347 lines
13 KiB
Python
347 lines
13 KiB
Python
import logging
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from typing import Optional
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from app.core.config import settings
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from app.services.mean_reversion_strategy import calculate_adx
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logger = logging.getLogger(__name__)
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# Trend filter defaults (configurable via settings)
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TREND_FILTER_ENABLED = getattr(settings, "TREND_FILTER_ENABLED", True)
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TREND_MA_SHORT = int(getattr(settings, "TREND_MA_SHORT", 20))
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TREND_MA_LONG = int(getattr(settings, "TREND_MA_LONG", 60))
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TREND_TOLERANCE_PCT = float(getattr(settings, "TREND_TOLERANCE_PCT", 0.005))
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# 급등 과열 필터 defaults
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OVERHEAT_FILTER_ENABLED = getattr(settings, "OVERHEAT_FILTER_ENABLED", True)
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OVERHEAT_3DAY_MAX = float(getattr(settings, "OVERHEAT_3DAY_MAX", 15.0))
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OVERHEAT_5DAY_MAX = float(getattr(settings, "OVERHEAT_5DAY_MAX", 20.0))
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OVERHEAT_10DAY_MAX = float(getattr(settings, "OVERHEAT_10DAY_MAX", 30.0))
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OVERHEAT_DISPARITY_MAX = float(getattr(settings, "OVERHEAT_DISPARITY_MAX", 108.0))
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def _ma(values: list, window: int) -> Optional[float]:
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try:
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if len(values) < window:
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return None
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return sum(values[-window:]) / window
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except Exception:
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return None
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def _passes_uptrend(closes: list, ma_short: int = TREND_MA_SHORT, ma_long: int = TREND_MA_LONG, tol: float = TREND_TOLERANCE_PCT) -> Optional[bool]:
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if not closes or len(closes) < max(ma_short, ma_long):
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return None
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ma_s = _ma(closes, ma_short)
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ma_l = _ma(closes, ma_long)
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close = closes[-1]
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if ma_s is None or ma_l is None:
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return None
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# 허용 오차 적용: 거의 골든크로스/이탈 직후도 허용
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return (ma_s >= ma_l * (1 - tol)) and (close >= ma_s * (1 - tol))
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def analyze_overheat(closes: list, ma20: Optional[float]) -> dict:
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"""급등 과열 필터 - 단기 급등 종목 매수 차단"""
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details = {}
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reasons = []
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if len(closes) >= 4:
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gain_3d = ((closes[-1] - closes[-4]) / closes[-4]) * 100 if closes[-4] > 0 else 0
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details["gain_3d"] = round(gain_3d, 1)
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if gain_3d > OVERHEAT_3DAY_MAX:
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reasons.append(f"3일 +{gain_3d:.1f}% (>{OVERHEAT_3DAY_MAX}%)")
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if len(closes) >= 6:
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gain_5d = ((closes[-1] - closes[-6]) / closes[-6]) * 100 if closes[-6] > 0 else 0
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details["gain_5d"] = round(gain_5d, 1)
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if gain_5d > OVERHEAT_5DAY_MAX:
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reasons.append(f"5일 +{gain_5d:.1f}% (>{OVERHEAT_5DAY_MAX}%)")
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if len(closes) >= 11:
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gain_10d = ((closes[-1] - closes[-11]) / closes[-11]) * 100 if closes[-11] > 0 else 0
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details["gain_10d"] = round(gain_10d, 1)
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if gain_10d > OVERHEAT_10DAY_MAX:
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reasons.append(f"10일 +{gain_10d:.1f}% (>{OVERHEAT_10DAY_MAX}%)")
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if ma20 and ma20 > 0:
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disparity = (closes[-1] / ma20) * 100
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details["disparity_20"] = round(disparity, 1)
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if disparity > OVERHEAT_DISPARITY_MAX:
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reasons.append(f"이격도 {disparity:.1f}% (>{OVERHEAT_DISPARITY_MAX}%)")
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blocked = len(reasons) > 0
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reason = "급등과열: " + ", ".join(reasons) if blocked else ""
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return {"blocked": blocked, "reason": reason, "details": details}
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def analyze_custom_signals(chart_data):
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"""커스텀 매매 공식 기반 신호 분석 - 이격도 필터 적용"""
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closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
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if len(closes) < 120:
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return None
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ma20 = sum(closes[-20:]) / 20
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ma60 = sum(closes[-60:]) / 60
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ma120 = sum(closes[-120:]) / 120
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close = closes[-1]
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disparity = (close / ma20) * 100 if ma20 else 100
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if len(closes) >= 4:
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price_3days_ago = closes[-4]
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recent_gain = ((close - price_3days_ago) / price_3days_ago) * 100 if price_3days_ago > 0 else 0
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if recent_gain > 10:
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return {"signal": None, "description": f"급등 종목 추격 금지 (3일 +{recent_gain:.1f}%)"}
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if ma20 > ma60 > ma120 and close > ma20:
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if disparity <= 102:
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return {"signal": "buy", "description": "정배열 눌림목 매수 (이격도 안정)"}
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else:
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return {"signal": None, "description": f"정배열이나 이격 과열 ({disparity:.1f}%)"}
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elif ma20 < ma60 < ma120 and close < ma20:
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return {"signal": "sell", "description": "역배열 하락 매도타점"}
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recent_lows = [float(item.get("stck_lwpr", 0)) for item in chart_data[-5:]]
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if any(low < ma20 for low in recent_lows) and close > ma20:
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if disparity <= 101:
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return {"signal": "buy", "description": "눌림목 진입 매수타점"}
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else:
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return {"signal": None, "description": f"눌림목이나 이격 과열 ({disparity:.1f}%)"}
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stop_loss_line = ma60
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take_profit_line = ma20 * 1.05
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if close < stop_loss_line:
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return {"signal": "exit", "description": "손절라인 이탈"}
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if close > take_profit_line:
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return {"signal": "take_profit", "description": "익절라인 돌파"}
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return None
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def analyze_macd(chart_data: list) -> dict:
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if len(chart_data) < 26:
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return {"signal": None, "description": "데이터 부족"}
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prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-26:]]
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if len(prices) < 26:
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return {"signal": None, "description": "데이터 부족"}
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def calculate_ema(prices, period):
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ema = [sum(prices[:period]) / period]
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multiplier = 2 / (period + 1)
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for price in prices[period:]:
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ema.append((price - ema[-1]) * multiplier + ema[-1])
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return ema
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ema12 = calculate_ema(prices, 12)
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ema26 = calculate_ema(prices, 26)
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if len(ema12) < 2 or len(ema26) < 2:
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return {"signal": None, "description": "데이터 부족"}
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macd = [ema12[i] - ema26[i] for i in range(len(ema26))]
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signal = calculate_ema(macd, 9)
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if len(signal) < 2:
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return {"signal": None, "description": "데이터 부족"}
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if macd[-2] <= signal[-2] and macd[-1] > signal[-1]:
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return {"signal": "buy", "description": "MACD 골든크로스"}
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elif macd[-2] >= signal[-2] and macd[-1] < signal[-1]:
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return {"signal": "sell", "description": "MACD 데드크로스"}
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return {"signal": None, "description": "신호 없음"}
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def analyze_rsi(chart_data: list) -> dict:
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if len(chart_data) < 15:
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return {"signal": None, "description": "데이터 부족"}
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prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-15:]]
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gains = []
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losses = []
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for i in range(1, len(prices)):
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change = prices[i] - prices[i-1]
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if change > 0:
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gains.append(change)
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losses.append(0)
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else:
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gains.append(0)
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losses.append(abs(change))
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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
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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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if len(chart_data) >= 16:
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prev_prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-16:-1]]
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prev_gains = []
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prev_losses = []
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for i in range(1, len(prev_prices)):
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change = prev_prices[i] - prev_prices[i-1]
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if change > 0:
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prev_gains.append(change)
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prev_losses.append(0)
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else:
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prev_gains.append(0)
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prev_losses.append(abs(change))
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prev_avg_gain = sum(prev_gains[-14:]) / 14
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prev_avg_loss = sum(prev_losses[-14:]) / 14
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if prev_avg_loss == 0:
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rsi_prev = 100
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else:
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prev_rs = prev_avg_gain / prev_avg_loss
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rsi_prev = 100 - (100 / (1 + prev_rs))
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else:
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rsi_prev = rsi
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if rsi < 30:
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return {"signal": None, "description": f"RSI 과매도 심화 ({rsi:.1f}) - 반등 대기"}
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elif 30 <= rsi <= 45 and rsi > rsi_prev:
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return {"signal": "buy", "description": f"RSI 바닥 탈출 반등 ({rsi:.1f})"}
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elif rsi > 70:
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return {"signal": "sell", "description": f"RSI 과매수 ({rsi:.1f})"}
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elif 45 <= rsi <= 60:
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return {"signal": "buy", "description": f"RSI 안정 상승 ({rsi:.1f})"}
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return {"signal": None, "description": f"RSI 중립 ({rsi:.1f})"}
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def analyze_bollinger(chart_data: list) -> dict:
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if len(chart_data) < 20:
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return {"signal": None, "description": "데이터 부족"}
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prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-20:]]
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ma20 = sum(prices) / len(prices)
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variance = sum((p - ma20) ** 2 for p in prices) / len(prices)
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std_dev = variance ** 0.5
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upper_band = ma20 + (2 * std_dev)
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lower_band = ma20 - (2 * std_dev)
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current_price = prices[-1]
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if current_price < lower_band:
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return {"signal": "buy", "description": "볼린저 하한선 터치"}
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elif current_price > upper_band:
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return {"signal": "sell", "description": "볼린저 상한선 돌파"}
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return {"signal": None, "description": "볼린저 중립"}
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def analyze_moving_average(chart_data: list) -> dict:
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if len(chart_data) < 60:
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return {"signal": None, "description": "데이터 부족"}
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prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-60:]]
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ma5 = sum(prices[-5:]) / 5
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ma20 = sum(prices[-20:]) / 20
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ma60 = sum(prices[-60:]) / 60
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current_price = prices[-1]
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if ma5 > ma20 and current_price > ma5:
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return {"signal": "buy", "description": "5일선 > 20일선"}
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elif ma20 > ma60 and current_price > ma20:
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return {"signal": "buy", "description": "20일선 > 60일선"}
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elif ma5 < ma20 < ma60 and current_price < ma5:
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return {"signal": "sell", "description": "이동평균 역배열"}
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return {"signal": None, "description": "이동평균 중립"}
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def analyze_stochastic(chart_data: list) -> dict:
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if len(chart_data) < 14:
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return {"signal": None, "description": "데이터 부족"}
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recent_data = chart_data[-14:]
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highs = [float(item.get("stck_hgpr", 0)) for item in recent_data]
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lows = [float(item.get("stck_lwpr", 0)) for item in recent_data]
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closes = [float(item.get("stck_clpr", 0)) for item in recent_data]
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highest_high = max(highs)
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lowest_low = min(lows)
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current_close = closes[-1]
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if highest_high == lowest_low:
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return {"signal": None, "description": "데이터 부족"}
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k = ((current_close - lowest_low) / (highest_high - lowest_low)) * 100
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if k < 30:
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return {"signal": "buy", "description": f"스토캐스틱 과매도 (%K: {k:.1f})"}
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elif k > 70:
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return {"signal": "sell", "description": f"스토캐스틱 과매수 (%K: {k:.1f})"}
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return {"signal": None, "description": f"스토캐스틱 중립 (%K: {k:.1f})"}
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def analyze_volatility_breakout(chart_data: list, k: float = 0.5) -> dict:
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if len(chart_data) < 2:
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return {"signal": None, "description": "데이터 부족"}
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today = chart_data[-1]
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yesterday = chart_data[-2]
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current_price = float(today.get("stck_clpr", 0))
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open_price = float(today.get("stck_oprc", 0))
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prev_high = float(yesterday.get("stck_hgpr", 0))
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prev_low = float(yesterday.get("stck_lwpr", 0))
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prev_range = prev_high - prev_low
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if prev_range == 0 or open_price == 0:
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return {"signal": None, "description": "데이터 부족"}
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target_price = open_price + (prev_range * k)
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if current_price >= target_price:
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return {"signal": "buy", "description": f"변동성 돌파 발생 (목표가: {target_price:.0f})"}
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return {"signal": None, "description": "변동성 돌파 미달"}
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def analyze_adx(chart_data: list) -> dict:
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if len(chart_data) < 29:
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return {"signal": None, "description": "데이터 부족", "adx": None}
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highs = [float(item.get("stck_hgpr", 0)) for item in chart_data]
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lows = [float(item.get("stck_lwpr", 0)) for item in chart_data]
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closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
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adx = calculate_adx(highs, lows, closes)
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if adx is None:
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return {"signal": None, "description": "ADX 계산 불가", "adx": None}
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if adx < 20:
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return {"signal": "buy", "description": f"ADX({adx:.0f}<20) 횡보장 — 평균회귀 유리", "adx": adx}
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if adx > 30:
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return {"signal": "sell", "description": f"ADX({adx:.0f}>30) 강추세 — 평균회귀 위험", "adx": adx}
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return {"signal": None, "description": f"ADX({adx:.0f}) 중립", "adx": adx}
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def analyze_obv(chart_data: list) -> dict:
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if len(chart_data) < 2:
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return {"signal": None, "description": "데이터 부족"}
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volumes = [int(item.get("acml_vol", 0)) for item in chart_data[-5:]]
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closes = [float(item.get("stck_clpr", 0)) for item in chart_data[-5:]]
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if len(volumes) < 2 or len(closes) < 2:
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return {"signal": None, "description": "데이터 부족"}
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if volumes[-1] > volumes[-2] * 1.5 and closes[-1] > closes[-2]:
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return {"signal": "buy", "description": "거래량 증가와 상승"}
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elif volumes[-1] < volumes[-2] * 0.5 and closes[-1] < closes[-2]:
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return {"signal": "sell", "description": "거래량 감소와 하락"}
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return {"signal": None, "description": "거래량 중립"}
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