────
+    async def predict_async(self, chart_data: list) -> Dict[str, Any]:
+        """asyncio.to_thread로 CPU 추론을 워커 스레드에서 실행.
+
+        async 핸들러/스케줄러에서는 이 메서드를 사용하세요.
+        무거운 RandomForest 배치 학습에는 ProcessPoolExecutor도 고려 가능.
+        """
+        return await asyncio.to_thread(self.predict, chart_data)
+
+    async def train_async(
+        self, X_tabular: np.ndarray, X_seq: np.ndarray, y: np.ndarray
+    ) -> Dict[str, Any]:
+        """asyncio.to_thread로 CPU 학습을 워커 스레드에서 실행."""
+        return await asyncio.to_thread(self.train, X_tabular, X_seq, y)
+    # ─────────────────────────────────────────────────────────────────────
+
     def _save_models(self):
         """모델 파일 저장"""
         self._ensure_dir()
@@ -486,7 +484,6 @@ class MLEnsemble:
         if self.scaler is not None:
             joblib.dump(self.scaler, self.model_dir / "scaler.pkl")
 
-        # 메트릭 저장
         import json
         with open(self.model_dir / "metrics.json", "w") as f:
             json.dump(self.training_metrics, f, indent=2, ensure_ascii=False, default=str)
@@ -519,7 +516,6 @@ class MLEnsemble:
                 )
                 self.lstm_model.load(lstm_path)
 
-            # 메트릭 로드
             metrics_path = self.model_dir / "metrics.json"
             if metrics_path.exists():
                 import json

backend/app/services/tensorflow_predictor.py

@@ -1,3 +1,5 @@
+import asyncio
+import functools
 import logging
 from typing import List, Dict, Any, Optional
 
@@ -19,6 +21,9 @@ class TensorFlowPredictor:
     - If TensorFlow or model/scaler files are missing, raises TensorFlowNotAvailable
       so callers can handle gracefully (e.g., return 503 or a friendly message).
     - Fetches recent daily closes from KIS API.
+
+    이슈 7: model.predict()는 CPU-bound 동기 호출이므로 predict_stock() 내에서
+    asyncio.to_thread()로 워커 스레드에 위임해 이벤트 루프 블로킹을 방지한다.
     """
 
     def __init__(self, model_path: str = "models/lstm_model.h5", scaler_path: str = "models/scaler.pkl", time_steps: int = 60):
@@ -27,7 +32,6 @@ class TensorFlowPredictor:
         self.time_steps = time_steps
 
         try:
-            # Local import to avoid hard dependency when not installed
             import tensorflow as tf  # noqa: F401
             from tensorflow import keras  # noqa: F401
             import joblib  # noqa: F401
@@ -35,7 +39,6 @@ class TensorFlowPredictor:
             logger.warning(f"TensorFlow or joblib not available: {e}")
             raise TensorFlowNotAvailable("TensorFlow/joblib not installed in backend environment")
 
-        # Load resources
         try:
             from tensorflow import keras
             import joblib
@@ -47,20 +50,16 @@ class TensorFlowPredictor:
             raise TensorFlowNotAvailable(f"Model or scaler not found: {e}")
 
     async def fetch_kis_closes(self, stock_code: str, days: int = 60) -> np.ndarray:
-        """Fetch recent daily closes from KIS. Returns array oldest→latest.
-        Uses korea_investment_api.get_daily_chart(stock_code, "D").
-        """
+        """Fetch recent daily closes from KIS. Returns array oldest→latest."""
         try:
             resp = await korea_investment_api.get_daily_chart(stock_code, "D")
             output = resp.get("output") or []
             if not output:
                 raise RuntimeError("empty_chart")
-            # KIS usually returns newest first; reverse to chronological
             closes_desc = [float(item.get("stck_clpr", 0) or 0) for item in output]
             closes = list(reversed(closes_desc))
             arr = np.array(closes[-days:], dtype=float)
             if arr.size < days:
-                # If fewer than requested, still return what's available
                 logger.info(f"{stock_code}: only {arr.size} closes available (requested {days})")
             return arr
         except Exception as e:
@@ -68,26 +67,34 @@ class TensorFlowPredictor:
             raise
 
     async def predict_stock(self, stock_code: str) -> Dict[str, Any]:
-        """Return prediction dict with expected_return and simple signal."""
+        """Return prediction dict with expected_return and simple signal.
+
+        model.predict()를 asyncio.to_thread()로 워커 스레드에서 실행해
+        Keras/TF의 GIL-holding 연산이 이벤트 루프를 막지 않게 한다.
+        """
         try:
             prices = await self.fetch_kis_closes(stock_code, days=self.time_steps)
             if prices.size == 0:
                 raise RuntimeError("no_price_data")
 
-            # Normalize and shape for LSTM (1, time_steps, 1)
             scaled = self.scaler.transform(prices.reshape(-1, 1))
             if scaled.shape[0] < self.time_steps:
-                # Pad at the front if needed
                 pad = np.repeat(scaled[0:1], self.time_steps - scaled.shape[0], axis=0)
                 scaled = np.vstack([pad, scaled])
             X_input = scaled[-self.time_steps:].reshape(1, self.time_steps, 1)
 
-            # Predict
-            predicted_scaled = self.model.predict(X_input, verbose=0)
+            # ── 이슈 7: CPU-bound model.predict → 워커 스레드로 분리 ──────
+            predict_fn = functools.partial(self.model.predict, X_input, verbose=0)
+            predicted_scaled = await asyncio.to_thread(predict_fn)
+            # ─────────────────────────────────────────────────────────────
+
             predicted_price = float(self.scaler.inverse_transform(predicted_scaled)[0][0])
             current_price = float(prices[-1])
 
-            expected_return = ((predicted_price - current_price) / current_price * 100.0) if current_price > 0 else 0.0
+            expected_return = (
+                (predicted_price - current_price) / current_price * 100.0
+                if current_price > 0 else 0.0
+            )
             signal = "BUY" if predicted_price > current_price * 1.02 else "HOLD"
 
             return {
@@ -101,7 +108,6 @@ class TensorFlowPredictor:
             raise
         except Exception as e:
             logger.error(f"Prediction failed for {stock_code}: {e}")
-            # Degrade gracefully
             return {
                 "stock_code": stock_code,
                 "error": str(e),

mcp-server/config.py

@@ -45,5 +45,14 @@ class Settings:
     CHART_DAYS = int(os.getenv("CHART_DAYS", "100"))
     JSON_COMPACT = os.getenv("JSON_COMPACT", "false").lower() == "true"
 
+    # CORS (이슈 5)
+    # 서버-서버 통신용: 브라우저 credentials 불필요.
+    # Docker 환경: MCP_CORS_ORIGINS=http://backend:8000
+    # 개발 환경:   MCP_CORS_ORIGINS=http://localhost:8000,http://127.0.0.1:8000
+    MCP_CORS_ORIGINS = os.getenv(
+        "MCP_CORS_ORIGINS",
+        "http://localhost:8000,http://127.0.0.1:8000,http://backend:8000",
+    )
+
 
 settings = Settings()

mcp-server/main.py

@@ -29,13 +29,22 @@ app = FastAPI(
     version="1.0.0"
 )
 
-# CORS 설정
+# CORS 설정 (이슈 5)
+# 문제: allow_origins=["*"] + allow_credentials=True 조합은 CORS 스펙 위반 (브라우저 차단)
+# 해결: 환경변수 MCP_CORS_ORIGINS로 origin 제한 + allow_credentials=False (서버-서버 통신 불필요)
+_raw_cors = settings.MCP_CORS_ORIGINS
+_allowed_origins = (
+    [o.strip() for o in _raw_cors.split(",") if o.strip()]
+    if _raw_cors
+    else ["http://localhost:8000", "http://127.0.0.1:8000", "http://backend:8000"]
+)
+logger.info(f"MCP CORS allowed origins: {_allowed_origins}")
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
+    allow_origins=_allowed_origins,
+    allow_credentials=False,   # 서버-서버 통신; 브라우저 세션 쿠키 불필요
+    allow_methods=["GET", "POST"],
+    allow_headers=["Content-Type", "Authorization"],
 )
 
 
@@ -95,17 +104,13 @@ async def analyze(request: AnalysisRequest):
         data = request.data
 
         if analysis_type == "RULE_VALIDATION":
-            # 규칙 검증
             rule = data.get("rule")
             market_data = data.get("market_data")
-            
             if not rule or not market_data:
                 raise HTTPException(
                     status_code=400,
                     detail="rule and market_data are required"
                 )
-            
-            # 뉴스 정보 추가 수집
             stock_code = market_data.get("stock_code")
             news_data = []
             if stock_code:
@@ -115,80 +120,56 @@ async def analyze(request: AnalysisRequest):
                         news_data = news_result["news"]
                 except Exception as e:
                     logger.warning(f"Failed to fetch news for validation: {e}")
-
             result = await ai_client.validate_trading_rule(rule, market_data, news_data)
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         elif analysis_type == "MARKET_ANALYSIS":
-            # 시장 분석
             stock_code = data.get("stock_code")
             market_data = data.get("market_data")
             news = data.get("news", [])
-            
             if not stock_code or not market_data:
                 raise HTTPException(
                     status_code=400,
                     detail="stock_code and market_data are required"
                 )
-            
             result = await ai_client.analyze_market(stock_code, market_data, news)
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         elif analysis_type == "RISK_ASSESSMENT":
-            # 리스크 평가
-            portfolio = data.get("portfolio")
+            # 이슈 4: request_mcp_risk_assessment()가 trade_plan/portfolio/signals 전달
+            portfolio = data.get("portfolio") or {}
             trade_plan = data.get("trade_plan")
-            
-            if not portfolio or not trade_plan:
+            signals = data.get("signals") or {}
+            if not trade_plan:
                 raise HTTPException(
                     status_code=400,
-                    detail="portfolio and trade_plan are required"
+                    detail="trade_plan is required"
                 )
-            
             result = await ai_client.assess_risk(portfolio, trade_plan)
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         elif analysis_type == "FAIR_PRICE":
-            # 적정가 분석
             stock_code = data.get("stock_code")
             market_data = data.get("market_data")
             financial_data = data.get("financial_data")
-            
             if not stock_code or not market_data:
                 raise HTTPException(
                     status_code=400,
                     detail="stock_code and market_data are required"
                 )
-            
             result = await ai_client.calculate_fair_price(
                 stock_code, market_data, financial_data
             )
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         elif analysis_type == "RULE_OPTIMIZATION":
-            # 트레이딩 규칙 최적화/생성
             stock_code = data.get("stock_code")
             market_data = data.get("market_data")
-            
             if not stock_code or not market_data:
                 raise HTTPException(
                     status_code=400,
                     detail="stock_code and market_data are required"
                 )
-            
-            # 뉴스 정보 추가 수집
             news_data = []
             try:
                 news_result = await mcp_tools.get_news(stock_code, limit=settings.NEWS_LIMIT)
@@ -196,29 +177,19 @@ async def analyze(request: AnalysisRequest):
                     news_data = news_result["news"]
             except Exception as e:
                 logger.warning(f"Failed to fetch news for optimization: {e}")
-                
             result = await ai_client.optimize_trading_rules(stock_code, market_data, news_data)
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         elif analysis_type == "GENERATE_JOURNAL":
-            # 매매일지 생성
             trade_info = data.get("trade_info")
             market_data = data.get("market_data")
-            
             if not trade_info or not market_data:
                 raise HTTPException(
                     status_code=400,
                     detail="trade_info and market_data are required"
                 )
-                
             result = await ai_client.generate_trade_journal(trade_info, market_data)
-            return {
-                "analysis_type": analysis_type,
-                "result": result
-            }
+            return {"analysis_type": analysis_type, "result": result}
 
         else:
             raise HTTPException(