React + FastAPI 풀 마이그레이션 — Streamlit 제거
- backend/ — FastAPI + JWT + 모든 REST 엔드포인트 - frontend/ — Next.js 14 + Tailwind + 7페이지 (대시보드/트레이드/거래소/자동매매/설정/내정보/로그인) - core_logic.py — 신호계산/알림 로직 분리 (기존 app_streamlit.py 에서 추출) - users_db.py + bcrypt 인증, exchange_keys.py + Fernet 암호화 - trades_db.py — 진입/청산 lifecycle 추적, signal_events raw 로그 - settings_db.py — 모든 운영 파라미터 DB 영속 저장 (RSI/거래량/펀딩비 임계값 포함) - docker-compose: frontend / backend / postgres + Traefik 라우팅 - assets/logo.svg — JUNGGOMOA 그라디언트 로고 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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chpark
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"""
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Streamlit 의존이 없는 핵심 비즈니스 로직.
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- Binance Futures API 데이터 수집
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- 지표 / 신호 계산
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- 알림 (텔레그램) + 트레이드 lifecycle 기록
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- 알림 / 일일 리포트 백그라운드 루프
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기존 app_streamlit.py 에서 그대로 추출. FastAPI 에서도 그대로 import.
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"""
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import os
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import time
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import threading
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import requests
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import pandas as pd
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import numpy as np
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from datetime import datetime, timezone, timedelta
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import ta
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import urllib3
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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import alert_state
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import settings_db
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import trades_db
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BASE = "https://fapi.binance.com"
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KST = timedelta(hours=9)
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STOP_LOSS_PCT = 0.0075
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LONG_SIGNALS = {"strong_long_signal", "long_signal", "vol_long_signal", "reversal_long_signal"}
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SHORT_SIGNALS = {"strong_short_signal", "short_signal", "vol_short_signal", "reversal_short_signal"}
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TF_LABEL_MAP = {
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"1m": "1분봉", "3m": "3분봉", "5m": "5분봉",
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"15m": "15분봉", "30m": "30분봉",
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"1h": "1시간봉", "4h": "4시간봉", "12h": "12시간봉",
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"1d": "1일봉", "3d": "3일봉", "1M": "1개월봉",
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}
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SIG_DEFS = [
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("strong_long_signal", "strong_long", "🟢 강한 롱", "long"),
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("strong_short_signal", "strong_short", "🔴 강한 숏", "short"),
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("long_signal", "long", "🔼 일반 롱", "long"),
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("short_signal", "short", "🔽 일반 숏", "short"),
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("vol_long_signal", "vol_long", "🔼 볼륨 롱", "long"),
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("vol_short_signal", "vol_short", "🔽 볼륨 숏", "short"),
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("reversal_long_signal", "rev_long", "🔄 롱 추세 꺾임 감지", "long"),
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("reversal_short_signal","rev_short", "🔄 숏 추세 꺾임 감지", "short"),
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("short_caution_signal", "short_caution","⚠️ 숏 주의", "caution"),
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]
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# ── 설정 helpers ──
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def TELEGRAM_TOKEN(): return settings_db.get("telegram_token", "")
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def TELEGRAM_CHAT_ID(): return settings_db.get("telegram_chat_id", "")
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def ALERT_COOLDOWN(): return settings_db.get_int("alert_cooldown_sec", 600)
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def STOP_LOSS_PCT_v(): return settings_db.get_float("stop_loss_pct", 0.0075)
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# ── 텔레그램 ──
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def send_telegram(message: str):
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token = TELEGRAM_TOKEN()
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chat_id = TELEGRAM_CHAT_ID()
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if not token or not chat_id:
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print("[텔레그램] 토큰/chat_id 미설정 — skip")
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return
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try:
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url = f"https://api.telegram.org/bot{token}/sendMessage"
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requests.post(url, data={"chat_id": chat_id, "text": message}, timeout=10)
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except Exception as e:
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print(f"[텔레그램 오류] {e}")
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# ── Binance Futures fetch ──
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def get_klines(symbol="BTCUSDT", interval="5m", limit=375):
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url = f"{BASE}/fapi/v1/klines"
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r = requests.get(url, params={"symbol": symbol, "interval": interval, "limit": limit}, timeout=10, verify=False)
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df = pd.DataFrame(r.json(), columns=[
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"open_time","open","high","low","close","volume",
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"close_time","quote_vol","trades","taker_buy_vol","taker_sell_vol","ignore"
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])
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for c in ["open","high","low","close","volume","taker_buy_vol","taker_sell_vol"]:
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df[c] = df[c].astype(float)
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df["taker_sell_vol"] = df["volume"] - df["taker_buy_vol"]
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df["open_time"] = pd.to_datetime(df["open_time"], unit="ms") + KST
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return df
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def get_funding_rate(symbol="BTCUSDT", limit=100):
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url = f"{BASE}/fapi/v1/fundingRate"
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r = requests.get(url, params={"symbol": symbol, "limit": limit}, timeout=10, verify=False)
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df = pd.DataFrame(r.json())
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if df.empty:
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return df
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df["fundingRate"] = df["fundingRate"].astype(float) * 100
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df["fundingTime"] = pd.to_datetime(df["fundingTime"], unit="ms") + KST
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return df
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def get_open_interest_history(symbol="BTCUSDT", period="5m", limit=100):
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url = f"{BASE}/futures/data/openInterestHist"
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r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10, verify=False)
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df = pd.DataFrame(r.json())
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if df.empty:
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return df
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df["sumOpenInterest"] = df["sumOpenInterest"].astype(float)
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") + KST
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return df
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def get_long_short_ratio(symbol="BTCUSDT", period="5m", limit=500):
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url = f"{BASE}/futures/data/topLongShortPositionRatio"
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r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10, verify=False)
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df = pd.DataFrame(r.json())
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if df.empty:
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return df
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df["longShortRatio"] = df["longShortRatio"].astype(float)
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") + KST
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return df
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def _to_floor_freq(period):
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return {"1m":"1min","3m":"3min","5m":"5min","15m":"15min","30m":"30min","1h":"1h","4h":"4h","12h":"12h","1d":"1D","3d":"3D","1M":"1ME"}.get(period, period)
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# ── 지표 + 신호 ──
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def compute_indicators(df, interval="5m"):
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c = df["close"]
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df["MA7"] = c.rolling(7).mean()
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df["MA25"] = c.rolling(25).mean()
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df["MA99"] = c.rolling(99).mean()
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df["MA200"] = c.rolling(200).mean()
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df["BB_mid"] = c.rolling(20).mean()
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df["BB_std"] = c.rolling(20).std()
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df["BB_upper"] = df["BB_mid"] + 2 * df["BB_std"]
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df["BB_lower"] = df["BB_mid"] - 2 * df["BB_std"]
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df["RSI"] = ta.momentum.RSIIndicator(c, window=14).rsi()
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macd = ta.trend.MACD(c, window_slow=26, window_fast=12, window_sign=9)
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df["MACD"] = macd.macd()
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df["MACD_signal"] = macd.macd_signal()
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df["MACD_hist"] = macd.macd_diff()
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stoch = ta.momentum.StochRSIIndicator(c, window=14, smooth1=3, smooth2=3)
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df["StochRSI_k"] = stoch.stochrsi_k() * 100
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df["StochRSI_d"] = stoch.stochrsi_d() * 100
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df["ATR"] = ta.volatility.AverageTrueRange(df["high"], df["low"], df["close"], window=14).average_true_range()
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df = compute_signals(df, interval)
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return df
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def compute_signals(df, interval="5m"):
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LONG_RSI_MAX = settings_db.get_float("long_rsi_max", 75.0)
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SHORT_RSI_MIN = settings_db.get_float("short_rsi_min", 25.0)
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SLONG_RSI_MAX = settings_db.get_float("strong_long_rsi_max", 65.0)
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SSHORT_RSI_MIN = settings_db.get_float("strong_short_rsi_min", 35.0)
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BODY_PCT_MIN = settings_db.get_float("body_pct_min", 0.002)
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REV_BODY_PCT = settings_db.get_float("reversal_body_pct", 0.003)
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REV_VOL_MULT = settings_db.get_float("reversal_vol_mult", 1.3)
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VOL_EXH_MULT = settings_db.get_float("vol_exhaustion_mult", 3.0)
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VOL_NET_MULT = settings_db.get_float("vol_net_mult", 2.0)
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OI_ACTIVE_PCT = settings_db.get_float("oi_active_pct", 0.001)
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FR_SHORT_EXTREME = settings_db.get_float("fr_short_extreme", -0.007)
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df["bull_ma_2"] = (df["close"] > df["MA7"]) & (df["close"] > df["MA25"])
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df["bear_ma_2"] = (df["close"] < df["MA7"]) & (df["close"] < df["MA25"])
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df["bull_ma"] = (df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"])
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df["bear_ma"] = (df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"])
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bb_range = (df["BB_upper"] - df["BB_lower"]).replace(0, float("nan"))
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df["bb_pos"] = (df["close"] - df["BB_lower"]) / bb_range
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body_pct = (df["close"] - df["open"]) / df["open"].replace(0, float("nan"))
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df["long_signal"] = df["bull_ma_2"] & (df["RSI"] < LONG_RSI_MAX) & (df["MACD_hist"] > df["MACD_hist"].shift(1)) & (df["close"] > df["BB_mid"]) & (body_pct >= BODY_PCT_MIN)
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df["short_signal"] = df["bear_ma_2"] & (df["RSI"] > SHORT_RSI_MIN) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & (df["close"] < df["BB_mid"]) & (body_pct <= -BODY_PCT_MIN)
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df["long_signal"] = df["long_signal"] & (df["long_signal"].rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0)
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df["short_signal"] = df["short_signal"] & (df["short_signal"].rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0)
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if "sumOpenInterest" in df.columns and df["sumOpenInterest"].notna().sum() > 5:
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oi_series = df["sumOpenInterest"].ffill()
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else:
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oi_series = df["close"] * df["volume"]
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df["oi_up"] = oi_series > oi_series.shift(1)
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df["oi_down"] = oi_series < oi_series.shift(1)
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df["oi_up_2"] = df["oi_up"] & df["oi_up"].shift(1).fillna(False)
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df["oi_down_2"] = df["oi_down"] & df["oi_down"].shift(1).fillna(False)
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df["oi_active"] = oi_series.pct_change().abs() > OI_ACTIVE_PCT
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df["taker_buy_dom"] = df["taker_buy_vol"] > df["taker_sell_vol"]
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df["taker_sell_dom"] = df["taker_sell_vol"] > df["taker_buy_vol"]
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df["taker_buy_2"] = df["taker_buy_dom"] & df["taker_buy_dom"].shift(1).fillna(False)
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df["taker_sell_2"] = df["taker_sell_dom"] & df["taker_sell_dom"].shift(1).fillna(False)
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df["fr_long_favor"] = df["taker_buy_vol"].rolling(3).mean() > df["taker_sell_vol"].rolling(3).mean()
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df["fr_short_favor"] = df["taker_sell_vol"].rolling(3).mean() > df["taker_buy_vol"].rolling(3).mean()
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df["strong_long_signal"] = df["bull_ma_2"] & (df["RSI"] < SLONG_RSI_MAX) & (df["MACD_hist"] > df["MACD_hist"].shift(1)) & df["oi_up_2"] & df["taker_buy_2"] & df["fr_long_favor"] & (df["close"] > df["open"])
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df["strong_short_signal"] = df["bear_ma_2"] & (df["RSI"] > SSHORT_RSI_MIN) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & df["oi_down_2"] & df["taker_sell_2"] & df["fr_short_favor"] & (df["close"] < df["open"])
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df["strong_long_signal"] = df["strong_long_signal"] & (df["strong_long_signal"].rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0)
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df["strong_short_signal"] = df["strong_short_signal"] & (df["strong_short_signal"].rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0)
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vol_avg = df["volume"].rolling(10).mean()
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spike = df["volume"] > vol_avg * VOL_EXH_MULT
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buy_spike = spike & (df["taker_buy_vol"] > df["taker_sell_vol"])
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sell_spike = spike & (df["taker_sell_vol"] > df["taker_buy_vol"])
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df["exhaustion_short"] = buy_spike.shift(1).fillna(False)
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df["exhaustion_long"] = sell_spike.shift(1).fillna(False)
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_vol_min_map = {"1m": 33, "3m": 100, "5m": 100, "15m": 300, "30m": 600, "1h": 1200, "2h": 2400, "4h": 4800, "12h": 14400, "1d": 28800, "3d": 86400, "1M": 864000}
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_vol_min = _vol_min_map.get(interval, 100)
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df["sell_net"] = df["taker_sell_vol"] - df["taker_buy_vol"]
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sell_net_avg = df["sell_net"].rolling(10).mean()
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sell_spike_strong = (
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(df["sell_net"] > sell_net_avg * VOL_NET_MULT) &
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(df["sell_net"] > 0) &
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(df["taker_sell_vol"] > _vol_min) &
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df["oi_active"]
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)
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cooldown_vol_short = sell_spike_strong.rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0
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df["vol_short_signal"] = sell_spike_strong & cooldown_vol_short
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df["buy_net"] = df["taker_buy_vol"] - df["taker_sell_vol"]
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buy_net_avg = df["buy_net"].rolling(10).mean()
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buy_spike_strong = (
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(df["buy_net"] > buy_net_avg * VOL_NET_MULT) &
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(df["buy_net"] > 0) &
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(df["taker_buy_vol"] > _vol_min) &
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df["oi_active"]
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)
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cooldown_vol_long = buy_spike_strong.rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0
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df["vol_long_signal"] = buy_spike_strong & cooldown_vol_long
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if "fundingRate" in df.columns and "sumOpenInterest" in df.columns:
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fr_extreme = df["fundingRate"] <= FR_SHORT_EXTREME
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raw_signal = df["oi_down_2"] & fr_extreme
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cooldown_mask = raw_signal.rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0
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df["short_caution_signal"] = raw_signal & cooldown_mask
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else:
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df["short_caution_signal"] = False
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prior_close = df["close"].shift(1)
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prior_close_3 = df["close"].shift(3)
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was_up = prior_close > prior_close_3
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was_down = prior_close < prior_close_3
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candle_body_pct = (df["close"] - df["open"]) / df["open"].replace(0, float("nan"))
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vol_avg3 = df["volume"].rolling(3).mean().shift(1)
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vol_strong = df["volume"] > vol_avg3 * REV_VOL_MULT
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rev_short_raw = was_up & (candle_body_pct < -REV_BODY_PCT) & vol_strong
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rev_long_raw = was_down & (candle_body_pct > REV_BODY_PCT) & vol_strong
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df["reversal_short_signal"] = rev_short_raw & (rev_short_raw.rolling(3, min_periods=1).sum().shift(1).fillna(0) == 0)
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df["reversal_long_signal"] = rev_long_raw & (rev_long_raw.rolling(3, min_periods=1).sum().shift(1).fillna(0) == 0)
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return df
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def build_signal_df(symbol, interval, klines_limit=200):
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"""알림 / API 공용 - klines + OI + FR 머지 + 지표/신호 계산"""
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df = get_klines(symbol, interval, klines_limit)
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oi_period = interval if interval in ["5m","15m","30m","1h","4h","12h","1d","3d","1M"] else "5m"
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try:
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oi = get_open_interest_history(symbol, oi_period, min(klines_limit, 500))
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if not oi.empty:
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oi_m = oi[["timestamp","sumOpenInterest"]].rename(columns={"timestamp":"open_time"})
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df["open_time_r"] = df["open_time"].dt.floor(_to_floor_freq(oi_period))
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oi_m["open_time"] = oi_m["open_time"].dt.floor(_to_floor_freq(oi_period))
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df = df.merge(oi_m, left_on="open_time_r", right_on="open_time", how="left", suffixes=("","_oi"))
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df = df.drop(columns=["open_time_r","open_time_oi"], errors="ignore")
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df["sumOpenInterest"] = df["sumOpenInterest"].ffill()
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except Exception: pass
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try:
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fr = get_funding_rate(symbol, 200)
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if not fr.empty:
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fr_m = fr[["fundingTime","fundingRate"]].rename(columns={"fundingTime":"open_time"})
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fr_m["open_time"] = fr_m["open_time"].dt.floor(_to_floor_freq("1h"))
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df["open_time_r2"] = df["open_time"].dt.floor(_to_floor_freq("1h"))
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df = df.merge(fr_m, left_on="open_time_r2", right_on="open_time", how="left", suffixes=("","_fr"))
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df = df.drop(columns=["open_time_r2","open_time_fr"], errors="ignore")
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df["fundingRate"] = df["fundingRate"].ffill().fillna(0)
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except Exception: pass
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df = compute_indicators(df, interval)
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return df
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# ── 알림 코어 ──
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def check_and_alert(df, symbol, interval):
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now = time.time()
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if df is None or df.empty:
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return
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forming_ct = df.iloc[-1]["open_time"]
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|
||||
if interval not in alert_state.synced_intervals:
|
||||
for sig, key, _, _ in SIG_DEFS:
|
||||
if sig not in df.columns:
|
||||
continue
|
||||
triggered = df[df[sig].fillna(False)]
|
||||
if not triggered.empty:
|
||||
alert_state.last_fired_candle[(interval, key)] = triggered.iloc[-1]["open_time"]
|
||||
alert_state.synced_intervals.add(interval)
|
||||
print(f"[알림스레드] {interval} 초기 sync 완료")
|
||||
return
|
||||
|
||||
new_pending = []
|
||||
for p in alert_state.pending_groups:
|
||||
if p["interval"] != interval:
|
||||
new_pending.append(p)
|
||||
continue
|
||||
ct = p["candle_time"]
|
||||
row_match = df[df["open_time"] == ct]
|
||||
if row_match.empty:
|
||||
continue
|
||||
row = row_match.iloc[0]
|
||||
any_still_true = any(bool(row.get(s, False)) for s in p["sig_cols"])
|
||||
if any_still_true:
|
||||
if ct == forming_ct:
|
||||
new_pending.append(p)
|
||||
else:
|
||||
send_telegram(f"[취소 알림]\n{p['msg']}")
|
||||
le = alert_state.long_entry.get(interval)
|
||||
se = alert_state.short_entry.get(interval)
|
||||
if p["direction"] == "long" and le is not None and le.get("open_time") == ct:
|
||||
trades_db.record_exit(symbol, interval, "long", ct, float(row["close"]), "cancelled")
|
||||
alert_state.long_entry[interval] = None
|
||||
elif p["direction"] == "short" and se is not None and se.get("open_time") == ct:
|
||||
trades_db.record_exit(symbol, interval, "short", ct, float(row["close"]), "cancelled")
|
||||
alert_state.short_entry[interval] = None
|
||||
alert_state.pending_groups = new_pending
|
||||
|
||||
recent = df.tail(3)
|
||||
eligible = []
|
||||
for sig, key, sub_label, direction in SIG_DEFS:
|
||||
if sig not in recent.columns:
|
||||
continue
|
||||
triggered = recent[recent[sig].fillna(False)]
|
||||
seen_key = (interval, sig)
|
||||
prev_seen = alert_state.signal_seen_count.get(seen_key)
|
||||
if triggered.empty:
|
||||
if prev_seen:
|
||||
alert_state.signal_seen_count[seen_key] = {"candle_time": prev_seen["candle_time"], "count": 0}
|
||||
continue
|
||||
candle_time = triggered.iloc[-1]["open_time"]
|
||||
state_key = (interval, key)
|
||||
if candle_time == alert_state.last_fired_candle.get(state_key):
|
||||
continue
|
||||
if now - alert_state.last_alert.get(state_key, 0) <= ALERT_COOLDOWN():
|
||||
continue
|
||||
if prev_seen is None or prev_seen["candle_time"] != candle_time:
|
||||
alert_state.signal_seen_count[seen_key] = {"candle_time": candle_time, "count": 1}
|
||||
else:
|
||||
alert_state.signal_seen_count[seen_key] = {"candle_time": candle_time, "count": prev_seen["count"] + 1}
|
||||
count = alert_state.signal_seen_count[seen_key]["count"]
|
||||
stable_min = settings_db.get_int("forming_stable_polls", 2)
|
||||
if candle_time == forming_ct and count < stable_min:
|
||||
continue
|
||||
eligible.append({
|
||||
"sig": sig, "key": key, "sub_label": sub_label,
|
||||
"direction": direction, "candle_time": candle_time, "row": triggered.iloc[-1],
|
||||
})
|
||||
|
||||
groups = {"long": [], "short": [], "caution": []}
|
||||
for e in eligible:
|
||||
groups[e["direction"]].append(e)
|
||||
|
||||
tf_label = TF_LABEL_MAP.get(interval, interval)
|
||||
|
||||
def _send_group(group):
|
||||
if not group:
|
||||
return
|
||||
candle_time = group[0]["candle_time"]
|
||||
candle_time_str = pd.Timestamp(candle_time).strftime("%Y-%m-%d %H:%M")
|
||||
sub_labels = " + ".join(e["sub_label"] for e in group)
|
||||
direction = group[0]["direction"]
|
||||
trades_db.log_signal_events(symbol, interval, group)
|
||||
if direction == "caution":
|
||||
msg = f"{sub_labels} 신호\n{symbol} {tf_label}\n시간: {candle_time_str}"
|
||||
send_telegram(msg)
|
||||
else:
|
||||
entry_price = float(group[0]["row"]["open"])
|
||||
sl_pct = STOP_LOSS_PCT_v()
|
||||
stop_price = entry_price * (1 - sl_pct) if direction == "long" else entry_price * (1 + sl_pct)
|
||||
msg = (
|
||||
f"{sub_labels} 진입 신호\n{symbol} {tf_label}\n"
|
||||
f"시간: {candle_time_str}\n진입가: {entry_price:,.2f}\n손절가: {stop_price:,.2f}"
|
||||
)
|
||||
entry_record = {"price": entry_price, "stop": stop_price, "open_time": candle_time, "entry_msg": msg}
|
||||
if interval in ("30m", "1h"):
|
||||
opposite_dict = alert_state.short_entry if direction == "long" else alert_state.long_entry
|
||||
opposite_label = "숏" if direction == "long" else "롱"
|
||||
opposite_direction = "short" if direction == "long" else "long"
|
||||
for opp_interval, opp_rec in list(opposite_dict.items()):
|
||||
if opp_rec is None:
|
||||
continue
|
||||
send_telegram(
|
||||
f"[반대 신호 감지 - {opposite_label} 청산 권장]\n"
|
||||
f"--- 기존 진입 ---\n{opp_rec['entry_msg']}\n"
|
||||
f"--- 반대 신호 ---\n{msg}"
|
||||
)
|
||||
trades_db.record_exit(symbol, opp_interval, opposite_direction,
|
||||
opp_rec.get("open_time"), entry_price, "reversal")
|
||||
opposite_dict[opp_interval] = None
|
||||
if direction == "long":
|
||||
alert_state.long_entry[interval] = entry_record
|
||||
else:
|
||||
alert_state.short_entry[interval] = entry_record
|
||||
trades_db.record_entry(symbol, interval, direction,
|
||||
[e["sig"] for e in group],
|
||||
candle_time, entry_price, stop_price)
|
||||
send_telegram(msg)
|
||||
for e in group:
|
||||
alert_state.last_alert[(interval, e["key"])] = now
|
||||
alert_state.last_fired_candle[(interval, e["key"])] = e["candle_time"]
|
||||
if candle_time == forming_ct:
|
||||
alert_state.pending_groups.append({
|
||||
"interval": interval, "direction": direction, "candle_time": candle_time,
|
||||
"msg": msg, "sig_cols": [e["sig"] for e in group],
|
||||
})
|
||||
|
||||
_send_group(groups.get("long", []))
|
||||
_send_group(groups.get("short", []))
|
||||
_send_group(groups.get("caution", []))
|
||||
|
||||
current_price = float(df.iloc[-1]["close"])
|
||||
le = alert_state.long_entry.get(interval)
|
||||
se = alert_state.short_entry.get(interval)
|
||||
if le is not None and current_price <= le["stop"]:
|
||||
send_telegram(f"[손절가알림]\n{le['entry_msg']}\n현재가: {current_price:,.2f}")
|
||||
trades_db.record_exit(symbol, interval, "long", le.get("open_time"), current_price, "stop_loss")
|
||||
alert_state.long_entry[interval] = None
|
||||
if se is not None and current_price >= se["stop"]:
|
||||
send_telegram(f"[손절가알림]\n{se['entry_msg']}\n현재가: {current_price:,.2f}")
|
||||
trades_db.record_exit(symbol, interval, "short", se.get("open_time"), current_price, "stop_loss")
|
||||
alert_state.short_entry[interval] = None
|
||||
|
||||
|
||||
# ── 백그라운드 루프 (FastAPI startup 에서 호출) ──
|
||||
def alert_timeframes():
|
||||
return settings_db.get_list("alert_timeframes", default=["5m", "15m", "30m", "1h"])
|
||||
|
||||
|
||||
def alert_loop():
|
||||
while True:
|
||||
poll = max(10, settings_db.get_int("polling_interval_sec", 30))
|
||||
if not settings_db.get_bool("alert_enabled", True):
|
||||
time.sleep(poll)
|
||||
continue
|
||||
with alert_state.alert_lock:
|
||||
symbol = alert_state.alert_symbol
|
||||
for interval in alert_timeframes():
|
||||
try:
|
||||
df = build_signal_df(symbol, interval, 200)
|
||||
check_and_alert(df, symbol, interval)
|
||||
except Exception as e:
|
||||
print(f"[알림스레드 오류] {interval}: {e}")
|
||||
time.sleep(poll)
|
||||
|
||||
|
||||
def daily_report_loop():
|
||||
while True:
|
||||
try:
|
||||
if not settings_db.get_bool("daily_report_enabled", True):
|
||||
time.sleep(60)
|
||||
continue
|
||||
now_kst = (datetime.now(timezone.utc) + KST).replace(tzinfo=None)
|
||||
today_str = now_kst.strftime("%Y-%m-%d")
|
||||
if alert_state.last_report_date is None:
|
||||
alert_state.last_report_date = today_str
|
||||
print(f"[일일리포트] 스레드 기동 -- 다음 자정({today_str} 24:00 KST) 까지 대기")
|
||||
elif alert_state.last_report_date != today_str:
|
||||
print(f"[일일리포트] 자정 통과 감지 -> 발송 ({today_str})")
|
||||
# send_daily_report 는 app_streamlit.py 안에 있는 그대로 사용 (없어도 silent skip)
|
||||
try:
|
||||
from app_streamlit import send_daily_report
|
||||
with alert_state.alert_lock:
|
||||
symbol = alert_state.alert_symbol
|
||||
send_daily_report(symbol)
|
||||
except Exception as e:
|
||||
print(f"[일일리포트 호출 실패] {e}")
|
||||
alert_state.last_report_date = today_str
|
||||
except Exception as e:
|
||||
print(f"[일일리포트 스레드 오류] {e}")
|
||||
time.sleep(60)
|
||||
|
||||
|
||||
def start_background_threads():
|
||||
"""FastAPI startup 에서 호출. 한 번만 시작."""
|
||||
if not alert_state.alert_started:
|
||||
t = threading.Thread(target=alert_loop, daemon=True, name="alert_loop")
|
||||
t.start()
|
||||
alert_state.alert_started = True
|
||||
if not alert_state.daily_report_started:
|
||||
dr = threading.Thread(target=daily_report_loop, daemon=True, name="daily_report_loop")
|
||||
dr.start()
|
||||
alert_state.daily_report_started = True
|
||||
Reference in New Issue
Block a user