387 lines
16 KiB
Python
387 lines
16 KiB
Python
import os
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import json
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import numpy as np
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from modules.services.ollama import OllamaManager
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from modules.analysis.technical import TechnicalAnalyzer
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from modules.analysis.deep_learning import PricePredictor
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# [최적화] 워커 프로세스별 전역 변수 (LSTM 모델 캐싱)
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_lstm_predictor = None
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def get_predictor():
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"""워커 프로세스 내에서 PricePredictor 인스턴스를 싱글톤으로 관리"""
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global _lstm_predictor
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if _lstm_predictor is None:
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print(f"[Worker {os.getpid()}] Initializing LSTM Predictor...")
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_lstm_predictor = PricePredictor()
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print(f"[Worker {os.getpid()}] LSTM Device: {_lstm_predictor.device}"
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f" | AMP: {_lstm_predictor.use_amp}")
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return _lstm_predictor
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def calculate_position_size(total_capital, current_price, volatility, score, ai_confidence,
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max_per_stock=3000000):
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"""
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[v2.0] 변동성 기반 포지션 사이징 (Modified Kelly Criterion)
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핵심 원칙:
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1. 변동성이 높으면 → 적은 수량 (리스크 관리)
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2. 확신도(score)가 높으면 → 많은 수량 (기회 포착)
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3. AI 신뢰도가 높으면 → 가산 비중
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4. 절대 한 종목에 전체 자산의 15% 이상 투자하지 않음
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Returns:
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int: 매수 수량 (0이면 매수 안 함)
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"""
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if current_price <= 0 or total_capital <= 0:
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return 0
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# 1. 기본 투자금 (전체 자산의 10%)
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base_invest = total_capital * 0.10
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# 2. 변동성 조절 계수 (변동성 높을수록 투자금 감소)
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# 변동성 1% → 1.0배, 2% → 0.75배, 3% → 0.5배, 5%+ → 0.3배
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if volatility <= 1.0:
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vol_factor = 1.2 # 안정적 종목은 약간 증가
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elif volatility <= 2.0:
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vol_factor = 1.0
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elif volatility <= 3.0:
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vol_factor = 0.7
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elif volatility <= 5.0:
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vol_factor = 0.45
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else:
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vol_factor = 0.3 # 고변동 종목
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# 3. 확신도 조절 계수 (score가 높을수록 투자금 증가)
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# score 0.6 → 0.5배, 0.7 → 1.0배, 0.8 → 1.5배, 0.9+ → 2.0배
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if score >= 0.85:
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conf_factor = 2.0
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elif score >= 0.75:
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conf_factor = 1.5
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elif score >= 0.65:
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conf_factor = 1.0
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else:
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conf_factor = 0.5
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# 4. AI 신뢰도 가산
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ai_bonus = 1.0
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if ai_confidence >= 0.85:
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ai_bonus = 1.3
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elif ai_confidence >= 0.7:
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ai_bonus = 1.1
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# 5. 최종 투자금 계산
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invest_amount = base_invest * vol_factor * conf_factor * ai_bonus
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# 상한 제한
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invest_amount = min(invest_amount, max_per_stock)
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invest_amount = min(invest_amount, total_capital * 0.15) # 최대 15%
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invest_amount = min(invest_amount, total_capital) # 잔고 초과 방지
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# 수량 계산
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qty = int(invest_amount / current_price)
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return max(0, qty)
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def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
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macro_status=None, holding_info=None):
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"""
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[v2.0] 종목 분석 + 매매 판단 (ProcessPoolExecutor에서 실행)
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[v2.0 개선사항]
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1. ATR 기반 동적 손절/익절 + 트레일링 스탑
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2. 포지션 사이징 (변동성 + 확신도 기반)
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3. 시장상황별 동적 매수/매도 임계값
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4. 보유종목에 대한 분석 기반 매도 판단
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5. ADX/OBV/MTF 통합 기술적 분석
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6. 강화된 AI 프롬프트 (종목 고유 뉴스 분석)
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"""
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try:
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print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles)...")
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# ===== 1. 기술적 지표 계산 =====
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current_price = prices[-1] if prices else 0
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tech_score, rsi, volatility, vol_ratio, ma_info = TechnicalAnalyzer.get_technical_score(
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current_price, prices, volume_history=None)
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# ===== 2. ATR 기반 동적 손절/익절 =====
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sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(prices)
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# ===== 3. LSTM 주가 예측 =====
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lstm_predictor = get_predictor()
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if lstm_predictor:
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lstm_predictor.training_status['current_ticker'] = ticker
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pred_result = lstm_predictor.train_and_predict(prices, ticker=ticker)
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lstm_score = 0.5
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ai_confidence = 0.5
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ai_loss = 1.0
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if pred_result:
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ai_confidence = pred_result.get('confidence', 0.5)
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ai_loss = pred_result.get('loss', 1.0)
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change_magnitude = min(abs(pred_result['change_rate']), 5.0) / 5.0
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if pred_result['trend'] == 'UP':
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lstm_score = 0.5 + (change_magnitude * ai_confidence * 0.4)
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else:
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lstm_score = 0.5 - (change_magnitude * ai_confidence * 0.4)
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lstm_score = max(0.0, min(1.0, lstm_score))
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# ===== 4. 수급 분석 (외인/기관) =====
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investor_score = 0.0
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frgn_net_buy = 0
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orgn_net_buy = 0
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consecutive_frgn_buy = 0
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consecutive_orgn_buy = 0
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if investor_trend:
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for day in investor_trend:
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frgn_net_buy += day['foreigner']
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orgn_net_buy += day['institutional']
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if day['foreigner'] > 0:
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consecutive_frgn_buy += 1
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if day['institutional'] > 0:
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consecutive_orgn_buy += 1
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# 외인 수급 점수 (강화)
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if frgn_net_buy > 0:
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investor_score += 0.03
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if consecutive_frgn_buy >= 3:
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investor_score += 0.04
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if consecutive_frgn_buy >= 5:
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investor_score += 0.03 # 5일 연속 매수 = 추가 보너스
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# 기관 수급 점수 (신규)
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if orgn_net_buy > 0:
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investor_score += 0.02
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if consecutive_orgn_buy >= 3:
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investor_score += 0.03
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# 외인+기관 동시 순매수 = 강력 신호
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if frgn_net_buy > 0 and orgn_net_buy > 0:
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investor_score += 0.03
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print(f" 💰 [Investor] Both Foreign & Institutional Buying!")
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# ===== 5. AI 뉴스 분석 (강화된 프롬프트) =====
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if pred_result:
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pred_price = pred_result.get('predicted', 0)
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pred_change = pred_result.get('change_rate', 0)
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else:
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pred_price = current_price
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pred_change = 0.0
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ollama = OllamaManager()
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prompt = f"""
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[System Instruction]
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1. Role: You are a legendary quant trader with 30 years of experience in Korean stock market.
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2. You MUST analyze the data objectively and respond with a JSON object.
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[Market Data for Stock {ticker}]
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- Current Price: {current_price:,.0f} KRW
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- Technical Score: {tech_score:.3f} (RSI: {rsi:.1f})
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- Moving Average: {ma_info['trend']} (Price is {ma_info['position']})
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- ADX Trend Strength: {ma_info.get('adx', 20):.1f} ({ma_info.get('adx_trend', 'N/A')})
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- Multi-Timeframe: {ma_info.get('mtf_alignment', 'N/A')}
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- AI Prediction: {pred_price:.0f} KRW ({pred_change:+.2f}%)
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- AI Confidence: {ai_confidence:.2f} (Training Loss: {ai_loss:.4f})
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- Volatility: {volatility:.2f}%
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- Volume Ratio: {vol_ratio:.1f}x
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- ATR Stop Loss: {sl_tp['stop_loss_pct']:.1f}% / Take Profit: {sl_tp['take_profit_pct']:.1f}%
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- Investor Trend (5 Days): Foreigner Net Buy {frgn_net_buy}, Institutional Net Buy {orgn_net_buy}
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[Decision Framework]
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- Strong BUY signals: Foreigners+Institutions buying, Golden Cross, ADX>25 with bullish trend, AI high confidence UP
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- Moderate BUY: RSI<40 with bullish reversal, Price near Bollinger Lower Band
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- SELL signals: RSI>70, Dead Cross, ADX>25 with bearish trend, Foreigners selling
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- AVOID/HOLD: ADX<20 (sideways), Mixed signals, Low confidence
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[News Data]
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{json.dumps(news_items[:5] if news_items else [], ensure_ascii=False)}
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[Response Format - JSON Only]
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{{"sentiment_score": 0.0 to 1.0, "reason": "Brief analysis reason"}}
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"""
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ai_resp = ollama.request_inference(prompt)
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sentiment_score = 0.5
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ai_reason = ""
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try:
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data = json.loads(ai_resp)
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sentiment_score = float(data.get("sentiment_score", 0.5))
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sentiment_score = max(0.0, min(1.0, sentiment_score)) # 범위 강제
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ai_reason = data.get("reason", "")
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except Exception:
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print(f" ⚠️ AI response parse failed, using neutral (0.5)")
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# ===== 6. 통합 점수 (동적 가중치 v2.0) =====
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# ADX가 높으면 (추세가 강하면) LSTM과 기술적 분석 비중 증가
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adx_val = ma_info.get('adx', 20)
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if ai_confidence >= 0.85 and adx_val >= 25:
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# 강한 추세 + 높은 AI 신뢰도: AI 최우선
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w_tech, w_news, w_ai = 0.15, 0.15, 0.70
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print(f" 🤖 [Ultra High Confidence + Strong Trend] AI Weight 70%")
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elif ai_confidence >= 0.85:
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w_tech, w_news, w_ai = 0.20, 0.20, 0.60
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print(f" 🤖 [High Confidence] AI Weight 60%")
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elif adx_val >= 30:
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# 매우 강한 추세: 기술적 분석 우선
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w_tech, w_news, w_ai = 0.50, 0.20, 0.30
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print(f" 📊 [Very Strong Trend ADX={adx_val:.0f}] Tech Weight 50%")
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elif adx_val < 20:
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# 비추세/횡보: 뉴스와 수급 중시
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w_tech, w_news, w_ai = 0.30, 0.40, 0.30
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print(f" 📰 [Sideways ADX={adx_val:.0f}] News Weight 40%")
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else:
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w_tech, w_news, w_ai = 0.35, 0.30, 0.35
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total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score)
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# 수급 가산점 (최대 +0.15)
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total_score += min(investor_score, 0.15)
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total_score = min(total_score, 1.0)
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# ===== 7. 시장 상황별 동적 임계값 =====
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buy_threshold = 0.60
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sell_threshold = 0.30
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if macro_status:
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macro_state = macro_status.get('status', 'SAFE')
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if macro_state == 'DANGER':
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buy_threshold = 999.0 # 매수 완전 차단
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sell_threshold = 0.45 # 매도 기준 상향 (빨리 탈출)
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print(f" 🚨 [DANGER Market] Buy BLOCKED, Sell threshold raised to 0.45")
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elif macro_state == 'CAUTION':
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buy_threshold = 0.72 # 매수 기준 대폭 상향 (보수적)
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sell_threshold = 0.38 # 매도 기준도 상향
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print(f" ⚠️ [CAUTION Market] Buy threshold raised to 0.72")
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# ===== 8. 매매 결정 =====
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decision = "HOLD"
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decision_reason = ""
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# --- 보유 종목 분석 기반 매도 (신규) ---
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if holding_info:
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holding_yield = holding_info.get('yield', 0.0)
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holding_qty = holding_info.get('qty', 0)
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peak_price = holding_info.get('peak_price', current_price)
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if holding_qty > 0:
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# A. 동적 손절 (ATR 기반)
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if holding_yield <= sl_tp['stop_loss_pct']:
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decision = "SELL"
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decision_reason = f"Dynamic Stop Loss ({holding_yield:.1f}% <= {sl_tp['stop_loss_pct']:.1f}%)"
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# B. 동적 익절 (ATR 기반)
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elif holding_yield >= sl_tp['take_profit_pct']:
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decision = "SELL"
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decision_reason = f"Dynamic Take Profit ({holding_yield:.1f}% >= {sl_tp['take_profit_pct']:.1f}%)"
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# C. 트레일링 스탑 (최고가 대비 하락)
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elif peak_price > 0:
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drop_from_peak = ((current_price - peak_price) / peak_price) * 100
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if drop_from_peak <= -sl_tp['trailing_stop_pct'] and holding_yield > 2.0:
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# 수익 상태에서만 트레일링 스탑 작동 (2% 이상 수익 확보)
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decision = "SELL"
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decision_reason = (f"Trailing Stop ({drop_from_peak:.1f}% from peak, "
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f"threshold: -{sl_tp['trailing_stop_pct']:.1f}%)")
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# D. 분석 기반 매도 (점수가 매도 임계값 이하)
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if decision == "HOLD" and total_score <= sell_threshold:
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decision = "SELL"
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decision_reason = f"Analysis Signal (Score: {total_score:.2f} <= {sell_threshold:.2f})"
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# E. 추세 반전 매도 (ADX 강한 하락추세)
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if decision == "HOLD" and adx_val >= 30:
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plus_di = ma_info.get('adx', 0) # 참고용
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mtf_align = ma_info.get('mtf_alignment', '')
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if mtf_align == 'STRONG_BEAR' and holding_yield < 0:
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decision = "SELL"
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decision_reason = f"Strong Bear Trend Reversal (MTF: {mtf_align})"
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# --- 매수 판단 ---
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if decision == "HOLD":
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# 강한 단일 신호 매수 (기준 강화)
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strong_signal = False
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strong_reason = ""
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# [강화] 복합 조건 매수 (단일 지표가 아닌 복합 조건)
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if tech_score >= 0.75 and lstm_score >= 0.6 and sentiment_score >= 0.6:
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strong_signal = True
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strong_reason = "Triple Confirmation (Tech+AI+News)"
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elif lstm_score >= 0.80 and ai_confidence >= 0.85 and adx_val >= 25:
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strong_signal = True
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strong_reason = f"High Confidence AI + Strong Trend (ADX={adx_val:.0f})"
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elif investor_score >= 0.10 and tech_score >= 0.60 and total_score >= 0.60:
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strong_signal = True
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strong_reason = "Institutional Buying + Good Fundamentals"
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elif ma_info.get('mtf_alignment') == 'STRONG_BULL' and tech_score >= 0.60:
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strong_signal = True
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strong_reason = f"Strong Multi-Timeframe Bullish + Tech {tech_score:.2f}"
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if strong_signal and total_score >= buy_threshold - 0.05:
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# 강한 신호는 임계값 약간 완화 허용
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decision = "BUY"
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decision_reason = strong_reason
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print(f" 🎯 [{strong_reason}] → BUY!")
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elif total_score >= buy_threshold:
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decision = "BUY"
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decision_reason = f"Score {total_score:.2f} >= threshold {buy_threshold:.2f}"
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# ===== 9. 포지션 사이징 =====
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suggested_qty = 0
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if decision == "BUY":
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# 기본 자산 1000만원으로 가정 (실제 run_cycle에서 덮어씀)
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suggested_qty = calculate_position_size(
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total_capital=10000000,
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current_price=current_price,
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volatility=volatility,
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score=total_score,
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ai_confidence=ai_confidence
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)
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if suggested_qty == 0:
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decision = "HOLD"
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decision_reason = "Position size too small"
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print(f" └─ Scores: Tech={tech_score:.2f} News={sentiment_score:.2f} "
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f"LSTM={lstm_score:.2f} Inv={investor_score:.2f} → "
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f"Total={total_score:.2f} [{decision}]"
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f"{f' ({decision_reason})' if decision_reason else ''}")
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return {
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"ticker": ticker,
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"score": total_score,
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"tech": tech_score,
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"sentiment": sentiment_score,
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"lstm_score": lstm_score,
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"investor_score": investor_score,
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"volatility": volatility,
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"volume_ratio": vol_ratio,
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"prediction": pred_result,
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"decision": decision,
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"decision_reason": decision_reason,
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"current_price": current_price,
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"ma_info": ma_info,
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"sl_tp": sl_tp,
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"suggested_qty": suggested_qty,
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"ai_confidence": ai_confidence,
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"ai_reason": ai_reason
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}
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except Exception as e:
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print(f"❌ [Worker Error] Failed to analyze {ticker}: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return {
|
|
"ticker": ticker,
|
|
"score": 0.0,
|
|
"decision": "HOLD",
|
|
"decision_reason": f"Error: {str(e)}",
|
|
"current_price": 0,
|
|
"sl_tp": {'stop_loss_pct': -5.0, 'take_profit_pct': 8.0, 'trailing_stop_pct': 3.0},
|
|
"suggested_qty": 0,
|
|
"error": str(e)
|
|
}
|