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app.py

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+"""
+OKX QUANT TERMINAL  ·  v2 — ELITE SIGNAL ENGINE
+Hedge-fund-style crypto market analytics dashboard built on Gradio.
+
+Uses ONLY OKX's public (unauthenticated) market-data REST API:
+  - /api/v5/market/candles
+  - /api/v5/market/ticker
+  - /api/v5/market/tickers
+  - /api/v5/market/books
+  - /api/v5/public/funding-rate      (türev duyarlılığı, isteğe bağlı)
+
+No API key, no order placement, no account access. This is a read-only
+analytics / signal-generation terminal, not an execution bot.
+
+v2 SİNYAL MOTORU — YENİLİKLER
+  * Yeni faktörler: Supertrend, Ichimoku bulutu, Donchian kırılımı (hacim teyitli),
+    piyasa yapısı (HH/HL – LH/LL), CMF, MFI, mum formasyonları, Bollinger/Keltner sıkışması
+  * Rejim ağırlıkları artık ADX ile SÜREKLİ (yumuşak) geçiş yapar (eski: sert eşik)
+  * Skor tanh ile ±10'a yumuşak sıkıştırılır; "GÜÇLÜ" sinyal için faktör konsensüsü + MTF şartı
+  * 2 kademeli çoklu zaman dilimi (MTF) teyidi
+  * Canlı mikro-yapı katmanı: emir defteri dengesizliği, funding rate, yakın destek/direnç
+  * Yapısal (S/R) stop + ATR stop, 3 kademeli hedef, hesap büyüklüğüne göre pozisyon boyutu
+  * Gerçek işlem simülasyonu (SL/TP/zaman çıkışı + komisyon): kazanma oranı, profit factor,
+    beklenti, R çarpanı, SQN, drawdown, Al&Tut karşılaştırması, yarım-Kelly risk önerisi
+  * Bileşenli GÜVEN skoru + A+ … D kalite notu, faktör katkı grafiği, sinyal işaretli fiyat grafiği
+  * "Sadece kapanmış mumlar" modu (repaint yok), veri önbelleği, paralel veri çekimi
+"""
+
+import math
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import requests
+import gradio as gr
+
+try:  # Hugging Face Spaces (ZeroGPU) — yerelde yoksa sessizce atla
+    import spaces
+    _gpu = spaces.GPU(duration=30)
+except Exception:
+    def _gpu(fn):
+        return fn
+
+# ---------------------------------------------------------------------------
+# CONFIG
+# ---------------------------------------------------------------------------
+OKX_BASE = "https://www.okx.com"
+TIMEOUT = 10
+CACHE_TTL = 8  # saniye — aynı mum verisini sekmeler arasında tekrar çekmemek için
+
+POPULAR_SYMBOLS = [
+    "BTC-USDT", "ETH-USDT", "SOL-USDT", "BNB-USDT", "XRP-USDT",
+    "DOGE-USDT", "ADA-USDT", "AVAX-USDT", "LINK-USDT", "TON-USDT",
+    "TRX-USDT", "DOT-USDT", "MATIC-USDT", "LTC-USDT", "NEAR-USDT",
+    "SUI-USDT", "APT-USDT", "ARB-USDT", "OP-USDT", "INJ-USDT",
+]
+
+BAR_OPTIONS = ["1m", "3m", "5m", "15m", "30m", "1H", "2H", "4H", "6H", "12H", "1D", "1W"]
+
+# approximate periods-per-year for each bar size, used to annualize risk stats
+BAR_TO_PERIODS_PER_YEAR = {
+    "1m": 365 * 24 * 60, "3m": 365 * 24 * 20, "5m": 365 * 24 * 12,
+    "15m": 365 * 24 * 4, "30m": 365 * 24 * 2, "1H": 365 * 24,
+    "2H": 365 * 12, "4H": 365 * 6, "6H": 365 * 4, "12H": 365 * 2,
+    "1D": 365, "1W": 52,
+}
+
+CHART_BG = "#0a0e14"
+
+# her zaman dilimi için "bir üst" zaman dilimi -> çoklu zaman dilimi (MTF) teyidi için
+MTF_MAP = {
+    "1m": "15m", "3m": "30m", "5m": "1H", "15m": "4H", "30m": "4H",
+    "1H": "4H", "2H": "1D", "4H": "1D", "6H": "1D", "12H": "1W",
+    "1D": "1W", "1W": "1W",
+}
+SLOW_BARS = {"4H", "6H", "12H", "1D", "1W"}  # bu vadelerde emir defteri etkisi yarıya iner
+
+# --- Sinyal motoru parametreleri ---
+SIGNAL_THRESHOLD = 2.0      # |skor| >= bu değer -> AL / SAT
+STRONG_THRESHOLD = 4.5      # |skor| >= bu değer + konsensüs -> GÜÇLÜ
+WARMUP_BARS = 80            # Ichimoku (52+26) ısınması için minimum bar
+BACKTEST_LOOKBACK = 220     # walk-forward test penceresi (bar)
+SL_ATR, TP_ATR = 1.5, 2.5   # backtest ve varsayılan işlem planı (ATR çarpanları)
+MAX_HOLD_BARS = 24          # zaman çıkışı
+FEE_PER_SIDE = 0.001        # OKX spot taker ≈ %0.1 / yön
+TP_R_MULTIPLES = (TP_ATR / SL_ATR, 2.5, 4.0)  # TP1/TP2/TP3 (R cinsinden)
+
+
+# ---------------------------------------------------------------------------
+# OKX PUBLIC API WRAPPERS  (önbellek + yeniden deneme)
+# ---------------------------------------------------------------------------
+_CACHE = {}
+_CACHE_LOCK = threading.Lock()
+
+
+def _cached(key, ttl, loader):
+    now = time.time()
+    with _CACHE_LOCK:
+        hit = _CACHE.get(key)
+        if hit and now - hit[0] < ttl:
+            return hit[1]
+    val = loader()
+    with _CACHE_LOCK:
+        _CACHE[key] = (time.time(), val)
+        if len(_CACHE) > 200:  # eski kayıtları buda
+            for k in sorted(_CACHE, key=lambda x: _CACHE[x][0])[:60]:
+                _CACHE.pop(k, None)
+    return val
+
+
+def _get(path, params=None, retries=2):
+    for attempt in range(retries + 1):
+        try:
+            r = requests.get(f"{OKX_BASE}{path}", params=params, timeout=TIMEOUT)
+            r.raise_for_status()
+            data = r.json()
+            code = str(data.get("code"))
+            if code == "50011" and attempt < retries:  # OKX rate-limit
+                time.sleep(0.5 * (attempt + 1))
+                continue
+            if code != "0":
+                raise RuntimeError(data.get("msg") or "OKX API hata döndürdü.")
+            return data["data"]
+        except requests.exceptions.RequestException as e:
+            if attempt < retries:
+                time.sleep(0.3 * (attempt + 1))
+                continue
+            raise RuntimeError(f"Ağ hatası: {e}")
+
+
+def fetch_candles(inst_id, bar="1H", limit=300):
+    def _load():
+        raw = _get("/api/v5/market/candles", {"instId": inst_id, "bar": bar, "limit": limit})
+        if not raw:
+            raise RuntimeError(f"{inst_id} için mum verisi bulunamadı (sembolü kontrol edin).")
+        cols = ["ts", "o", "h", "l", "c", "vol", "volCcy", "volCcyQuote", "confirm"]
+        df = pd.DataFrame(raw, columns=cols)
+        for c in ["o", "h", "l", "c", "vol"]:
+            df[c] = df[c].astype(float)
+        df["confirm"] = df["confirm"].astype(int)
+        df["ts"] = pd.to_datetime(df["ts"].astype(np.int64), unit="ms")
+        return df.sort_values("ts").reset_index(drop=True)
+
+    return _cached(("candles", inst_id, bar, limit), CACHE_TTL, _load).copy()
+
+
+def fetch_ticker(inst_id):
+    raw = _get("/api/v5/market/ticker", {"instId": inst_id})
+    return raw[0] if raw else {}
+
+
+def fetch_all_tickers(inst_type="SPOT"):
+    return _cached(("tickers", inst_type), 20, lambda: _get("/api/v5/market/tickers", {"instType": inst_type}))
+
+
+def fetch_orderbook(inst_id, depth=20):
+    raw = _get("/api/v5/market/books", {"instId": inst_id, "sz": depth})
+    return raw[0] if raw else {}
+
+
+def fetch_funding_rate(inst_id):
+    """Spot paritenin (BTC-USDT) sürekli vadeli karşılığının (BTC-USDT-SWAP) funding oranı."""
+    parts = inst_id.split("-")
+    if len(parts) != 2:
+        return None
+    raw = _get("/api/v5/public/funding-rate", {"instId": f"{inst_id}-SWAP"})
+    if not raw:
+        return None
+    return float(raw[0].get("fundingRate") or 0.0)
+
+
+# ---------------------------------------------------------------------------
+# TECHNICAL INDICATORS
+# ---------------------------------------------------------------------------
+def ema(s, p):
+    return s.ewm(span=p, adjust=False).mean()
+
+
+def sma(s, p):
+    return s.rolling(p).mean()
+
+
+def rsi(s, p=14):
+    d = s.diff()
+    gain = d.clip(lower=0)
+    loss = -d.clip(upper=0)
+    ag = gain.ewm(alpha=1 / p, adjust=False).mean()
+    al = loss.ewm(alpha=1 / p, adjust=False).mean()
+    rs = ag / al.replace(0, np.nan)
+    return (100 - 100 / (1 + rs)).fillna(50)
+
+
+def macd(s, fast=12, slow=26, signal=9):
+    line = ema(s, fast) - ema(s, slow)
+    sig = ema(line, signal)
+    return line, sig, line - sig
+
+
+def bollinger(s, p=20, k=2):
+    mid = sma(s, p)
+    std = s.rolling(p).std()
+    return mid + k * std, mid, mid - k * std
+
+
+def atr(df, p=14):
+    h, l, c = df["h"], df["l"], df["c"]
+    pc = c.shift(1)
+    tr = pd.concat([h - l, (h - pc).abs(), (l - pc).abs()], axis=1).max(axis=1)
+    return tr.ewm(alpha=1 / p, adjust=False).mean()
+
+
+def stochastic(df, k_p=14, d_p=3):
+    lo = df["l"].rolling(k_p).min()
+    hi = df["h"].rolling(k_p).max()
+    k = 100 * (df["c"] - lo) / (hi - lo).replace(0, np.nan)
+    d = k.rolling(d_p).mean()
+    return k.fillna(50), d.fillna(50)
+
+
+def vwap_window(df):
+    tp = (df["h"] + df["l"] + df["c"]) / 3
+    return (tp * df["vol"]).cumsum() / df["vol"].cumsum().replace(0, np.nan)
+
+
+def adx(df, p=14):
+    """ADX + yönsel indikatörler (+DI / -DI). Trend gücünü ölçer (0-100)."""
+    h, l, c = df["h"], df["l"], df["c"]
+    up_move = h.diff()
+    down_move = -l.diff()
+    plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0.0)
+    minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0.0)
+    tr = pd.concat([h - l, (h - c.shift(1)).abs(), (l - c.shift(1)).abs()], axis=1).max(axis=1)
+    atr_ = tr.ewm(alpha=1 / p, adjust=False).mean()
+    plus_di = 100 * pd.Series(plus_dm, index=df.index).ewm(alpha=1 / p, adjust=False).mean() / atr_.replace(0, np.nan)
+    minus_di = 100 * pd.Series(minus_dm, index=df.index).ewm(alpha=1 / p, adjust=False).mean() / atr_.replace(0, np.nan)
+    dx = ((plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)) * 100
+    adx_ = dx.ewm(alpha=1 / p, adjust=False).mean()
+    return adx_.fillna(0), plus_di.fillna(0), minus_di.fillna(0)
+
+
+def obv(df):
+    """On-Balance Volume: hacmin fiyat yönüyle kümülatif toplamı."""
+    direction = np.sign(df["c"].diff()).fillna(0)
+    return (direction * df["vol"]).cumsum()
+
+
+def supertrend(df, period=10, mult=3.0):
+    """Supertrend: ATR bantlı trend takipçisi. dir=+1 yükseliş, -1 düşüş. (nedensel, geleceğe bakmaz)"""
+    h, l, c = df["h"].to_numpy(), df["l"].to_numpy(), df["c"].to_numpy()
+    a = atr(df, period).to_numpy()
+    hl2 = (h + l) / 2
+    ub, lb = hl2 + mult * a, hl2 - mult * a
+    n = len(c)
+    fub, flb = ub.copy(), lb.copy()
+    direction = np.ones(n)
+    line = np.full(n, np.nan)
+    if n:
+        line[0] = flb[0]
+    for i in range(1, n):
+        fub[i] = ub[i] if (ub[i] < fub[i - 1] or c[i - 1] > fub[i - 1]) else fub[i - 1]
+        flb[i] = lb[i] if (lb[i] > flb[i - 1] or c[i - 1] < flb[i - 1]) else flb[i - 1]
+        if c[i] > fub[i - 1]:
+            direction[i] = 1
+        elif c[i] < flb[i - 1]:
+            direction[i] = -1
+        else:
+            direction[i] = direction[i - 1]
+        line[i] = flb[i] if direction[i] == 1 else fub[i]
+    return pd.Series(line, index=df.index), pd.Series(direction, index=df.index)
+
+
+def ichimoku(df):
+    """Tenkan/Kijun + bulut. Bulut, 26 bar önce hesaplanan Senkou A/B'nin bugüne kaydırılmış halidir
+    (yani nedensel: gelecek veri kullanılmaz)."""
+    h, l = df["h"], df["l"]
+    tenkan = (h.rolling(9).max() + l.rolling(9).min()) / 2
+    kijun = (h.rolling(26).max() + l.rolling(26).min()) / 2
+    span_a = (tenkan + kijun) / 2
+    span_b = (h.rolling(52).max() + l.rolling(52).min()) / 2
+    return tenkan, kijun, span_a.shift(26), span_b.shift(26)
+
+
+def mfi(df, p=14):
+    """Money Flow Index: hacim ağırlıklı RSI."""
+    tp = (df["h"] + df["l"] + df["c"]) / 3
+    rmf = tp * df["vol"]
+    d = tp.diff()
+    pos = rmf.where(d > 0, 0.0).rolling(p).sum()
+    neg = rmf.where(d < 0, 0.0).rolling(p).sum()
+    out = 100 - 100 / (1 + pos / neg.replace(0, np.nan))
+    out = out.where(~((neg == 0) & (pos > 0)), 100.0)
+    return out.fillna(50)
+
+
+def cmf(df, p=20):
+    """Chaikin Money Flow: kapanışın bar aralığındaki konumuna göre hacim akışı (-1..+1)."""
+    rng = (df["h"] - df["l"]).replace(0, np.nan)
+    mfm = ((df["c"] - df["l"]) - (df["h"] - df["c"])) / rng
+    mfv = mfm.fillna(0) * df["vol"]
+    return (mfv.rolling(p).sum() / df["vol"].rolling(p).sum().replace(0, np.nan)).fillna(0)
+
+
+def market_structure(df, w=10):
+    """+1: Higher-High & Higher-Low (yükseliş yapısı), -1: Lower-High & Lower-Low, 0: karışık."""
+    hi, lo = df["h"].rolling(w).max(), df["l"].rolling(w).min()
+    up = (hi > hi.shift(w)) & (lo > lo.shift(w))
+    dn = (hi < hi.shift(w)) & (lo < lo.shift(w))
+    return pd.Series(np.where(up, 1.0, np.where(dn, -1.0, 0.0)), index=df.index)
+
+
+def squeeze_indicator(df, ind):
+    """TTM tarzı sıkışma: Bollinger bantları Keltner kanalının İÇİNE girerse volatilite sıkışmıştır.
+    Dönüş: (sıkışma_açık, son 3 barda sıkışma çözüldü mü, momentum)"""
+    kc_mid = ind["ema20"]
+    kc_u, kc_l = kc_mid + 1.5 * ind["atr"], kc_mid - 1.5 * ind["atr"]
+    on = (ind["bb_lower"] > kc_l) & (ind["bb_upper"] < kc_u)
+    release = (~on) & on.shift(1, fill_value=False)
+    rel3 = release.astype(float).rolling(3, min_periods=1).max()
+    hh, ll = df["h"].rolling(20).max(), df["l"].rolling(20).min()
+    mom = df["c"] - ((hh + ll) / 2 + sma(df["c"], 20)) / 2
+    return on.astype(float), rel3, mom
+
+
+CANDLE_PATTERNS = [
+    ("pat_hammer", "Çekiç", 1),
+    ("pat_bull_eng", "Yükseliş Yutan Mum", 1),
+    ("pat_morning", "Sabah Yıldızı", 1),
+    ("pat_shooting", "Kayan Yıldız", -1),
+    ("pat_bear_eng", "Düşüş Yutan Mum", -1),
+    ("pat_evening", "Akşam Yıldızı", -1),
+]
+
+
+def candle_patterns(df):
+    """Vektörel mum formasyonu tespiti (yalnızca geçmiş barlar kullanılır)."""
+    o, h, l, c = df["o"], df["h"], df["l"], df["c"]
+    body = (c - o).abs()
+    rng = (h - l).replace(0, np.nan)
+    upper = h - np.maximum(o, c)
+    lower = np.minimum(o, c) - l
+    avg_body = body.rolling(10, min_periods=3).mean()
+    prior_down = c.shift(1) < c.shift(4)
+    prior_up = c.shift(1) > c.shift(4)
+
+    hammer = (lower >= 0.6 * rng) & (body <= 0.3 * rng) & (upper <= 0.15 * rng) & prior_down
+    shooting = (upper >= 0.6 * rng) & (body <= 0.3 * rng) & (lower <= 0.15 * rng) & prior_up
+    bull_eng = ((c.shift(1) < o.shift(1)) & (c > o) & (c >= o.shift(1)) & (o <= c.shift(1))
+                & (body > body.shift(1)) & prior_down)
+    bear_eng = ((c.shift(1) > o.shift(1)) & (c < o) & (c <= o.shift(1)) & (o >= c.shift(1))
+                & (body > body.shift(1)) & prior_up)
+    big2 = body.shift(2) > avg_body.shift(2)
+    small1 = body.shift(1) < 0.35 * body.shift(2)
+    mid2 = (o.shift(2) + c.shift(2)) / 2
+    morning = (c.shift(2) < o.shift(2)) & big2 & small1 & (c > o) & (c > mid2)
+    evening = (c.shift(2) > o.shift(2)) & big2 & small1 & (c < o) & (c < mid2)
+
+    raw = {"pat_hammer": hammer, "pat_shooting": shooting, "pat_bull_eng": bull_eng,
+           "pat_bear_eng": bear_eng, "pat_morning": morning, "pat_evening": evening}
+    return {k: v.fillna(False).astype(float) for k, v in raw.items()}
+
+
+def find_swings(series, window=3):
+    """Yerel tepe/dip (swing high/low) indekslerini bulur."""
+    n = len(series)
+    highs, lows = [], []
+    for i in range(window, n - window):
+        seg = series.iloc[i - window:i + window + 1]
+        if series.iloc[i] == seg.max():
+            highs.append(i)
+        if series.iloc[i] == seg.min():
+            lows.append(i)
+    return highs, lows
+
+
+def detect_divergence(df, ind, lookback=60, window=3):
+    """Fiyat ile RSI arasındaki son swing'leri karşılaştırarak klasik uyumsuzluğu tespit eder."""
+    n = len(df)
+    lb = min(lookback, n)
+    close = df["c"].iloc[-lb:].reset_index(drop=True)
+    rsi_s = ind["rsi"].iloc[-lb:].reset_index(drop=True)
+    highs, lows = find_swings(close, window)
+    result = {"bullish": False, "bearish": False}
+    if len(highs) >= 2:
+        h1, h2 = highs[-2], highs[-1]
+        if close.iloc[h2] > close.iloc[h1] and rsi_s.iloc[h2] < rsi_s.iloc[h1]:
+            result["bearish"] = True
+    if len(lows) >= 2:
+        l1, l2 = lows[-2], lows[-1]
+        if close.iloc[l2] < close.iloc[l1] and rsi_s.iloc[l2] > rsi_s.iloc[l1]:
+            result["bullish"] = True
+    return result
+
+
+def find_sr_levels(df, atr_v, window=5, lookback=200, max_levels=4):
+    """Swing tepe/diplerini kümeleyerek destek/direnç seviyeleri çıkarır.
+    Dönüş: (destekler, dirençler) — her biri [(seviye, temas_sayısı), ...], fiyata yakınlık sırasıyla."""
+    sub = df.iloc[-lookback:].reset_index(drop=True)
+    highs, _ = find_swings(sub["h"], window)
+    _, lows = find_swings(sub["l"], window)
+    pts = [float(sub["h"].iloc[i]) for i in highs] + [float(sub["l"].iloc[i]) for i in lows]
+    if not pts:
+        return [], []
+    price = float(df["c"].iloc[-1])
+    tol = max(0.6 * (atr_v if atr_v and atr_v > 0 else 0.0), price * 0.002)
+    pts.sort()
+    clusters, cur = [], [pts[0]]
+    for p in pts[1:]:
+        if p - float(np.mean(cur)) <= tol:
+            cur.append(p)
+        else:
+            clusters.append(cur)
+            cur = [p]
+    clusters.append(cur)
+    levels = [(float(np.mean(c)), len(c)) for c in clusters]
+    supports = sorted([lv for lv in levels if lv[0] < price], key=lambda x: -x[0])[:max_levels]
+    resists = sorted([lv for lv in levels if lv[0] > price], key=lambda x: x[0])[:max_levels]
+    return supports, resists
+
+
+def compute_indicators(df):
+    close = df["c"]
+    ind = {}
+    ind["rsi"] = rsi(close)
+    ind["macd_line"], ind["macd_signal"], ind["macd_hist"] = macd(close)
+    ind["ema20"] = ema(close, 20)
+    ind["ema50"] = ema(close, 50)
+    ind["ema200"] = ema(close, 200 if len(close) >= 200 else max(5, len(close) // 2))
+    ind["bb_upper"], ind["bb_mid"], ind["bb_lower"] = bollinger(close)
+    ind["atr"] = atr(df)
+    ind["stoch_k"], ind["stoch_d"] = stochastic(df)
+    ind["vwap"] = vwap_window(df)
+    ind["adx"], ind["plus_di"], ind["minus_di"] = adx(df)
+    ind["obv"] = obv(df)
+    ind["obv_ema"] = ema(ind["obv"], 20)
+    # --- v2 ek faktörler ---
+    ind["st_line"], ind["st_dir"] = supertrend(df)
+    ind["tenkan"], ind["kijun"], ind["ich_a"], ind["ich_b"] = ichimoku(df)
+    ind["mfi"] = mfi(df)
+    ind["cmf"] = cmf(df)
+    ind["rvol"] = (df["vol"] / sma(df["vol"], 20).replace(0, np.nan)).fillna(1.0)
+    ind["dc_hi20"] = df["h"].rolling(20).max().shift(1)
+    ind["dc_lo20"] = df["l"].rolling(20).min().shift(1)
+    ind["struct"] = market_structure(df)
+    ind["sq_on"], ind["sq_rel3"], ind["sq_mom"] = squeeze_indicator(df, ind)
+    ind["atr_rank"] = (ind["atr"] / close).rolling(100, min_periods=30).apply(
+        lambda x: float((x <= x[-1]).mean()), raw=True).fillna(0.5)
+    for name, ser in candle_patterns(df).items():
+        ind[name] = ser
+    # hızlı, nedensel erişim için numpy önbelleği (backtest döngüsünde .iloc yerine kullanılır)
+    arr = {k: v.to_numpy(dtype=float) for k, v in ind.items()}
+    for k in ("o", "h", "l", "c", "vol"):
+        arr[k] = df[k].to_numpy(dtype=float)
+    ind["_arr"] = arr
+    return ind
+
+
+# ---------------------------------------------------------------------------
+# COMPOSITE SIGNAL ENGINE v2 (multi-factor, rejim-uyarlamalı, konsensüs filtreli)
+# ---------------------------------------------------------------------------
+def _score_at(ind, i, verbose=False):
+    """Çoklu faktör puanlamasını verilen bar indeksinde hesaplar (yalnızca o ana kadarki veri).
+
+    İki faktör ailesi vardır:
+      T (TREND)     : MACD, EMA dizilimi, VWAP, OBV, Supertrend, Ichimoku, Donchian kırılımı,
+                      piyasa yapısı, CMF, sıkışma kırılımı, ADX/DI yönü
+      R (REVERSION) : RSI, Bollinger, Stokastik, MFI, mum formasyonları
+
+    ADX'e göre iki ailenin ağırlığı SÜREKLİ olarak kayar (w: 0=yatay … 1=güçlü trend).
+    Güçlü trendde karşı-trend (aşırı alım/satım) sinyalleri bastırılır; yatay piyasada trend takibi bastırılır.
+    Sonuç, tanh ile ±10 aralığına yumuşak sıkıştırılır.
+    Canlı sinyal ve tarihsel backtest AYNI fonksiyonu kullanır (tutarlılık + gelecek sızıntısı yok)."""
+    A = ind["_arr"]
+    price = A["c"][i]
+    items = []  # (etiket, grup, ham_puan, açıklama)
+
+    def add(label, group, raw, text):
+        items.append((label, group, float(raw), text))
+
+    # --- RSI (R) ---
+    rsi_v = A["rsi"][i]
+    if rsi_v < 30:
+        add("RSI", "R", 2, f"RSI aşırı satım bölgesinde ({rsi_v:.1f})")
+    elif rsi_v > 70:
+        add("RSI", "R", -2, f"RSI aşırı alım bölgesinde ({rsi_v:.1f})")
+    else:
+        add("RSI", "R", 0, f"RSI nötr bölgede ({rsi_v:.1f})")
+
+    # --- MACD histogramı (T) ---
+    hist_v = A["macd_hist"][i]
+    hist_prev = A["macd_hist"][i - 1] if i > 0 else 0.0
+    if hist_v > 0 and hist_v > hist_prev:
+        add("MACD", "T", 1.5, "MACD histogramı pozitif ve güçleniyor")
+    elif hist_v > 0:
+        add("MACD", "T", 0.5, "MACD histogramı pozitif")
+    elif hist_v < 0 and hist_v < hist_prev:
+        add("MACD", "T", -1.5, "MACD histogramı negatif ve zayıflıyor")
+    else:
+        add("MACD", "T", -0.5, "MACD histogramı negatif")
+
+    # --- EMA dizilimi (T) ---
+    e20, e50, e200 = A["ema20"][i], A["ema50"][i], A["ema200"][i]
+    if price > e20 > e50 > e200:
+        add("EMA Dizilimi", "T", 2, "Güçlü yükseliş trendi (Fiyat > EMA20 > EMA50 > EMA200)")
+    elif price > e20 and e20 > e50:
+        add("EMA Dizilimi", "T", 1, "Yükseliş trendi (kısa vadeli ortalamaların üzerinde)")
+    elif price < e20 < e50 < e200:
+        add("EMA Dizilimi", "T", -2, "Güçlü düşüş trendi (Fiyat < EMA20 < EMA50 < EMA200)")
+    elif price < e20 and e20 < e50:
+        add("EMA Dizilimi", "T", -1, "Düşüş trendi (kısa vadeli ortalamaların altında)")
+    else:
+        add("EMA Dizilimi", "T", 0, "Trend yatay / belirsiz")
+
+    # --- Bollinger konumu (R) ---
+    bb_u, bb_l = A["bb_upper"][i], A["bb_lower"][i]
+    if bb_u == bb_u and bb_l == bb_l and bb_u != bb_l:
+        bb_pos = (price - bb_l) / (bb_u - bb_l)
+        if bb_pos < 0.15:
+            add("Bollinger", "R", 1, "Fiyat Bollinger alt bandına yakın (olası tepki)")
+        elif bb_pos > 0.85:
+            add("Bollinger", "R", -1, "Fiyat Bollinger üst bandına yakın (olası tepki)")
+
+    # --- Stokastik (R) ---
+    k_v = A["stoch_k"][i]
+    if k_v < 20:
+        add("Stokastik", "R", 1, f"Stokastik aşırı satım bölgesinde ({k_v:.1f})")
+    elif k_v > 80:
+        add("Stokastik", "R", -1, f"Stokastik aşırı alım bölgesinde ({k_v:.1f})")
+
+    # --- MFI (R) ---
+    mfi_v = A["mfi"][i]
+    if mfi_v < 20:
+        add("MFI", "R", 1, f"MFI (hacimli RSI) aşırı satımda ({mfi_v:.1f})")
+    elif mfi_v > 80:
+        add("MFI", "R", -1, f"MFI (hacimli RSI) aşırı alımda ({mfi_v:.1f})")
+
+    # --- Mum formasyonları (R) ---
+    hits = []
+    for key, name, sgn in CANDLE_PATTERNS:
+        if A[key][i] > 0:
+            hits.append((name, sgn))
+    if hits:
+        raw = max(-1.5, min(1.5, float(sum(s for _, s in hits))))
+        add("Mum Formasyonu", "R", raw, "Mum formasyonu: " + ", ".join(n for n, _ in hits))
+
+    # --- VWAP (T) ---
+    vwap_v = A["vwap"][i]
+    if vwap_v == vwap_v:
+        if price > vwap_v:
+            add("VWAP", "T", 0.5, "Fiyat pencere VWAP'ının üzerinde")
+        else:
+            add("VWAP", "T", -0.5, "Fiyat pencere VWAP'ının altında")
+
+    # --- OBV (T) ---
+    obv_v, obv_e = A["obv"][i], A["obv_ema"][i]
+    if obv_e == obv_e:
+        if obv_v > obv_e:
+            add("OBV", "T", 0.5, "OBV hacim akışı yükseliş yönünde")
+        else:
+            add("OBV", "T", -0.5, "OBV hacim akışı düşüş yönünde")
+
+    # --- CMF (T) ---
+    cmf_v = A["cmf"][i]
+    if cmf_v > 0.05:
+        add("CMF", "T", 0.5, f"Chaikin para akışı pozitif ({cmf_v:+.2f}) — alıcılar baskın")
+    elif cmf_v < -0.05:
+        add("CMF", "T", -0.5, f"Chaikin para akışı negatif ({cmf_v:+.2f}) — satıcılar baskın")
+
+    # --- Supertrend (T) ---
+    st_d = A["st_dir"][i]
+    st_flip = i > 0 and A["st_dir"][i] != A["st_dir"][i - 1]
+    if st_d > 0:
+        add("Supertrend", "T", 1.5 + (0.5 if st_flip else 0),
+            "Supertrend YÜKSELİŞ moduna döndü (taze sinyal)" if st_flip else "Supertrend yükseliş modunda")
+    else:
+        add("Supertrend", "T", -(1.5 + (0.5 if st_flip else 0)),
+            "Supertrend DÜŞÜŞ moduna döndü (taze sinyal)" if st_flip else "Supertrend düşüş modunda")
+
+    # --- Ichimoku (T) ---
+    sa, sb = A["ich_a"][i], A["ich_b"][i]
+    if sa == sa and sb == sb:
+        top, bot = max(sa, sb), min(sa, sb)
+        if price > top:
+            add("Ichimoku Bulutu", "T", 1.5, "Fiyat Ichimoku bulutunun üzerinde (boğa bölgesi)")
+        elif price < bot:
+            add("Ichimoku Bulutu", "T", -1.5, "Fiyat Ichimoku bulutunun altında (ayı bölgesi)")
+        else:
+            add("Ichimoku Bulutu", "T", 0, "Fiyat Ichimoku bulutunun içinde (kararsız bölge)")
+        tk, kj = A["tenkan"][i], A["kijun"][i]
+        if tk == tk and kj == kj:
+            add("Tenkan/Kijun", "T", 0.5 if tk > kj else -0.5,
+                "Tenkan > Kijun (kısa vadeli momentum yukarı)" if tk > kj else "Tenkan < Kijun (kısa vadeli momentum aşağı)")
+
+    # --- Donchian kırılımı (T) ---
+    dhi, dlo, rvol = A["dc_hi20"][i], A["dc_lo20"][i], A["rvol"][i]
+    if dhi == dhi and dlo == dlo:
+        if price > dhi:
+            if rvol >= 1.2:
+                add("Donchian Kırılımı", "T", 1.5, f"20 barlık tepe HACİMLİ kırıldı (göreli hacim {rvol:.1f}x)")
+            else:
+                add("Donchian Kırılımı", "T", 0.75, f"20 barlık tepe kırıldı ama hacim zayıf ({rvol:.1f}x)")
+        elif price < dlo:
+            if rvol >= 1.2:
+                add("Donchian Kırılımı", "T", -1.5, f"20 barlık dip HACİMLİ kırıldı (göreli hacim {rvol:.1f}x)")
+            else:
+                add("Donchian Kırılımı", "T", -0.75, f"20 barlık dip kırıldı ama hacim zayıf ({rvol:.1f}x)")
+
+    # --- Piyasa yapısı (T) ---
+    st_v = A["struct"][i]
+    if st_v > 0:
+        add("Piyasa Yapısı", "T", 1, "Piyasa yapısı: Higher-High / Higher-Low (yükseliş yapısı)")
+    elif st_v < 0:
+        add("Piyasa Yapısı", "T", -1, "Piyasa yapısı: Lower-High / Lower-Low (düşüş yapısı)")
+
+    # --- Sıkışma (Bollinger ⊂ Keltner) ---
+    squeeze_on = A["sq_on"][i] > 0
+    mom = A["sq_mom"][i]
+    if A["sq_rel3"][i] > 0 and mom == mom and mom != 0:
+        d = 1 if mom > 0 else -1
+        add("Sıkışma Kırılımı", "T", 1.5 * d,
+            "Volatilite sıkışması yukarı yönde çözüldü" if d > 0 else "Volatilite sıkışması aşağı yönde çözüldü")
+    elif squeeze_on:
+        add("Sıkışma", "T", 0, "Volatilite sıkışması aktif — kırılım bekleniyor, sinyal güveni düşürüldü")
+
+    # --- REJİM (ADX) — sürekli ağırlıklandırma ---
+    adx_v = A["adx"][i]
+    w = min(max((adx_v - 15.0) / 15.0, 0.0), 1.0) if adx_v == adx_v else 0.0  # 0 = yatay, 1 = güçlü trend
+    trend_w = 0.5 + 0.8 * w
+    rev_w = 1.3 - 0.95 * w
+    di_dir = 1 if A["plus_di"][i] > A["minus_di"][i] else -1
+    add("ADX / DI Yönü", "T", di_dir * w, "")  # yönsel bonus; w kadar ölçeklenir
+    if w >= 0.67:
+        regime = "TREND"
+        add("Rejim", "T", 0, f"ADX güçlü trend gösteriyor ({adx_v:.1f}) — trend faktörleri ağırlıklı, karşı-trend sinyaller bastırıldı")
+    elif w <= 0.33:
+        regime = "YATAY"
+        add("Rejim", "T", 0, f"ADX zayıf/yatay piyasa ({adx_v:.1f}) — ortalamaya dönüş faktörleri ağırlıklı")
+    else:
+        regime = "GEÇİŞ"
+        add("Rejim", "T", 0, f"ADX geçiş bölgesinde ({adx_v:.1f}) — faktör ağırlıkları dengeli karışım")
+
+    contribs = [(lab, (trend_w if g == "T" else rev_w) * raw, raw) for lab, g, raw, _ in items]
+    raw_total = sum(c for _, c, _ in contribs)
+    if squeeze_on:
+        raw_total *= 0.75
+    score = 10.0 * math.tanh(raw_total / 9.0)
+    bull_w = sum(c for _, c, _ in contribs if c > 0)
+    bear_w = -sum(c for _, c, _ in contribs if c < 0)
+
+    out = {
+        "score": score, "price": price, "adx": adx_v, "w": w, "regime": regime,
+        "bull_w": bull_w, "bear_w": bear_w, "squeeze_on": bool(squeeze_on),
+        "atr_rank": A["atr_rank"][i], "pat_hits": hits,
+    }
+    if verbose:
+        def tone(raw):
+            return "up" if raw > 0 else ("down" if raw < 0 else "flat")
+        out["reasons"] = [(tone(raw), text) for _, _, raw, text in items if text]
+        out["contribs"] = [(lab, c) for lab, c, raw in contribs if abs(c) > 1e-9]
+    return out
+
+
+def compute_score_series(ind, n):
+    """Son BACKTEST_LOOKBACK bar için (canlı bar dahil) skor serisi. Dönüş: (başlangıç_indeksi, [skor_dict,...])"""
+    start = min(max(WARMUP_BARS, n - BACKTEST_LOOKBACK), n - 1)
+    return start, [_score_at(ind, i) for i in range(start, n)]
+
+
+# ---------------------------------------------------------------------------
+# BACKTEST — yön doğruluğu + gerçek işlem simülasyonu (SL/TP/zaman çıkışı + komisyon)
+# ---------------------------------------------------------------------------
+def directional_accuracy(df, start, scores, forward=5, threshold=SIGNAL_THRESHOLD):
+    """Klasik test: sinyal 'forward' bar sonra doğru yönü göstermiş mi? (rejim kırılımlı)"""
+    c = df["c"].to_numpy()
+    n = len(c)
+    stats = {"all": [0, 0], "trend": [0, 0], "range": [0, 0]}
+    for i in range(start, n - forward):
+        s = scores[i - start]
+        if abs(s["score"]) < threshold:
+            continue
+        correct = (s["score"] > 0) == (c[i + forward] > c[i])
+        regime = "trend" if s["adx"] >= 25 else "range"
+        for key in ("all", regime):
+            stats[key][1] += 1
+            if correct:
+                stats[key][0] += 1
+    if stats["all"][1] < 5:
+        return None
+
+    def pack(key):
+        ok, t = stats[key]
+        return {"accuracy": ok / t, "n_signals": t} if t > 0 else None
+
+    return {
+        "accuracy": stats["all"][0] / stats["all"][1], "correct": stats["all"][0],
+        "n_signals": stats["all"][1], "forward_bars": forward,
+        "trend_regime": pack("trend"), "range_regime": pack("range"),
+    }
+
+
+def simulate_trades(df, ind, start, scores, threshold=SIGNAL_THRESHOLD):
+    """Sinyalleri sırayla (üst üste binmeden) işleme çevirir.
+    Giriş: sinyal barını izleyen barın AÇILIŞI (gerçekçi, ileri sızıntı yok).
+    Çıkış: SL (1.5×ATR) / TP (2.5×ATR) / 24 bar zaman çıkışı. Aynı barda ikisi de değerse SL öncelikli (kötümser)."""
+    A = ind["_arr"]
+    o, h, l, c, atr_a = A["o"], A["h"], A["l"], A["c"], A["atr"]
+    n = len(c)
+    trades = []
+    i = start
+    while i <= n - 2:
+        s = scores[i - start]["score"]
+        a = atr_a[i]
+        if abs(s) < threshold or not (a > 0):
+            i += 1
+            continue
+        d = 1 if s > 0 else -1
+        entry = o[i + 1]
+        risk = SL_ATR * a
+        sl, tp = entry - d * risk, entry + d * TP_ATR * a
+        last = min(n - 1, i + 1 + MAX_HOLD_BARS)
+        exit_i, exit_px, why = None, None, None
+        for j in range(i + 1, last + 1):
+            hit_sl = (l[j] <= sl) if d > 0 else (h[j] >= sl)
+            hit_tp = (h[j] >= tp) if d > 0 else (l[j] <= tp)
+            if hit_sl:
+                exit_i, exit_px, why = j, sl, "SL"
+                break
+            if hit_tp:
+                exit_i, exit_px, why = j, tp, "TP"
+                break
+        if exit_i is None:
+            if last == i + 1 + MAX_HOLD_BARS:
+                exit_i, exit_px, why = last, c[last], "ZAMAN"
+            else:
+                break  # veri bitti, işlem hâlâ açık — sonuçlandırılamaz
+        pnl = d * (exit_px - entry) / entry - 2 * FEE_PER_SIDE
+        r_mult = pnl / (risk / entry)
+        trades.append({"signal_i": i, "entry_i": i + 1, "exit_i": exit_i, "dir": d, "entry": entry,
+                       "exit": exit_px, "pnl": pnl, "r": r_mult, "why": why, "score": s})
+        i = exit_i
+    return trades
+
+
+def summarize_trades(trades, df, start):
+    if len(trades) < 3:
+        return None
+    n = len(df)
+    pnl = np.array([t["pnl"] for t in trades])
+    r = np.array([t["r"] for t in trades])
+    wins, losses = pnl[pnl > 0], pnl[pnl <= 0]
+    gross_loss = abs(losses.sum())
+    pf = float(wins.sum() / gross_loss) if gross_loss > 0 else (99.0 if wins.sum() > 0 else 0.0)
+    win_rate = len(wins) / len(pnl)
+    win_r, loss_r = r[r > 0], r[r <= 0]
+    avg_win_r = float(win_r.mean()) if len(win_r) else 0.0
+    avg_loss_r = float(abs(loss_r.mean())) if len(loss_r) else 0.0
+    kelly = None
+    if avg_win_r > 0 and avg_loss_r > 0:
+        kelly = win_rate - (1 - win_rate) / (avg_win_r / avg_loss_r)
+    sqn = float(np.sqrt(min(len(r), 100)) * r.mean() / r.std()) if r.std() > 0 else 0.0
+
+    mult = np.ones(n - start)
+    for t in trades:
+        mult[t["exit_i"] - start] *= (1 + t["pnl"])
+    equity = np.cumprod(mult)
+    peak = np.maximum.accumulate(equity)
+    max_dd = float(((equity - peak) / peak).min())
+    c = df["c"].to_numpy()
+    bh = c[start:] / c[start]
+
+    streak = best = 0
+    for p in pnl:
+        streak = streak + 1 if p <= 0 else 0
+        best = max(best, streak)
+
+    def side(dirn):
+        sub = [t for t in trades if t["dir"] == dirn]
+        return {"n": len(sub), "win_rate": (sum(1 for t in sub if t["pnl"] > 0) / len(sub)) if sub else None}
+
+    stats = {
+        "n": len(trades), "win_rate": win_rate, "profit_factor": pf, "expectancy": float(pnl.mean()),
+        "avg_r": float(r.mean()), "sqn": sqn, "max_dd": max_dd, "total_return": float(equity[-1] - 1),
+        "bh_return": float(bh[-1] - 1), "max_consec_loss": best, "avg_win_r": avg_win_r,
+        "avg_loss_r": avg_loss_r, "kelly": kelly, "long": side(1), "short": side(-1),
+    }
+    return stats, equity, bh
+
+
+def run_backtest(df, ind, start, scores):
+    n = len(df)
+    if n - start < 20:
+        return None
+    acc = directional_accuracy(df, start, scores)
+    trades = simulate_trades(df, ind, start, scores)
+    summ = summarize_trades(trades, df, start)
+    bt = {"start": start, "acc": acc, "trades": trades, "stats": None, "equity": None, "bh": None}
+    if summ:
+        bt["stats"], bt["equity"], bt["bh"] = summ
+    return bt
+
+
+# ---------------------------------------------------------------------------
+# CANLI KATMAN — üst zaman dilimleri, emir defteri, funding (backtest EDİLMEZ)
+# ---------------------------------------------------------------------------
+def _mtf_score(symbol, bar, closed_only):
+    hdf = fetch_candles(symbol, bar, 300)
+    if closed_only:
+        hdf = hdf[hdf["confirm"] == 1].reset_index(drop=True)
+    hind = compute_indicators(hdf)
+    r = _score_at(hind, len(hdf) - 1)
+    return {"bar": bar, "score": r["score"]}
+
+
+def _fetch_live_extras(symbol, bar, mtf, micro, closed_only):
+    """MTF / emir defteri / funding çağrılarını PARALEL yapar; hata veren parça sessizce atlanır."""
+    tasks = {}
+    with ThreadPoolExecutor(max_workers=4) as ex:
+        if mtf and symbol and bar:
+            b1 = MTF_MAP.get(bar)
+            if b1 and b1 != bar:
+                tasks["mtf1"] = ex.submit(_mtf_score, symbol, b1, closed_only)
+                b2 = MTF_MAP.get(b1)
+                if b2 and b2 not in (b1, bar):
+                    tasks["mtf2"] = ex.submit(_mtf_score, symbol, b2, closed_only)
+        if micro and symbol:
+            tasks["ob"] = ex.submit(fetch_orderbook, symbol, 20)
+            tasks["fund"] = ex.submit(fetch_funding_rate, symbol)
+        out = {}
+        for k, f in tasks.items():
+            try:
+                out[k] = f.result()
+            except Exception:
+                out[k] = None
+    return out
+
+
+def build_trade_plan(price, direction, atr_v, supports, resists, acct=None, risk_pct=None):
+    """Yapısal (destek/direnç) stop uygun aralıktaysa onu, değilse ATR stop'unu kullanır.
+    3 kademeli hedef + hesap büyüklüğüne göre pozisyon boyutu."""
+    d = direction
+    stop, stop_type = price - d * SL_ATR * atr_v, f"ATR × {SL_ATR}"
+    cand = None
+    if d > 0 and supports:
+        cand = supports[0][0] - 0.25 * atr_v
+    elif d < 0 and resists:
+        cand = resists[0][0] + 0.25 * atr_v
+    if cand is not None:
+        dist = abs(price - cand)
+        if 0.8 * atr_v <= dist <= 3.0 * atr_v and (cand < price if d > 0 else cand > price):
+            stop, stop_type = cand, "Yapısal (S/R arkası)"
+    risk = abs(price - stop)
+    plan = {
+        "direction": "LONG" if d > 0 else "SHORT", "entry": price, "stop_loss": stop, "stop_type": stop_type,
+        "risk_dist": risk, "risk_frac": risk / price,
+        "targets": [(price + d * risk * m, m) for m in TP_R_MULTIPLES], "sizing": None,
+    }
+    if acct and acct > 0 and risk_pct and risk_pct > 0 and risk > 0:
+        risk_amt = acct * risk_pct / 100.0
+        qty = risk_amt / risk
+        notional = qty * price
+        plan["sizing"] = {"risk_amount": risk_amt, "qty": qty, "notional": notional, "leverage": notional / acct}
+    return plan
+
+
+def compute_confidence(score, agree, mtf_state, hist_q, vol_q):
+    mag = min(abs(score) / 8.0, 1.0)
+    conf = 100.0 * (0.35 * mag + 0.25 * agree + 0.15 * mtf_state + 0.20 * hist_q + 0.05 * vol_q)
+    parts = {"Skor Büyüklüğü": mag, "Faktör Uyumu": agree, "MTF Teyidi": mtf_state,
+             "Tarihsel Kenar": hist_q, "Volatilite Rejimi": vol_q}
+    return min(conf, 100.0), parts
+
+
+def grade_from_conf(conf):
+    for thr, g, col in ((75, "A+", "#3ddc97"), (62, "A", "#7fe6b8"), (50, "B", "#f2b84b"), (38, "C", "#ff9aa8")):
+        if conf >= thr:
+            return g, col
+    return "D", "#ff5c72"
+
+
+def generate_signal(df, symbol=None, bar=None, mtf=True, micro=True, closed_only=False,
+                    acct=None, risk_pct=None):
+    ind = compute_indicators(df)
+    n = len(df)
+    start, scores = compute_score_series(ind, n)
+    base = _score_at(ind, n - 1, verbose=True)
+    score, price, w = base["score"], base["price"], base["w"]
+    reasons, contribs = list(base["reasons"]), list(base["contribs"])
+    tags = []
+    if base["squeeze_on"]:
+        tags.append(("SIKIŞMA AKTİF", "#f2b84b"))
+    for name, sgn in base["pat_hits"]:
+        tags.append((name.upper(), "#3ddc97" if sgn > 0 else "#ff5c72"))
+
+    def overlay(label, delta, text):
+        nonlocal score
+        score += delta
+        reasons.append(("up" if delta > 0 else "down", text))
+        contribs.append((label, delta))
+
+    # --- Uyumsuzluk (RSI divergence) — reversion sinyali, rejime göre ağırlıklı ---
+    div = detect_divergence(df, ind)
+    div_weight = 1.5 - 1.1 * w  # güçlü trendde 0.4, yatayda 1.5 (eski davranışla aynı uçlar)
+    if div["bullish"]:
+        overlay("Yükseliş Uyumsuzluğu", div_weight, "Yükseliş uyumsuzluğu tespit edildi (fiyat düşük dip, RSI yüksek dip)")
+        tags.append(("YÜKSELİŞ UYUMSUZLUĞU", "#3ddc97"))
+    if div["bearish"]:
+        overlay("Düşüş Uyumsuzluğu", -div_weight, "Düşüş uyumsuzluğu tespit edildi (fiyat yüksek tepe, RSI düşük tepe)")
+        tags.append(("DÜŞÜŞ UYUMSUZLUĞU", "#ff5c72"))
+
+    # --- Skor ivmesi ---
+    if n - 4 >= start:
+        momentum = base["score"] - scores[n - 4 - start]["score"]
+        if momentum > 1.5:
+            overlay("Skor İvmesi", 0.3, "Kompozit skor son barlarda hızla güçleniyor")
+        elif momentum < -1.5:
+            overlay("Skor İvmesi", -0.3, "Kompozit skor son barlarda hızla zayıflıyor")
+
+    # --- Canlı ekstralar (paralel çekim) ---
+    extras = _fetch_live_extras(symbol, bar, mtf, micro, closed_only)
+    sgn = 1 if score > 0 else (-1 if score < 0 else 0)
+
+    # --- 2 kademeli MTF teyidi ---
+    mtf_info, align_flags = [], []
+    for key, wt in (("mtf1", 1.0), ("mtf2", 0.6)):
+        m = extras.get(key)
+        if not m:
+            continue
+        hs = m["score"]
+        aligned = None
+        if abs(hs) >= 1.0 and sgn != 0:
+            aligned = bool(np.sign(hs) == sgn)
+            align_flags.append(aligned)
+            if aligned:
+                overlay(f"{m['bar']} Teyidi", sgn * wt, f"{m['bar']} zaman diliminde de aynı yönlü teyit var (skor {hs:+.1f})")
+            else:
+                overlay(f"{m['bar']} Çelişkisi", -sgn * wt, f"{m['bar']} zaman dilimi ters yönlü sinyal veriyor (skor {hs:+.1f}) — dikkat")
+        mtf_info.append({"bar": m["bar"], "score": round(hs, 2), "aligned": aligned})
+    mtf_state = (sum(1.0 for a in align_flags if a) / len(align_flags)) if align_flags else 0.5
+    mtf_opposed = any(a is False for a in align_flags)
+
+    # --- Emir defteri dengesizliği (derinlik ağırlıklı, kısa vadede anlamlı) ---
+    imb = None
+    ob = extras.get("ob")
+    if ob and ob.get("bids") and ob.get("asks"):
+        bw = sum(float(x[1]) / (k + 1) for k, x in enumerate(ob["bids"]))
+        aw = sum(float(x[1]) / (k + 1) for k, x in enumerate(ob["asks"]))
+        if bw + aw > 0:
+            imb = (bw - aw) / (bw + aw)
+            if abs(imb) >= 0.2:
+                delta = max(-0.8, min(0.8, imb * 2)) * (0.5 if bar in SLOW_BARS else 1.0)
+                overlay("Emir Defteri", delta,
+                        f"Emir defterinde {'alış' if imb > 0 else 'satış'} baskısı (dengesizlik {imb * 100:+.0f}%)")
+
+    # --- Funding rate (kalabalık pozisyon = kontraryen) ---
+    fr = extras.get("fund")
+    if fr is not None:
+        if fr > 0.0005:
+            overlay("Funding", -0.6, f"Funding aşırı pozitif ({fr * 100:.3f}%) — kalabalık long, tasfiye riski")
+        elif fr > 0.0002:
+            overlay("Funding", -0.3, f"Funding yüksek pozitif ({fr * 100:.3f}%) — long tarafı kalabalık")
+        elif fr < -0.0005:
+            overlay("Funding", 0.6, f"Funding aşırı negatif ({fr * 100:.3f}%) — kalabalık short, sıkışma potansiyeli")
+        elif fr < -0.0002:
+            overlay("Funding", 0.3, f"Funding negatif ({fr * 100:.3f}%) — short tarafı kalabalık")
+
+    # --- Destek / direnç yakınlığı ---
+    atr_v = float(ind["atr"].iloc[-1])
+    supports, resists = find_sr_levels(df, atr_v)
+    sgn = 1 if score > 0 else (-1 if score < 0 else 0)
+    if atr_v > 0 and sgn != 0:
+        if sgn > 0:
+            if resists and resists[0][1] >= 2 and (resists[0][0] - price) / atr_v <= 1.0:
+                lv, tc = resists[0]
+                overlay("Yakın Direnç", -0.8, f"Yakın direnç {fmt_price(lv)} ({tc} temas, {(lv - price) / atr_v:.1f} ATR) — yükselişi sınırlayabilir")
+            elif supports and supports[0][1] >= 2 and (price - supports[0][0]) / atr_v <= 0.5:
+                lv, tc = supports[0]
+                overlay("Destek Üstünde", 0.4, f"Fiyat güçlü destek {fmt_price(lv)} ({tc} temas) üzerinde")
+        else:
+            if supports and supports[0][1] >= 2 and (price - supports[0][0]) / atr_v <= 1.0:
+                lv, tc = supports[0]
+                overlay("Yakın Destek", 0.8, f"Yakın destek {fmt_price(lv)} ({tc} temas, {(price - lv) / atr_v:.1f} ATR) — düşüşü sınırlayabilir")
+            elif resists and resists[0][1] >= 2 and (resists[0][0] - price) / atr_v <= 0.5:
+                lv, tc = resists[0]
+                overlay("Direnç Altında", -0.4, f"Fiyat güçlü direnç {fmt_price(lv)} ({tc} temas) altında")
+
+    score = max(-10.0, min(10.0, score))
+
+    # --- Konsensüs + sınıflandırma ---
+    direction = 1 if score > 0 else -1
+    tot = base["bull_w"] + base["bear_w"]
+    agree = ((base["bull_w"] if direction > 0 else base["bear_w"]) / tot) if tot > 0 else 0.5
+    if abs(score) >= STRONG_THRESHOLD and agree >= 0.65 and not mtf_opposed:
+        signal, color = ("GÜÇLÜ AL", "#3ddc97") if score > 0 else ("GÜÇLÜ SAT", "#ff5c72")
+    elif abs(score) >= SIGNAL_THRESHOLD:
+        signal, color = ("AL", "#7fe6b8") if score > 0 else ("SAT", "#ff9aa8")
+        if abs(score) >= STRONG_THRESHOLD:
+            reasons.append(("flat", f"Skor güçlü bölgede ancak konsensüs ({agree * 100:.0f}%) veya MTF filtresini geçemedi — GÜÇLÜ yerine normal sinyal verildi"))
+    else:
+        signal, color = "BEKLE / NÖTR", "#f2b84b"
+    is_neutral = signal == "BEKLE / NÖTR"
+
+    # --- Tarihsel doğrulama ---
+    backtest = run_backtest(df, ind, start, scores)
+    hist_q = 0.4
+    if backtest:
+        qs = []
+        acc = backtest["acc"]
+        if acc:
+            p_shrunk = (acc["correct"] + 5) / (acc["n_signals"] + 10)  # Bayes büzülmesi (önsel %50)
+            qs.append(min(max((p_shrunk - 0.45) / 0.20, 0.0), 1.0))
+        st = backtest["stats"]
+        if st:
+            qs.append(min(max((st["profit_factor"] - 0.8) / 0.8, 0.0), 1.0))
+        if qs:
+            hist_q = float(np.mean(qs))
+    rank = base["atr_rank"]
+    vol_q = 1.0 if 0.10 <= rank <= 0.90 else 0.5
+    if rank > 0.90:
+        reasons.append(("flat", f"Volatilite aşırı yüksek (ATR yüzdeliği %{rank * 100:.0f}) — stoplar geniş tutulmalı, güven düşürüldü"))
+    conf, conf_parts = compute_confidence(score, agree, mtf_state, hist_q, vol_q)
+    grade, grade_color = ("—", "#7c8797") if is_neutral else grade_from_conf(conf)
+
+    plan = None
+    if not is_neutral and atr_v > 0:
+        plan = build_trade_plan(price, direction, atr_v, supports, resists, acct, risk_pct)
+
+    return {
+        "signal": signal, "score": round(score, 2), "confidence": round(conf, 1), "grade": grade,
+        "grade_color": grade_color, "color": color, "reasons": reasons, "contribs": contribs,
+        "indicators": ind, "price": price, "divergence": div, "mtf": mtf_info, "plan": plan,
+        "backtest": backtest, "regime": base["regime"], "adx": base["adx"], "atr_rank": rank,
+        "squeeze_on": base["squeeze_on"], "agree": agree, "bull_w": base["bull_w"], "bear_w": base["bear_w"],
+        "conf_parts": conf_parts, "tags": tags, "funding": fr, "imbalance": imb,
+        "supports": supports, "resists": resists, "atr": atr_v,
+    }
+
+
+# ---------------------------------------------------------------------------
+# RISK ANALYTICS
+# ---------------------------------------------------------------------------
+def risk_metrics(df, bar):
+    ret = df["c"].pct_change().dropna()
+    if len(ret) < 5:
+        return None
+    periods = BAR_TO_PERIODS_PER_YEAR.get(bar, 365)
+    vol_annual = ret.std() * np.sqrt(periods)
+    mean_annual = ret.mean() * periods
+    sharpe = mean_annual / vol_annual if vol_annual > 0 else 0.0
+    cum = (1 + ret).cumprod()
+    peak = cum.cummax()
+    dd = (cum - peak) / peak
+    var95 = np.percentile(ret, 5)
+    tail = ret[ret <= var95]
+    cvar95 = tail.mean() if len(tail) else var95
+    return {
+        "volatility_annual": vol_annual, "return_annual": mean_annual, "sharpe": sharpe,
+        "max_drawdown": dd.min(), "current_dd": dd.iloc[-1],
+        "var95_bar": var95, "cvar95_bar": cvar95,
+    }
+
+
+# ---------------------------------------------------------------------------
+# FORMAT HELPERS
+# ---------------------------------------------------------------------------
+def _isnan(v):
+    return v is None or (isinstance(v, float) and math.isnan(v))
+
+
+def fmt_price(v):
+    if _isnan(v):
+        return "—"
+    av = abs(v)
+    if av >= 1000:
+        return f"{v:,.2f}"
+    if av >= 1:
+        return f"{v:,.4f}"
+    if av >= 0.01:
+        return f"{v:.6f}"
+    return f"{v:.8f}"
+
+
+def fmt_pct(v):
+    if _isnan(v):
+        return "—"
+    sign = "+" if v > 0 else ""
+    return f"{sign}{v * 100:.2f}%"
+
+
+def fmt_large(v):
+    if _isnan(v):
+        return "—"
+    av = abs(v)
+    if av >= 1e9:
+        return f"{v / 1e9:.2f}B"
+    if av >= 1e6:
+        return f"{v / 1e6:.2f}M"
+    if av >= 1e3:
+        return f"{v / 1e3:.2f}K"
+    return f"{v:.2f}"
+
+
+def metric_card(label, value, sub="", tone="neutral"):
+    tone_class = {"up": "mc-up", "down": "mc-down", "neutral": "mc-neutral"}.get(tone, "mc-neutral")
+    sub_html = f'<div class="mc-sub">{sub}</div>' if sub else ""
+    return (f'<div class="metric-card {tone_class}"><div class="mc-label">{label}</div>'
+            f'<div class="mc-value">{value}</div>{sub_html}</div>')
+
+
+def section(title):
+    return f'<div class="section-title">{title}</div>'
+
+
+def cards_row(cards):
+    return f'<div class="metric-row">{"".join(cards)}</div>' if cards else ""
+
+
+def error_box(msg):
+    return f'<div class="error-box">⚠ {msg}</div>'
+
+
+# ---------------------------------------------------------------------------
+# CHART BUILDERS
+# ---------------------------------------------------------------------------
+def _base_layout(fig, title, height):
+    fig.update_layout(
+        template="plotly_dark", height=height, margin=dict(l=10, r=10, t=40, b=10),
+        paper_bgcolor=CHART_BG, plot_bgcolor=CHART_BG, title=title,
+        legend=dict(orientation="h", y=1.05, font=dict(size=10)),
+        font=dict(family="IBM Plex Mono, monospace", size=11),
+    )
+    return fig
+
+
+def _empty_fig(msg, title="", height=220):
+    fig = go.Figure()
+    fig.add_annotation(text=msg, x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False,
+                       font=dict(size=13, color="#7c8797"))
+    fig.update_xaxes(visible=False)
+    fig.update_yaxes(visible=False)
+    return _base_layout(fig, title, height)
+
+
+def build_price_chart(df, ind, symbol, bar):
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.75, 0.25], vertical_spacing=0.03)
+    fig.add_trace(go.Candlestick(
+        x=df["ts"], open=df["o"], high=df["h"], low=df["l"], close=df["c"], name=symbol,
+        increasing_line_color="#3ddc97", decreasing_line_color="#ff5c72"), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["ema20"], name="EMA20",
+                              line=dict(color="#5b8cff", width=1)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["ema50"], name="EMA50",
+                              line=dict(color="#f2b84b", width=1)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["bb_upper"], name="BB Üst",
+                              line=dict(color="rgba(150,160,255,0.45)", width=1, dash="dot")), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["bb_lower"], name="BB Alt",
+                              line=dict(color="rgba(150,160,255,0.45)", width=1, dash="dot"),
+                              fill="tonexty", fillcolor="rgba(150,160,255,0.05)"), row=1, col=1)
+    vol_colors = np.where(df["c"] >= df["o"], "#3ddc97", "#ff5c72")
+    fig.add_trace(go.Bar(x=df["ts"], y=df["vol"], name="Hacim", marker_color=vol_colors, opacity=0.55),
+                  row=2, col=1)
+    fig.update_layout(xaxis_rangeslider_visible=False)
+    return _base_layout(fig, f"{symbol} · {bar}", 520)
+
+
+def build_signal_price_chart(df, ind, symbol, bar, res):
+    """Sinyal sekmesinin ana grafiği: mum + Supertrend + Ichimoku bulutu + S/R + geçmiş sinyal işaretleri + işlem planı."""
+    n = len(df)
+    v0 = max(0, n - 200)
+    sl = slice(v0, n)
+    ts = df["ts"].iloc[sl]
+    fig = go.Figure()
+    fig.add_trace(go.Candlestick(
+        x=ts, open=df["o"].iloc[sl], high=df["h"].iloc[sl], low=df["l"].iloc[sl], close=df["c"].iloc[sl],
+        name=symbol, increasing_line_color="#3ddc97", decreasing_line_color="#ff5c72"))
+    # Ichimoku bulutu
+    fig.add_trace(go.Scatter(x=ts, y=ind["ich_a"].iloc[sl], line=dict(width=0), showlegend=False, hoverinfo="skip"))
+    fig.add_trace(go.Scatter(x=ts, y=ind["ich_b"].iloc[sl], line=dict(width=0), name="Ichimoku Bulutu",
+                              fill="tonexty", fillcolor="rgba(120,140,255,0.10)", hoverinfo="skip"))
+    # Supertrend (yükseliş/düşüş ayrı renk)
+    st_line, st_dir = ind["st_line"].iloc[sl], ind["st_dir"].iloc[sl]
+    fig.add_trace(go.Scatter(x=ts, y=st_line.where(st_dir > 0), name="Supertrend ▲",
+                              line=dict(color="#3ddc97", width=1.5), connectgaps=False))
+    fig.add_trace(go.Scatter(x=ts, y=st_line.where(st_dir < 0), name="Supertrend ▼",
+                              line=dict(color="#ff5c72", width=1.5), connectgaps=False))
+    # Destek / direnç
+    for lv, tc in res["supports"][:3]:
+        fig.add_hline(y=lv, line=dict(color="rgba(61,220,151,0.55)", dash="dash", width=1),
+                      annotation_text=f"D {fmt_price(lv)} ×{tc}", annotation_position="left",
+                      annotation_font=dict(size=9, color="#3ddc97"))
+    for lv, tc in res["resists"][:3]:
+        fig.add_hline(y=lv, line=dict(color="rgba(255,92,114,0.55)", dash="dash", width=1),
+                      annotation_text=f"R {fmt_price(lv)} ×{tc}", annotation_position="left",
+                      annotation_font=dict(size=9, color="#ff5c72"))
+    # Geçmiş sinyal işlemleri
+    bt = res["backtest"]
+    if bt and bt["trades"]:
+        atr_a = ind["_arr"]["atr"]
+        for d, sym_, col, nm in ((1, "triangle-up", "#3ddc97", "Geçmiş LONG"), (-1, "triangle-down", "#ff5c72", "Geçmiş SHORT")):
+            tr = [t for t in bt["trades"] if t["dir"] == d and t["entry_i"] >= v0]
+            if not tr:
+                continue
+            xs = [df["ts"].iloc[t["entry_i"]] for t in tr]
+            ys = [(df["l"].iloc[t["entry_i"]] - 0.6 * atr_a[t["entry_i"]]) if d > 0
+                  else (df["h"].iloc[t["entry_i"]] + 0.6 * atr_a[t["entry_i"]]) for t in tr]
+            hov = [f"{nm} · skor {t['score']:+.1f}<br>Sonuç: {t['why']} ({t['pnl'] * 100:+.2f}% · {t['r']:+.2f}R)" for t in tr]
+            fig.add_trace(go.Scatter(x=xs, y=ys, mode="markers", name=nm, text=hov, hoverinfo="text",
+                                      marker=dict(symbol=sym_, size=9, color=col, line=dict(width=0.5, color="#0a0e14"))))
+    # Güncel işlem planı
+    plan = res["plan"]
+    if plan:
+        x0, x1 = df["ts"].iloc[max(0, n - 30)], df["ts"].iloc[-1]
+        lines = [("GİRİŞ", plan["entry"], "#e6ebf2"), ("SL", plan["stop_loss"], "#ff5c72")]
+        lines += [(f"TP{k + 1}", px, "#3ddc97") for k, (px, _) in enumerate(plan["targets"])]
+        for lab, y, col in lines:
+            fig.add_shape(type="line", x0=x0, x1=x1, y0=y, y1=y, line=dict(color=col, width=1.4, dash="dot"))
+            fig.add_annotation(x=x1, y=y, text=f"{lab} {fmt_price(y)}", showarrow=False, xanchor="left",
+                               font=dict(size=9, color=col))
+    fig.update_layout(xaxis_rangeslider_visible=False)
+    return _base_layout(fig, f"{symbol} · {bar} · Sinyal Haritası", 560)
+
+
+def build_factor_chart(contribs, symbol):
+    items = [(l, v) for l, v in contribs if abs(v) > 1e-9]
+    if not items:
+        return _empty_fig("Faktör verisi yok", f"{symbol} · Faktör Katkıları")
+    items.sort(key=lambda x: x[1])
+    labels, vals = [l for l, _ in items], [v for _, v in items]
+    fig = go.Figure(go.Bar(
+        x=vals, y=labels, orientation="h", marker_color=["#3ddc97" if v > 0 else "#ff5c72" for v in vals],
+        text=[f"{v:+.2f}" for v in vals], textposition="outside", cliponaxis=False))
+    fig.update_layout(showlegend=False, xaxis_title="Ağırlıklı katkı (rejim ağırlığı uygulanmış)")
+    return _base_layout(fig, f"{symbol} · Faktör Katkıları (skoru ne yönlendiriyor?)", max(300, 25 * len(items) + 100))
+
+
+def build_backtest_chart(bt, df, symbol):
+    if not bt or not bt.get("stats"):
+        return _empty_fig("Backtest için yeterli işlem oluşmadı (en az 3 işlem gerekir)", f"{symbol} · Sinyal Backtesti")
+    start = bt["start"]
+    ts = df["ts"].iloc[start:]
+    fig = make_subplots(rows=2, cols=1, row_heights=[0.62, 0.38], vertical_spacing=0.14,
+                         subplot_titles=("Sinyal Stratejisi vs Al&Tut (komisyon dahil, tam pozisyon)", "İşlem Bazında R Çarpanı"))
+    fig.add_trace(go.Scatter(x=ts, y=bt["equity"], name="Sinyal Stratejisi", line=dict(color="#5b8cff", width=2, shape="hv")),
+                  row=1, col=1)
+    fig.add_trace(go.Scatter(x=ts, y=bt["bh"], name="Al ve Tut", line=dict(color="#7c8797", width=1.2)), row=1, col=1)
+    rs = [t["r"] for t in bt["trades"]]
+    fig.add_trace(go.Bar(x=list(range(1, len(rs) + 1)), y=rs, name="R",
+                          marker_color=["#3ddc97" if r > 0 else "#ff5c72" for r in rs],
+                          text=[t["why"] for t in bt["trades"]], hoverinfo="text+y"), row=2, col=1)
+    fig.update_xaxes(title_text="İşlem #", row=2, col=1)
+    return _base_layout(fig, f"{symbol} · Walk-Forward İşlem Simülasyonu", 470)
+
+
+def build_signal_chart(df, ind, symbol):
+    fig = make_subplots(rows=4, cols=1, shared_xaxes=True, row_heights=[0.27, 0.25, 0.24, 0.24],
+                         vertical_spacing=0.05,
+                         subplot_titles=("RSI (14)", "MACD (12,26,9)", "Stokastik (14,3)", "ADX / DI (14)"))
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["rsi"], name="RSI", line=dict(color="#5b8cff")), row=1, col=1)
+    fig.add_hline(y=70, line=dict(color="#ff5c72", dash="dot", width=1), row=1, col=1)
+    fig.add_hline(y=30, line=dict(color="#3ddc97", dash="dot", width=1), row=1, col=1)
+
+    hist_colors = np.where(ind["macd_hist"] >= 0, "#3ddc97", "#ff5c72")
+    fig.add_trace(go.Bar(x=df["ts"], y=ind["macd_hist"], name="Histogram", marker_color=hist_colors), row=2, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["macd_line"], name="MACD",
+                              line=dict(color="#f2b84b", width=1)), row=2, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["macd_signal"], name="Sinyal",
+                              line=dict(color="#5b8cff", width=1)), row=2, col=1)
+
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["stoch_k"], name="%K", line=dict(color="#5b8cff")), row=3, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["stoch_d"], name="%D", line=dict(color="#f2b84b")), row=3, col=1)
+    fig.add_hline(y=80, line=dict(color="#ff5c72", dash="dot", width=1), row=3, col=1)
+    fig.add_hline(y=20, line=dict(color="#3ddc97", dash="dot", width=1), row=3, col=1)
+
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["adx"], name="ADX", line=dict(color="#e6ebf2", width=1.5)), row=4, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["plus_di"], name="+DI", line=dict(color="#3ddc97", width=1)), row=4, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=ind["minus_di"], name="-DI", line=dict(color="#ff5c72", width=1)), row=4, col=1)
+    fig.add_hline(y=25, line=dict(color="#f2b84b", dash="dot", width=1), row=4, col=1)
+
+    return _base_layout(fig, f"{symbol} · Momentum Göstergeleri", 720)
+
+
+def build_equity_chart(df, symbol):
+    ret = df["c"].pct_change().fillna(0)
+    cum = (1 + ret).cumprod()
+    peak = cum.cummax()
+    dd = (cum - peak) / peak
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.65, 0.35], vertical_spacing=0.06,
+                         subplot_titles=("Kümülatif Getiri (Al ve Tut)", "Drawdown"))
+    fig.add_trace(go.Scatter(x=df["ts"], y=cum, name="Kümülatif Getiri", line=dict(color="#5b8cff"),
+                              fill="tozeroy", fillcolor="rgba(91,140,255,0.08)"), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df["ts"], y=dd, name="Drawdown", line=dict(color="#ff5c72"),
+                              fill="tozeroy", fillcolor="rgba(255,92,114,0.12)"), row=2, col=1)
+    fig.update_layout(showlegend=False)
+    return _base_layout(fig, f"{symbol} · Risk Profili", 440)
+
+
+def build_depth_chart(bids, asks, symbol):
+    bids_sorted = sorted(bids, key=lambda x: -x[0])
+    asks_sorted = sorted(asks, key=lambda x: x[0])
+    bid_prices = [p for p, _ in bids_sorted]
+    bid_cum = np.cumsum([s for _, s in bids_sorted])
+    ask_prices = [p for p, _ in asks_sorted]
+    ask_cum = np.cumsum([s for _, s in asks_sorted])
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=bid_prices, y=bid_cum, name="Alış (Bid)", line=dict(color="#3ddc97", width=2),
+                              fill="tozeroy", fillcolor="rgba(61,220,151,0.15)"))
+    fig.add_trace(go.Scatter(x=ask_prices, y=ask_cum, name="Satış (Ask)", line=dict(color="#ff5c72", width=2),
+                              fill="tozeroy", fillcolor="rgba(255,92,114,0.15)"))
+    fig.update_layout(xaxis_title="Fiyat", yaxis_title="Kümülatif Miktar")
+    return _base_layout(fig, f"{symbol} · Emir Defteri Derinliği", 380)
+
+
+# ---------------------------------------------------------------------------
+# TAB CALLBACKS
+# ---------------------------------------------------------------------------
+def load_overview(symbol, bar):
+    try:
+        df = fetch_candles(symbol, bar, 300)
+        ticker = fetch_ticker(symbol)
+        ind = compute_indicators(df)
+        last = float(ticker.get("last") or df["c"].iloc[-1])
+        open24h = float(ticker.get("open24h") or df["c"].iloc[0])
+        high24h = float(ticker.get("high24h") or df["h"].max())
+        low24h = float(ticker.get("low24h") or df["l"].min())
+        vol24h = float(ticker.get("volCcy24h") or df["vol"].sum())
+        chg = (last - open24h) / open24h if open24h else 0.0
+        tone = "up" if chg >= 0 else "down"
+        cards = [
+            metric_card("SON FİYAT", f"${fmt_price(last)}", "", tone),
+            metric_card("24S DEĞİŞİM", fmt_pct(chg), "", tone),
+            metric_card("24S YÜKSEK", f"${fmt_price(high24h)}"),
+            metric_card("24S DÜŞÜK", f"${fmt_price(low24h)}"),
+            metric_card("24S HACİM (USDT)", f"${fmt_large(vol24h)}"),
+        ]
+        return cards_row(cards), build_price_chart(df, ind, symbol, bar)
+    except Exception as e:
+        return error_box(f"Veri alınamadı: {e}"), go.Figure()
+
+
+def _reasons_html(reasons):
+    icon = {"up": "▲", "down": "▼", "flat": "•"}
+    return "".join(f'<li class="r-{t}"><span class="ri">{icon[t]}</span>{txt}</li>' for t, txt in reasons)
+
+
+def load_signal(symbol, bar, closed_only=False, acct=10000, risk_pct=1.0):
+    empty5 = lambda html: (html, go.Figure(), go.Figure(), go.Figure(), go.Figure())
+    try:
+        df = fetch_candles(symbol, bar, 300)
+        if closed_only:
+            df = df[df["confirm"] == 1].reset_index(drop=True)
+        if len(df) < 30:
+            return empty5(error_box("Sinyal için yeterli mum verisi yok (en az 30 bar gerekir)."))
+        acct = float(acct or 0)
+        risk_pct = float(risk_pct or 0)
+        res = generate_signal(df, symbol=symbol, bar=bar, mtf=True, micro=True, closed_only=closed_only,
+                              acct=acct, risk_pct=risk_pct)
+        ind, plan, bt = res["indicators"], res["plan"], res["backtest"]
+
+        # ---- Ana panel ----
+        regime_col = {"TREND": "#5b8cff", "YATAY": "#f2b84b", "GEÇİŞ": "#7c8797"}[res["regime"]]
+        panel = (
+            f'<div class="signal-panel">'
+            f'<div class="signal-badge" style="--tone:{res["color"]}">{res["signal"]}</div>'
+            f'<div class="signal-meta"><div class="mc-label">KOMPOZİT SKOR</div>'
+            f'<div class="mc-value">{res["score"]:+.2f} / ±10</div></div>'
+            f'<div class="signal-meta"><div class="mc-label">GÜVEN</div>'
+            f'<div class="mc-value">%{res["confidence"]}</div></div>'
+            f'<div class="signal-meta"><div class="mc-label">KALİTE NOTU</div>'
+            f'<div class="mc-value" style="color:{res["grade_color"]}">{res["grade"]}</div></div>'
+            f'<div class="signal-meta"><div class="mc-label">REJİM</div>'
+            f'<div class="mc-value" style="color:{regime_col}">{res["regime"]}</div></div>'
+            f'</div>'
+        )
+        tot = res["bull_w"] + res["bear_w"]
+        bull_pct = (res["bull_w"] / tot * 100) if tot > 0 else 50.0
+        consensus = (
+            f'<div class="consensus"><div class="mc-label">FAKTÖR KONSENSÜSÜ · BOĞA %{bull_pct:.0f} / AYI %{100 - bull_pct:.0f}</div>'
+            f'<div class="cbar"><div class="cbull" style="width:{bull_pct:.1f}%"></div>'
+            f'<div class="cbear" style="width:{100 - bull_pct:.1f}%"></div></div></div>'
+        )
+        conf_tags = "".join(
+            f'<div class="tag" style="color:var(--text-muted)">{k} %{v * 100:.0f}</div>' for k, v in res["conf_parts"].items())
+        tag_html = ""
+        if res["tags"]:
+            tag_html = '<div class="tags" style="margin-bottom:12px;">' + "".join(
+                f'<div class="tag" style="border-color:{c};color:{c};">{t}</div>' for t, c in res["tags"]) + "</div>"
+        conf_html = f'<div class="tags" style="margin-bottom:12px;">{conf_tags}</div>'
+
+        # ---- İşlem planı + pozisyon boyutu ----
+        plan_cards, size_cards = [], []
+        if plan:
+            up = plan["direction"] == "LONG"
+            plan_cards = [
+                metric_card("ÖNERİLEN YÖN", plan["direction"], "", "up" if up else "down"),
+                metric_card("GİRİŞ", f"${fmt_price(plan['entry'])}"),
+                metric_card("STOP-LOSS", f"${fmt_price(plan['stop_loss'])}",
+                            f"{plan['stop_type']} · %{plan['risk_frac'] * 100:.2f} risk", "down"),
+            ]
+            for k, (px, m) in enumerate(plan["targets"]):
+                plan_cards.append(metric_card(f"HEDEF {k + 1}", f"${fmt_price(px)}", f"{m:.2f}R", "up"))
+            sz = plan["sizing"]
+            if sz:
+                size_cards = [
+                    metric_card("RİSK TUTARI", f"${fmt_large(sz['risk_amount'])}", f"Hesabın %{risk_pct:g}'i"),
+                    metric_card("POZİSYON (USDT)", f"${fmt_large(sz['notional'])}", f"Miktar: {sz['qty']:.6g}"),
+                    metric_card("KALDIRAÇ ETKİSİ", f"{sz['leverage']:.2f}x",
+                                "Spot ile yetmez — kaldıraç gerekir" if sz["leverage"] > 1.0 else "Spot ile uygulanabilir",
+                                "down" if sz["leverage"] > 1.0 else "up"),
+                ]
+        stats = bt["stats"] if bt else None
+        if stats is not None:
+            k = stats["kelly"]
+            if stats["n"] < 20:
+                kv, ks, kt = "—", "Örnek az (<20 işlem)", "neutral"
+            elif k is None or k <= 0:
+                kv, ks, kt = "Kenar yok", "Kelly ≤ 0", "down"
+            else:
+                half = min(k / 2, 0.03)
+                kv, ks, kt = f"%{half * 100:.2f}", f"Yarım-Kelly (tavan %3) · bilgi amaçlı", "up"
+            size_cards.append(metric_card("ÖNERİLEN RİSK", kv, ks, kt))
+
+        # ---- Backtest kartları ----
+        bt_cards = []
+        if stats:
+            L, S = stats["long"], stats["short"]
+            wr = lambda x: f"%{x['win_rate'] * 100:.0f}" if x["win_rate"] is not None else "—"
+            bt_cards = [
+                metric_card("İŞLEM SAYISI", f"{stats['n']}", f"Long {L['n']} ({wr(L)}) · Short {S['n']} ({wr(S)})"),
+                metric_card("KAZANMA ORANI", f"%{stats['win_rate'] * 100:.1f}", f"Maks. üst üste kayıp: {stats['max_consec_loss']}",
+                            "up" if stats["win_rate"] >= 0.45 else "down"),
+                metric_card("PROFIT FACTOR", f"{stats['profit_factor']:.2f}", "Brüt kazanç / brüt kayıp",
+                            "up" if stats["profit_factor"] >= 1.2 else "down"),
+                metric_card("BEKLENTİ / İŞLEM", fmt_pct(stats["expectancy"]), f"Ort. {stats['avg_r']:+.2f}R (komisyon dahil)",
+                            "up" if stats["expectancy"] > 0 else "down"),
+                metric_card("SQN", f"{stats['sqn']:.2f}", "Sistem kalitesi (>2 iyi, >3 çok iyi)",
+                            "up" if stats["sqn"] >= 2 else "neutral"),
+                metric_card("STRATEJİ MAKS. DD", fmt_pct(stats["max_dd"]), "", "down"),
+                metric_card("STRATEJİ vs AL&TUT", f"{fmt_pct(stats['total_return'])} / {fmt_pct(stats['bh_return'])}",
+                            "Aynı dönem, komisyon dahil", "up" if stats["total_return"] > stats["bh_return"] else "down"),
+            ]
+        acc_cards = []
+        if bt and bt["acc"]:
+            a = bt["acc"]
+            acc_cards.append(metric_card("YÖN DOĞRULUĞU", f"%{a['accuracy'] * 100:.1f}",
+                                         f"{a['n_signals']} sinyal · {a['forward_bars']} bar ileri",
+                                         "up" if a["accuracy"] >= 0.5 else "down"))
+            if a.get("trend_regime"):
+                t = a["trend_regime"]
+                acc_cards.append(metric_card("DOĞRULUK · TREND", f"%{t['accuracy'] * 100:.1f}",
+                                             f"{t['n_signals']} sinyal (ADX ≥ 25)", "up" if t["accuracy"] >= 0.5 else "down"))
+            if a.get("range_regime"):
+                t = a["range_regime"]
+                acc_cards.append(metric_card("DOĞRULUK · YATAY", f"%{t['accuracy'] * 100:.1f}",
+                                             f"{t['n_signals']} sinyal (ADX &lt; 25)", "up" if t["accuracy"] >= 0.5 else "down"))
+
+        # ---- Piyasa bağlamı ----
+        ctx = []
+        for m in res["mtf"]:
+            if m["aligned"] is None:
+                ctx.append(metric_card(f"{m['bar']} TEYİDİ", "Nötr", f"Üst zaman dilimi skoru: {m['score']}"))
+            else:
+                ctx.append(metric_card(f"{m['bar']} TEYİDİ", "Uyumlu ✓" if m["aligned"] else "Ters Yönlü ⚠",
+                                       f"Üst zaman dilimi skoru: {m['score']}", "up" if m["aligned"] else "down"))
+        if res["funding"] is not None:
+            f_ = res["funding"]
+            ctx.append(metric_card("FUNDING (8S)", f"{f_ * 100:+.4f}%", "Pozitif = long kalabalık",
+                                   "down" if f_ > 0.0002 else ("up" if f_ < -0.0002 else "neutral")))
+        if res["imbalance"] is not None:
+            im = res["imbalance"]
+            ctx.append(metric_card("DEFTER DENGESİ", f"{im * 100:+.0f}%", "Derinlik ağırlıklı",
+                                   "up" if im > 0.2 else ("down" if im < -0.2 else "neutral")))
+        if res["supports"]:
+            lv, tc = res["supports"][0]
+            ctx.append(metric_card("YAKIN DESTEK", f"${fmt_price(lv)}", f"{tc} temas · {(res['price'] - lv) / res['atr']:.1f} ATR", "up"))
+        if res["resists"]:
+            lv, tc = res["resists"][0]
+            ctx.append(metric_card("YAKIN DİRENÇ", f"${fmt_price(lv)}", f"{tc} temas · {(lv - res['price']) / res['atr']:.1f} ATR", "down"))
+        ctx.append(metric_card("VOLATİLİTE YÜZDELİĞİ", f"%{res['atr_rank'] * 100:.0f}", "ATR/fiyat, son 100 bar",
+                               "down" if res["atr_rank"] > 0.9 else "neutral"))
+
+        reasons_box = (f'<div class="reasons-box"><div class="mc-label">FAKTÖR ANALİZİ</div>'
+                       f'<ul>{_reasons_html(res["reasons"])}</ul></div>')
+        bt_note = ""
+        if stats:
+            bt_note = ('<div class="note">Backtest: son ~220 barda sinyaller üst üste binmeden işleme çevrilir; giriş bir sonraki barın açılışı, '
+                       f'çıkış SL {SL_ATR}×ATR / TP {TP_ATR}×ATR / {MAX_HOLD_BARS} bar; komisyon %{FEE_PER_SIDE * 100:.1f}/yön dahildir. '
+                       'Emir defteri, funding, S/R ve MTF katmanları canlıdır, geçmişe uygulanamaz.</div>')
+
+        html = (panel + consensus + conf_html + tag_html
+                + (section("İŞLEM PLANI") + cards_row(plan_cards) if plan_cards else "")
+                + (section("POZİSYON BOYUTU") + cards_row(size_cards) if size_cards else "")
+                + (section("GEÇMİŞ PERFORMANS (WALK-FORWARD)") + cards_row(bt_cards) + cards_row(acc_cards) + bt_note
+                   if (bt_cards or acc_cards) else "")
+                + section("PİYASA BAĞLAMI") + cards_row(ctx) + reasons_box)
+        return (html, build_signal_price_chart(df, ind, symbol, bar, res), build_factor_chart(res["contribs"], symbol),
+                build_backtest_chart(bt, df, symbol), build_signal_chart(df, ind, symbol))
+    except Exception as e:
+        return empty5(error_box(f"Veri alınamadı: {e}"))
+
+
+def load_risk(symbol, bar):
+    try:
+        df = fetch_candles(symbol, bar, 300)
+        rm = risk_metrics(df, bar)
+        if rm is None:
+            return error_box("Risk hesaplaması için yeterli veri yok."), go.Figure()
+        cards = [
+            metric_card("YILLIKLANDIRILMIŞ VOLATİLİTE", fmt_pct(rm["volatility_annual"])),
+            metric_card("YILLIKLANDIRILMIŞ GETİRİ", fmt_pct(rm["return_annual"]), "",
+                        "up" if rm["return_annual"] >= 0 else "down"),
+            metric_card("SHARPE ORANI", f"{rm['sharpe']:.2f}", "", "up" if rm["sharpe"] > 0 else "down"),
+            metric_card("MAKS. DRAWDOWN", fmt_pct(rm["max_drawdown"]), "", "down"),
+            metric_card("GÜNCEL DRAWDOWN", fmt_pct(rm["current_dd"]), "",
+                        "down" if rm["current_dd"] < 0 else "neutral"),
+            metric_card("VaR %95 (BAR)", fmt_pct(rm["var95_bar"]), "", "down"),
+            metric_card("CVaR %95 (BAR)", fmt_pct(rm["cvar95_bar"]), "", "down"),
+        ]
+        return cards_row(cards), build_equity_chart(df, symbol)
+    except Exception as e:
+        return error_box(f"Veri alınamadı: {e}"), go.Figure()
+
+
+def load_orderbook(symbol, depth):
+    try:
+        ob = fetch_orderbook(symbol, int(depth))
+        bids = [(float(p), float(s)) for p, s, *_ in ob.get("bids", [])]
+        asks = [(float(p), float(s)) for p, s, *_ in ob.get("asks", [])]
+        if not bids or not asks:
+            return error_box("Emir defteri verisi alınamadı."), go.Figure()
+        best_bid, best_ask = bids[0][0], asks[0][0]
+        spread = best_ask - best_bid
+        spread_pct = spread / best_ask if best_ask else 0.0
+        bid_vol = sum(s for _, s in bids)
+        ask_vol = sum(s for _, s in asks)
+        imbalance = (bid_vol - ask_vol) / (bid_vol + ask_vol) if (bid_vol + ask_vol) else 0.0
+        tone = "up" if imbalance > 0.05 else ("down" if imbalance < -0.05 else "neutral")
+        cards = [
+            metric_card("EN İYİ ALIŞ", f"${fmt_price(best_bid)}", "", "up"),
+            metric_card("EN İYİ SATIŞ", f"${fmt_price(best_ask)}", "", "down"),
+            metric_card("SPREAD", f"${fmt_price(spread)}", fmt_pct(spread_pct)),
+            metric_card("DEFTER DENGESİZLİĞİ", fmt_pct(imbalance), "Pozitif = alış baskısı", tone),
+        ]
+        return cards_row(cards), build_depth_chart(bids, asks, symbol)
+    except Exception as e:
+        return error_box(f"Veri alınamadı: {e}"), go.Figure()
+
+
+@_gpu
+def refresh_all(symbol, bar, depth, closed_only, acct, risk_pct):
+    ov_html, ov_fig = load_overview(symbol, bar)
+    sig = load_signal(symbol, bar, closed_only, acct, risk_pct)
+    risk_html, risk_fig = load_risk(symbol, bar)
+    ob_html, ob_fig = load_orderbook(symbol, depth)
+    return (ov_html, ov_fig, *sig, risk_html, risk_fig, ob_html, ob_fig)
+
+
+def refresh_signal(symbol, bar, closed_only, acct, risk_pct):
+    """Yalnızca sinyal sekmesini yeniler (risk/kapanmış-mum ayarı değişince hızlı güncelleme)."""
+    return load_signal(symbol, bar, closed_only, acct, risk_pct)
+
+
+SCAN_MODES = ["Tümü (skora göre)", "Yalnızca AL yönlü", "Yalnızca SAT yönlü", "Yalnızca yüksek kalite (A / A+)"]
+
+
+def _scan_one(sym, bar, all_tickers, closed_only):
+    df = fetch_candles(sym, bar, 300)
+    if closed_only:
+        df = df[df["confirm"] == 1].reset_index(drop=True)
+    # MTF ve mikro-yapı katmanları taramada kapalı (API yükünü azaltmak için); tekil analizde açık.
+    res = generate_signal(df, symbol=sym, bar=bar, mtf=False, micro=False, closed_only=closed_only)
+    rm = risk_metrics(df, bar)
+    t = all_tickers.get(sym, {})
+    last = float(t.get("last") or df["c"].iloc[-1])
+    open24h = float(t.get("open24h") or df["c"].iloc[0])
+    chg24h = (last - open24h) / open24h if open24h else 0.0
+    div = res["divergence"]
+    div_txt = "Yükseliş" if div["bullish"] else ("Düşüş" if div["bearish"] else "—")
+    bt = res["backtest"]
+    st = bt["stats"] if bt else None
+    acc = bt["acc"] if bt else None
+    return {
+        "Sembol": sym, "Fiyat": last, "24S %": round(chg24h * 100, 2), "Sinyal": res["signal"],
+        "Skor": res["score"], "Kalite": res["grade"], "Güven %": res["confidence"], "Rejim": res["regime"],
+        "RSI": round(res["indicators"]["rsi"].iloc[-1], 1), "ADX": round(res["adx"], 1),
+        "Sıkışma": "●" if res["squeeze_on"] else "—", "Uyumsuzluk": div_txt,
+        "Kazanma %": round(st["win_rate"] * 100, 1) if st else None,
+        "Profit Factor": round(st["profit_factor"], 2) if st else None,
+        "Beklenti %": round(st["expectancy"] * 100, 2) if st else None,
+        "Yön Doğruluğu %": round(acc["accuracy"] * 100, 1) if acc else None,
+        "Volatilite (Yıllık) %": round(rm["volatility_annual"] * 100, 1) if rm else None,
+        "Sharpe": round(rm["sharpe"], 2) if rm else None,
+    }
+
+
+def run_scanner(symbols_text, bar, closed_only=False, mode=SCAN_MODES[0], progress=gr.Progress()):
+    symbols = [s.strip().upper() for s in symbols_text.split(",") if s.strip()][:25]
+    if not symbols:
+        return pd.DataFrame()
+    try:
+        all_tickers = {t["instId"]: t for t in fetch_all_tickers("SPOT")}
+    except Exception:
+        all_tickers = {}
+
+    rows, done = [], 0
+    with ThreadPoolExecutor(max_workers=4) as ex:  # ağ beklemesini paralelle (OKX limiti için 4 ile sınırlı)
+        futs = {ex.submit(_scan_one, s, bar, all_tickers, closed_only): s for s in symbols}
+        for f in as_completed(futs):
+            done += 1
+            progress(done / len(symbols), desc=f"{futs[f]} tarandı ({done}/{len(symbols)})")
+            try:
+                rows.append(f.result())
+            except Exception:
+                continue
+    if not rows:
+        return pd.DataFrame()
+    out = pd.DataFrame(rows)
+    if mode == SCAN_MODES[1]:
+        out = out[out["Skor"] >= SIGNAL_THRESHOLD].sort_values("Skor", ascending=False)
+    elif mode == SCAN_MODES[2]:
+        out = out[out["Skor"] <= -SIGNAL_THRESHOLD].sort_values("Skor", ascending=True)
+    elif mode == SCAN_MODES[3]:
+        out = out[out["Kalite"].isin(["A", "A+"])].sort_values("Güven %", ascending=False)
+    else:
+        out = out.sort_values("Skor", ascending=False)
+    return out.reset_index(drop=True)
+
+
+# ---------------------------------------------------------------------------
+# STYLE — dark "trading terminal" identity (deliberate, not default SaaS chrome)
+# ---------------------------------------------------------------------------
+HEAD = """
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@500;600;700&family=IBM+Plex+Sans:wght@400;500;600&family=IBM+Plex+Mono:wght@400;500;600&display=swap" rel="stylesheet">
+"""
+
+CSS = """
+:root {
+  --bg-void: #0a0e14;
+  --bg-panel: #12171f;
+  --line: #232a35;
+  --text-primary: #e6ebf2;
+  --text-muted: #7c8797;
+  --accent-long: #3ddc97;
+  --accent-short: #ff5c72;
+  --accent-signal: #f2b84b;
+  --accent-primary: #5b8cff;
+}
+.gradio-container { background: var(--bg-void) !important; color: var(--text-primary) !important;
+  font-family: 'IBM Plex Sans', system-ui, sans-serif !important; }
+h1, h2, h3 { font-family: 'Space Grotesk', sans-serif !important; letter-spacing: -0.01em; }
+
+#masthead { display:flex; justify-content:space-between; align-items:center; flex-wrap:wrap; gap:10px;
+  border-bottom: 1px solid var(--line); padding: 14px 4px 18px 4px; margin-bottom: 6px; }
+#masthead .wordmark { font-family:'Space Grotesk',sans-serif; font-weight:700; font-size:1.5rem; letter-spacing:-0.02em; }
+#masthead .wordmark span { color: var(--accent-primary); }
+#masthead .tags { display:flex; gap:8px; flex-wrap:wrap; }
+#masthead .tag { font-size:0.7rem; padding:3px 9px; border:1px solid var(--line); border-radius:3px;
+  color:var(--text-muted); font-family:'IBM Plex Mono',monospace; }
+
+.metric-row { display:flex; flex-wrap:wrap; gap:10px; margin:8px 0 16px 0; }
+.metric-card { background: var(--bg-panel); border:1px solid var(--line); border-radius:4px;
+  padding:12px 16px; min-width:150px; flex:1; }
+.mc-label { font-size:0.66rem; color:var(--text-muted); letter-spacing:0.04em; margin-bottom:6px; }
+.mc-value { font-size:1.24rem; font-weight:600; color:var(--text-primary);
+  font-family:'IBM Plex Mono',monospace; }
+.mc-sub { font-size:0.72rem; color:var(--text-muted); margin-top:4px; font-family:'IBM Plex Mono',monospace; }
+.metric-card.mc-up .mc-value { color: var(--accent-long); }
+.metric-card.mc-down .mc-value { color: var(--accent-short); }
+
+.section-title { font-family:'Space Grotesk',sans-serif; font-size:0.78rem; font-weight:600; letter-spacing:0.08em;
+  color:var(--text-muted); margin:18px 0 0 0; padding-top:12px; border-top:1px solid var(--line); }
+
+.signal-panel { display:flex; align-items:center; flex-wrap:wrap; gap:20px; background:var(--bg-panel);
+  border:1px solid var(--line); border-radius:4px; padding:18px 20px; margin-bottom:12px; }
+.signal-badge { font-family:'Space Grotesk',sans-serif; font-weight:700; font-size:1.6rem; color:var(--tone);
+  border:2px solid var(--tone); border-radius:4px; padding:10px 22px; }
+.signal-meta .mc-value { font-size:1.35rem; }
+
+.consensus { background:var(--bg-panel); border:1px solid var(--line); border-radius:4px; padding:12px 16px; margin-bottom:12px; }
+.cbar { display:flex; height:8px; border-radius:2px; overflow:hidden; background:var(--line); }
+.cbull { background: var(--accent-long); }
+.cbear { background: var(--accent-short); }
+
+.tags { display:flex; gap:8px; flex-wrap:wrap; }
+.tags .tag { font-size:0.72rem; font-weight:600; padding:5px 12px; border:1px solid var(--line);
+  border-radius:3px; font-family:'IBM Plex Mono',monospace; letter-spacing:0.03em; }
+
+.reasons-box { background:var(--bg-panel); border:1px solid var(--line); border-radius:4px; padding:16px 20px; margin-top:8px; }
+.reasons-box ul { margin:8px 0 0 0; padding-left:0; list-style:none; }
+.reasons-box li { margin-bottom:6px; color:var(--text-primary); font-size:0.9rem; }
+.reasons-box .ri { display:inline-block; width:18px; font-size:0.7rem; }
+.reasons-box .r-up .ri { color: var(--accent-long); }
+.reasons-box .r-down .ri { color: var(--accent-short); }
+.reasons-box .r-flat .ri { color: var(--text-muted); }
+
+.note { color:var(--text-muted); font-size:0.74rem; line-height:1.5; margin:-6px 0 8px 0; }
+
+.error-box { background: rgba(255,92,114,0.08); border:1px solid var(--accent-short); color:var(--accent-short);
+  padding:14px 18px; border-radius:4px; font-family:'IBM Plex Mono',monospace; font-size:0.85rem; }
+
+.disclaimer { border-top:1px solid var(--line); margin-top:22px; padding-top:14px; color:var(--text-muted);
+  font-size:0.76rem; line-height:1.55; }
+"""
+
+# ---------------------------------------------------------------------------
+# GRADIO UI
+# ---------------------------------------------------------------------------
+with gr.Blocks(title="OKX Quant Terminal") as demo:
+
+    gr.HTML(
+        '<div id="masthead">'
+        '<div class="wordmark">OKX <span>QUANT</span> TERMINAL</div>'
+        '<div class="tags"><div class="tag">ELITE SIGNAL ENGINE v2</div>'
+        '<div class="tag">PUBLIC MARKET DATA</div>'
+        '<div class="tag">READ-ONLY · EMİR GÖNDERİLMEZ</div></div>'
+        '</div>'
+    )
+
+    with gr.Row():
+        symbol_dd = gr.Dropdown(choices=POPULAR_SYMBOLS, value="BTC-USDT", allow_custom_value=True,
+                                 label="Sembol", scale=2)
+        bar_dd = gr.Dropdown(choices=BAR_OPTIONS, value="1H", label="Zaman Dilimi", scale=1)
+        depth_slider = gr.Slider(5, 50, value=20, step=5, label="Defter Derinliği", scale=1)
+        refresh_btn = gr.Button("🔄 Yenile", variant="primary", scale=1)
+        auto_cb = gr.Checkbox(label="Otomatik (15sn)", value=False, scale=1)
+
+    with gr.Row():
+        closed_cb = gr.Checkbox(label="Sadece kapanmış mumlarla sinyal (repaint yok)", value=False, scale=2)
+        acct_num = gr.Number(value=10000, label="Hesap Büyüklüğü (USDT)", minimum=0, scale=1)
+        risk_sl = gr.Slider(0.25, 5, value=1, step=0.25, label="İşlem Başına Risk (%)", scale=1)
+
+    timer = gr.Timer(15, active=False)
+
+    with gr.Tabs():
+        with gr.Tab("📊 Genel Bakış"):
+            ov_html = gr.HTML()
+            ov_plot = gr.Plot()
+        with gr.Tab("🎯 Sinyal Motoru"):
+            sig_html = gr.HTML()
+            sig_map_plot = gr.Plot()
+            sig_factor_plot = gr.Plot()
+            sig_bt_plot = gr.Plot()
+            sig_ind_plot = gr.Plot()
+        with gr.Tab("⚠️ Risk Analitiği"):
+            risk_html = gr.HTML()
+            risk_plot = gr.Plot()
+        with gr.Tab("📖 Emir Defteri"):
+            ob_html = gr.HTML()
+            ob_plot = gr.Plot()
+        with gr.Tab("🔍 Çoklu Varlık Tarayıcı"):
+            gr.Markdown("Popüler pariteleri kompozit sinyal skoruna göre sıralar. Kendi listenizi virgülle ayırıp girebilirsiniz (en fazla 25 sembol).")
+            with gr.Row():
+                scan_symbols = gr.Textbox(value=",".join(POPULAR_SYMBOLS), label="Semboller", scale=3)
+                scan_bar = gr.Dropdown(choices=BAR_OPTIONS, value="4H", label="Zaman Dilimi", scale=1)
+                scan_mode = gr.Dropdown(choices=SCAN_MODES, value=SCAN_MODES[0], label="Filtre", scale=2)
+            scan_btn = gr.Button("🔍 Taramayı Başlat", variant="primary")
+            scan_out = gr.Dataframe(interactive=False, wrap=True)
+
+    gr.HTML(
+        '<div class="disclaimer">Bu panel yalnızca eğitim ve bilgilendirme amaçlıdır; yatırım tavsiyesi '
+        'değildir. Tüm veriler OKX genel (public) piyasa API\'sinden alınır; hiçbir API anahtarı '
+        'kullanılmaz ve hiçbir gerçek emir gönderilmez. Kripto varlıklar yüksek oynaklık içerir; geçmiş '
+        'performans (backtest dahil) gelecekteki sonuçların göstergesi değildir. Backtest sonuçları '
+        'kısa örneklem üzerindedir ve istatistiksel olarak sınırlıdır.</div>'
+    )
+
+    io_inputs = [symbol_dd, bar_dd, depth_slider, closed_cb, acct_num, risk_sl]
+    io_outputs = [ov_html, ov_plot, sig_html, sig_map_plot, sig_factor_plot, sig_bt_plot, sig_ind_plot,
+                  risk_html, risk_plot, ob_html, ob_plot]
+    sig_inputs = [symbol_dd, bar_dd, closed_cb, acct_num, risk_sl]
+    sig_outputs = [sig_html, sig_map_plot, sig_factor_plot, sig_bt_plot, sig_ind_plot]
+
+    refresh_btn.click(refresh_all, io_inputs, io_outputs)
+    timer.tick(refresh_all, io_inputs, io_outputs)
+    auto_cb.change(lambda active: gr.Timer(active=active), auto_cb, timer)
+    closed_cb.change(refresh_signal, sig_inputs, sig_outputs)
+    acct_num.submit(refresh_signal, sig_inputs, sig_outputs)
+    risk_sl.release(refresh_signal, sig_inputs, sig_outputs)
+    scan_btn.click(run_scanner, [scan_symbols, scan_bar, closed_cb, scan_mode], scan_out)
+    demo.load(refresh_all, io_inputs, io_outputs)
+
+demo.queue().launch(
+    theme=gr.themes.Base(primary_hue="blue", neutral_hue="slate"),
+    css=CSS,
+    head=HEAD,
+    ssr_mode=False,
+)

requirements.txt

@@ -0,0 +1,6 @@
+spaces
+gradio
+pandas
+numpy
+plotly
+requests