import sys
import os
sys.stdout.reconfigure(line_buffering=True)
os.environ["PYTHONUNBUFFERED"] = "1"

import time
import requests
from dotenv import load_dotenv
load_dotenv()
import pandas as pd
import numpy as np
from datetime import datetime, timezone, timedelta
import threading
import streamlit as st
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import ta
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)


# ──────────────────────────────────────────────
# 페이지 설정 (반드시 최상단)
# ──────────────────────────────────────────────
st.set_page_config(
    page_title="BTC/ETH Futures Dashboard",
    layout="wide",
    initial_sidebar_state="collapsed"
)

# 라이트모드 강제 CSS
st.markdown("""
<style>
    html, body, [data-testid="stAppViewContainer"], [data-testid="stHeader"] {
        background-color: #ffffff !important;
        color: #131722 !important;
    }
    [data-testid="stSidebar"] { background-color: #f0f3fa !important; }
    .stSelectbox > div > div { background-color: #ffffff !important; color: #131722 !important; }
    .stButton > button { background-color: #238636 !important; color: #ffffff !important; border: none !important; }
    header { background-color: #f0f0f0 !important; }
</style>
""", unsafe_allow_html=True)

# ──────────────────────────────────────────────
# 설정
# ──────────────────────────────────────────────
TELEGRAM_TOKEN   = os.getenv("TELEGRAM_TOKEN", "")
TELEGRAM_CHAT_ID = os.getenv("TELEGRAM_CHAT_ID", "")
ALERT_COOLDOWN   = 300

BASE = "https://fapi.binance.com"
KST  = timedelta(hours=9)

_last_alert = {"strong_long": 0, "strong_short": 0, "long": 0, "short": 0, "vol_long": 0, "vol_short": 0, "short_caution": 0}

# ──────────────────────────────────────────────
# 텔레그램
# ──────────────────────────────────────────────
def send_telegram(message: str):
    try:
        url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage"
        requests.post(url, data={"chat_id": TELEGRAM_CHAT_ID, "text": message}, timeout=10)
    except Exception as e:
        print(f"[텔레그램 오류] {e}")

def check_and_alert(df, symbol, interval):
    now = time.time()
    last = df.iloc[-1]
    for sig, key, msg in [
        ("strong_long_signal",  "strong_long",  f"🟢 강한 롱 진입 신호\n{symbol} {interval}"),
        ("strong_short_signal", "strong_short", f"🔴 강한 숏 진입 신호\n{symbol} {interval}"),
        ("long_signal",         "long",         f"🔼 롱 진입 신호\n{symbol} {interval}"),
        ("short_signal",        "short",        f"🔽 숏 진입 신호\n{symbol} {interval}"),
        ("vol_long_signal",     "vol_long",     f"🔼 볼륨급등 롱 신호\n{symbol} {interval}"),
        ("vol_short_signal",    "vol_short",    f"🔽 볼륨급등 숏 신호\n{symbol} {interval}"),
        ("short_caution_signal","short_caution",f"⚠️ 숏 진입 주의 신호\n{symbol} {interval}"),
    ]:
        if last.get(sig, False) and now - _last_alert[key] > ALERT_COOLDOWN:
            send_telegram(msg)
            _last_alert[key] = now

# ──────────────────────────────────────────────
# 데이터 수집
# ──────────────────────────────────────────────
def get_klines(symbol="BTCUSDT", interval="5m", limit=375):
    url = f"{BASE}/fapi/v1/klines"
    r = requests.get(url, params={"symbol": symbol, "interval": interval, "limit": limit}, timeout=10, verify=False)
    df = pd.DataFrame(r.json(), columns=[
        "open_time","open","high","low","close","volume",
        "close_time","quote_vol","trades","taker_buy_vol","taker_sell_vol","ignore"
    ])
    for c in ["open","high","low","close","volume","taker_buy_vol","taker_sell_vol"]:
        df[c] = df[c].astype(float)
    df["taker_sell_vol"] = df["volume"] - df["taker_buy_vol"]
    df["open_time"] = pd.to_datetime(df["open_time"], unit="ms") + KST
    return df

def get_funding_rate(symbol="BTCUSDT", limit=100):
    url = f"{BASE}/fapi/v1/fundingRate"
    r = requests.get(url, params={"symbol": symbol, "limit": limit}, timeout=10, verify=False)
    df = pd.DataFrame(r.json())
    df["fundingRate"] = df["fundingRate"].astype(float) * 100
    df["fundingTime"] = pd.to_datetime(df["fundingTime"], unit="ms") + KST
    return df

def get_open_interest_history(symbol="BTCUSDT", period="5m", limit=100):
    url = f"{BASE}/futures/data/openInterestHist"
    r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10, verify=False)
    df = pd.DataFrame(r.json())
    df["sumOpenInterest"] = df["sumOpenInterest"].astype(float)
    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") + KST
    return df

def get_long_short_ratio(symbol="BTCUSDT", period="5m", limit=500):
    url = f"{BASE}/futures/data/topLongShortPositionRatio"
    r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10, verify=False)
    df = pd.DataFrame(r.json())
    df["longShortRatio"] = df["longShortRatio"].astype(float)
    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") + KST
    return df

def get_taker_buy_sell_ratio(symbol="BTCUSDT", period="5m", limit=100):
    url = f"{BASE}/futures/data/takerlongshortRatio"
    r = requests.get(url, params={"symbol": symbol, "period": period, "limit": limit}, timeout=10, verify=False)
    df = pd.DataFrame(r.json())
    df["buySellRatio"] = df["buySellRatio"].astype(float)
    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") + KST
    return df

# ──────────────────────────────────────────────
# 지표 계산
# ──────────────────────────────────────────────
def compute_indicators(df, interval="5m"):
    c = df["close"]
    df["MA7"]   = c.rolling(7).mean()
    df["MA25"]  = c.rolling(25).mean()
    df["MA99"]  = c.rolling(99).mean()
    df["MA200"] = c.rolling(200).mean()
    df["BB_mid"]   = c.rolling(20).mean()
    df["BB_std"]   = c.rolling(20).std()
    df["BB_upper"] = df["BB_mid"] + 2 * df["BB_std"]
    df["BB_lower"] = df["BB_mid"] - 2 * df["BB_std"]
    df["RSI"]  = ta.momentum.RSIIndicator(c, window=14).rsi()
    macd = ta.trend.MACD(c, window_slow=26, window_fast=12, window_sign=9)
    df["MACD"] = macd.macd()
    df["MACD_signal"] = macd.macd_signal()
    df["MACD_hist"]   = macd.macd_diff()
    stoch = ta.momentum.StochRSIIndicator(c, window=14, smooth1=3, smooth2=3)
    df["StochRSI_k"] = stoch.stochrsi_k() * 100
    df["StochRSI_d"] = stoch.stochrsi_d() * 100
    df["ATR"] = ta.volatility.AverageTrueRange(df["high"], df["low"], df["close"], window=14).average_true_range()
    df = compute_signals(df, interval)
    return df

def compute_signals(df, interval="5m"):
    df["bull_ma_2"] = (
        (df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"])
    )
    df["bear_ma_2"] = (
        (df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"])
    )
    df["bull_ma"] = (
        (df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"]) &
        (df["MA25"] > df["MA99"])
    )
    df["bear_ma"] = (
        (df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"]) &
        (df["MA25"] < df["MA99"])
    )
    df["long_signal"]  = df["bull_ma_2"] & (df["RSI"] < 60) & (df["MACD_hist"] > df["MACD_hist"].shift(1)) & (df["close"] > df["BB_mid"])
    df["short_signal"] = df["bear_ma_2"] & (df["RSI"] > 35) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & (df["close"] < df["BB_mid"])
    df["long_signal"]  = df["long_signal"]  & (df["long_signal"].rolling(5,  min_periods=1).sum().shift(1).fillna(0) == 0)
    df["short_signal"] = df["short_signal"] & (df["short_signal"].rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0)

    if "sumOpenInterest" in df.columns and df["sumOpenInterest"].notna().sum() > 5:
        oi_series = df["sumOpenInterest"].ffill()
    else:
        oi_series = df["close"] * df["volume"]
    df["oi_up"]    = oi_series > oi_series.shift(1)
    df["oi_down"]  = oi_series < oi_series.shift(1)
    df["oi_up_2"]   = df["oi_up"]  & df["oi_up"].shift(1).fillna(False)
    df["oi_down_2"] = df["oi_down"] & df["oi_down"].shift(1).fillna(False)

    df["taker_buy_dom"]  = df["taker_buy_vol"]  > df["taker_sell_vol"]
    df["taker_sell_dom"] = df["taker_sell_vol"] > df["taker_buy_vol"]
    df["taker_buy_2"]  = df["taker_buy_dom"]  & df["taker_buy_dom"].shift(1).fillna(False)
    df["taker_sell_2"] = df["taker_sell_dom"] & df["taker_sell_dom"].shift(1).fillna(False)

    df["fr_long_favor"]  = df["taker_buy_vol"].rolling(3).mean()  > df["taker_sell_vol"].rolling(3).mean()
    df["fr_short_favor"] = df["taker_sell_vol"].rolling(3).mean() > df["taker_buy_vol"].rolling(3).mean()

    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"]
    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"]
    df["strong_long_signal"]  = df["strong_long_signal"]  & (df["strong_long_signal"].rolling(10,  min_periods=1).sum().shift(1).fillna(0) == 0)
    df["strong_short_signal"] = df["strong_short_signal"] & (df["strong_short_signal"].rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0)

    vol_avg    = df["volume"].rolling(10).mean()
    spike      = df["volume"] > vol_avg * 3
    buy_spike  = spike & (df["taker_buy_vol"]  > df["taker_sell_vol"])
    sell_spike = spike & (df["taker_sell_vol"] > df["taker_buy_vol"])
    df["exhaustion_short"] = buy_spike.shift(1).fillna(False)
    df["exhaustion_long"]  = sell_spike.shift(1).fillna(False)

    _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}
    _vol_min = _vol_min_map.get(interval, 100)

    df["sell_net"] = df["taker_sell_vol"] - df["taker_buy_vol"]
    sell_net_avg = df["sell_net"].rolling(10).mean()
    sell_spike_strong = (
        (df["sell_net"] > sell_net_avg * 2) &
        (df["sell_net"] > 0) &
        (df["taker_sell_vol"] > _vol_min) &
        df["oi_up"]
    )
    cooldown_vol_short = sell_spike_strong.rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0
    df["vol_short_signal"] = sell_spike_strong & cooldown_vol_short

    df["buy_net"] = df["taker_buy_vol"] - df["taker_sell_vol"]
    buy_net_avg = df["buy_net"].rolling(10).mean()
    buy_spike_strong = (
        (df["buy_net"] > buy_net_avg * 2) &
        (df["buy_net"] > 0) &
        (df["taker_buy_vol"] > _vol_min) &
        df["oi_up"]
    )
    cooldown_vol_long = buy_spike_strong.rolling(10, min_periods=1).sum().shift(1).fillna(0) == 0
    df["vol_long_signal"] = buy_spike_strong & cooldown_vol_long

    if "fundingRate" in df.columns and "sumOpenInterest" in df.columns:
        fr_extreme = df["fundingRate"] <= -0.007
        raw_signal = df["oi_down_2"] & fr_extreme
        cooldown_mask = raw_signal.rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0
        df["short_caution_signal"] = raw_signal & cooldown_mask
    else:
        df["short_caution_signal"] = False

    return df

# ──────────────────────────────────────────────
# 차트 빌드
# ──────────────────────────────────────────────
COLORS = {
    "bg":    "#ffffff",
    "grid":  "#e0e3eb",
    "text":  "#131722",
    "green": "#26a69a",
    "red":   "#ef5350",
    "yellow":"#f5ce05",
    "blue":  "#2962ff",
    "purple":"#9c27b0",
    "orange":"#ff9800",
    "MA7":   "#f5ce05",
    "MA25":  "#ef5350",
    "MA99":  "#9c27b0",
    "MA200": "#2962ff",
    "BB":    "rgba(41,98,255,0.1)",
}

def _to_floor_freq(period):
    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)

def build_chart(symbol, interval, candle_limit=200):
    df = get_klines(symbol, interval, candle_limit)
    oi_period = interval if interval in ["5m","15m","30m","1h","4h","12h","1d","3d","1M"] else "5m"

    try:
        oi = get_open_interest_history(symbol, oi_period, 200)
        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

    try:
        fr = get_funding_rate(symbol, 200)
        if not fr.empty:
            fr_m = fr[["fundingTime","fundingRate"]].rename(columns={"fundingTime":"open_time"})
            fr_m["open_time"] = fr_m["open_time"].dt.floor(_to_floor_freq("1h"))
            df["open_time_r2"] = df["open_time"].dt.floor(_to_floor_freq("1h"))
            df = df.merge(fr_m, left_on="open_time_r2", right_on="open_time", how="left", suffixes=("","_fr"))
            df = df.drop(columns=["open_time_r2","open_time_fr"], errors="ignore")
            df["fundingRate"] = df["fundingRate"].ffill().fillna(0)
    except: pass

    try:
        ls = get_long_short_ratio(symbol, oi_period, 200)
        if not ls.empty:
            ls_m = ls[["timestamp","longShortRatio"]].rename(columns={"timestamp":"open_time"})
            df["open_time_r3"] = df["open_time"].dt.floor(_to_floor_freq(oi_period))
            ls_m["open_time"] = ls_m["open_time"].dt.floor(_to_floor_freq(oi_period))
            df = df.merge(ls_m, left_on="open_time_r3", right_on="open_time", how="left", suffixes=("","_ls"))
            df = df.drop(columns=["open_time_r3","open_time_ls"], errors="ignore")
            df["longShortRatio"] = df["longShortRatio"].ffill()
    except: pass

    df = compute_indicators(df, interval)

    try:
        if interval != "1h":
            df_1h = get_klines(symbol, "1h", 150)
            df_1h["MA7"]   = df_1h["close"].rolling(7).mean()
            df_1h["MA25"]  = df_1h["close"].rolling(25).mean()
            df_1h["MA99"]  = df_1h["close"].rolling(99).mean()
            df_1h["MA200"] = df_1h["close"].rolling(200).mean()
            df_1h["h1_bull_2"] = (
                (df_1h["close"] > df_1h["MA7"]) & (df_1h["MA7"] > df_1h["MA25"])
            )
            df_1h["h1_bear_2"] = (
                (df_1h["close"] < df_1h["MA7"]) & (df_1h["MA7"] < df_1h["MA25"])
            )
            df_1h["h1_bull"] = (
                (df_1h["close"] > df_1h["MA7"]) &
                (df_1h["MA7"]   > df_1h["MA25"]) &
                (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")
            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

    df["long_signal"]         = df["long_signal"]        & df["taker_buy_dom"]
    df["short_signal"]        = df["short_signal"]       & df["taker_sell_dom"]
    df["strong_long_signal"]  = df["strong_long_signal"]
    df["strong_short_signal"] = df["strong_short_signal"]
    df["short_caution_signal"]= df["short_caution_signal"]
    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"]
    )

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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
            elif sig_name in _short_sigs:
                y_val = d["high"] * 1.0002
            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:
            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)

    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()
        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)

    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)

    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)

    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)

    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["red"], width=1.5), 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)

    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)

    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
    )

    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

    lo, hi = tight(pd.concat([df["low"], df["high"]]), pad=0.02)
    if lo: fig.update_yaxes(range=[lo, hi], row=1, col=1)

    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)

    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)

    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)

    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)

    fig.update_yaxes(range=[0, 100], row=6, col=1)

    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","12h","1d","3d","1M"] 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)

# ──────────────────────────────────────────────
# 메인 UI
# ──────────────────────────────────────────────
def main():
    global _alert_started, _alert_symbol, _alert_interval

    if not _alert_started:
        t = threading.Thread(target=_alert_loop, daemon=True)
        t.start()
        _alert_started = True

    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","12h","1d","3d","1M"], 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, col5d = st.columns(4)
        with col5a:
            refresh_btn = st.button("🔄 새로고침")
        with col5b:
            auto = st.checkbox("자동갱신", value=True)
        with col5c:
            show_legend = st.checkbox("범례", value=False)
        with col5d:
            mobile_mode = st.checkbox("모바일", value=False)

    candle_limit = 53 if mobile_mode else 200

    with _alert_lock:
        _alert_symbol   = symbol
        _alert_interval = interval

    try:
        with st.spinner("데이터 로딩 중..."):
            fig, df = build_chart(symbol, interval, candle_limit)

        try:
            fr_df = get_funding_rate(symbol, 1)
            if not fr_df.empty:
                rate = fr_df["fundingRate"].iloc[-1]
                if rate <= -0.007:
                    st.error(f"🚨 극단적 숏스퀴즈 위험 | FR: {rate:.4f}% | 숏 신규진입 절대 금지")
                elif rate <= -0.005:
                    st.warning(f"⚠️ 숏스퀴즈 경보 구간 | FR: {rate:.4f}% | 숏 진입 시 청산가 재확인 필수")
                elif rate >= 0.005:
                    st.info(f"📈 롱 과열 구간 | FR: {rate:.4f}% | 롱스퀴즈 주의")
                else:
                    st.success(f"✅ FR 정상 | {rate:.4f}%")
        except: pass

        fig.update_layout(showlegend=show_legend)
        st.plotly_chart(fig, use_container_width=True, config={
            "scrollZoom": True,
            "doubleClick": "reset",
            "displayModeBar": True,
            "modeBarButtonsToRemove": ["lasso2d", "select2d"],
        })

        st.caption(f"마지막 갱신: {datetime.now().strftime('%H:%M:%S')}")

    except Exception as e:
        st.error(f"데이터 로드 오류: {e}")
        import traceback
        st.code(traceback.format_exc())

    if auto:
        time.sleep(refresh_sec)
        st.rerun()

if __name__ == "__main__":
    main()