gahusb
7ea1a21487
Atomic mv of root V1 assets (main_server.py + modules/ + data/ + tests/ + entry scripts + docs + logs) into signal_v1/ subdirectory. load_dotenv() updated to load web-ai/.env explicitly via Path. Adds web-ai/CLAUDE.md (workspace guide) and web-ai/start.bat (signal_v1 entry wrapper). Prepares for signal_v2/ Phase 2. Tests: signal_v1/tests/unit baseline preserved (no regression). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
572 lines
24 KiB
Python
572 lines
24 KiB
Python
import os
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import json
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import time
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import numpy as np
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from modules.services.llm_client import get_llm_client
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from modules.analysis.technical import TechnicalAnalyzer
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from modules.analysis.deep_learning import ModelRegistry
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from modules.analysis.market_regime import MarketRegimeDetector
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from modules.analysis.ai_council import get_council
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from modules.analysis.ensemble import get_ensemble
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from modules.config import Config
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# AI Council 마지막 호출 시각 캐시 (종목별, 과다 호출 방지)
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_council_last_call: dict = {}
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def get_predictor(ticker=None):
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"""워커 프로세스 내에서 ModelRegistry로 종목별 PricePredictor 관리"""
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registry = ModelRegistry.get_instance()
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return registry.get_predictor(ticker or "default")
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def get_ollama():
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"""LLMClient 싱글톤 반환 (Gemini 우선, Ollama 폴백)"""
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return get_llm_client()
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def calculate_position_size(total_capital, current_price, volatility, score, ai_confidence,
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max_per_stock=3000000, ticker=None):
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"""
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[v3.1] Modified Kelly Criterion 기반 포지션 사이징
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핵심 원칙:
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1. Kelly Fraction: f* = (p*b - q) / b (과거 실전 승률 + 손익비 기반)
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- 데이터 부족 시 보수적 기본값 8% 사용
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- Half-Kelly 적용으로 변동성 과대추정 보완
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2. 변동성 조절: ATR 기반 변동성에 따라 Kelly 비중 추가 조절
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3. 확신도 조절: 앙상블 score에 따른 최종 배수
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4. AI 신뢰도 가산: LSTM confidence 기반 (상한 0.80 반영)
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5. 상한: min(종목당 최대, 자산의 20%, 실제 자산)
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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. Kelly Fraction 기반 기본 투자 비중
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ensemble = get_ensemble()
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kelly_f = ensemble.get_kelly_fraction(ticker=ticker, half_kelly=True)
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base_invest = total_capital * kelly_f
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# 2. 변동성 조절 계수 (ATR% 기반, 변동성 높을수록 축소)
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if volatility <= 1.0:
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vol_factor = 1.2
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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
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# 3. 앙상블 확신도 조절 계수 (score 기반)
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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 (LSTM confidence 상한 0.80 반영)
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ai_bonus = 1.0
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if ai_confidence >= 0.75:
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ai_bonus = 1.2
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elif ai_confidence >= 0.65:
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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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invest_amount = min(invest_amount, max_per_stock) # 종목당 최대
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invest_amount = min(invest_amount, total_capital * 0.20) # 자산 20% 상한
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invest_amount = min(invest_amount, total_capital)
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qty = int(invest_amount / current_price)
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kelly_pct = invest_amount / total_capital * 100 if total_capital > 0 else 0
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print(f" [Kelly] f={kelly_f:.2%} invest={invest_amount:,.0f}won ({kelly_pct:.1f}%) qty={qty}")
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return max(0, qty)
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def analyze_stock_process(ticker, ohlcv_data, news_items, investor_trend=None,
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macro_status=None, holding_info=None, total_capital=None):
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"""
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[v3.1] 종목 분석 + 매매 판단 (ProcessPoolExecutor에서 실행)
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[v3.1 개선사항]
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1. AdaptiveEnsemble 연동: 하드코딩 가중치 → 학습 기반 동적 가중치
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2. Kelly Criterion 기반 포지션 사이징 (calculate_position_size)
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3. 파일 mtime 동기화: 메인 프로세스의 record_trade 결과를 워커에 반영
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[v3.0 기능 유지]
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4. OHLCV 전체 수신 (실제 고가/저가/거래량 사용)
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5. 종목별 ModelRegistry (가중치 덮어쓰기 방지)
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6. 강화된 LLM 프롬프트
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"""
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try:
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# [v3.1] 메인 프로세스가 갱신한 앙상블 가중치 파일 감지 → 재로드
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get_ensemble().reload_if_stale()
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# OHLCV 데이터 분리 (하위호환: list 형태도 허용)
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if isinstance(ohlcv_data, dict):
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prices = ohlcv_data.get('close', [])
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high_prices = ohlcv_data.get('high') or None
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low_prices = ohlcv_data.get('low') or None
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volume_history = ohlcv_data.get('volume') or None
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open_prices = ohlcv_data.get('open') or None
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else:
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# 하위 호환: 기존 close 리스트
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prices = ohlcv_data if isinstance(ohlcv_data, list) else []
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high_prices = None
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low_prices = None
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volume_history = None
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open_prices = None
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# volume이 모두 0이거나 비어있으면 None 처리
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if volume_history and all(v == 0 for v in volume_history):
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volume_history = None
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print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles, "
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f"OHLCV={'yes' if high_prices else 'close-only'}, "
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f"Vol={'yes' if volume_history else 'no'})...")
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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=volume_history)
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# ===== 2. ATR 기반 동적 손절/익절 (실제 고가/저가 사용) =====
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sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(
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prices, high_prices=high_prices, low_prices=low_prices)
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# ===== 3. 볼린저밴드 위치 계산 =====
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bb_upper, bb_mid, bb_lower = TechnicalAnalyzer.calculate_bollinger_bands(prices)
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if bb_upper > bb_lower:
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bb_pos = (current_price - bb_lower) / (bb_upper - bb_lower) # 0=하단, 1=상단
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if bb_pos <= 0.2:
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bb_zone = "하단(과매도)"
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elif bb_pos >= 0.8:
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bb_zone = "상단(과매수)"
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else:
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bb_zone = f"중간({bb_pos:.0%})"
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else:
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bb_pos = 0.5
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bb_zone = "중간"
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# ===== 4. LSTM 주가 예측 (ModelRegistry 사용) =====
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lstm_predictor = get_predictor(ticker)
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if lstm_predictor:
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lstm_predictor.training_status['current_ticker'] = ticker
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# LSTM에 전달할 OHLCV 딕셔너리 구성
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lstm_ohlcv = {
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'close': prices,
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'open': open_prices or prices,
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'high': high_prices or prices,
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'low': low_prices or prices,
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'volume': volume_history or []
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}
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pred_result = lstm_predictor.train_and_predict(lstm_ohlcv, 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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# ===== 5. 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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# 연속 매수일 수: 가장 최근부터 역순으로 연속된 양수 일수만 카운트
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for day in reversed(investor_trend):
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if day['foreigner'] > 0:
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consecutive_frgn_buy += 1
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else:
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break
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for day in reversed(investor_trend):
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if day['institutional'] > 0:
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consecutive_orgn_buy += 1
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else:
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break
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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
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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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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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# ===== 6. 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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news_summary = "; ".join(
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[n.get('title', '') for n in (news_items or [])[:3] if n.get('title')]
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) or "뉴스 없음"
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# 거시경제 상태
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macro_state = macro_status.get('status', 'SAFE') if macro_status else 'SAFE'
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# 거래량 급증 여부
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vol_surge = "급증(x{:.1f})".format(vol_ratio) if vol_ratio >= 2.0 else "정상"
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# 보유종목 수익률
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holding_yield_str = ""
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if holding_info and holding_info.get('qty', 0) > 0:
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yld = holding_info.get('yield', 0.0)
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holding_yield_str = f" | 보유수익률={yld:+.1f}%"
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ollama = get_ollama()
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prompt = (
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f"Korean stock analyst. JSON only: {{\"sentiment_score\":0.0-1.0,\"reason\":\"1 sentence\"}}\n"
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f"Stock {ticker} ₩{current_price:,.0f}{holding_yield_str}\n"
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f"Market={macro_state} | "
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f"Tech={tech_score:.2f} RSI={rsi:.1f} MA={ma_info['trend']} ADX={ma_info.get('adx',20):.0f} "
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f"MTF={ma_info.get('mtf_alignment','N/A')}\n"
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f"BB={bb_zone} | AI={pred_change:+.2f}% conf={ai_confidence:.0%} | "
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f"Vol={volatility:.1f}% VolRatio={vol_surge}\n"
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f"Flow: Frgn={frgn_net_buy:+,}({consecutive_frgn_buy}d) "
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f"Inst={orgn_net_buy:+,}({consecutive_orgn_buy}d)\n"
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f"News: {news_summary}"
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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))
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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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# ===== 7. 통합 점수 (AdaptiveEnsemble v3.1) =====
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# 하드코딩 가중치 → 학습 기반 동적 가중치 (과거 매매 결과 반영)
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adx_val = ma_info.get('adx', 20)
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ensemble = get_ensemble()
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weights = ensemble.get_weights(
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ticker=ticker,
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adx=adx_val,
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macro_state=macro_state,
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ai_confidence=ai_confidence
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)
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print(f" [Ensemble] tech={weights.tech:.2f} news={weights.sentiment:.2f} "
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f"lstm={weights.lstm:.2f} (adx={adx_val:.0f} conf={ai_confidence:.2f})")
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total_score = ensemble.compute_ensemble_score(
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tech_score=tech_score,
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sentiment_score=sentiment_score,
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lstm_score=lstm_score,
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investor_score=investor_score,
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weights=weights
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)
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# ===== 7.5. 시장 레짐 감지 (코스피 수준 기반) =====
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kospi_price = 0.0
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kospi_change_val = 0.0
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regime_analysis = None
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if macro_status:
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kospi_info = macro_status.get('indicators', {}).get('KOSPI', {})
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kospi_price = float(kospi_info.get('price', 0) or 0)
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kospi_change_val = float(kospi_info.get('change', 0) or 0)
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if Config.MARKET_REGIME_ENABLED and kospi_price > 0:
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regime_analysis = MarketRegimeDetector.detect(kospi_price, kospi_change_val)
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print(
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f" 📈 [Regime] {MarketRegimeDetector.get_regime_label(kospi_price)} "
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f"risk={regime_analysis.risk_level} "
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f"buy_adj={regime_analysis.buy_threshold_adj:+.2f} "
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f"pos=x{regime_analysis.position_size_adj:.2f}"
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)
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# ===== 8. 시장 상황별 동적 임계값 =====
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buy_threshold = 0.60
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sell_threshold = 0.30
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danger_force_sell = False # DANGER 긴급 매도 플래그
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if macro_status:
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if macro_state == 'DANGER':
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buy_threshold = 999.0
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sell_threshold = 0.35 # 이전 0.45에서 하향 (더 적극적 손절)
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print(f" 🚨 [DANGER Market] Buy BLOCKED, Sell threshold lowered to 0.35")
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# 보유 중이고 손실이면 즉시 매도 플래그
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if holding_info and holding_info.get('qty', 0) > 0:
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hy = holding_info.get('yield', 0.0)
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if hy < -3.0:
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danger_force_sell = True
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print(f" 🚨 [DANGER + Loss {hy:.1f}%] Emergency Sell Triggered")
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elif macro_state == 'CAUTION':
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buy_threshold = 0.72
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sell_threshold = 0.38
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print(f" ⚠️ [CAUTION Market] Buy threshold raised to 0.72")
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# 레짐 기반 임계값 추가 조정 (거시경제 판단 이후 적용)
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if regime_analysis and macro_state != 'DANGER':
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buy_threshold = round(
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max(0.55, buy_threshold + regime_analysis.buy_threshold_adj), 3
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)
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# ===== 9. 매매 결정 =====
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decision = "HOLD"
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decision_reason = ""
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# DANGER 긴급 매도 (손실 보유종목)
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if danger_force_sell:
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decision = "SELL"
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decision_reason = f"Emergency DANGER Market + Loss ({holding_info.get('yield', 0.0):.1f}%)"
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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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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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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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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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decision = "SELL"
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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 >= 0.78 and ai_confidence >= 0.75 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 =====
|
|
# total_capital: 호출 측에서 실제 잔고 전달 (없으면 보수적 기본값 5M)
|
|
_capital = total_capital if (total_capital and total_capital > 0) else 5_000_000
|
|
suggested_qty = 0
|
|
if decision == "BUY":
|
|
suggested_qty = calculate_position_size(
|
|
total_capital=_capital,
|
|
current_price=current_price,
|
|
volatility=volatility,
|
|
score=total_score,
|
|
ai_confidence=ai_confidence,
|
|
ticker=ticker
|
|
)
|
|
if suggested_qty == 0:
|
|
decision = "HOLD"
|
|
decision_reason = "Position size too small"
|
|
|
|
# 레짐 기반 포지션 크기 조정 (이미 계산된 수량에 배수 적용)
|
|
if regime_analysis and suggested_qty > 0:
|
|
adjusted_qty = int(suggested_qty * regime_analysis.position_size_adj)
|
|
if adjusted_qty != suggested_qty:
|
|
print(f" 📐 [Regime] 포지션 조정: {suggested_qty} → {adjusted_qty}주 "
|
|
f"(x{regime_analysis.position_size_adj:.2f})")
|
|
suggested_qty = max(0, adjusted_qty)
|
|
if suggested_qty == 0:
|
|
decision = "HOLD"
|
|
decision_reason = "Regime position size adjustment → 0"
|
|
|
|
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 ''}")
|
|
|
|
# ===== 11. AI 전문가 회의 (선택적, Config.AI_COUNCIL_ENABLED) =====
|
|
council_decision = None
|
|
if Config.AI_COUNCIL_ENABLED:
|
|
now = time.time()
|
|
last_call = _council_last_call.get(ticker, 0)
|
|
if now - last_call >= Config.AI_COUNCIL_MIN_INTERVAL:
|
|
_council_last_call[ticker] = now
|
|
council_data = {
|
|
"current_price": current_price,
|
|
"kospi_price": kospi_price,
|
|
"macro_state": macro_state,
|
|
"tech_score": tech_score,
|
|
"rsi": rsi,
|
|
"adx": adx_val,
|
|
"volatility": volatility,
|
|
"bb_zone": bb_zone,
|
|
"mtf_alignment": ma_info.get('mtf_alignment', 'N/A'),
|
|
"lstm_predicted": (
|
|
pred_result.get('predicted', current_price)
|
|
if pred_result else current_price
|
|
),
|
|
"lstm_change_rate": (
|
|
pred_result.get('change_rate', 0) if pred_result else 0
|
|
),
|
|
"ai_confidence": ai_confidence,
|
|
"lstm_score": lstm_score,
|
|
"sentiment_score": sentiment_score,
|
|
"investor_score": investor_score,
|
|
"frgn_net_buy": frgn_net_buy,
|
|
"consecutive_frgn_buy": consecutive_frgn_buy,
|
|
"is_holding": (
|
|
holding_info.get('qty', 0) > 0 if holding_info else False
|
|
),
|
|
"holding_yield": (
|
|
holding_info.get('yield', 0.0) if holding_info else 0.0
|
|
),
|
|
"total_score": total_score,
|
|
}
|
|
try:
|
|
council = get_council(get_ollama())
|
|
council_decision = council.convene(
|
|
ticker, council_data,
|
|
regime_analysis=regime_analysis,
|
|
fast_mode=Config.AI_COUNCIL_FAST_MODE,
|
|
)
|
|
# 모델 교체 권고 경고 출력
|
|
if council_decision.model_replacement_recommended:
|
|
print(
|
|
f" ⚠️ [Council] 모델 교체 권고: "
|
|
f"{council_decision.recommended_model}"
|
|
)
|
|
# 회의 결정이 기존 결정과 다르고 신뢰도 높으면 우선 적용
|
|
if council_decision.confidence >= 0.75:
|
|
council_final = council_decision.final_decision.upper()
|
|
if council_final != decision:
|
|
print(
|
|
f" 🔄 [Council Override] {decision} → {council_final} "
|
|
f"(conf={council_decision.confidence:.2f})"
|
|
)
|
|
decision = council_final
|
|
decision_reason = (
|
|
f"AI Council ({council_decision.confidence:.0%}): "
|
|
f"{council_decision.majority_reasoning[:80]}"
|
|
)
|
|
# BUY로 전환된 경우 수량 재계산
|
|
if decision == "BUY" and suggested_qty == 0:
|
|
suggested_qty = calculate_position_size(
|
|
total_capital=_capital,
|
|
current_price=current_price,
|
|
volatility=volatility,
|
|
score=council_decision.confidence,
|
|
ai_confidence=ai_confidence,
|
|
ticker=ticker,
|
|
)
|
|
except Exception as _ce:
|
|
print(f" [Council] 회의 오류: {_ce}")
|
|
|
|
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,
|
|
"regime": {
|
|
"kospi_level": kospi_price,
|
|
"regime": regime_analysis.regime.value if regime_analysis else "unknown",
|
|
"description": regime_analysis.description if regime_analysis else "",
|
|
"risk_level": regime_analysis.risk_level if regime_analysis else "LOW",
|
|
"model_recommendation": (
|
|
regime_analysis.model_recommendation if regime_analysis else ""
|
|
),
|
|
} if regime_analysis else None,
|
|
"council": {
|
|
"final": council_decision.final_decision,
|
|
"confidence": council_decision.confidence,
|
|
"model_health": council_decision.model_health_score,
|
|
"replace_recommended": council_decision.model_replacement_recommended,
|
|
"recommended_model": council_decision.recommended_model,
|
|
"summary": council_decision.council_summary,
|
|
} if council_decision else None,
|
|
}
|
|
|
|
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)
|
|
}
|