갱신시간 위치 KST 수정

app_streamlit.py

@@ -1,7 +1,3 @@
-"""
-BTC/ETH Futures Trading Dashboard — Streamlit 버전
-"""
-
 import sys
 import os
 sys.stdout.reconfigure(line_buffering=True)
@@ -19,7 +15,9 @@ import streamlit as st
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import ta
-from pyngrok import ngrok
+import urllib3
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
 
 # ──────────────────────────────────────────────
 # 페이지 설정 (반드시 최상단)
@@ -49,7 +47,7 @@ st.markdown("""
 # ──────────────────────────────────────────────
 TELEGRAM_TOKEN   = os.getenv("TELEGRAM_TOKEN", "")
 TELEGRAM_CHAT_ID = os.getenv("TELEGRAM_CHAT_ID", "")
-ALERT_COOLDOWN   = 30
+ALERT_COOLDOWN   = 300
 
 BASE = "https://fapi.binance.com"
 KST  = timedelta(hours=9)
@@ -62,7 +60,7 @@ _last_alert = {"strong_long": 0, "strong_short": 0, "long": 0, "short": 0, "vol_
 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=5)
+        requests.post(url, data={"chat_id": TELEGRAM_CHAT_ID, "text": message}, timeout=10)
     except Exception as e:
         print(f"[텔레그램 오류] {e}")
 
@@ -85,9 +83,9 @@ def check_and_alert(df, symbol, interval):
 # ──────────────────────────────────────────────
 # 데이터 수집
 # ──────────────────────────────────────────────
-def get_klines(symbol="BTCUSDT", interval="5m", limit=1500):
+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)
+    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"
@@ -100,7 +98,7 @@ def get_klines(symbol="BTCUSDT", interval="5m", limit=1500):
 
 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)
+    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
@@ -108,7 +106,7 @@ def get_funding_rate(symbol="BTCUSDT", limit=100):
 
 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)
+    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
@@ -116,7 +114,7 @@ def get_open_interest_history(symbol="BTCUSDT", period="5m", limit=100):
 
 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)
+    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
@@ -124,7 +122,7 @@ def get_long_short_ratio(symbol="BTCUSDT", period="5m", limit=500):
 
 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)
+    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
@@ -156,14 +154,12 @@ def compute_indicators(df, interval="5m"):
     return df
 
 def compute_signals(df, interval="5m"):
-    # 일반 신호용 — MA7/MA25 2개 기준
     df["bull_ma_2"] = (
         (df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"])
     )
     df["bear_ma_2"] = (
         (df["close"] < df["MA7"]) & (df["MA7"] < df["MA25"])
     )
-    # 강한 신호용 — MA7/MA25/MA99 3개 기준
     df["bull_ma"] = (
         (df["close"] > df["MA7"]) & (df["MA7"] > df["MA25"]) &
         (df["MA25"] > df["MA99"])
@@ -172,9 +168,8 @@ def compute_signals(df, interval="5m"):
         (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["StochRSI_k"] < 80)
+    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"] > 40) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & (df["close"] < df["BB_mid"]) & (df["StochRSI_k"] > 20)
+    df["short_signal"] = df["bear_ma_2"] & (df["RSI"] > 35) & (df["MACD_hist"] < df["MACD_hist"].shift(1)) & (df["close"] < df["BB_mid"])
-    # 쿨다운: 직전 5캔들 내 같은 신호 있으면 억제
     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)
 
@@ -197,7 +192,6 @@ def compute_signals(df, interval="5m"):
 
     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"]
-    # 강한 신호 쿨다운: 직전 10캔들 내 억제
     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)
 
@@ -208,9 +202,7 @@ def compute_signals(df, interval="5m"):
     df["exhaustion_short"] = buy_spike.shift(1).fillna(False)
     df["exhaustion_long"]  = sell_spike.shift(1).fillna(False)
 
-    # ── Taker Sell Net(Sell-Buy) 극값 강한 숏 신호 (MA배열 무관, 볼륨 우선) ──
+    _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}
-    # 시간봉별 볼륨 임계값 (5분봉 기준 100, 비례 적용)
-    _vol_min_map = {"1m": 33, "3m": 100, "5m": 100, "15m": 300, "30m": 600, "1h": 1200, "2h": 2400, "4h": 4800, "1d": 28800}
     _vol_min = _vol_min_map.get(interval, 100)
 
     df["sell_net"] = df["taker_sell_vol"] - df["taker_buy_vol"]
@@ -224,7 +216,6 @@ def compute_signals(df, interval="5m"):
     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
 
-    # ── Taker Buy Net(Buy-Sell) 극값 강한 롱 신호 (MA배열 무관, 볼륨 우선) ──
     df["buy_net"] = df["taker_buy_vol"] - df["taker_sell_vol"]
     buy_net_avg = df["buy_net"].rolling(10).mean()
     buy_spike_strong = (
@@ -236,11 +227,9 @@ def compute_signals(df, interval="5m"):
     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
 
-    # ── OI 2캔들 하락 + FR ≤ -0.007% → 숏 주의 신호 (쿨다운 5캔들) ──
     if "fundingRate" in df.columns and "sumOpenInterest" in df.columns:
         fr_extreme = df["fundingRate"] <= -0.007
         raw_signal = df["oi_down_2"] & fr_extreme
-        # 쿨다운: 직전 5캔들 내 이미 신호 있으면 억제
         cooldown_mask = raw_signal.rolling(5, min_periods=1).sum().shift(1).fillna(0) == 0
         df["short_caution_signal"] = raw_signal & cooldown_mask
     else:
@@ -269,14 +258,14 @@ COLORS = {
 }
 
 def _to_floor_freq(period):
-    return {"1m":"1min","3m":"3min","5m":"5min","15m":"15min","30m":"30min","1h":"1h","4h":"4h"}.get(period, 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):
+def build_chart(symbol, interval, candle_limit=200):
-    df = get_klines(symbol, interval, 1500)
+    df = get_klines(symbol, interval, candle_limit)
-    oi_period = interval if interval in ["5m","15m","30m","1h","4h"] else "5m"
+    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, 500)
+        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))
@@ -287,7 +276,7 @@ def build_chart(symbol, interval):
     except: pass
 
     try:
-        fr = get_funding_rate(symbol, 500)
+        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"))
@@ -298,7 +287,7 @@ def build_chart(symbol, interval):
     except: pass
 
     try:
-        ls = get_long_short_ratio(symbol, oi_period, 500)
+        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))
@@ -310,22 +299,19 @@ def build_chart(symbol, interval):
 
     df = compute_indicators(df, interval)
 
-    # ── 1시간봉 MA 배열 — 각 캔들 시점별로 소급 체크 ──
     try:
         if interval != "1h":
-            df_1h = get_klines(symbol, "1h", 300)
+            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()
-            # 일반 신호용 2개 기준
             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"])
             )
-            # 강한 신호용 3개 기준
             df_1h["h1_bull"] = (
                 (df_1h["close"] > df_1h["MA7"]) &
                 (df_1h["MA7"]   > df_1h["MA25"]) &
@@ -342,7 +328,6 @@ def build_chart(symbol, interval):
             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())
@@ -360,17 +345,11 @@ def build_chart(symbol, interval):
         df["h1_bull"] = False
         df["h1_bear"] = False
 
-    # 각 캔들 시점의 1시간봉 배열로 신호 억제
+    df["long_signal"]         = df["long_signal"]        & df["taker_buy_dom"]
-    # 일반 신호 — 1h 2개 기준
+    df["short_signal"]        = df["short_signal"]       & df["taker_sell_dom"]
-    h1_bull_2 = df.get("h1_bull_2", df["h1_bull"])
+    df["strong_long_signal"]  = df["strong_long_signal"]
-    h1_bear_2 = df.get("h1_bear_2", df["h1_bear"])
+    df["strong_short_signal"] = df["strong_short_signal"]
-    df["long_signal"]        = df["long_signal"]        & h1_bull_2 & df["taker_buy_dom"]
+    df["short_caution_signal"]= df["short_caution_signal"]
-    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"]
@@ -385,7 +364,6 @@ def build_chart(symbol, interval):
                         "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"],
@@ -393,31 +371,24 @@ def build_chart(symbol, interval):
         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)"),
@@ -436,9 +407,9 @@ def build_chart(symbol, interval):
             _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
+                y_val = d["low"] * 0.9998
             elif sig_name in _short_sigs:
-                y_val = d["high"] * 1.0002  # 캔들 상단 위
+                y_val = d["high"] * 1.0002
             else:
                 y_val = d["close"]
             fig.add_trace(go.Scatter(
@@ -450,7 +421,6 @@ def build_chart(symbol, interval):
                 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),
@@ -458,20 +428,16 @@ def build_chart(symbol, interval):
                 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)
@@ -479,26 +445,22 @@ def build_chart(symbol, interval):
         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(
@@ -512,7 +474,6 @@ def build_chart(symbol, interval):
         row=1, col=1
     )
 
-    # ── 레이아웃 ──
     fig.update_layout(
         height=1600,
         paper_bgcolor="#ffffff",
@@ -540,34 +501,27 @@ def build_chart(symbol, interval):
         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)
@@ -589,7 +543,7 @@ def _alert_loop():
                 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"
+            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:
@@ -606,51 +560,49 @@ def _alert_loop():
             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")
+        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 = st.columns(3)
+        now_kst = datetime.now(timezone.utc) + KST
+        st.markdown(f"<div style='text-align:right; font-size:12px; color:#888; margin-bottom:2px;'>🕐 마지막 갱신: {now_kst.strftime('%Y-%m-%d %H:%M:%S')} KST</div>", unsafe_allow_html=True)
+        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
 
-    # 알림 타겟 업데이트
     with _alert_lock:
         _alert_symbol   = symbol
         _alert_interval = interval
 
-    # 데이터 로드 & 차트
     try:
         with st.spinner("데이터 로딩 중..."):
-            fig, df = build_chart(symbol, interval)
+            fig, df = build_chart(symbol, interval, candle_limit)
 
-        # FR 배너
         try:
             fr_df = get_funding_rate(symbol, 1)
             if not fr_df.empty:
@@ -668,27 +620,21 @@ def main():
         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())
 
-    # 자동갱신
     if auto:
         time.sleep(refresh_sec)
         st.rerun()
 
 if __name__ == "__main__":
-    try:
-        public_url = ngrok.connect(8501)
-        print(f"핸드폰: {public_url}")
-    except Exception as e:
-        print(f"ngrok 오류: {e}")
-
     main()