ILSEON-RYU
695 lines
34 KiB
Python
695 lines
34 KiB
Python
"""
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BTC/ETH Futures Trading Dashboard — Streamlit 버전
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"""
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import sys
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import os
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sys.stdout.reconfigure(line_buffering=True)
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os.environ["PYTHONUNBUFFERED"] = "1"
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import time
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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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 threading
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import streamlit as st
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import ta
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from pyngrok import ngrok
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# ──────────────────────────────────────────────
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# 페이지 설정 (반드시 최상단)
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# ──────────────────────────────────────────────
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st.set_page_config(
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page_title="BTC/ETH Futures Dashboard",
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layout="wide",
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initial_sidebar_state="collapsed"
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)
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# 라이트모드 강제 CSS
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st.markdown("""
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<style>
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html, body, [data-testid="stAppViewContainer"], [data-testid="stHeader"] {
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background-color: #ffffff !important;
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color: #131722 !important;
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}
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[data-testid="stSidebar"] { background-color: #f0f3fa !important; }
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.stSelectbox > div > div { background-color: #ffffff !important; color: #131722 !important; }
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.stButton > button { background-color: #238636 !important; color: #ffffff !important; border: none !important; }
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header { background-color: #f0f0f0 !important; }
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</style>
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""", unsafe_allow_html=True)
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# ──────────────────────────────────────────────
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# 설정
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# ──────────────────────────────────────────────
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TELEGRAM_TOKEN = os.getenv("TELEGRAM_TOKEN", "")
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TELEGRAM_CHAT_ID = os.getenv("TELEGRAM_CHAT_ID", "")
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ALERT_COOLDOWN = 30
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BASE = "https://fapi.binance.com"
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KST = timedelta(hours=9)
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_last_alert = {"strong_long": 0, "strong_short": 0, "long": 0, "short": 0, "vol_long": 0, "vol_short": 0, "short_caution": 0}
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# ──────────────────────────────────────────────
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# 텔레그램
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# ──────────────────────────────────────────────
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def send_telegram(message: str):
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try:
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url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage"
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requests.post(url, data={"chat_id": TELEGRAM_CHAT_ID, "text": message}, timeout=5)
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except Exception as e:
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print(f"[텔레그램 오류] {e}")
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def check_and_alert(df, symbol, interval):
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now = time.time()
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last = df.iloc[-1]
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for sig, key, msg in [
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("strong_long_signal", "strong_long", f"🟢 강한 롱 진입 신호\n{symbol} {interval}"),
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("strong_short_signal", "strong_short", f"🔴 강한 숏 진입 신호\n{symbol} {interval}"),
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("long_signal", "long", f"🔼 롱 진입 신호\n{symbol} {interval}"),
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("short_signal", "short", f"🔽 숏 진입 신호\n{symbol} {interval}"),
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("vol_long_signal", "vol_long", f"🔼 볼륨급등 롱 신호\n{symbol} {interval}"),
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("vol_short_signal", "vol_short", f"🔽 볼륨급등 숏 신호\n{symbol} {interval}"),
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("short_caution_signal","short_caution",f"⚠️ 숏 진입 주의 신호\n{symbol} {interval}"),
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]:
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if last.get(sig, False) and now - _last_alert[key] > ALERT_COOLDOWN:
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send_telegram(msg)
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_last_alert[key] = now
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# ──────────────────────────────────────────────
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# 데이터 수집
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# ──────────────────────────────────────────────
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def get_klines(symbol="BTCUSDT", interval="5m", limit=1500):
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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)
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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)
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df = pd.DataFrame(r.json())
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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)
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df = pd.DataFrame(r.json())
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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)
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df = pd.DataFrame(r.json())
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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 get_taker_buy_sell_ratio(symbol="BTCUSDT", period="5m", limit=100):
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url = f"{BASE}/futures/data/takerlongshortRatio"
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r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10)
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df = pd.DataFrame(r.json())
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df["buySellRatio"] = df["buySellRatio"].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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# ──────────────────────────────────────────────
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# 지표 계산
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# ──────────────────────────────────────────────
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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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# 일반 신호용 — MA7/MA25 2개 기준
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df["bull_ma_2"] = (
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(df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"])
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)
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df["bear_ma_2"] = (
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(df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"])
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)
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# 강한 신호용 — MA7/MA25/MA99 3개 기준
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df["bull_ma"] = (
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(df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"]) &
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(df["MA25"] > df["MA99"])
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)
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df["bear_ma"] = (
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(df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"]) &
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(df["MA25"] < df["MA99"])
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)
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df["long_signal"] = df["bull_ma_2"] & (df["RSI"] < 60) & (df["MACD_hist"] > df["MACD_hist"].shift(1)) & (df["close"] > df["BB_mid"]) & (df["StochRSI_k"] < 80)
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df["short_signal"] = df["bear_ma_2"] & (df["RSI"] > 40) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & (df["close"] < df["BB_mid"]) & (df["StochRSI_k"] > 20)
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# 쿨다운: 직전 5캔들 내 같은 신호 있으면 억제
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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["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"] & (df["RSI"] < 65) & (df["MACD_hist"] > df["MACD_hist"].shift(1)) & df["oi_up_2"] & df["taker_buy_2"] & df["fr_long_favor"]
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df["strong_short_signal"] = df["bear_ma"] & (df["RSI"] > 35) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & df["oi_down_2"] & df["taker_sell_2"] & df["fr_short_favor"]
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# 강한 신호 쿨다운: 직전 10캔들 내 억제
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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 * 3
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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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# ── Taker Sell Net(Sell-Buy) 극값 강한 숏 신호 (MA배열 무관, 볼륨 우선) ──
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# 시간봉별 볼륨 임계값 (5분봉 기준 100, 비례 적용)
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_vol_min_map = {"1m": 33, "3m": 100, "5m": 100, "15m": 300, "30m": 600, "1h": 1200, "2h": 2400, "4h": 4800, "1d": 28800}
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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 * 2) &
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(df["sell_net"] > 0) &
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(df["taker_sell_vol"] > _vol_min) &
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df["oi_up"]
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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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# ── Taker Buy Net(Buy-Sell) 극값 강한 롱 신호 (MA배열 무관, 볼륨 우선) ──
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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 * 2) &
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(df["buy_net"] > 0) &
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(df["taker_buy_vol"] > _vol_min) &
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df["oi_up"]
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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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# ── OI 2캔들 하락 + FR ≤ -0.007% → 숏 주의 신호 (쿨다운 5캔들) ──
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if "fundingRate" in df.columns and "sumOpenInterest" in df.columns:
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fr_extreme = df["fundingRate"] <= -0.007
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raw_signal = df["oi_down_2"] & fr_extreme
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# 쿨다운: 직전 5캔들 내 이미 신호 있으면 억제
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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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return df
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# ──────────────────────────────────────────────
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# 차트 빌드
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# ──────────────────────────────────────────────
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COLORS = {
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"bg": "#ffffff",
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"grid": "#e0e3eb",
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"text": "#131722",
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"green": "#26a69a",
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"red": "#ef5350",
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"yellow":"#f5ce05",
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"blue": "#2962ff",
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"purple":"#9c27b0",
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"orange":"#ff9800",
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"MA7": "#f5ce05",
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"MA25": "#ef5350",
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"MA99": "#9c27b0",
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"MA200": "#2962ff",
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"BB": "rgba(41,98,255,0.1)",
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}
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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"}.get(period, period)
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def build_chart(symbol, interval):
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df = get_klines(symbol, interval, 1500)
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oi_period = interval if interval in ["5m","15m","30m","1h","4h"] else "5m"
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try:
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oi = get_open_interest_history(symbol, oi_period, 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: pass
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try:
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fr = get_funding_rate(symbol, 500)
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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: pass
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try:
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ls = get_long_short_ratio(symbol, oi_period, 500)
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if not ls.empty:
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ls_m = ls[["timestamp","longShortRatio"]].rename(columns={"timestamp":"open_time"})
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df["open_time_r3"] = df["open_time"].dt.floor(_to_floor_freq(oi_period))
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ls_m["open_time"] = ls_m["open_time"].dt.floor(_to_floor_freq(oi_period))
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df = df.merge(ls_m, left_on="open_time_r3", right_on="open_time", how="left", suffixes=("","_ls"))
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df = df.drop(columns=["open_time_r3","open_time_ls"], errors="ignore")
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df["longShortRatio"] = df["longShortRatio"].ffill()
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except: pass
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df = compute_indicators(df, interval)
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# ── 1시간봉 MA 배열 — 각 캔들 시점별로 소급 체크 ──
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try:
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if interval != "1h":
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df_1h = get_klines(symbol, "1h", 300)
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df_1h["MA7"] = df_1h["close"].rolling(7).mean()
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df_1h["MA25"] = df_1h["close"].rolling(25).mean()
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df_1h["MA99"] = df_1h["close"].rolling(99).mean()
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df_1h["MA200"] = df_1h["close"].rolling(200).mean()
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# 일반 신호용 2개 기준
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df_1h["h1_bull_2"] = (
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(df_1h["close"] > df_1h["MA7"]) & (df_1h["MA7"] > df_1h["MA25"])
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)
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df_1h["h1_bear_2"] = (
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(df_1h["close"] < df_1h["MA7"]) & (df_1h["MA7"] < df_1h["MA25"])
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)
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# 강한 신호용 3개 기준
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df_1h["h1_bull"] = (
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(df_1h["close"] > df_1h["MA7"]) &
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(df_1h["MA7"] > df_1h["MA25"]) &
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(df_1h["MA25"] > df_1h["MA99"])
|
|
)
|
|
df_1h["h1_bear"] = (
|
|
(df_1h["close"] < df_1h["MA7"]) &
|
|
(df_1h["MA7"] < df_1h["MA25"]) &
|
|
(df_1h["MA25"] < df_1h["MA99"])
|
|
)
|
|
df_1h_m = df_1h[["open_time","h1_bull","h1_bear","h1_bull_2","h1_bear_2"]].copy()
|
|
df["open_time_1h"] = df["open_time"].dt.floor("1h")
|
|
df_1h_m["open_time"] = df_1h_m["open_time"].dt.floor("1h")
|
|
df = df.merge(df_1h_m, left_on="open_time_1h", right_on="open_time",
|
|
how="left", suffixes=("","_1h"))
|
|
df = df.drop(columns=["open_time_1h","open_time_1h_x","open_time_1h_y"], errors="ignore")
|
|
# ffill은 당일(KST 날짜) 내에서만 허용 — 전날 신호 전이 방지
|
|
df["_date"] = df["open_time"].dt.date
|
|
for col in ["h1_bull","h1_bear","h1_bull_2","h1_bear_2"]:
|
|
df[col] = df.groupby("_date")[col].transform(lambda x: x.ffill())
|
|
df.drop(columns=["_date"], inplace=True)
|
|
df["h1_bull"] = df["h1_bull"].fillna(False)
|
|
df["h1_bear"] = df["h1_bear"].fillna(False)
|
|
df["h1_bull_2"] = df["h1_bull_2"].fillna(False)
|
|
df["h1_bear_2"] = df["h1_bear_2"].fillna(False)
|
|
else:
|
|
df["h1_bull"] = df["bull_ma"]
|
|
df["h1_bear"] = df["bear_ma"]
|
|
df["h1_bull_2"] = df["bull_ma_2"]
|
|
df["h1_bear_2"] = df["bear_ma_2"]
|
|
except:
|
|
df["h1_bull"] = False
|
|
df["h1_bear"] = False
|
|
|
|
# 각 캔들 시점의 1시간봉 배열로 신호 억제
|
|
# 일반 신호 — 1h 2개 기준
|
|
h1_bull_2 = df.get("h1_bull_2", df["h1_bull"])
|
|
h1_bear_2 = df.get("h1_bear_2", df["h1_bear"])
|
|
df["long_signal"] = df["long_signal"] & h1_bull_2 & df["taker_buy_dom"]
|
|
df["short_signal"] = df["short_signal"] & h1_bear_2 & df["taker_sell_dom"]
|
|
# 강한 신호 — 1h 3개 기준
|
|
df["strong_long_signal"] = df["strong_long_signal"] & df["h1_bull"]
|
|
df["strong_short_signal"]= df["strong_short_signal"]& df["h1_bear"]
|
|
df["short_caution_signal"]= df["short_caution_signal"] & h1_bear_2
|
|
|
|
df["long_exhaustion_warn"] = False
|
|
|
|
t = df["open_time"]
|
|
|
|
fig = make_subplots(
|
|
rows=7, cols=1,
|
|
shared_xaxes=True,
|
|
row_heights=[0.38, 0.10, 0.10, 0.10, 0.10, 0.11, 0.11],
|
|
vertical_spacing=0.01,
|
|
subplot_titles=["", "Taker Buy/Sell Volume", "Open Interest",
|
|
"Funding Rate (%)", "Long/Short Ratio (탑트레이더)",
|
|
"RSI / StochRSI", "MACD"]
|
|
)
|
|
|
|
# ── 캔들 ──
|
|
fig.add_trace(go.Candlestick(
|
|
x=t, open=df["open"], high=df["high"], low=df["low"], close=df["close"],
|
|
increasing_line_color=COLORS["green"], decreasing_line_color=COLORS["red"],
|
|
increasing_fillcolor=COLORS["green"], decreasing_fillcolor=COLORS["red"],
|
|
name="캔들", line=dict(width=1)
|
|
), row=1, col=1)
|
|
|
|
# ── BB ──
|
|
fig.add_trace(go.Scatter(x=t, y=df["BB_upper"], line=dict(color=COLORS["BB"].replace("0.1","0.6"), width=0.8), name="BB상단", showlegend=False), row=1, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["BB_lower"], line=dict(color=COLORS["BB"].replace("0.1","0.6"), width=0.8), fill="tonexty", fillcolor=COLORS["BB"], name="BB하단", showlegend=False), row=1, col=1)
|
|
|
|
# ── MA ──
|
|
for ma, col in [("MA200", COLORS["MA200"]), ("MA99", COLORS["MA99"]), ("MA25", COLORS["MA25"]), ("MA7", COLORS["MA7"])]:
|
|
fig.add_trace(go.Scatter(x=t, y=df[ma], line=dict(color=col, width=1.2), name=ma), row=1, col=1)
|
|
|
|
# ── 탑트레이더 L/S ──
|
|
if "longShortRatio" in df.columns:
|
|
fig.add_trace(go.Scatter(x=t, y=df["longShortRatio"], line=dict(color=COLORS["orange"], width=1), name="탑트레이더 L/S"), row=1, col=1)
|
|
|
|
# ── FR 선 ──
|
|
if "fundingRate" in df.columns:
|
|
fig.add_trace(go.Scatter(x=t, y=df["fundingRate"], line=dict(color=COLORS["purple"], width=1), name="Funding Rate"), row=1, col=1)
|
|
|
|
# ── OI 선 ──
|
|
if "sumOpenInterest" in df.columns:
|
|
fig.add_trace(go.Scatter(x=t, y=df["sumOpenInterest"], line=dict(color=COLORS["blue"], width=1), name="OI"), row=1, col=1)
|
|
|
|
# ── Taker sell/buy scatter ──
|
|
fig.add_trace(go.Scatter(x=t, y=df["taker_sell_vol"], mode="markers", marker=dict(color=COLORS["red"], size=3), name="Taker Sell"), row=1, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["taker_buy_vol"], mode="markers", marker=dict(color=COLORS["green"], size=3), name="Taker Buy"), row=1, col=1)
|
|
|
|
# ── 신호 마커 ──
|
|
for mask, sym, color, sig_name in [
|
|
(df["exhaustion_short"], "star", COLORS["red"], "매수소진(숏)"),
|
|
(df["exhaustion_long"], "star", COLORS["green"], "매도소진(롱)"),
|
|
(df.get("long_exhaustion_warn", pd.Series([False]*len(df), index=df.index)), "x", COLORS["orange"], "롱소진경고(숏전환)"),
|
|
(df["strong_short_signal"],"triangle-down", COLORS["red"], "강한 숏 진입 신호"),
|
|
(df.get("vol_short_signal", pd.Series([False]*len(df), index=df.index)), "triangle-down", COLORS["orange"], "볼륨급등 숏 신호"),
|
|
(df.get("vol_long_signal", pd.Series([False]*len(df), index=df.index)), "triangle-up", "#00bfff", "볼륨급등 롱 신호"),
|
|
(df["strong_long_signal"], "triangle-up", COLORS["green"], "강한 롱 진입 신호"),
|
|
(df["short_signal"], "triangle-down", COLORS["orange"], "숏 진입 신호"),
|
|
(df["long_signal"], "triangle-up", COLORS["blue"], "롱 진입 신호"),
|
|
(df.get("short_caution_signal", pd.Series([False]*len(df), index=df.index)), "diamond", "#ff00ff", "숏 진입(주의)"),
|
|
]:
|
|
d = df[mask]
|
|
if not d.empty:
|
|
cd = [[v] for v in d["open_time"].dt.strftime("%m/%d %H:%M").tolist()]
|
|
_long_sigs = ["강한 롱 진입 신호", "볼륨급등 롱 신호", "롱 진입 신호", "매도소진(롱)"]
|
|
_short_sigs = ["강한 숏 진입 신호", "볼륨급등 숏 신호", "숏 진입 신호", "매수소진(숏)", "롱소진경고(숏전환)", "숏 진입(주의)"]
|
|
if sig_name in _long_sigs:
|
|
y_val = d["low"] * 0.9998 # 캔들 하단 아래
|
|
elif sig_name in _short_sigs:
|
|
y_val = d["high"] * 1.0002 # 캔들 상단 위
|
|
else:
|
|
y_val = d["close"]
|
|
fig.add_trace(go.Scatter(
|
|
x=d["open_time"], y=y_val,
|
|
mode="markers", marker=dict(symbol=sym, color=color, size=10),
|
|
name=sig_name,
|
|
customdata=cd,
|
|
hovertemplate="<b>" + sig_name + "</b><br>신호: %{customdata[0]}<br>가격: %{y:,.1f}<extra></extra>",
|
|
showlegend=True,
|
|
), row=1, col=1)
|
|
else:
|
|
# 신호 없어도 범례에 항상 표시
|
|
fig.add_trace(go.Scatter(
|
|
x=[None], y=[None],
|
|
mode="markers", marker=dict(symbol=sym, color=color, size=10),
|
|
name=sig_name,
|
|
showlegend=True,
|
|
), row=1, col=1)
|
|
|
|
# ── Taker Volume 바 ──
|
|
buy_vol = df["taker_buy_vol"] - df["taker_sell_vol"]
|
|
colors_v = [COLORS["green"] if v >= 0 else COLORS["red"] for v in buy_vol]
|
|
fig.add_trace(go.Bar(x=t, y=buy_vol, marker_color=colors_v, name="Taker Net"), row=2, col=1)
|
|
if "sumOpenInterest" not in df.columns:
|
|
df["spike_avg"] = df["volume"].rolling(10).mean()
|
|
spike_mask = df["volume"] > df["spike_avg"] * 3
|
|
fig.add_trace(go.Scatter(x=t, y=df["spike_avg"] * 3, line=dict(color=COLORS["yellow"], width=0.8, dash="dot"), name="스파이크 기준(3x)"), row=2, col=1)
|
|
|
|
# ── OI ──
|
|
if "sumOpenInterest" in df.columns:
|
|
fig.add_trace(go.Scatter(x=t, y=df["sumOpenInterest"], line=dict(color=COLORS["purple"], width=1.5), fill="tozeroy", fillcolor="rgba(156,39,176,0.15)", name="OI"), row=3, col=1)
|
|
|
|
# ── FR ──
|
|
if "fundingRate" in df.columns:
|
|
fr_colors = [COLORS["red"] if v < 0 else COLORS["green"] for v in df["fundingRate"]]
|
|
fig.add_trace(go.Bar(x=t, y=df["fundingRate"], marker_color=fr_colors, name="FR"), row=4, col=1)
|
|
fig.add_hline(y=0.005, line=dict(color=COLORS["orange"], width=1, dash="dash"), row=4, col=1)
|
|
fig.add_hline(y=-0.005, line=dict(color=COLORS["orange"], width=1, dash="dash"), row=4, col=1)
|
|
fig.add_hline(y=-0.007, line=dict(color=COLORS["red"], width=1, dash="dash"), row=4, col=1)
|
|
|
|
# ── L/S Ratio ──
|
|
if "longShortRatio" in df.columns:
|
|
fig.add_trace(go.Scatter(x=t, y=df["longShortRatio"], line=dict(color=COLORS["orange"], width=1.5), name="탑트레이더 L/S"), row=5, col=1)
|
|
fig.add_hline(y=1.0, line=dict(color=COLORS["grid"], width=0.8, dash="dash"), row=5, col=1)
|
|
|
|
# ── RSI / StochRSI ──
|
|
fig.add_trace(go.Scatter(x=t, y=df["RSI"], line=dict(color=COLORS["blue"], width=1.5), name="RSI(14)"), row=6, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["StochRSI_k"],line=dict(color=COLORS["yellow"], width=1.2, dash="dot"), name="StochRSI K"), row=6, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["StochRSI_d"],line=dict(color=COLORS["orange"], width=1.0, dash="dot"), name="StochRSI D"), row=6, col=1)
|
|
for lvl in [20, 50, 80]:
|
|
fig.add_hline(y=lvl, line=dict(color=COLORS["grid"], width=0.6, dash="dash"), row=6, col=1)
|
|
|
|
# ── MACD ──
|
|
hist_colors = [COLORS["green"] if v >= 0 else COLORS["red"] for v in df["MACD_hist"].fillna(0)]
|
|
fig.add_trace(go.Bar(x=t, y=df["MACD_hist"], marker_color=hist_colors, name="MACD Hist"), row=7, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["MACD"], line=dict(color=COLORS["blue"], width=1.2), name="MACD"), row=7, col=1)
|
|
fig.add_trace(go.Scatter(x=t, y=df["MACD_signal"], line=dict(color=COLORS["orange"], width=1.2), name="Signal"), row=7, col=1)
|
|
fig.add_hline(y=0, line=dict(color=COLORS["grid"], width=0.5), row=7, col=1)
|
|
|
|
# ── 현재가 라인 ──
|
|
last_price = df["close"].iloc[-1]
|
|
fig.add_hline(y=last_price, line=dict(color=COLORS["yellow"], width=1, dash="dash"), row=1, col=1)
|
|
fig.add_annotation(
|
|
x=df["open_time"].iloc[-1], y=last_price,
|
|
text=f"▶ {last_price:,.1f}",
|
|
showarrow=False,
|
|
font=dict(color=COLORS["yellow"], size=12),
|
|
xanchor="left", yanchor="middle",
|
|
bgcolor="rgba(245,206,5,0.15)",
|
|
bordercolor=COLORS["yellow"], borderwidth=1,
|
|
row=1, col=1
|
|
)
|
|
|
|
# ── 레이아웃 ──
|
|
fig.update_layout(
|
|
height=1600,
|
|
paper_bgcolor="#ffffff",
|
|
plot_bgcolor="#ffffff",
|
|
font=dict(color=COLORS["text"], size=11, family="monospace"),
|
|
legend=dict(bgcolor="rgba(255,255,255,0.95)", bordercolor=COLORS["grid"], borderwidth=1,
|
|
orientation="h", x=0, y=1.02, yanchor="bottom", font=dict(size=10)),
|
|
xaxis_rangeslider_visible=False,
|
|
margin=dict(l=60, r=100, t=60, b=20),
|
|
hovermode="x unified",
|
|
dragmode="pan",
|
|
showlegend=False,
|
|
)
|
|
|
|
for i in range(1, 8):
|
|
fig.update_xaxes(showgrid=True, gridcolor=COLORS["grid"], zeroline=False, row=i, col=1,
|
|
showline=True, linecolor=COLORS["grid"])
|
|
fig.update_yaxes(showgrid=True, gridcolor=COLORS["grid"], zeroline=False, row=i, col=1,
|
|
showline=True, linecolor=COLORS["grid"])
|
|
|
|
def tight(series, pad=0.03):
|
|
s = series.dropna()
|
|
if s.empty: return None, None
|
|
lo, hi = s.min(), s.max()
|
|
margin = (hi - lo) * pad if (hi - lo) > 0 else abs(lo) * pad + 1
|
|
return lo - margin, hi + margin
|
|
|
|
# 캔들 Y축
|
|
lo, hi = tight(pd.concat([df["low"], df["high"]]), pad=0.02)
|
|
if lo: fig.update_yaxes(range=[lo, hi], row=1, col=1)
|
|
|
|
# Taker Volume — 98% 분위수 기준 대칭 범위
|
|
buy_vol = df["taker_buy_vol"] - df["taker_sell_vol"]
|
|
abs_max = buy_vol.abs().quantile(0.98) * 1.5
|
|
if abs_max > 0: fig.update_yaxes(range=[-abs_max, abs_max], row=2, col=1)
|
|
|
|
# OI
|
|
if "sumOpenInterest" in df.columns:
|
|
lo, hi = tight(df["sumOpenInterest"], pad=0.05)
|
|
if lo: fig.update_yaxes(range=[lo, hi], row=3, col=1)
|
|
|
|
# FR
|
|
if "fundingRate" in df.columns:
|
|
lo, hi = tight(df["fundingRate"], pad=0.2)
|
|
if lo: fig.update_yaxes(range=[lo, hi], row=4, col=1)
|
|
|
|
# L/S
|
|
if "longShortRatio" in df.columns:
|
|
lo, hi = tight(df["longShortRatio"], pad=0.05)
|
|
if lo: fig.update_yaxes(range=[lo, hi], row=5, col=1)
|
|
|
|
# RSI
|
|
fig.update_yaxes(range=[0, 100], row=6, col=1)
|
|
|
|
# MACD
|
|
macd_all = pd.concat([df["MACD"], df["MACD_signal"], df["MACD_hist"]]).dropna()
|
|
lo, hi = tight(macd_all, pad=0.1)
|
|
if lo: fig.update_yaxes(range=[lo, hi], row=7, col=1)
|
|
|
|
return fig, df
|
|
|
|
# ──────────────────────────────────────────────
|
|
# 알림 스레드
|
|
# ──────────────────────────────────────────────
|
|
_alert_symbol = "BTCUSDT"
|
|
_alert_interval = "5m"
|
|
_alert_lock = threading.Lock()
|
|
_alert_started = False
|
|
|
|
def _alert_loop():
|
|
while True:
|
|
try:
|
|
with _alert_lock:
|
|
symbol = _alert_symbol
|
|
interval = _alert_interval
|
|
df = get_klines(symbol, interval, 50)
|
|
oi_period = interval if interval in ["5m","15m","30m","1h","4h"] else "5m"
|
|
try:
|
|
oi = get_open_interest_history(symbol, oi_period, 50)
|
|
if not oi.empty:
|
|
oi_m = oi[["timestamp","sumOpenInterest"]].rename(columns={"timestamp":"open_time"})
|
|
df["open_time_r"] = df["open_time"].dt.floor(_to_floor_freq(oi_period))
|
|
oi_m["open_time"] = oi_m["open_time"].dt.floor(_to_floor_freq(oi_period))
|
|
df = df.merge(oi_m, left_on="open_time_r", right_on="open_time", how="left", suffixes=("","_oi"))
|
|
df = df.drop(columns=["open_time_r","open_time_oi"], errors="ignore")
|
|
df["sumOpenInterest"] = df["sumOpenInterest"].ffill()
|
|
except: pass
|
|
df = compute_indicators(df, interval)
|
|
check_and_alert(df, symbol, interval)
|
|
except Exception as e:
|
|
print(f"[알림스레드 오류] {e}")
|
|
time.sleep(30)
|
|
|
|
def _to_floor_freq(period):
|
|
return {"1m":"1min","3m":"3min","5m":"5min","15m":"15min","30m":"30min","1h":"1h","4h":"4h"}.get(period, period)
|
|
|
|
# ──────────────────────────────────────────────
|
|
# 메인 UI
|
|
# ──────────────────────────────────────────────
|
|
def main():
|
|
global _alert_started, _alert_symbol, _alert_interval
|
|
|
|
# 알림 스레드 시작 (1회만)
|
|
if not _alert_started:
|
|
t = threading.Thread(target=_alert_loop, daemon=True)
|
|
t.start()
|
|
_alert_started = True
|
|
|
|
# 헤더
|
|
col1, col2, col3, col4, col5 = st.columns([2, 1, 1, 1, 2])
|
|
with col1:
|
|
st.markdown("### 📊 Futures Dashboard")
|
|
with col2:
|
|
symbol = st.selectbox("심볼", ["BTCUSDT","ETHUSDT","SOLUSDT","BNBUSDT"], index=0, label_visibility="collapsed")
|
|
with col3:
|
|
interval = st.selectbox("시간축", ["1m","3m","5m","15m","30m","1h","4h"], index=2, label_visibility="collapsed")
|
|
with col4:
|
|
refresh_sec = st.number_input("갱신(초)", min_value=10, max_value=300, value=30)
|
|
with col5:
|
|
col5a, col5b, col5c = st.columns(3)
|
|
with col5a:
|
|
refresh_btn = st.button("🔄 새로고침")
|
|
with col5b:
|
|
auto = st.checkbox("자동갱신", value=True)
|
|
with col5c:
|
|
show_legend = st.checkbox("범례", value=False)
|
|
|
|
# 알림 타겟 업데이트
|
|
with _alert_lock:
|
|
_alert_symbol = symbol
|
|
_alert_interval = interval
|
|
|
|
# 데이터 로드 & 차트
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try:
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with st.spinner("데이터 로딩 중..."):
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fig, df = build_chart(symbol, interval)
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# FR 배너
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try:
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fr_df = get_funding_rate(symbol, 1)
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if not fr_df.empty:
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rate = fr_df["fundingRate"].iloc[-1]
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if rate <= -0.007:
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st.error(f"🚨 극단적 숏스퀴즈 위험 | FR: {rate:.4f}% | 숏 신규진입 절대 금지")
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elif rate <= -0.005:
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st.warning(f"⚠️ 숏스퀴즈 경보 구간 | FR: {rate:.4f}% | 숏 진입 시 청산가 재확인 필수")
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elif rate >= 0.005:
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st.info(f"📈 롱 과열 구간 | FR: {rate:.4f}% | 롱스퀴즈 주의")
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else:
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st.success(f"✅ FR 정상 | {rate:.4f}%")
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except: pass
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fig.update_layout(showlegend=show_legend)
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st.plotly_chart(fig, use_container_width=True, config={
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"scrollZoom": True,
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"displayModeBar": True,
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"modeBarButtonsToRemove": ["lasso2d", "select2d"],
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})
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st.caption(f"마지막 갱신: {datetime.now().strftime('%H:%M:%S')}")
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except Exception as e:
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st.error(f"데이터 로드 오류: {e}")
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import traceback
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st.code(traceback.format_exc())
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|
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# 자동갱신
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if auto:
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time.sleep(refresh_sec)
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|
st.rerun()
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|
|
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if __name__ == "__main__":
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try:
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public_url = ngrok.connect(8501)
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print(f"핸드폰: {public_url}")
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except Exception as e:
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print(f"ngrok 오류: {e}")
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main()
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