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shuim
2026-03-06 10:01:13 +09:00
parent d6841a06f7
commit c93b35ba04
11 changed files with 1051 additions and 586 deletions
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79
backend/app/services/korea_investment_api.py
View File

@@ -511,6 +511,76 @@ class KoreaInvestmentAPI:
logger.error(f"Error fetching paginated chart for {stock_code}: {e}")
raise
async def get_daily_ohlcv_range(
self, stock_code: str, start_date: str, end_date: str
) -> list:
"""날짜 범위 일봉 OHLCV 전체 조회 (FHKST03010100)
KIS API는 1회 최대 100행 반환 → 날짜를 소급하며 start_date까지 전부 수집.
Args:
stock_code: 종목코드 (예: "005930")
start_date: 조회 시작일 YYYYMMDD
end_date: 조회 종료일 YYYYMMDD
Returns:
[{"stck_bsop_date": "20240101", "stck_oprc": ..., "stck_hgpr": ...,
"stck_lwpr": ..., "stck_clpr": ..., "acml_vol": ..., "prdy_ctrt": ...}, ...]
최신 → 오래된 순 정렬 (KIS API 기본값)
"""
all_data: list = []
current_end = end_date
while True:
params = {
"FID_COND_MRKT_DIV_CODE": "J",
"FID_INPUT_ISCD": stock_code,
"FID_INPUT_DATE_1": start_date,
"FID_INPUT_DATE_2": current_end,
"FID_PERIOD_DIV_CODE": "D",
"FID_ORG_ADJ_PRC": "0", # 수정주가
}
try:
result, _ = await self._make_request(
"GET",
"/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice",
"FHKST03010100",
params=params,
)
except Exception as exc:
logger.error(
"get_daily_ohlcv_range %s %s~%s 실패: %s",
stock_code, start_date, current_end, exc,
)
break
if not result or result.get("rt_cd") != "0":
logger.warning(
"get_daily_ohlcv_range %s: %s",
stock_code, result.get("msg1") if result else "no response",
)
break
output = result.get("output2") or []
if not output:
break
all_data.extend(output)
# 가장 오래된 행의 날짜를 확인하고 그보다 하루 전으로 소급
oldest_date = output[-1].get("stck_bsop_date", "")
if not oldest_date or oldest_date <= start_date:
break
oldest_dt = datetime.strptime(oldest_date, "%Y%m%d")
current_end = (oldest_dt - timedelta(days=1)).strftime("%Y%m%d")
if current_end < start_date:
break
await asyncio.sleep(self.min_request_interval)
return all_data
async def get_overseas_price(self, symbol: str, exchange_code: str = "NASD") -> Dict[str, Any]:
"""해외주식 현재가 조회 (NASD, NYSE, AMEX)"""
try:
@@ -975,7 +1045,7 @@ class KoreaInvestmentAPI:
"FID_BLNG_CLS_CODE": "0",
"FID_TRGT_CLS_CODE": "111111111",
"FID_TRGT_EXLS_CLS_CODE": "000000",
"FID_INPUT_PRICE_1": "",
"FID_INPUT_PRICE_1": "5000", # 5,000원 이상 종목만 조회
"FID_INPUT_PRICE_2": "",
"FID_VOL_CNT": "",
"FID_INPUT_DATE_1": ""
@@ -1032,3 +1102,10 @@ class KoreaInvestmentAPI:
# 싱글톤 인스턴스
korea_investment_api = KoreaInvestmentAPI()
# 실계좌 시세조회 전용 싱글톤 (KIS_REAL_APP_KEY 설정 시 초기화)
# 모의투자 모드에서도 시세/차트/종목 조회는 실계좌 API를 사용한다.
try:
real_market_api = KoreaInvestmentAPI.for_real_market_data()
except Exception:
real_market_api = None

642
backend/app/services/market_analyzer.py
View File

@@ -8,7 +8,7 @@ from pydantic import BaseModel
from app.core.config import settings
from app.core.market_schedule import is_market_open
from app.services.korea_investment_api import korea_investment_api
from app.services.korea_investment_api import korea_investment_api, real_market_api as _real_market_api
from app.services.mean_reversion_strategy import (
MarketRegime, detect_market_regime, calculate_atr, calculate_adx
)
@@ -77,7 +77,8 @@ async def check_market_regime() -> dict:
if _market_regime_cache["result"] is not None and (now_ts - _market_regime_cache["ts"]) < _MARKET_REGIME_CACHE_TTL:
return _market_regime_cache["result"]
kospi_chart = await korea_investment_api.get_daily_chart("069500", "D", limit=240)
_api = _real_market_api or korea_investment_api
kospi_chart = await _api.get_daily_chart("069500", "D", limit=240)
if "output2" not in kospi_chart or not kospi_chart["output2"]:
return {"regime": "neutral", "message": "시장 지수 확인 불가", "position_multiplier": 0.5}
@@ -88,6 +89,22 @@ async def check_market_regime() -> dict:
if len(closes) < 60:
return {"regime": "neutral", "message": "데이터 부족", "position_multiplier": 0.5}
def _cache_and_return(result: dict) -> dict:
_market_regime_cache["result"] = result
_market_regime_cache["ts"] = time.monotonic()
return result
# ── 수동 비상 정지 ────────────────────────────────────────────
if settings.TRADING_HALTED:
return _cache_and_return({
"regime": "halted",
"regime_200d": "bear",
"message": "⛔ TRADING_HALTED=true — 수동 비상 정지 중 (매수 전면 차단)",
"position_multiplier": 0.0,
"min_score": 9999,
"max_trades": 0,
})
current_price = closes[0]
ma20 = sum(closes[:20]) / 20
ma60 = sum(closes[:60]) / 60
@@ -100,6 +117,23 @@ async def check_market_regime() -> dict:
is_bull_200 = current_price > ma20
ma200 = ma60
# ── 당일 급락 감지 (사이드카/전쟁 등) ───────────────────────
# closes[0]이 오늘 데이터일 때만 적용 (일봉 미확정 시 전일 종가가 closes[0]이 되어 오탐 발생)
latest_date = chart_data[0].get("stck_bsop_date", "") if chart_data else ""
today_str = _get_kst_now().strftime("%Y%m%d")
if latest_date == today_str and len(closes) >= 2 and closes[1] > 0:
today_change_pct = (closes[0] - closes[1]) / closes[1] * 100
if today_change_pct <= settings.MARKET_CRASH_HALT_PCT:
return _cache_and_return({
"regime": "crash",
"regime_200d": "bear",
"message": f"🚨 당일 급락 감지 ({today_change_pct:.1f}% ≤ {settings.MARKET_CRASH_HALT_PCT}%) — 매수 전면 차단",
"position_multiplier": 0.0,
"min_score": 9999,
"max_trades": 0,
"today_change_pct": round(today_change_pct, 2),
})
consecutive_down = 0
for i in range(len(closes) - 1):
if closes[i] < closes[i + 1]:
@@ -109,11 +143,6 @@ async def check_market_regime() -> dict:
drop_from_ma20 = ((current_price - ma20) / ma20) * 100
def _cache_and_return(result: dict) -> dict:
_market_regime_cache["result"] = result
_market_regime_cache["ts"] = time.monotonic()
return result
if current_price < ma60 or consecutive_down >= 5:
return _cache_and_return({
"regime": "strong_bearish",
@@ -153,274 +182,318 @@ async def check_market_regime() -> dict:
return {"regime": "neutral", "message": "시장 지수 확인 실패", "position_multiplier": 0.5}
async def analyze_stock(stock_code: str, stock_name: str, criteria: RecommendationCriteria) -> Optional[StockRecommendation]:
def _analyze_chart_items(
chart_data: List[dict],
stock_code: str,
stock_name: str,
criteria: "RecommendationCriteria",
) -> Optional[StockRecommendation]:
"""KIS 형식 chart_data (oldest first)로 기술적 분석 수행 — API 호출 없음.
analyze_stock / analyze_stock_for_batch_db 양쪽에서 공유하는 순수 분석 로직.
"""
latest = chart_data[-1] if chart_data else None
if not latest:
return None
if not stock_name:
stock_name = latest.get("hts_kor_isnm", stock_code)
current_price = float(latest.get("stck_clpr", 0))
price_date = latest.get("stck_bsop_date", "")
volume = int(latest.get("acml_vol", 0) or 0)
if current_price == 0:
logger.debug("필터[가격0] %s", stock_code)
return None
# 가격 필터: 5,000원 미만 종목 제외 (CLAUDE.md 섹션 5.4)
if current_price < settings.MIN_PRICE_KRW:
logger.info("필터[저가] %s: %.0f원 < %d원", stock_code, current_price, settings.MIN_PRICE_KRW)
return None
# 전일 거래량 필터: 100만주 미만 종목 제외 (유동성 부족)
if volume < 1_000_000:
logger.info("필터[거래량] %s: %d주 < 100만주", stock_code, volume)
return None
if len(chart_data) >= 2:
prev_price = float(chart_data[-2].get("stck_clpr", current_price))
change_rate = ((current_price - prev_price) / prev_price) * 100 if prev_price > 0 else 0
else:
change_rate = 0
closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
volumes = [int(item.get("acml_vol", 0)) for item in chart_data]
ma5 = sum(closes[-5:]) / 5 if len(closes) >= 5 else None
ma20 = sum(closes[-20:]) / 20 if len(closes) >= 20 else None
ma60 = sum(closes[-60:]) / 60 if len(closes) >= 60 else None
avg_vol_20 = sum(volumes[-20:]) / 20 if len(volumes) >= 20 else 0
volume_ratio = (volume / avg_vol_20) if avg_vol_20 > 0 else 0
if not criteria.for_batch and avg_vol_20 < 50000 and current_price < 50000:
return None
disparity = (current_price / ma20) * 100 if ma20 else 100
trend_ok = None
if TREND_FILTER_ENABLED:
trend_ok = _passes_uptrend(closes, TREND_MA_SHORT, TREND_MA_LONG)
rsi = None
if len(closes) >= 15:
prices_for_rsi = closes[-15:]
gains = []
losses = []
for i in range(1, len(prices_for_rsi)):
diff = prices_for_rsi[i] - prices_for_rsi[i-1]
gains.append(max(diff, 0))
losses.append(max(-diff, 0))
avg_gain = sum(gains[-14:]) / 14
avg_loss = sum(losses[-14:]) / 14
if avg_loss == 0:
rsi = 100.0
else:
rs = avg_gain / avg_loss
rsi = 100 - (100 / (1 + rs))
stochastic_k = None
stochastic_d = None
if len(chart_data) >= 14:
recent = chart_data[-14:]
highs = [float(item.get("stck_hgpr", 0)) for item in recent]
lows = [float(item.get("stck_lwpr", 0)) for item in recent]
closes_for_stoch = [float(item.get("stck_clpr", 0)) for item in recent]
highest_high = max(highs)
lowest_low = min(lows)
if highest_high != lowest_low:
stochastic_k = ((closes_for_stoch[-1] - lowest_low) / (highest_high - lowest_low)) * 100
stochastic_d_vals = []
for j in range(len(recent)-3, len(recent)):
slice_high = max([float(item.get("stck_hgpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
slice_low = min([float(item.get("stck_lwpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
if slice_high != slice_low:
stochastic_d_vals.append(((float(recent[j].get("stck_clpr", 0)) - slice_low) / (slice_high - slice_low)) * 100)
if stochastic_d_vals:
stochastic_d = sum(stochastic_d_vals) / len(stochastic_d_vals)
bollinger_upper = bollinger_lower = bollinger_middle = None
if len(closes) >= 20:
window = closes[-20:]
middle = sum(window) / len(window)
variance = sum((p - middle) ** 2 for p in window) / len(window)
stddev = variance ** 0.5
bollinger_middle = middle
bollinger_upper = middle + 2 * stddev
bollinger_lower = middle - 2 * stddev
signals = []
score = 0
if disparity > criteria.max_disparity:
score -= 5
signals.append(f"이격도 과열({disparity:.1f}% > {criteria.max_disparity}%) - 매수금지")
if criteria.use_overheat_filter and OVERHEAT_FILTER_ENABLED:
overheat = analyze_overheat(closes, ma20)
if overheat["blocked"]:
score -= 10
signals.append(overheat["reason"])
if criteria.use_volatility_breakout:
vb_result = analyze_volatility_breakout(chart_data)
if vb_result and vb_result["signal"] == "buy":
signals.append(vb_result["description"])
score += 4
custom_result = analyze_custom_signals(chart_data)
if custom_result:
if custom_result["signal"] == "buy":
signals.append(custom_result["description"])
score += 3
elif custom_result["signal"] == "sell":
signals.append(custom_result["description"])
score -= 2
elif custom_result["signal"] == "exit":
signals.append(custom_result["description"])
score -= 5
elif custom_result["signal"] == "take_profit":
signals.append(custom_result["description"])
score += 2
if criteria.use_macd:
result = analyze_macd(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 2
if criteria.use_rsi:
result = analyze_rsi(chart_data)
if result["signal"] == "buy":
if volume_ratio >= 1.2:
signals.append(result["description"] + " + 거래량증가")
score += 3
else:
signals.append(result["description"] + " (거래량부족)")
score += 1
if criteria.use_bollinger:
result = analyze_bollinger(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_moving_average:
result = analyze_moving_average(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 2
if criteria.use_stochastic:
result = analyze_stochastic(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_adx:
result = analyze_adx(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_obv:
result = analyze_obv(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if volume_ratio >= 1.5 and change_rate > 0:
signals.append(f"거래량 실린 상승 ({volume_ratio:.1f}배)")
score += 3
elif volume_ratio < 0.8:
score -= 1
if TREND_FILTER_ENABLED and trend_ok is not None:
if trend_ok:
signals.append(f"추세상승(>{TREND_MA_SHORT}MA & {TREND_MA_SHORT}MA>{TREND_MA_LONG}MA, tol={int(TREND_TOLERANCE_PCT*10000)/100:.2f}%)")
score += 1
else:
signals.append(f"추세약함({TREND_MA_SHORT}MA≤{TREND_MA_LONG}MA 또는 종가≤{TREND_MA_SHORT}MA)")
if criteria.min_volume_ratio > 0 and volume_ratio < criteria.min_volume_ratio:
return None
ml_score_val = None
ml_confidence_val = None
ml_signal_val = None
if settings.ML_ENSEMBLE_ENABLED:
try:
from app.services.ml_ensemble import get_ensemble
ensemble = get_ensemble()
if ensemble.is_trained:
ml_result = ensemble.predict(chart_data)
if "error" not in ml_result:
ml_score_val = ml_result["ensemble_score"]
ml_confidence_val = ml_result["confidence"]
ml_signal_val = ml_result["signal"]
rule_score_normalized = (score / 15.0) * 100.0
combined_score = int(
rule_score_normalized * settings.ML_ENSEMBLE_RULE_WEIGHT +
ml_score_val * settings.ML_ENSEMBLE_ML_WEIGHT
)
score = max(0, int(combined_score / 100.0 * 15.0))
signals.append(f"ML앙상블: {ml_signal_val}({ml_score_val}점, 신뢰도{ml_confidence_val:.0%})")
except Exception:
pass
atr_14_val = None
adx_14_val = None
try:
chart_response = await korea_investment_api.get_daily_chart(stock_code, "D")
if len(chart_data) >= 15:
_h = [float(d.get("stck_hgpr", 0)) for d in chart_data]
_l = [float(d.get("stck_lwpr", 0)) for d in chart_data]
_c = [float(d.get("stck_clpr", 0)) for d in chart_data]
atr_14_val = calculate_atr(_h, _l, _c)
if len(chart_data) >= 29:
adx_14_val = calculate_adx(_h, _l, _c)
except Exception as _e:
logger.debug(f"ATR/ADX 계산 실패 {stock_code}: {_e}")
if criteria.for_batch or len(signals) > 0 or score > 0:
final_score = max(1, score) if len(signals) > 0 else score
return StockRecommendation(
stock_code=stock_code,
stock_name=stock_name,
current_price=current_price,
change_rate=change_rate,
signals=signals if signals else ["조건 분석 중"],
score=final_score,
price_date=price_date,
volume=volume,
rsi=rsi,
ma5=ma5,
ma20=ma20,
ma60=ma60,
stochastic_k=stochastic_k,
stochastic_d=stochastic_d,
bollinger_upper=bollinger_upper,
bollinger_lower=bollinger_lower,
bollinger_middle=bollinger_middle,
ml_score=ml_score_val,
ml_confidence=ml_confidence_val,
ml_signal=ml_signal_val,
atr_14=atr_14_val,
adx_14=adx_14_val,
)
return None
async def analyze_stock(stock_code: str, stock_name: str, criteria: RecommendationCriteria) -> Optional[StockRecommendation]:
"""KIS API로 차트를 조회한 뒤 _analyze_chart_items에 위임."""
try:
_api = _real_market_api or korea_investment_api
chart_response = await _api.get_daily_chart(stock_code, "D")
if "output2" not in chart_response or not chart_response["output2"]:
return None
chart_data = list(reversed(chart_response["output2"]))
latest = chart_data[-1] if chart_data else None
if not latest:
return None
if not stock_name:
stock_name = latest.get("hts_kor_isnm", stock_code)
current_price = float(latest.get("stck_clpr", 0))
price_date = latest.get("stck_bsop_date", "")
volume = int(latest.get("acml_vol", 0) or 0)
if current_price == 0:
return None
# 가격 필터: 5,000원 미만 종목 제외 (CLAUDE.md 섹션 5.4)
if current_price < settings.MIN_PRICE_KRW:
return None
# 전일 거래량 필터: 100만주 미만 종목 제외 (유동성 부족)
if volume < 1_000_000:
return None
if len(chart_data) >= 2:
prev_price = float(chart_data[-2].get("stck_clpr", current_price))
change_rate = ((current_price - prev_price) / prev_price) * 100 if prev_price > 0 else 0
else:
change_rate = 0
closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
volumes = [int(item.get("acml_vol", 0)) for item in chart_data]
ma5 = sum(closes[-5:]) / 5 if len(closes) >= 5 else None
ma20 = sum(closes[-20:]) / 20 if len(closes) >= 20 else None
ma60 = sum(closes[-60:]) / 60 if len(closes) >= 60 else None
avg_vol_20 = sum(volumes[-20:]) / 20 if len(volumes) >= 20 else 0
volume_ratio = (volume / avg_vol_20) if avg_vol_20 > 0 else 0
if not criteria.for_batch and avg_vol_20 < 50000 and current_price < 50000:
return None
disparity = (current_price / ma20) * 100 if ma20 else 100
trend_ok = None
if TREND_FILTER_ENABLED:
trend_ok = _passes_uptrend(closes, TREND_MA_SHORT, TREND_MA_LONG)
rsi = None
if len(closes) >= 15:
prices_for_rsi = closes[-15:]
gains = []
losses = []
for i in range(1, len(prices_for_rsi)):
diff = prices_for_rsi[i] - prices_for_rsi[i-1]
gains.append(max(diff, 0))
losses.append(max(-diff, 0))
avg_gain = sum(gains[-14:]) / 14
avg_loss = sum(losses[-14:]) / 14
if avg_loss == 0:
rsi = 100.0
else:
rs = avg_gain / avg_loss
rsi = 100 - (100 / (1 + rs))
stochastic_k = None
stochastic_d = None
if len(chart_data) >= 14:
recent = chart_data[-14:]
highs = [float(item.get("stck_hgpr", 0)) for item in recent]
lows = [float(item.get("stck_lwpr", 0)) for item in recent]
closes_for_stoch = [float(item.get("stck_clpr", 0)) for item in recent]
highest_high = max(highs)
lowest_low = min(lows)
if highest_high != lowest_low:
stochastic_k = ((closes_for_stoch[-1] - lowest_low) / (highest_high - lowest_low)) * 100
stochastic_d_vals = []
for j in range(len(recent)-3, len(recent)):
slice_high = max([float(item.get("stck_hgpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
slice_low = min([float(item.get("stck_lwpr", 0)) for item in recent[j-3 if j-3>=0 else 0:j+1]])
if slice_high != slice_low:
stochastic_d_vals.append(((float(recent[j].get("stck_clpr", 0)) - slice_low) / (slice_high - slice_low)) * 100)
if stochastic_d_vals:
stochastic_d = sum(stochastic_d_vals) / len(stochastic_d_vals)
bollinger_upper = bollinger_lower = bollinger_middle = None
if len(closes) >= 20:
window = closes[-20:]
middle = sum(window) / len(window)
variance = sum((p - middle) ** 2 for p in window) / len(window)
stddev = variance ** 0.5
bollinger_middle = middle
bollinger_upper = middle + 2 * stddev
bollinger_lower = middle - 2 * stddev
signals = []
score = 0
if disparity > criteria.max_disparity:
score -= 5
signals.append(f"이격도 과열({disparity:.1f}% > {criteria.max_disparity}%) - 매수금지")
if criteria.use_overheat_filter and OVERHEAT_FILTER_ENABLED:
overheat = analyze_overheat(closes, ma20)
if overheat["blocked"]:
score -= 10
signals.append(overheat["reason"])
if criteria.use_volatility_breakout:
vb_result = analyze_volatility_breakout(chart_data)
if vb_result and vb_result["signal"] == "buy":
signals.append(vb_result["description"])
score += 4
custom_result = analyze_custom_signals(chart_data)
if custom_result:
if custom_result["signal"] == "buy":
signals.append(custom_result["description"])
score += 3
elif custom_result["signal"] == "sell":
signals.append(custom_result["description"])
score -= 2
elif custom_result["signal"] == "exit":
signals.append(custom_result["description"])
score -= 5
elif custom_result["signal"] == "take_profit":
signals.append(custom_result["description"])
score += 2
if criteria.use_macd:
result = analyze_macd(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 2
if criteria.use_rsi:
result = analyze_rsi(chart_data)
if result["signal"] == "buy":
if volume_ratio >= 1.2:
signals.append(result["description"] + " + 거래량증가")
score += 3
else:
signals.append(result["description"] + " (거래량부족)")
score += 1
if criteria.use_bollinger:
result = analyze_bollinger(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_moving_average:
result = analyze_moving_average(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 2
if criteria.use_stochastic:
result = analyze_stochastic(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_adx:
result = analyze_adx(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if criteria.use_obv:
result = analyze_obv(chart_data)
if result["signal"] == "buy":
signals.append(result["description"])
score += 1
if volume_ratio >= 1.5 and change_rate > 0:
signals.append(f"거래량 실린 상승 ({volume_ratio:.1f}배)")
score += 3
elif volume_ratio < 0.8:
score -= 1
if TREND_FILTER_ENABLED and trend_ok is not None:
if trend_ok:
signals.append(f"추세상승(>{TREND_MA_SHORT}MA & {TREND_MA_SHORT}MA>{TREND_MA_LONG}MA, tol={int(TREND_TOLERANCE_PCT*10000)/100:.2f}%)")
score += 1
else:
signals.append(f"추세약함({TREND_MA_SHORT}MA≤{TREND_MA_LONG}MA 또는 종가≤{TREND_MA_SHORT}MA)")
if criteria.min_volume_ratio > 0 and volume_ratio < criteria.min_volume_ratio:
return None
ml_score_val = None
ml_confidence_val = None
ml_signal_val = None
if settings.ML_ENSEMBLE_ENABLED:
try:
from app.services.ml_ensemble import get_ensemble
ensemble = get_ensemble()
if ensemble.is_trained:
ml_result = ensemble.predict(chart_data)
if "error" not in ml_result:
ml_score_val = ml_result["ensemble_score"]
ml_confidence_val = ml_result["confidence"]
ml_signal_val = ml_result["signal"]
rule_score_normalized = (score / 15.0) * 100.0
combined_score = int(
rule_score_normalized * settings.ML_ENSEMBLE_RULE_WEIGHT +
ml_score_val * settings.ML_ENSEMBLE_ML_WEIGHT
)
score = max(0, int(combined_score / 100.0 * 15.0))
signals.append(f"ML앙상블: {ml_signal_val}({ml_score_val}점, 신뢰도{ml_confidence_val:.0%})")
except Exception:
pass
atr_14_val = None
adx_14_val = None
try:
if len(chart_data) >= 15:
_h = [float(d.get("stck_hgpr", 0)) for d in chart_data]
_l = [float(d.get("stck_lwpr", 0)) for d in chart_data]
_c = [float(d.get("stck_clpr", 0)) for d in chart_data]
atr_14_val = calculate_atr(_h, _l, _c)
if len(chart_data) >= 29:
adx_14_val = calculate_adx(_h, _l, _c)
except Exception as _e:
logger.debug(f"ATR/ADX 계산 실패 {stock_code}: {_e}")
if criteria.for_batch or len(signals) > 0 or score > 0:
final_score = max(1, score) if len(signals) > 0 else score
return StockRecommendation(
stock_code=stock_code,
stock_name=stock_name,
current_price=current_price,
change_rate=change_rate,
signals=signals if signals else ["조건 분석 중"],
score=final_score,
price_date=price_date,
volume=volume,
rsi=rsi,
ma5=ma5,
ma20=ma20,
ma60=ma60,
stochastic_k=stochastic_k,
stochastic_d=stochastic_d,
bollinger_upper=bollinger_upper,
bollinger_lower=bollinger_lower,
bollinger_middle=bollinger_middle,
ml_score=ml_score_val,
ml_confidence=ml_confidence_val,
ml_signal=ml_signal_val,
atr_14=atr_14_val,
adx_14=adx_14_val,
)
return None
return _analyze_chart_items(chart_data, stock_code, stock_name, criteria)
except Exception as e:
logger.error(f"Error analyzing {stock_code}: {str(e)}")
return None
async def analyze_stock_for_batch_db(
chart_data_newest_first: List[dict],
stock_code: str,
stock_name: str,
) -> Optional[StockRecommendation]:
"""DB에서 가져온 chart_data (newest first)로 배치 분석 — KIS API 호출 없음."""
criteria = RecommendationCriteria(
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=0,
max_disparity=200.0,
for_batch=True,
)
try:
chart_data = list(reversed(chart_data_newest_first)) # oldest first
return _analyze_chart_items(chart_data, stock_code, stock_name, criteria)
except Exception as e:
logger.error(f"Error analyzing {stock_code}: {str(e)}")
return None
async def analyze_stock_for_batch(stock_code: str, stock_name: str) -> Optional[StockRecommendation]:
criteria = RecommendationCriteria(
use_macd=True,
@@ -453,27 +526,30 @@ async def analyze_stock_with_indicators(stock_code: str, stock_name: str) -> Opt
return await analyze_stock(stock_code, stock_name, criteria)
async def get_all_tradable_stocks() -> List[tuple]:
SKIP_KEYWORDS = [
"ETF", "레버리지", "인버스", "KODEX", "TIGER", "ARIRANG",
"KBSTAR", "HANARO", "SOL", "ACE", "KOSEF", "KINDEX", "RISE",
"선물", "스팩", "리츠", "SPAC",
]
try:
_list_api = _real_market_api or korea_investment_api
await asyncio.sleep(3.0)
kospi_result = await korea_investment_api.get_stock_list("J")
kospi_result = await _list_api.get_stock_list("J")
await asyncio.sleep(3.0)
kosdaq_result = await korea_investment_api.get_stock_list("Q")
kosdaq_result = await _list_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:
for result in (kospi_result, kosdaq_result):
if "output" in result:
for item in result["output"]:
stock_code = item.get("mksc_shrn_iscd", "")
stock_name = item.get("hts_kor_isnm", "")
if not stock_code or not stock_name:
continue
if any(kw in stock_name for kw in SKIP_KEYWORDS):
continue
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)