MCP 수정중

backend/app/api/endpoints/mcp.py

@@ -72,6 +72,9 @@ async def request_mcp_risk_assessment(
         - approve=False → 주문 스킵
         - MCP 장애 / 타임아웃 → fail-open (approve=True, 매매 중단 방지)
     """
+    if not settings.MCP_VALIDATION_ENABLED:
+        return True, "mcp_disabled"
+
     try:
         client = get_http_client()
     except RuntimeError:

backend/app/api/endpoints/recommendations.py

@@ -3,7 +3,6 @@ from typing import List, Optional
 from datetime import datetime, date, timedelta
 import asyncio
 import logging
-import httpx
 from collections import namedtuple
 
 from app.core.database import get_db, AsyncSessionLocal
@@ -662,72 +661,49 @@ async def execute_auto_trading(db: AsyncSession = Depends(get_db)):
 
 @router.get("/ai/{stock_code}")
 async def get_ai_recommendation(stock_code: str):
-    from app.services.korea_investment_api import korea_investment_api
+    """종목 기술적 분석 — AI 미사용, 전략 엔진(RSI/BB/MA/ATR/ADX) 직접 계산."""
+    from app.services.market_analyzer import analyze_stock_for_batch
     try:
-        price_data = {}
-        try:
-            price_data = await korea_investment_api.get_current_price(stock_code)
-        except Exception as e:
-            logger.warning(f"Failed to fetch price data locally: {e}")
+        rec = await analyze_stock_for_batch(stock_code, stock_code)
+        if rec is None:
+            raise HTTPException(status_code=404, detail="분석 데이터를 가져올 수 없습니다.")
 
-        news_data = []
-        try:
-            async with httpx.AsyncClient() as client:
-                mcp_url = f"{settings.MCP_SERVER_URL}/tools/call"
-                payload = {
-                    "tool_name": "get_news",
-                    "parameters": {"stock_code": stock_code, "limit": 3}
-                }
-                response = await client.post(mcp_url, json=payload, timeout=10.0)
-                if response.status_code == 200:
-                    result = response.json()
-                    if result.get("status") == "success":
-                        data = result.get("result", {})
-                        if "news" in data:
-                            news_data = data["news"]
-                        else:
-                            news_data = data
-        except Exception as e:
-            logger.warning(f"Failed to fetch news via MCP: {e}")
+        signals = rec.signals or []
+        score = rec.score or 0
 
-        analysis_result = "AI 분석을 수행할 수 없습니다."
-        try:
-            async with httpx.AsyncClient() as client:
-                analyze_url = f"{settings.MCP_SERVER_URL}/analyze"
-                market_summary = {}
-                if "output" in price_data:
-                    output = price_data["output"]
-                    market_summary = {
-                        "current_price": output.get("stck_prpr"),
-                        "change_rate": output.get("prdy_ctrt"),
-                        "volume": output.get("acml_vol"),
-                        "high": output.get("stck_hgpr"),
-                        "low": output.get("stck_lwpr")
-                    }
+        # 전략 신호 기반 요약 문자열 생성
+        if score >= 3:
+            summary = f"매수 신호 감지 (점수: {score}). 주요 신호: {', '.join(signals[:3])}"
+        elif score <= -2:
+            summary = f"매도/경고 신호 (점수: {score}). 주요 신호: {', '.join(signals[:3])}"
+        else:
+            summary = f"보유/관망 (점수: {score}). 신호: {', '.join(signals[:3]) if signals else '없음'}"
 
-                payload = {
-                    "analysis_type": "MARKET_ANALYSIS",
-                    "data": {
-                        "stock_code": stock_code,
-                        "market_data": market_summary,
-                        "news": news_data
-                    }
-                }
-                response = await client.post(analyze_url, json=payload, timeout=45.0)
-                if response.status_code == 200:
-                     analysis_res = response.json()
-                     if "result" in analysis_res:
-                         analysis_result = analysis_res["result"]
-        except Exception as e:
-            logger.error(f"Failed to fetch AI analysis: {e}")
-            
         return {
             "stock_code": stock_code,
-            "analysis": analysis_result,
-            "news": news_data,
-            "market_data": price_data.get("output", {})
+            "analysis": summary,
+            "signals": signals,
+            "score": score,
+            "market_data": {
+                "current_price": rec.current_price,
+                "change_rate": rec.change_rate,
+                "volume": rec.volume,
+                "rsi": rec.rsi,
+                "ma5": rec.ma5,
+                "ma20": rec.ma20,
+                "ma60": rec.ma60,
+                "bollinger_upper": rec.bollinger_upper,
+                "bollinger_lower": rec.bollinger_lower,
+                "bollinger_middle": rec.bollinger_middle,
+                "atr_14": rec.atr_14,
+                "adx_14": rec.adx_14,
+                "stochastic_k": rec.stochastic_k,
+                "stochastic_d": rec.stochastic_d,
+            }
         }
 
+    except HTTPException:
+        raise
     except Exception as e:
-        logger.error(f"AI Recommendation error: {e}")
+        logger.error(f"Strategy analysis error for {stock_code}: {e}")
         raise HTTPException(status_code=500, detail=str(e))