main_server.py가 중복 실행되면서 좀비 프로세스가 수행되는 오류 해결, process_tracker.py가 감시하면서 할당되지 않은 pid가 존재하면 좀비프로세스로 판단하여 kill

modules/strategy/process.py

@@ -16,130 +16,179 @@ def get_predictor():
         _lstm_predictor = PricePredictor()
     return _lstm_predictor
 
-def analyze_stock_process(ticker, prices, news_items):
+def analyze_stock_process(ticker, prices, news_items, investor_trend=None):
     """
     [CPU Intensive] 기술적 분석 및 AI 판단을 수행하는 함수
     (ProcessPoolExecutor에서 실행됨)
     """
-    print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles)...")
-    
-    # 1. 기술적 지표 계산
-    current_price = prices[-1] if prices else 0
-    # [수정] 변동성, 거래량 비율 반환 (거래량 데이터가 없으면 None 전달)
-    tech_score, rsi, volatility, vol_ratio = TechnicalAnalyzer.get_technical_score(current_price, prices, volume_history=None)
-    
-    # 2. LSTM 주가 예측
-    # [최적화] 전역 캐시된 Predictor 사용
-    lstm_predictor = get_predictor()
-    if lstm_predictor:
-        lstm_predictor.training_status['current_ticker'] = ticker
-    pred_result = lstm_predictor.train_and_predict(prices)
-    
-    lstm_score = 0.5 # 중립
-    ai_confidence = 0.5
-    ai_loss = 1.0
-
-    if pred_result:
-        ai_confidence = pred_result.get('confidence', 0.5)
-        ai_loss = pred_result.get('loss', 1.0)
-        
-        # 상승/하락 예측에 따라 점수 조정 (신뢰도 반영)
-        # 최대 5% 변동폭까지 반영
-        change_magnitude = min(abs(pred_result['change_rate']), 5.0) / 5.0 
-        
-        if pred_result['trend'] == 'UP':
-            # 상승 예측 시: 기본 0.5 + (강도 * 신뢰도 * 0.4) -> 최대 0.9
-            lstm_score = 0.5 + (change_magnitude * ai_confidence * 0.4)
-        else:
-            # 하락 예측 시: 기본 0.5 - (강도 * 신뢰도 * 0.4) -> 최소 0.1
-            lstm_score = 0.5 - (change_magnitude * ai_confidence * 0.4)
-            
-        lstm_score = max(0.0, min(1.0, lstm_score))
-
-    # 3. AI 뉴스 분석 
-    ollama = OllamaManager() 
-    prompt = f"""
-    [System Instruction]
-    1. Role: You are a Expert Quant Trader with 20 years of experience.
-    2. Market Data:
-       - Technical Score: {tech_score:.2f} (RSI: {rsi:.2f})
-       - AI Prediction: {pred_result['predicted']:.0f} KRW ({pred_result['change_rate']}%)
-       - AI Confidence: {ai_confidence:.2f} (Loss: {ai_loss:.4f})
-    3. Strategy:
-       - If AI Confidence > 0.8 and Trend is UP -> Strong BUY signal.
-       - If Tech Score > 0.7 -> BUY signal.
-       - If Trend is DOWN -> SELL/AVOID.
-    4. Task: Analyze the news and combine with market data to decide sentiment.
-    
-    News Data: {json.dumps(news_items, ensure_ascii=False)}
-    
-    Response (JSON):
-    {{
-        "sentiment_score": 0.8,
-        "reason": "AI confidence is high and news supports the uptrend."
-    }}
-    """
-    ai_resp = ollama.request_inference(prompt)
-    sentiment_score = 0.5
     try:
-        data = json.loads(ai_resp)
-        sentiment_score = float(data.get("sentiment_score", 0.5))
-    except:
-        pass
+        print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles)...")
         
-    # 4. 통합 점수 (동적 가중치)
-    # AI 신뢰도가 높으면 AI 비중을 대폭 상향
-    if ai_confidence >= 0.85:
-        w_tech, w_news, w_ai = 0.2, 0.2, 0.6
-        print(f"   🤖 [High Confidence] AI Weight Boosted to 60%")
-    else:
-        w_tech, w_news, w_ai = 0.4, 0.3, 0.3
+        # 1. 기술적 지표 계산
+        current_price = prices[-1] if prices else 0
+        # [수정] 변동성, 거래량 비율, MA 정보 반환
+        tech_score, rsi, volatility, vol_ratio, ma_info = TechnicalAnalyzer.get_technical_score(current_price, prices, volume_history=None)
         
-    total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score)
-    
-    decision = "HOLD"
-    
-    # [신규] 강한 단일 신호 매수 로직 (기준 강화)
-    strong_signal = False
-    strong_reason = ""
-    
-    if tech_score >= 0.80:
-        strong_signal = True
-        strong_reason = "Super Strong Technical"
-    elif lstm_score >= 0.80 and ai_confidence >= 0.8:
-        strong_signal = True
-        strong_reason = f"High Confidence AI Buy (Conf: {ai_confidence})"
-    elif sentiment_score >= 0.85:
-        strong_signal = True
-        strong_reason = "Strong News Sentiment"
-    
-    if strong_signal:
-        decision = "BUY"
-        print(f"   🎯 [{strong_reason}] Overriding to BUY!")
-    elif total_score >= 0.60: # (0.5 -> 0.6 상향 조정으로 보수적 접근)
-        decision = "BUY"
-    elif total_score <= 0.30:
-        decision = "SELL"
-    
-    print(f"   └─ Scores: Tech={tech_score:.2f} News={sentiment_score:.2f} LSTM={lstm_score:.2f}(Conf:{ai_confidence:.2f}) → Total={total_score:.2f} [{decision}]")
+        # 2. LSTM 주가 예측
+        # [최적화] 전역 캐시된 Predictor 사용
+        lstm_predictor = get_predictor()
+        if lstm_predictor:
+            lstm_predictor.training_status['current_ticker'] = ticker
+        pred_result = lstm_predictor.train_and_predict(prices)
         
-    # [신규] 변동성(Volatility) 계산
-    if len(prices) > 1:
-        prices_np = np.array(prices)
-        changes = np.diff(prices_np) / prices_np[:-1]
-        volatility = np.std(changes) * 100 # 퍼센트 단위
-    else:
-        volatility = 0.0
+        lstm_score = 0.5 # 중립
+        ai_confidence = 0.5
+        ai_loss = 1.0
 
-    return {
-        "ticker": ticker,
-        "score": total_score,
-        "tech": tech_score,
-        "sentiment": sentiment_score,
-        "lstm_score": lstm_score,
-        "volatility": volatility,
-        "volume_ratio": vol_ratio,
-        "prediction": pred_result,
-        "decision": decision,
-        "current_price": current_price
-    }
+        if pred_result:
+            ai_confidence = pred_result.get('confidence', 0.5)
+            ai_loss = pred_result.get('loss', 1.0)
+            
+            # 상승/하락 예측에 따라 점수 조정 (신뢰도 반영)
+            # 최대 5% 변동폭까지 반영
+            change_magnitude = min(abs(pred_result['change_rate']), 5.0) / 5.0 
+            
+            if pred_result['trend'] == 'UP':
+                # 상승 예측 시: 기본 0.5 + (강도 * 신뢰도 * 0.4) -> 최대 0.9
+                lstm_score = 0.5 + (change_magnitude * ai_confidence * 0.4)
+            else:
+                # 하락 예측 시: 기본 0.5 - (강도 * 신뢰도 * 0.4) -> 최소 0.1
+                lstm_score = 0.5 - (change_magnitude * ai_confidence * 0.4)
+                
+            lstm_score = max(0.0, min(1.0, lstm_score))
+
+        # [신규] 수급 분석 (외인/기관)
+        investor_score = 0.0
+        frgn_net_buy = 0
+        orgn_net_buy = 0
+        consecutive_frgn_buy = 0
+        
+        if investor_trend:
+            # 최근 5일 합산
+            for day in investor_trend:
+                frgn_net_buy += day['foreigner']
+                orgn_net_buy += day['institutional']
+                if day['foreigner'] > 0:
+                    consecutive_frgn_buy += 1
+            
+            # 외인 수급 점수 (단순화)
+            if frgn_net_buy > 0:
+                investor_score += 0.05
+            if consecutive_frgn_buy >= 3:
+                investor_score += 0.05
+            
+            if investor_score > 0:
+                print(f"   💰 [Investor] Foreign Buy Detected (Net: {frgn_net_buy})")
+
+        # 3. AI 뉴스 분석
+        # pred_result가 None일 경우 기본값 사용
+        if pred_result:
+            pred_price = pred_result.get('predicted', 0)
+            pred_change = pred_result.get('change_rate', 0)
+        else:
+            pred_price = current_price
+            pred_change = 0.0
+        
+        ollama = OllamaManager() 
+        prompt = f"""
+        [System Instruction]
+        1. Role: You are a Expert Quant Trader with 20 years of experience.
+        2. Market Data:
+        - Technical Score: {tech_score:.2f} (RSI: {rsi:.2f})
+        - Moving Average: {ma_info['trend']} (Price is {ma_info['position']})
+        - AI Prediction: {pred_price:.0f} KRW ({pred_change}%)
+        - AI Confidence: {ai_confidence:.2f} (Loss: {ai_loss:.4f})
+        - Investor Trend (5 Days): Foreigner Net Buy {frgn_net_buy}, Institutional Net Buy {orgn_net_buy}
+        3. Strategy:
+        - If Foreigners are buying AND Trend is UP -> Strong BUY.
+        - If AI Confidence > 0.8 and Trend is UP -> Strong BUY.
+        - If MA is Bullish (Golden Alignment) -> Positive Signal.
+        - If Price is above MA20 -> Support Uptrend.
+        - If Trend is DOWN -> SELL/AVOID.
+        4. Task: Analyze the news and combine with market data to decide sentiment.
+        
+        News Data: {json.dumps(news_items, ensure_ascii=False)}
+        
+        Response (JSON):
+        {{
+            "sentiment_score": 0.8,
+            "reason": "Foreigners buying and Golden Cross detected."
+        }}
+        """
+        ai_resp = ollama.request_inference(prompt)
+        sentiment_score = 0.5
+        try:
+            data = json.loads(ai_resp)
+            sentiment_score = float(data.get("sentiment_score", 0.5))
+        except:
+            pass
+            
+        # 4. 통합 점수 (동적 가중치)
+        # AI 신뢰도가 높으면 AI 비중을 대폭 상향
+        if ai_confidence >= 0.85:
+            w_tech, w_news, w_ai = 0.2, 0.2, 0.6
+            print(f"   🤖 [High Confidence] AI Weight Boosted to 60%")
+        else:
+            w_tech, w_news, w_ai = 0.4, 0.3, 0.3
+            
+        total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score)
+        
+        # [수신] 수급 가산점 추가 (최대 +0.1)
+        total_score += investor_score
+        total_score = min(total_score, 1.0)
+        
+        decision = "HOLD"
+        
+        # [신규] 강한 단일 신호 매수 로직 (기준 강화)
+        strong_signal = False
+        strong_reason = ""
+        
+        if tech_score >= 0.80:
+            strong_signal = True
+            strong_reason = "Super Strong Technical"
+        elif lstm_score >= 0.80 and ai_confidence >= 0.8:
+            strong_signal = True
+            strong_reason = f"High Confidence AI Buy (Conf: {ai_confidence})"
+        elif sentiment_score >= 0.85:
+            strong_signal = True
+            strong_reason = "Strong News Sentiment"
+        elif investor_score >= 0.1 and total_score >= 0.6: # 외인 수급이 좋고 전체 점수 양호
+            strong_signal = True
+            strong_reason = "Strong Foreigner Buying"
+        
+        if strong_signal:
+            decision = "BUY"
+            print(f"   🎯 [{strong_reason}] Overriding to BUY!")
+        elif total_score >= 0.60: # (0.5 -> 0.6 상향 조정으로 보수적 접근)
+            decision = "BUY"
+        elif total_score <= 0.30:
+            decision = "SELL"
+        
+        print(f"   └─ Scores: Tech={tech_score:.2f} News={sentiment_score:.2f} LSTM={lstm_score:.2f} → Total={total_score:.2f} [{decision}]")
+        
+        return {
+            "ticker": ticker,
+            "score": total_score,
+            "tech": tech_score,
+            "sentiment": sentiment_score,
+            "lstm_score": lstm_score,
+            "volatility": volatility,
+            "volume_ratio": vol_ratio,
+            "prediction": pred_result,
+            "decision": decision,
+            "current_price": current_price,
+            "ma_info": ma_info
+        }
+
+    except Exception as e:
+        print(f"❌ [Worker Error] Failed to analyze {ticker}: {e}")
+        import traceback
+        traceback.print_exc()
+        # 기본 실패 응답 반환 (프로세스 크래시 방지)
+        return {
+            "ticker": ticker,
+            "score": 0.0,
+            "decision": "HOLD",
+            "current_price": 0,
+            "error": str(e)
+        }