주식자동매매 AI 프로그램 초기 모델

2026-02-04 23:29:06 +09:00
parent 41df1a38d3
commit 7d5f62f844
20 changed files with 2987 additions and 0 deletions
--- a/modules/strategy/process.py
+++ b/modules/strategy/process.py
@@ -0,0 +1,145 @@
+import os
+import json
+import numpy as np
+from modules.services.ollama import OllamaManager
+from modules.analysis.technical import TechnicalAnalyzer
+from modules.analysis.deep_learning import PricePredictor
+
+# [최적화] 워커 프로세스별 전역 변수 (LSTM 모델 캐싱)
+_lstm_predictor = None
+
+def get_predictor():
+    """워커 프로세스 내에서 PricePredictor 인스턴스를 싱글톤으로 관리"""
+    global _lstm_predictor
+    if _lstm_predictor is None:
+        print(f"🧩 [Worker {os.getpid()}] Initializing LSTM Predictor...")
+        _lstm_predictor = PricePredictor()
+    return _lstm_predictor
+
+def analyze_stock_process(ticker, prices, news_items):
+    """
+    [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
+        
+    # 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)
+    
+    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}]")
+        
+    # [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
+
+    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
+    }