Delete app.py

app.py

@@ -1,600 +0,0 @@
-import subprocess
-import sys
-
-def install(pkg):
-    subprocess.check_call([sys.executable, "-m", "pip", "install", pkg, "-q"])
-
-packages = [
-    "gradio==4.44.0",
-    "requests",
-    "pandas",
-    "numpy",
-    "plotly",
-    "xgboost",
-    "scikit-learn",
-    "ta",
-]
-
-for pkg in packages:
-    try:
-        install(pkg)
-    except Exception as e:
-        print(f"Warning: could not install {pkg}: {e}")
-
-import gradio as gr
-import requests
-import pandas as pd
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import json
-import warnings
-warnings.filterwarnings("ignore")
-
-try:
-    import ta
-    TA_AVAILABLE = True
-except ImportError:
-    TA_AVAILABLE = False
-
-try:
-    from xgboost import XGBClassifier
-    XGB_AVAILABLE = True
-except ImportError:
-    XGB_AVAILABLE = False
-
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-
-# ─────────────────────────────────────────────
-# DATA FETCHING
-# ─────────────────────────────────────────────
-
-SYMBOLS = [
-    "BTCUSDT", "ETHUSDT", "SOLUSDT", "BNBUSDT", "XRPUSDT",
-    "ADAUSDT", "DOGEUSDT", "AVAXUSDT", "DOTUSDT", "MATICUSDT",
-    "LINKUSDT", "LTCUSDT", "UNIUSDT", "ATOMUSDT", "NEARUSDT",
-]
-
-INTERVALS = ["5m", "15m", "1h", "4h", "1d"]
-
-def fetch_klines(symbol: str, interval: str, limit: int = 300) -> pd.DataFrame:
-    url = "https://api.binance.com/api/v3/klines"
-    params = {"symbol": symbol, "interval": interval, "limit": limit}
-    try:
-        r = requests.get(url, params=params, timeout=10)
-        r.raise_for_status()
-        raw = r.json()
-    except Exception as e:
-        raise RuntimeError(f"Binance API hatası: {e}")
-
-    df = pd.DataFrame(raw, columns=[
-        "open_time","open","high","low","close","volume",
-        "close_time","quote_vol","trades","taker_buy_base",
-        "taker_buy_quote","ignore"
-    ])
-    for col in ["open","high","low","close","volume"]:
-        df[col] = df[col].astype(float)
-    df["open_time"] = pd.to_datetime(df["open_time"], unit="ms")
-    df.set_index("open_time", inplace=True)
-    return df[["open","high","low","close","volume"]]
-
-# ─────────────────────────────────────────────
-# INDICATORS
-# ─────────────────────────────────────────────
-
-def compute_ema(series: pd.Series, period: int) -> pd.Series:
-    return series.ewm(span=period, adjust=False).mean()
-
-def compute_rsi(series: pd.Series, period: int = 14) -> pd.Series:
-    delta = series.diff()
-    gain = delta.clip(lower=0).rolling(period).mean()
-    loss = (-delta.clip(upper=0)).rolling(period).mean()
-    rs = gain / loss.replace(0, np.nan)
-    return 100 - (100 / (1 + rs))
-
-def compute_macd(series: pd.Series):
-    fast = series.ewm(span=12, adjust=False).mean()
-    slow = series.ewm(span=26, adjust=False).mean()
-    macd_line = fast - slow
-    signal_line = macd_line.ewm(span=9, adjust=False).mean()
-    histogram = macd_line - signal_line
-    return macd_line, signal_line, histogram
-
-def compute_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
-    h_l = df["high"] - df["low"]
-    h_pc = (df["high"] - df["close"].shift()).abs()
-    l_pc = (df["low"] - df["close"].shift()).abs()
-    tr = pd.concat([h_l, h_pc, l_pc], axis=1).max(axis=1)
-    return tr.rolling(period).mean()
-
-def compute_vwap(df: pd.DataFrame) -> pd.Series:
-    tp = (df["high"] + df["low"] + df["close"]) / 3
-    cum_tp_vol = (tp * df["volume"]).cumsum()
-    cum_vol = df["volume"].cumsum()
-    return cum_tp_vol / cum_vol
-
-def add_indicators(df: pd.DataFrame) -> pd.DataFrame:
-    df = df.copy()
-    df["ema20"]  = compute_ema(df["close"], 20)
-    df["ema50"]  = compute_ema(df["close"], 50)
-    df["ema200"] = compute_ema(df["close"], 200)
-    df["rsi"]    = compute_rsi(df["close"], 14)
-    df["macd"], df["macd_signal"], df["macd_hist"] = compute_macd(df["close"])
-    df["atr"]    = compute_atr(df, 14)
-    df["vwap"]   = compute_vwap(df)
-
-    # Volume Profile (approximate: rolling 20-bar avg)
-    df["vol_ma20"] = df["volume"].rolling(20).mean()
-    df["vol_ratio"] = df["volume"] / df["vol_ma20"].replace(0, np.nan)
-
-    return df.dropna()
-
-# ─────────────────────────────────────────────
-# SIGNAL LOGIC
-# ─────────────────────────────────────────────
-
-def generate_signal(df: pd.DataFrame):
-    """Returns (signal, confidence_tech, sl, tp, rr) on last row."""
-    last = df.iloc[-1]
-    prev = df.iloc[-2]
-
-    buy_conditions = [
-        last["ema20"] > last["ema50"],
-        last["ema50"] > last["ema200"],
-        last["rsi"]   > 50,
-        (last["macd"] > last["macd_signal"]) and (prev["macd"] <= prev["macd_signal"]),
-        last["close"] > last["vwap"],
-    ]
-    sell_conditions = [
-        last["ema20"] < last["ema50"],
-        last["ema50"] < last["ema200"],
-        last["rsi"]   < 50,
-        (last["macd"] < last["macd_signal"]) and (prev["macd"] >= prev["macd_signal"]),
-        last["close"] < last["vwap"],
-    ]
-
-    buy_score  = sum(buy_conditions)
-    sell_score = sum(sell_conditions)
-
-    atr = last["atr"]
-    price = last["close"]
-
-    if buy_score >= 3:
-        signal = "AL 📈"
-        sl = round(price - 1.5 * atr, 4)
-        tp = round(price + 3.0 * atr, 4)
-        tech_conf = int(buy_score / 5 * 100)
-    elif sell_score >= 3:
-        signal = "SAT 📉"
-        sl = round(price + 1.5 * atr, 4)
-        tp = round(price - 3.0 * atr, 4)
-        tech_conf = int(sell_score / 5 * 100)
-    else:
-        signal = "BEKLE ⏳"
-        sl = round(price - 1.5 * atr, 4)
-        tp = round(price + 1.5 * atr, 4)
-        tech_conf = 30
-
-    rr = round(abs(tp - price) / abs(sl - price), 2) if abs(sl - price) > 0 else 0
-    return signal, tech_conf, sl, tp, rr
-
-# ─────────────────────────────────────────────
-# AI MODEL
-# ─────────────────────────────────────────────
-
-def build_features(df: pd.DataFrame) -> pd.DataFrame:
-    d = df.copy()
-    d["ret1"]   = d["close"].pct_change(1)
-    d["ret3"]   = d["close"].pct_change(3)
-    d["ret5"]   = d["close"].pct_change(5)
-    d["ema20_50_cross"] = (d["ema20"] - d["ema50"]) / d["close"]
-    d["ema50_200_cross"] = (d["ema50"] - d["ema200"]) / d["close"]
-    d["rsi_norm"] = d["rsi"] / 100
-    d["macd_norm"] = d["macd"] / d["close"]
-    d["macd_hist_norm"] = d["macd_hist"] / d["close"]
-    d["vwap_dist"] = (d["close"] - d["vwap"]) / d["close"]
-    d["atr_norm"] = d["atr"] / d["close"]
-    d["vol_ratio_feat"] = d["vol_ratio"].clip(0, 5)
-    return d.dropna()
-
-FEATURE_COLS = [
-    "ret1","ret3","ret5",
-    "ema20_50_cross","ema50_200_cross",
-    "rsi_norm","macd_norm","macd_hist_norm",
-    "vwap_dist","atr_norm","vol_ratio_feat"
-]
-
-def train_ai(df: pd.DataFrame):
-    feat_df = build_features(df)
-    if len(feat_df) < 60:
-        return None, None, 50
-
-    feat_df["target"] = (feat_df["close"].shift(-3) > feat_df["close"]).astype(int)
-    feat_df = feat_df.dropna()
-
-    X = feat_df[FEATURE_COLS].values
-    y = feat_df["target"].values
-
-    if len(X) < 40:
-        return None, None, 50
-
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(X)
-
-    split = int(len(X_scaled) * 0.8)
-    X_train, X_test = X_scaled[:split], X_scaled[split:]
-    y_train, y_test = y[:split], y[split:]
-
-    if not XGB_AVAILABLE:
-        return None, scaler, 50
-
-    try:
-        model = XGBClassifier(
-            n_estimators=50,
-            max_depth=3,
-            learning_rate=0.1,
-            use_label_encoder=False,
-            eval_metric="logloss",
-            verbosity=0,
-            n_jobs=1,
-        )
-        model.fit(X_train, y_train)
-
-        last_feat = build_features(df.tail(50)).tail(1)[FEATURE_COLS].values
-        last_scaled = scaler.transform(last_feat)
-        prob = model.predict_proba(last_scaled)[0][1]
-        ai_conf = int(prob * 100)
-        return model, scaler, ai_conf
-    except Exception:
-        return None, None, 50
-
-# ─────────────────────────────────────────────
-# CHART
-# ─────────────────────────────────────────────
-
-def build_chart(df: pd.DataFrame, symbol: str, signal: str, sl: float, tp: float) -> go.Figure:
-    plot_df = df.tail(120).copy()
-
-    fig = make_subplots(
-        rows=3, cols=1,
-        shared_xaxes=True,
-        row_heights=[0.6, 0.2, 0.2],
-        vertical_spacing=0.03,
-        subplot_titles=["", "RSI", "MACD"]
-    )
-
-    # Candlestick
-    fig.add_trace(go.Candlestick(
-        x=plot_df.index,
-        open=plot_df["open"], high=plot_df["high"],
-        low=plot_df["low"], close=plot_df["close"],
-        name="Fiyat",
-        increasing_line_color="#00e676",
-        decreasing_line_color="#ff5252",
-        increasing_fillcolor="#00e676",
-        decreasing_fillcolor="#ff5252",
-    ), row=1, col=1)
-
-    # EMAs
-    for period, color, width in [(20,"#64b5f6",1.5),(50,"#ffb74d",1.5),(200,"#ce93d8",2)]:
-        fig.add_trace(go.Scatter(
-            x=plot_df.index, y=plot_df[f"ema{period}"],
-            name=f"EMA{period}", line=dict(color=color, width=width)
-        ), row=1, col=1)
-
-    # VWAP
-    fig.add_trace(go.Scatter(
-        x=plot_df.index, y=plot_df["vwap"],
-        name="VWAP", line=dict(color="#f48fb1", width=1.5, dash="dot")
-    ), row=1, col=1)
-
-    # Signal arrows on last bar
-    last_idx = plot_df.index[-1]
-    last_close = plot_df["close"].iloc[-1]
-
-    if "AL" in signal:
-        fig.add_annotation(
-            x=last_idx, y=last_close * 0.995,
-            text="▲ AL", showarrow=False,
-            font=dict(color="#00e676", size=16, family="monospace"),
-            row=1, col=1
-        )
-    elif "SAT" in signal:
-        fig.add_annotation(
-            x=last_idx, y=last_close * 1.005,
-            text="▼ SAT", showarrow=False,
-            font=dict(color="#ff5252", size=16, family="monospace"),
-            row=1, col=1
-        )
-
-    # SL / TP horizontal lines
-    fig.add_hline(y=sl, line_dash="dash", line_color="#ff5252",
-                  annotation_text=f"SL: {sl:,.4f}", row=1, col=1)
-    fig.add_hline(y=tp, line_dash="dash", line_color="#00e676",
-                  annotation_text=f"TP: {tp:,.4f}", row=1, col=1)
-
-    # RSI
-    fig.add_trace(go.Scatter(
-        x=plot_df.index, y=plot_df["rsi"],
-        name="RSI", line=dict(color="#80cbc4", width=1.5)
-    ), row=2, col=1)
-    fig.add_hline(y=70, line_dash="dot", line_color="#ff5252", row=2, col=1)
-    fig.add_hline(y=30, line_dash="dot", line_color="#00e676", row=2, col=1)
-    fig.add_hline(y=50, line_dash="dot", line_color="#888888", row=2, col=1)
-
-    # MACD
-    colors = ["#00e676" if v >= 0 else "#ff5252" for v in plot_df["macd_hist"]]
-    fig.add_trace(go.Bar(
-        x=plot_df.index, y=plot_df["macd_hist"],
-        name="MACD Hist", marker_color=colors, opacity=0.7
-    ), row=3, col=1)
-    fig.add_trace(go.Scatter(
-        x=plot_df.index, y=plot_df["macd"],
-        name="MACD", line=dict(color="#64b5f6", width=1.2)
-    ), row=3, col=1)
-    fig.add_trace(go.Scatter(
-        x=plot_df.index, y=plot_df["macd_signal"],
-        name="Sinyal", line=dict(color="#ffb74d", width=1.2)
-    ), row=3, col=1)
-
-    fig.update_layout(
-        title=dict(
-            text=f"<b>{symbol}</b> — Trading Analizi",
-            font=dict(color="#e0e0e0", size=18, family="'Courier New', monospace")
-        ),
-        paper_bgcolor="#0d0d0d",
-        plot_bgcolor="#111111",
-        font=dict(color="#b0b0b0", family="'Courier New', monospace"),
-        xaxis_rangeslider_visible=False,
-        legend=dict(
-            bgcolor="rgba(20,20,20,0.8)",
-            bordercolor="#333",
-            borderwidth=1,
-            font=dict(size=10)
-        ),
-        height=750,
-        margin=dict(l=50, r=50, t=60, b=20),
-    )
-    for i in range(1, 4):
-        fig.update_xaxes(
-            gridcolor="#1e1e1e", zeroline=False,
-            showspikes=True, spikecolor="#444", row=i, col=1
-        )
-        fig.update_yaxes(
-            gridcolor="#1e1e1e", zeroline=False,
-            showspikes=True, spikecolor="#444", row=i, col=1
-        )
-
-    return fig
-
-# ─────────────────────────────────────────────
-# MAIN ANALYSIS
-# ─────────────────────────────────────────────
-
-def analyze(symbol: str, interval: str):
-    try:
-        df_raw = fetch_klines(symbol, interval, limit=300)
-    except Exception as e:
-        return None, f"❌ Veri alınamadı: {e}", "", "", "", "", "", ""
-
-    df = add_indicators(df_raw)
-
-    if len(df) < 50:
-        return None, "❌ Yeterli veri yok.", "", "", "", "", "", ""
-
-    signal, tech_conf, sl, tp, rr = generate_signal(df)
-    _, _, ai_conf = train_ai(df)
-
-    # Combined confidence
-    final_conf = int(0.5 * tech_conf + 0.5 * ai_conf)
-
-    # Direction
-    if "AL" in signal:
-        direction = "🟢 YUKARI"
-        direction_color = "#00e676"
-    elif "SAT" in signal:
-        direction = "🔴 AŞAĞI"
-        direction_color = "#ff5252"
-    else:
-        direction = "🟡 YATAY"
-        direction_color = "#ffb74d"
-
-    price = df["close"].iloc[-1]
-    rsi_val = df["rsi"].iloc[-1]
-    atr_val = df["atr"].iloc[-1]
-
-    fig = build_chart(df, symbol, signal, sl, tp)
-
-    signal_md = f"""
-<div style="
-  background: linear-gradient(135deg, #0d1117 0%, #161b22 100%);
-  border: 1px solid #30363d;
-  border-radius: 12px;
-  padding: 20px;
-  font-family: 'Courier New', monospace;
-  color: #e6edf3;
-">
-  <h2 style="margin:0 0 16px 0; color:#58a6ff; font-size:1.1em; letter-spacing:2px;">
-    ⚡ SİNYAL ANALİZİ — {symbol} / {interval}
-  </h2>
-  <table style="width:100%; border-collapse:collapse;">
-    <tr>
-      <td style="padding:8px 12px; color:#8b949e; width:40%;">📌 Son Sinyal</td>
-      <td style="padding:8px 12px; font-size:1.2em; font-weight:bold;">{signal}</td>
-    </tr>
-    <tr style="background:rgba(255,255,255,0.03);">
-      <td style="padding:8px 12px; color:#8b949e;">📊 Güven Skoru</td>
-      <td style="padding:8px 12px;">
-        <div style="background:#21262d; border-radius:6px; overflow:hidden; height:20px; width:100%;">
-          <div style="background: linear-gradient(90deg, #238636, #56d364); width:{final_conf}%; height:100%; display:flex; align-items:center; justify-content:center; font-size:12px; font-weight:bold;">{final_conf}%</div>
-        </div>
-      </td>
-    </tr>
-    <tr>
-      <td style="padding:8px 12px; color:#8b949e;">🎯 Teknik Skor</td>
-      <td style="padding:8px 12px;">{tech_conf}% &nbsp; | &nbsp; 🤖 AI Skoru: {ai_conf}%</td>
-    </tr>
-    <tr style="background:rgba(255,255,255,0.03);">
-      <td style="padding:8px 12px; color:#8b949e;">📈 Beklenen Yön</td>
-      <td style="padding:8px 12px; color:{direction_color}; font-weight:bold;">{direction}</td>
-    </tr>
-    <tr>
-      <td style="padding:8px 12px; color:#8b949e;">💰 Güncel Fiyat</td>
-      <td style="padding:8px 12px;">${price:,.4f}</td>
-    </tr>
-    <tr style="background:rgba(255,255,255,0.03);">
-      <td style="padding:8px 12px; color:#ff5252;">🛑 Stop Loss</td>
-      <td style="padding:8px 12px; color:#ff5252; font-weight:bold;">${sl:,.4f}</td>
-    </tr>
-    <tr>
-      <td style="padding:8px 12px; color:#00e676;">✅ Take Profit</td>
-      <td style="padding:8px 12px; color:#00e676; font-weight:bold;">${tp:,.4f}</td>
-    </tr>
-    <tr style="background:rgba(255,255,255,0.03);">
-      <td style="padding:8px 12px; color:#8b949e;">⚖️ Risk/Ödül</td>
-      <td style="padding:8px 12px;">{rr}x</td>
-    </tr>
-    <tr>
-      <td style="padding:8px 12px; color:#8b949e;">📉 RSI (14)</td>
-      <td style="padding:8px 12px;">{rsi_val:.1f}</td>
-    </tr>
-    <tr style="background:rgba(255,255,255,0.03);">
-      <td style="padding:8px 12px; color:#8b949e;">📏 ATR (14)</td>
-      <td style="padding:8px 12px;">${atr_val:,.4f}</td>
-    </tr>
-  </table>
-  <p style="margin:16px 0 0 0; font-size:0.75em; color:#484f58; text-align:center;">
-    ⚠️ Bu uygulama eğitim amaçlıdır. Finansal tavsiye değildir. Her zaman kendi araştırmanızı yapın.
-  </p>
-</div>
-"""
-
-    return fig, signal_md
-
-# ─────────────────────────────────────────────
-# GRADIO UI
-# ─────────────────────────────────────────────
-
-CSS = """
-body, .gradio-container {
-    background: #0a0a0f !important;
-    font-family: 'Courier New', monospace !important;
-}
-.gr-button {
-    background: linear-gradient(135deg, #1a1a2e, #16213e) !important;
-    border: 1px solid #0f3460 !important;
-    color: #e0e0e0 !important;
-    font-family: 'Courier New', monospace !important;
-    letter-spacing: 1px !important;
-    transition: all 0.2s !important;
-}
-.gr-button:hover {
-    background: linear-gradient(135deg, #0f3460, #533483) !important;
-    border-color: #533483 !important;
-    box-shadow: 0 0 12px rgba(83,52,131,0.5) !important;
-}
-.gr-button.primary {
-    background: linear-gradient(135deg, #0f3460, #533483) !important;
-    border-color: #7b68ee !important;
-    font-weight: bold !important;
-}
-.gr-dropdown, .gr-textbox, select, input {
-    background: #111 !important;
-    border: 1px solid #333 !important;
-    color: #e0e0e0 !important;
-    font-family: 'Courier New', monospace !important;
-}
-.gradio-container h1, .gradio-container h2, .gradio-container h3 {
-    font-family: 'Courier New', monospace !important;
-}
-"""
-
-HEADER_HTML = """
-<div style="
-  background: linear-gradient(135deg, #0d0d1a 0%, #0a0a0f 50%, #1a0a1a 100%);
-  border: 1px solid #1e1e3f;
-  border-radius: 16px;
-  padding: 28px 32px;
-  text-align: center;
-  font-family: 'Courier New', monospace;
-  margin-bottom: 8px;
-  position: relative;
-  overflow: hidden;
-">
-  <div style="
-    position: absolute; top: 0; left: 0; right: 0; height: 2px;
-    background: linear-gradient(90deg, transparent, #7b68ee, #00e5ff, #7b68ee, transparent);
-  "></div>
-  <h1 style="
-    margin: 0 0 6px 0;
-    font-size: 2em;
-    letter-spacing: 4px;
-    background: linear-gradient(90deg, #7b68ee, #00e5ff, #7b68ee);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    text-transform: uppercase;
-  ">⚡ CRYPTO SIGNAL PRO</h1>
-  <p style="margin:0; color:#555; letter-spacing:3px; font-size:0.75em; text-transform:uppercase;">
-    AI-Powered Technical Analysis &amp; Trading Signals
-  </p>
-  <div style="
-    display: flex; justify-content: center; gap: 24px; margin-top: 16px;
-    font-size: 0.7em; color: #444; letter-spacing: 2px;
-  ">
-    <span>EMA · RSI · MACD</span>
-    <span style="color:#333;">|</span>
-    <span>VWAP · ATR · Volume</span>
-    <span style="color:#333;">|</span>
-    <span>XGBoost AI</span>
-  </div>
-</div>
-"""
-
-def run_analysis(symbol, interval):
-    fig, signal_md = analyze(symbol, interval)
-    if fig is None:
-        return None, signal_md
-    return fig, signal_md
-
-with gr.Blocks(css=CSS, title="⚡ Crypto Signal Pro") as demo:
-    gr.HTML(HEADER_HTML)
-
-    with gr.Row():
-        sym_dd = gr.Dropdown(
-            choices=SYMBOLS,
-            value="BTCUSDT",
-            label="📊 Parite",
-            interactive=True,
-        )
-        int_dd = gr.Dropdown(
-            choices=INTERVALS,
-            value="1h",
-            label="⏱ Zaman Dilimi",
-            interactive=True,
-        )
-        analyze_btn = gr.Button("🔍 ANALİZ ET", variant="primary", scale=1)
-
-    chart_out = gr.Plot(label="Grafik", elem_id="chart-area")
-    signal_out = gr.HTML(label="Sinyal Özeti")
-
-    analyze_btn.click(
-        fn=run_analysis,
-        inputs=[sym_dd, int_dd],
-        outputs=[chart_out, signal_out],
-    )
-
-    gr.HTML("""
-    <div style="
-      text-align:center; padding:16px; 
-      color:#2a2a2a; font-size:0.7em;
-      font-family:'Courier New',monospace; letter-spacing:1px;
-    ">
-      CRYPTO SIGNAL PRO · Binance API · XGBoost AI · Plotly Charts<br>
-      ⚠️ Yatırım tavsiyesi değildir — DYOR
-    </div>
-    """)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)