476 lines
19 KiB
Python
476 lines
19 KiB
Python
import asyncio
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import logging
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import time
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import pytz
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from datetime import datetime
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from typing import List, Optional
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from pydantic import BaseModel
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from app.core.config import settings
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from app.core.market_schedule import is_market_open
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from app.services.korea_investment_api import korea_investment_api
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from app.services.mean_reversion_strategy import (
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MarketRegime, detect_market_regime, calculate_atr, calculate_adx
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)
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from app.services.technical_indicators import (
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TREND_FILTER_ENABLED, TREND_MA_SHORT, TREND_MA_LONG, TREND_TOLERANCE_PCT,
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OVERHEAT_FILTER_ENABLED, _passes_uptrend, analyze_overheat,
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analyze_custom_signals, analyze_macd, analyze_rsi, analyze_bollinger,
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analyze_moving_average, analyze_stochastic, analyze_volatility_breakout,
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analyze_adx, analyze_obv
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)
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logger = logging.getLogger(__name__)
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KST = pytz.timezone('Asia/Seoul')
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def _get_kst_now():
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return datetime.now(KST).replace(tzinfo=None)
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class RecommendationCriteria(BaseModel):
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use_macd: bool = False
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use_rsi: bool = False
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use_bollinger: bool = False
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use_moving_average: bool = False
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use_stochastic: bool = False
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use_adx: bool = False
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use_obv: bool = False
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use_volatility_breakout: bool = False
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use_overheat_filter: bool = True
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min_volume_ratio: float = 1.0
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max_disparity: float = 200.0
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for_batch: bool = False
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class StockRecommendation(BaseModel):
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stock_code: str
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stock_name: str
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current_price: float
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change_rate: float
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signals: List[str]
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score: int
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price_date: str = ""
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volume: Optional[int] = None
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rsi: Optional[float] = None
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ma5: Optional[float] = None
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ma20: Optional[float] = None
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ma60: Optional[float] = None
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stochastic_k: Optional[float] = None
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stochastic_d: Optional[float] = None
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bollinger_upper: Optional[float] = None
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bollinger_lower: Optional[float] = None
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bollinger_middle: Optional[float] = None
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ml_score: Optional[int] = None
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ml_confidence: Optional[float] = None
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ml_signal: Optional[str] = None
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atr_14: Optional[float] = None
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adx_14: Optional[float] = None
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_market_regime_cache: dict = {"result": None, "ts": 0.0}
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_MARKET_REGIME_CACHE_TTL = 120
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async def check_market_regime() -> dict:
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try:
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if not is_market_open():
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return {"regime": "neutral", "regime_200d": "bear", "message": "장외 시간 - 시장 레짐 판별 스킵", "position_multiplier": 0.5}
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now_ts = time.monotonic()
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if _market_regime_cache["result"] is not None and (now_ts - _market_regime_cache["ts"]) < _MARKET_REGIME_CACHE_TTL:
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return _market_regime_cache["result"]
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kospi_chart = await korea_investment_api.get_daily_chart("069500", "D", limit=240)
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if "output2" not in kospi_chart or not kospi_chart["output2"]:
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return {"regime": "neutral", "message": "시장 지수 확인 불가", "position_multiplier": 0.5}
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chart_data = kospi_chart["output2"]
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closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
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if len(closes) < 60:
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return {"regime": "neutral", "message": "데이터 부족", "position_multiplier": 0.5}
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current_price = closes[0]
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ma20 = sum(closes[:20]) / 20
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ma60 = sum(closes[:60]) / 60
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if len(closes) >= 205:
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regime_enum = detect_market_regime(list(reversed(closes)))
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is_bull_200 = regime_enum == MarketRegime.BULL
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ma200 = sum(closes[:200]) / 200
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else:
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is_bull_200 = current_price > ma20
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ma200 = ma60
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consecutive_down = 0
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for i in range(len(closes) - 1):
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if closes[i] < closes[i + 1]:
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consecutive_down += 1
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else:
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break
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drop_from_ma20 = ((current_price - ma20) / ma20) * 100
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def _cache_and_return(result: dict) -> dict:
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_market_regime_cache["result"] = result
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_market_regime_cache["ts"] = time.monotonic()
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return result
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if current_price < ma60 or consecutive_down >= 5:
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return _cache_and_return({
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"regime": "strong_bearish",
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"regime_200d": "bear",
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"message": f"강한 하락장 (코스피: {current_price:.0f}, MA200: {ma200:.0f}, {consecutive_down}일 연속하락)",
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"position_multiplier": 0.1,
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"min_score": settings.BEAR_MARKET_MIN_SCORE + 3,
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"max_trades": 1,
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"consecutive_down": consecutive_down,
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"drop_from_ma20": round(drop_from_ma20, 1),
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})
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if current_price < ma20:
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return _cache_and_return({
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"regime": "bearish",
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"regime_200d": "bear",
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"message": f"하락장 (코스피: {current_price:.0f} < MA20: {ma20:.0f}, MA200: {ma200:.0f})",
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"position_multiplier": settings.BEAR_MARKET_POSITION_RATIO,
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"min_score": settings.BEAR_MARKET_MIN_SCORE,
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"max_trades": settings.BEAR_MARKET_MAX_TRADES,
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"consecutive_down": consecutive_down,
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"drop_from_ma20": round(drop_from_ma20, 1),
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})
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return _cache_and_return({
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"regime": "bullish",
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"regime_200d": "bull" if is_bull_200 else "bear",
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"message": f"상승장 (코스피: {current_price:.0f}, MA200: {ma200:.0f}, Bull={is_bull_200})",
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"position_multiplier": 1.0,
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"min_score": 1,
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"max_trades": settings.MAX_DAILY_TRADES,
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"consecutive_down": consecutive_down,
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"drop_from_ma20": round(drop_from_ma20, 1),
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})
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except Exception as e:
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logger.error(f"시장 지수 확인 실패: {e}")
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return {"regime": "neutral", "message": "시장 지수 확인 실패", "position_multiplier": 0.5}
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async def analyze_stock(stock_code: str, stock_name: str, criteria: RecommendationCriteria) -> Optional[StockRecommendation]:
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try:
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chart_response = await korea_investment_api.get_daily_chart(stock_code, "D")
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if "output2" not in chart_response or not chart_response["output2"]:
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return None
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chart_data = list(reversed(chart_response["output2"]))
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latest = chart_data[-1] if chart_data else None
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if not latest:
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return None
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if not stock_name:
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stock_name = latest.get("hts_kor_isnm", stock_code)
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current_price = float(latest.get("stck_clpr", 0))
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price_date = latest.get("stck_bsop_date", "")
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volume = int(latest.get("acml_vol", 0) or 0)
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if current_price == 0:
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return None
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# 전일 거래량 필터: 100만주 미만 종목 제외 (유동성 부족)
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if volume < 1_000_000:
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return None
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if len(chart_data) >= 2:
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prev_price = float(chart_data[-2].get("stck_clpr", current_price))
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change_rate = ((current_price - prev_price) / prev_price) * 100 if prev_price > 0 else 0
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else:
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change_rate = 0
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closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
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volumes = [int(item.get("acml_vol", 0)) for item in chart_data]
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ma5 = sum(closes[-5:]) / 5 if len(closes) >= 5 else None
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ma20 = sum(closes[-20:]) / 20 if len(closes) >= 20 else None
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ma60 = sum(closes[-60:]) / 60 if len(closes) >= 60 else None
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avg_vol_20 = sum(volumes[-20:]) / 20 if len(volumes) >= 20 else 0
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volume_ratio = (volume / avg_vol_20) if avg_vol_20 > 0 else 0
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if not criteria.for_batch and avg_vol_20 < 50000 and current_price < 50000:
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return None
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disparity = (current_price / ma20) * 100 if ma20 else 100
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trend_ok = None
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if TREND_FILTER_ENABLED:
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trend_ok = _passes_uptrend(closes, TREND_MA_SHORT, TREND_MA_LONG)
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rsi = None
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if len(closes) >= 15:
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prices_for_rsi = closes[-15:]
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gains = []
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losses = []
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for i in range(1, len(prices_for_rsi)):
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diff = prices_for_rsi[i] - prices_for_rsi[i-1]
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gains.append(max(diff, 0))
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losses.append(max(-diff, 0))
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avg_gain = sum(gains[-14:]) / 14
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avg_loss = sum(losses[-14:]) / 14
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if avg_loss == 0:
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rsi = 100.0
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else:
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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stochastic_k = None
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stochastic_d = None
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if len(chart_data) >= 14:
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recent = chart_data[-14:]
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highs = [float(item.get("stck_hgpr", 0)) for item in recent]
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lows = [float(item.get("stck_lwpr", 0)) for item in recent]
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closes_for_stoch = [float(item.get("stck_clpr", 0)) for item in recent]
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highest_high = max(highs)
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lowest_low = min(lows)
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if highest_high != lowest_low:
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stochastic_k = ((closes_for_stoch[-1] - lowest_low) / (highest_high - lowest_low)) * 100
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stochastic_d_vals = []
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for j in range(len(recent)-3, len(recent)):
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slice_high = max([float(item.get("stck_hgpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
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slice_low = min([float(item.get("stck_lwpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
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if slice_high != slice_low:
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stochastic_d_vals.append(((float(recent[j].get("stck_clpr", 0)) - slice_low) / (slice_high - slice_low)) * 100)
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if stochastic_d_vals:
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stochastic_d = sum(stochastic_d_vals) / len(stochastic_d_vals)
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bollinger_upper = bollinger_lower = bollinger_middle = None
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if len(closes) >= 20:
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window = closes[-20:]
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middle = sum(window) / len(window)
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variance = sum((p - middle) ** 2 for p in window) / len(window)
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stddev = variance ** 0.5
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bollinger_middle = middle
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bollinger_upper = middle + 2 * stddev
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bollinger_lower = middle - 2 * stddev
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signals = []
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score = 0
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if disparity > criteria.max_disparity:
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score -= 5
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signals.append(f"이격도 과열({disparity:.1f}% > {criteria.max_disparity}%) - 매수금지")
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if criteria.use_overheat_filter and OVERHEAT_FILTER_ENABLED:
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overheat = analyze_overheat(closes, ma20)
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if overheat["blocked"]:
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score -= 10
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signals.append(overheat["reason"])
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if criteria.use_volatility_breakout:
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vb_result = analyze_volatility_breakout(chart_data)
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if vb_result and vb_result["signal"] == "buy":
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signals.append(vb_result["description"])
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score += 4
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custom_result = analyze_custom_signals(chart_data)
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if custom_result:
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if custom_result["signal"] == "buy":
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signals.append(custom_result["description"])
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score += 3
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elif custom_result["signal"] == "sell":
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signals.append(custom_result["description"])
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score -= 2
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elif custom_result["signal"] == "exit":
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signals.append(custom_result["description"])
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score -= 5
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elif custom_result["signal"] == "take_profit":
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signals.append(custom_result["description"])
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score += 2
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if criteria.use_macd:
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result = analyze_macd(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 2
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if criteria.use_rsi:
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result = analyze_rsi(chart_data)
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if result["signal"] == "buy":
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if volume_ratio >= 1.2:
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signals.append(result["description"] + " + 거래량증가")
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score += 3
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else:
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signals.append(result["description"] + " (거래량부족)")
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score += 1
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if criteria.use_bollinger:
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result = analyze_bollinger(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 1
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if criteria.use_moving_average:
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result = analyze_moving_average(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 2
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if criteria.use_stochastic:
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result = analyze_stochastic(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 1
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if criteria.use_adx:
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result = analyze_adx(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 1
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if criteria.use_obv:
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result = analyze_obv(chart_data)
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if result["signal"] == "buy":
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signals.append(result["description"])
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score += 1
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if volume_ratio >= 1.5 and change_rate > 0:
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signals.append(f"거래량 실린 상승 ({volume_ratio:.1f}배)")
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score += 3
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elif volume_ratio < 0.8:
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score -= 1
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if TREND_FILTER_ENABLED and trend_ok is not None:
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if trend_ok:
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signals.append(f"추세상승(>{TREND_MA_SHORT}MA & {TREND_MA_SHORT}MA>{TREND_MA_LONG}MA, tol={int(TREND_TOLERANCE_PCT*10000)/100:.2f}%)")
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score += 1
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else:
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signals.append(f"추세약함({TREND_MA_SHORT}MA≤{TREND_MA_LONG}MA 또는 종가≤{TREND_MA_SHORT}MA)")
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if criteria.min_volume_ratio > 0 and volume_ratio < criteria.min_volume_ratio:
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return None
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ml_score_val = None
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ml_confidence_val = None
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ml_signal_val = None
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if settings.ML_ENSEMBLE_ENABLED:
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try:
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from app.services.ml_ensemble import get_ensemble
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ensemble = get_ensemble()
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if ensemble.is_trained:
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ml_result = ensemble.predict(chart_data)
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if "error" not in ml_result:
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ml_score_val = ml_result["ensemble_score"]
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ml_confidence_val = ml_result["confidence"]
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ml_signal_val = ml_result["signal"]
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rule_score_normalized = (score / 15.0) * 100.0
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combined_score = int(
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rule_score_normalized * settings.ML_ENSEMBLE_RULE_WEIGHT +
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ml_score_val * settings.ML_ENSEMBLE_ML_WEIGHT
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)
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score = max(0, int(combined_score / 100.0 * 15.0))
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signals.append(f"ML앙상블: {ml_signal_val}({ml_score_val}점, 신뢰도{ml_confidence_val:.0%})")
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except Exception:
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pass
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atr_14_val = None
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adx_14_val = None
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try:
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if len(chart_data) >= 15:
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_h = [float(d.get("stck_hgpr", 0)) for d in chart_data]
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_l = [float(d.get("stck_lwpr", 0)) for d in chart_data]
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_c = [float(d.get("stck_clpr", 0)) for d in chart_data]
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atr_14_val = calculate_atr(_h, _l, _c)
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if len(chart_data) >= 29:
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adx_14_val = calculate_adx(_h, _l, _c)
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except Exception as _e:
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logger.debug(f"ATR/ADX 계산 실패 {stock_code}: {_e}")
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if criteria.for_batch or len(signals) > 0 or score > 0:
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final_score = max(1, score) if len(signals) > 0 else score
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return StockRecommendation(
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stock_code=stock_code,
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stock_name=stock_name,
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current_price=current_price,
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change_rate=change_rate,
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signals=signals if signals else ["조건 분석 중"],
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score=final_score,
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price_date=price_date,
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volume=volume,
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rsi=rsi,
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ma5=ma5,
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ma20=ma20,
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ma60=ma60,
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stochastic_k=stochastic_k,
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stochastic_d=stochastic_d,
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bollinger_upper=bollinger_upper,
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bollinger_lower=bollinger_lower,
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bollinger_middle=bollinger_middle,
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ml_score=ml_score_val,
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ml_confidence=ml_confidence_val,
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ml_signal=ml_signal_val,
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atr_14=atr_14_val,
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adx_14=adx_14_val,
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)
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return None
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except Exception as e:
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logger.error(f"Error analyzing {stock_code}: {str(e)}")
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return None
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async def analyze_stock_for_batch(stock_code: str, stock_name: str) -> Optional[StockRecommendation]:
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criteria = RecommendationCriteria(
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use_macd=True,
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use_rsi=True,
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use_bollinger=True,
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use_moving_average=True,
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use_stochastic=True,
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use_adx=True,
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use_obv=True,
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use_volatility_breakout=True,
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min_volume_ratio=0,
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max_disparity=200.0,
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for_batch=True,
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)
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return await analyze_stock(stock_code, stock_name, criteria)
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async def analyze_stock_with_indicators(stock_code: str, stock_name: str) -> Optional[StockRecommendation]:
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criteria = RecommendationCriteria(
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use_macd=True,
|
|
use_rsi=True,
|
|
use_bollinger=True,
|
|
use_moving_average=True,
|
|
use_stochastic=True,
|
|
use_adx=True,
|
|
use_obv=True,
|
|
use_volatility_breakout=True,
|
|
min_volume_ratio=1.2,
|
|
max_disparity=110.0
|
|
)
|
|
return await analyze_stock(stock_code, stock_name, criteria)
|
|
|
|
async def get_all_tradable_stocks() -> List[tuple]:
|
|
try:
|
|
await asyncio.sleep(3.0)
|
|
kospi_result = await korea_investment_api.get_stock_list("J")
|
|
await asyncio.sleep(3.0)
|
|
kosdaq_result = await korea_investment_api.get_stock_list("Q")
|
|
|
|
stocks = []
|
|
if "output" in kospi_result:
|
|
for item in kospi_result["output"]:
|
|
stock_code = item.get("mksc_shrn_iscd", "")
|
|
stock_name = item.get("hts_kor_isnm", "")
|
|
if stock_code and stock_name:
|
|
stocks.append((stock_code, stock_name))
|
|
|
|
if "output" in kosdaq_result:
|
|
for item in kosdaq_result["output"]:
|
|
stock_code = item.get("mksc_shrn_iscd", "")
|
|
stock_name = item.get("hts_kor_isnm", "")
|
|
if stock_code and stock_name:
|
|
stocks.append((stock_code, stock_name))
|
|
|
|
return stocks
|
|
except Exception as e:
|
|
logger.error(f"Error fetching stock list from KIS API: {e}", exc_info=True)
|
|
return [] |