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 ModelRegistry # [최적화] 워커 프로세스별 전역 변수 (Ollama 캐싱) _ollama_manager = None def get_predictor(ticker=None): """워커 프로세스 내에서 ModelRegistry로 종목별 PricePredictor 관리""" registry = ModelRegistry.get_instance() return registry.get_predictor(ticker or "default") def get_ollama(): """워커 프로세스 내에서 OllamaManager 인스턴스를 싱글톤으로 관리 - 종목마다 새 인스턴스를 만들면 Ollama에 동시 요청이 폭주해 데드락 발생""" global _ollama_manager if _ollama_manager is None: _ollama_manager = OllamaManager() return _ollama_manager def calculate_position_size(total_capital, current_price, volatility, score, ai_confidence, max_per_stock=3000000): """ [v2.0] 변동성 기반 포지션 사이징 (Modified Kelly Criterion) 핵심 원칙: 1. 변동성이 높으면 → 적은 수량 (리스크 관리) 2. 확신도(score)가 높으면 → 많은 수량 (기회 포착) 3. AI 신뢰도가 높으면 → 가산 비중 4. 절대 한 종목에 전체 자산의 15% 이상 투자하지 않음 Returns: int: 매수 수량 (0이면 매수 안 함) """ if current_price <= 0 or total_capital <= 0: return 0 # 1. 기본 투자금 (전체 자산의 10%) base_invest = total_capital * 0.10 # 2. 변동성 조절 계수 (변동성 높을수록 투자금 감소) if volatility <= 1.0: vol_factor = 1.2 elif volatility <= 2.0: vol_factor = 1.0 elif volatility <= 3.0: vol_factor = 0.7 elif volatility <= 5.0: vol_factor = 0.45 else: vol_factor = 0.3 # 3. 확신도 조절 계수 if score >= 0.85: conf_factor = 2.0 elif score >= 0.75: conf_factor = 1.5 elif score >= 0.65: conf_factor = 1.0 else: conf_factor = 0.5 # 4. AI 신뢰도 가산 ai_bonus = 1.0 if ai_confidence >= 0.85: ai_bonus = 1.3 elif ai_confidence >= 0.7: ai_bonus = 1.1 # 5. 최종 투자금 계산 invest_amount = base_invest * vol_factor * conf_factor * ai_bonus invest_amount = min(invest_amount, max_per_stock) invest_amount = min(invest_amount, total_capital * 0.15) invest_amount = min(invest_amount, total_capital) qty = int(invest_amount / current_price) return max(0, qty) def analyze_stock_process(ticker, ohlcv_data, news_items, investor_trend=None, macro_status=None, holding_info=None): """ [v3.0] 종목 분석 + 매매 판단 (ProcessPoolExecutor에서 실행) [v3.0 개선사항] 1. OHLCV 전체 수신 (실제 고가/저가/거래량 사용) 2. 종목별 ModelRegistry (가중치 덮어쓰기 방지) 3. 강화된 LLM 프롬프트 (거시경제 상태, 볼린저밴드, 거래량 급증, 보유 수익률) """ try: # OHLCV 데이터 분리 (하위호환: list 형태도 허용) if isinstance(ohlcv_data, dict): prices = ohlcv_data.get('close', []) high_prices = ohlcv_data.get('high') or None low_prices = ohlcv_data.get('low') or None volume_history = ohlcv_data.get('volume') or None open_prices = ohlcv_data.get('open') or None else: # 하위 호환: 기존 close 리스트 prices = ohlcv_data if isinstance(ohlcv_data, list) else [] high_prices = None low_prices = None volume_history = None open_prices = None # volume이 모두 0이거나 비어있으면 None 처리 if volume_history and all(v == 0 for v in volume_history): volume_history = None print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles, " f"OHLCV={'yes' if high_prices else 'close-only'}, " f"Vol={'yes' if volume_history else 'no'})...") # ===== 1. 기술적 지표 계산 ===== current_price = prices[-1] if prices else 0 tech_score, rsi, volatility, vol_ratio, ma_info = TechnicalAnalyzer.get_technical_score( current_price, prices, volume_history=volume_history) # ===== 2. ATR 기반 동적 손절/익절 (실제 고가/저가 사용) ===== sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp( prices, high_prices=high_prices, low_prices=low_prices) # ===== 3. 볼린저밴드 위치 계산 ===== bb_upper, bb_mid, bb_lower = TechnicalAnalyzer.calculate_bollinger_bands(prices) if bb_upper > bb_lower: bb_pos = (current_price - bb_lower) / (bb_upper - bb_lower) # 0=하단, 1=상단 if bb_pos <= 0.2: bb_zone = "하단(과매도)" elif bb_pos >= 0.8: bb_zone = "상단(과매수)" else: bb_zone = f"중간({bb_pos:.0%})" else: bb_pos = 0.5 bb_zone = "중간" # ===== 4. LSTM 주가 예측 (ModelRegistry 사용) ===== lstm_predictor = get_predictor(ticker) if lstm_predictor: lstm_predictor.training_status['current_ticker'] = ticker # LSTM에 전달할 OHLCV 딕셔너리 구성 lstm_ohlcv = { 'close': prices, 'open': open_prices or prices, 'high': high_prices or prices, 'low': low_prices or prices, 'volume': volume_history or [] } pred_result = lstm_predictor.train_and_predict(lstm_ohlcv, ticker=ticker) lstm_score = 0.5 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) change_magnitude = min(abs(pred_result['change_rate']), 5.0) / 5.0 if pred_result['trend'] == 'UP': lstm_score = 0.5 + (change_magnitude * ai_confidence * 0.4) else: lstm_score = 0.5 - (change_magnitude * ai_confidence * 0.4) lstm_score = max(0.0, min(1.0, lstm_score)) # ===== 5. 수급 분석 (외인/기관) ===== investor_score = 0.0 frgn_net_buy = 0 orgn_net_buy = 0 consecutive_frgn_buy = 0 consecutive_orgn_buy = 0 if investor_trend: for day in investor_trend: frgn_net_buy += day['foreigner'] orgn_net_buy += day['institutional'] if day['foreigner'] > 0: consecutive_frgn_buy += 1 if day['institutional'] > 0: consecutive_orgn_buy += 1 if frgn_net_buy > 0: investor_score += 0.03 if consecutive_frgn_buy >= 3: investor_score += 0.04 if consecutive_frgn_buy >= 5: investor_score += 0.03 if orgn_net_buy > 0: investor_score += 0.02 if consecutive_orgn_buy >= 3: investor_score += 0.03 if frgn_net_buy > 0 and orgn_net_buy > 0: investor_score += 0.03 print(f" 💰 [Investor] Both Foreign & Institutional Buying!") # ===== 6. AI 뉴스 분석 (강화된 프롬프트) ===== 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 news_summary = "; ".join( [n.get('title', '') for n in (news_items or [])[:3] if n.get('title')] ) or "뉴스 없음" # 거시경제 상태 macro_state = macro_status.get('status', 'SAFE') if macro_status else 'SAFE' # 거래량 급증 여부 vol_surge = "급증(x{:.1f})".format(vol_ratio) if vol_ratio >= 2.0 else "정상" # 보유종목 수익률 holding_yield_str = "" if holding_info and holding_info.get('qty', 0) > 0: yld = holding_info.get('yield', 0.0) holding_yield_str = f" | 보유수익률={yld:+.1f}%" ollama = get_ollama() prompt = ( f"Korean stock analyst. JSON only: {{\"sentiment_score\":0.0-1.0,\"reason\":\"1 sentence\"}}\n" f"Stock {ticker} ₩{current_price:,.0f}{holding_yield_str}\n" f"Market={macro_state} | " f"Tech={tech_score:.2f} RSI={rsi:.1f} MA={ma_info['trend']} ADX={ma_info.get('adx',20):.0f} " f"MTF={ma_info.get('mtf_alignment','N/A')}\n" f"BB={bb_zone} | AI={pred_change:+.2f}% conf={ai_confidence:.0%} | " f"Vol={volatility:.1f}% VolRatio={vol_surge}\n" f"Flow: Frgn={frgn_net_buy:+,}({consecutive_frgn_buy}d) " f"Inst={orgn_net_buy:+,}({consecutive_orgn_buy}d)\n" f"News: {news_summary}" ) ai_resp = ollama.request_inference(prompt) sentiment_score = 0.5 ai_reason = "" try: data = json.loads(ai_resp) sentiment_score = float(data.get("sentiment_score", 0.5)) sentiment_score = max(0.0, min(1.0, sentiment_score)) ai_reason = data.get("reason", "") except Exception: print(f" ⚠️ AI response parse failed, using neutral (0.5)") # ===== 7. 통합 점수 (동적 가중치 v2.0) ===== adx_val = ma_info.get('adx', 20) if ai_confidence >= 0.85 and adx_val >= 25: w_tech, w_news, w_ai = 0.15, 0.15, 0.70 print(f" 🤖 [Ultra High Confidence + Strong Trend] AI Weight 70%") elif ai_confidence >= 0.85: w_tech, w_news, w_ai = 0.20, 0.20, 0.60 print(f" 🤖 [High Confidence] AI Weight 60%") elif adx_val >= 30: w_tech, w_news, w_ai = 0.50, 0.20, 0.30 print(f" 📊 [Very Strong Trend ADX={adx_val:.0f}] Tech Weight 50%") elif adx_val < 20: w_tech, w_news, w_ai = 0.30, 0.40, 0.30 print(f" 📰 [Sideways ADX={adx_val:.0f}] News Weight 40%") else: w_tech, w_news, w_ai = 0.35, 0.30, 0.35 total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score) total_score += min(investor_score, 0.15) total_score = min(total_score, 1.0) # ===== 8. 시장 상황별 동적 임계값 ===== buy_threshold = 0.60 sell_threshold = 0.30 if macro_status: if macro_state == 'DANGER': buy_threshold = 999.0 sell_threshold = 0.45 print(f" 🚨 [DANGER Market] Buy BLOCKED, Sell threshold raised to 0.45") elif macro_state == 'CAUTION': buy_threshold = 0.72 sell_threshold = 0.38 print(f" ⚠️ [CAUTION Market] Buy threshold raised to 0.72") # ===== 9. 매매 결정 ===== decision = "HOLD" decision_reason = "" if holding_info: holding_yield = holding_info.get('yield', 0.0) holding_qty = holding_info.get('qty', 0) peak_price = holding_info.get('peak_price', current_price) if holding_qty > 0: if holding_yield <= sl_tp['stop_loss_pct']: decision = "SELL" decision_reason = f"Dynamic Stop Loss ({holding_yield:.1f}% <= {sl_tp['stop_loss_pct']:.1f}%)" elif holding_yield >= sl_tp['take_profit_pct']: decision = "SELL" decision_reason = f"Dynamic Take Profit ({holding_yield:.1f}% >= {sl_tp['take_profit_pct']:.1f}%)" elif peak_price > 0: drop_from_peak = ((current_price - peak_price) / peak_price) * 100 if drop_from_peak <= -sl_tp['trailing_stop_pct'] and holding_yield > 2.0: decision = "SELL" decision_reason = (f"Trailing Stop ({drop_from_peak:.1f}% from peak, " f"threshold: -{sl_tp['trailing_stop_pct']:.1f}%)") if decision == "HOLD" and total_score <= sell_threshold: decision = "SELL" decision_reason = f"Analysis Signal (Score: {total_score:.2f} <= {sell_threshold:.2f})" if decision == "HOLD" and adx_val >= 30: mtf_align = ma_info.get('mtf_alignment', '') if mtf_align == 'STRONG_BEAR' and holding_yield < 0: decision = "SELL" decision_reason = f"Strong Bear Trend Reversal (MTF: {mtf_align})" # --- 매수 판단 --- if decision == "HOLD": strong_signal = False strong_reason = "" if tech_score >= 0.75 and lstm_score >= 0.6 and sentiment_score >= 0.6: strong_signal = True strong_reason = "Triple Confirmation (Tech+AI+News)" elif lstm_score >= 0.80 and ai_confidence >= 0.85 and adx_val >= 25: strong_signal = True strong_reason = f"High Confidence AI + Strong Trend (ADX={adx_val:.0f})" elif investor_score >= 0.10 and tech_score >= 0.60 and total_score >= 0.60: strong_signal = True strong_reason = "Institutional Buying + Good Fundamentals" elif ma_info.get('mtf_alignment') == 'STRONG_BULL' and tech_score >= 0.60: strong_signal = True strong_reason = f"Strong Multi-Timeframe Bullish + Tech {tech_score:.2f}" if strong_signal and total_score >= buy_threshold - 0.05: decision = "BUY" decision_reason = strong_reason print(f" 🎯 [{strong_reason}] → BUY!") elif total_score >= buy_threshold: decision = "BUY" decision_reason = f"Score {total_score:.2f} >= threshold {buy_threshold:.2f}" # ===== 10. 포지션 사이징 ===== suggested_qty = 0 if decision == "BUY": suggested_qty = calculate_position_size( total_capital=10000000, current_price=current_price, volatility=volatility, score=total_score, ai_confidence=ai_confidence ) if suggested_qty == 0: decision = "HOLD" decision_reason = "Position size too small" print(f" └─ Scores: Tech={tech_score:.2f} News={sentiment_score:.2f} " f"LSTM={lstm_score:.2f} Inv={investor_score:.2f} → " f"Total={total_score:.2f} [{decision}]" f"{f' ({decision_reason})' if decision_reason else ''}") return { "ticker": ticker, "score": total_score, "tech": tech_score, "sentiment": sentiment_score, "lstm_score": lstm_score, "investor_score": investor_score, "volatility": volatility, "volume_ratio": vol_ratio, "prediction": pred_result, "decision": decision, "decision_reason": decision_reason, "current_price": current_price, "ma_info": ma_info, "sl_tp": sl_tp, "suggested_qty": suggested_qty, "ai_confidence": ai_confidence, "ai_reason": ai_reason } except Exception as e: 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) }