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app-1.py

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+import subprocess, sys
+
+def install(pkg):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", pkg, "-q"])
+
+for pkg in ["requests","pandas","numpy","plotly","xgboost","scikit-learn","ta"]:
+    try:
+        install(pkg)
+    except Exception as e:
+        print(f"Warning: {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 warnings
+warnings.filterwarnings("ignore")
+
+try:
+    from xgboost import XGBClassifier
+    XGB_AVAILABLE = True
+except ImportError:
+    XGB_AVAILABLE = False
+
+from sklearn.preprocessing import StandardScaler
+
+# ─────────────────────────────────────────────
+# 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, interval, limit=300):
+    url = "https://api.binance.com/api/v3/klines"
+    r = requests.get(url, params={"symbol":symbol,"interval":interval,"limit":limit}, timeout=10)
+    r.raise_for_status()
+    df = pd.DataFrame(r.json(), columns=[
+        "open_time","open","high","low","close","volume",
+        "close_time","quote_vol","trades","taker_buy_base","taker_buy_quote","ignore"
+    ])
+    for c in ["open","high","low","close","volume"]:
+        df[c] = df[c].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 ema(s, n):    return s.ewm(span=n, adjust=False).mean()
+def rsi(s, n=14):
+    d=s.diff(); g=d.clip(lower=0).rolling(n).mean(); l=(-d.clip(upper=0)).rolling(n).mean()
+    return 100-(100/(1+g/l.replace(0,np.nan)))
+def macd(s):
+    ml=s.ewm(span=12,adjust=False).mean()-s.ewm(span=26,adjust=False).mean()
+    sig=ml.ewm(span=9,adjust=False).mean(); return ml,sig,ml-sig
+def atr(df,n=14):
+    tr=pd.concat([df.high-df.low,(df.high-df.close.shift()).abs(),(df.low-df.close.shift()).abs()],axis=1).max(axis=1)
+    return tr.rolling(n).mean()
+def vwap(df):
+    tp=(df.high+df.low+df.close)/3; return (tp*df.volume).cumsum()/df.volume.cumsum()
+
+def add_indicators(df):
+    df=df.copy()
+    df["ema20"]=ema(df.close,20); df["ema50"]=ema(df.close,50); df["ema200"]=ema(df.close,200)
+    df["rsi"]=rsi(df.close)
+    df["macd"],df["macd_sig"],df["macd_hist"]=macd(df.close)
+    df["atr"]=atr(df); df["vwap"]=vwap(df)
+    df["vol_ma"]=df.volume.rolling(20).mean()
+    df["vol_ratio"]=df.volume/df.vol_ma.replace(0,np.nan)
+    return df.dropna()
+
+# ─────────────────────────────────────────────
+# SIGNAL LOGIC
+# ─────────────────────────────────────────────
+
+def generate_signal(df):
+    l,p=df.iloc[-1],df.iloc[-2]
+    bc=[l.ema20>l.ema50, l.ema50>l.ema200, l.rsi>50,
+        (l.macd>l.macd_sig)and(p.macd<=p.macd_sig), l.close>l.vwap]
+    sc=[l.ema20<l.ema50, l.ema50<l.ema200, l.rsi<50,
+        (l.macd<l.macd_sig)and(p.macd>=p.macd_sig), l.close<l.vwap]
+    bs,ss=sum(bc),sum(sc)
+    px,at=l.close,l.atr
+    if bs>=3:
+        return "AL 📈", int(bs/5*100), round(px-1.5*at,4), round(px+3*at,4)
+    elif ss>=3:
+        return "SAT 📉", int(ss/5*100), round(px+1.5*at,4), round(px-3*at,4)
+    else:
+        return "BEKLE ⏳", 30, round(px-1.5*at,4), round(px+1.5*at,4)
+
+# ─────────────────────────────────────────────
+# AI MODEL
+# ─────────────────────────────────────────────
+
+FCOLS=["r1","r3","r5","e2050","e50200","rsi_n","macd_n","mhist_n","vwap_d","atr_n","volr"]
+
+def featurize(df):
+    d=df.copy()
+    d["r1"]=d.close.pct_change(1); d["r3"]=d.close.pct_change(3); d["r5"]=d.close.pct_change(5)
+    d["e2050"]=(d.ema20-d.ema50)/d.close; d["e50200"]=(d.ema50-d.ema200)/d.close
+    d["rsi_n"]=d.rsi/100; d["macd_n"]=d.macd/d.close; d["mhist_n"]=d.macd_hist/d.close
+    d["vwap_d"]=(d.close-d.vwap)/d.close; d["atr_n"]=d.atr/d.close; d["volr"]=d.vol_ratio.clip(0,5)
+    return d.dropna()
+
+def train_ai(df):
+    fd=featurize(df)
+    if len(fd)<60 or not XGB_AVAILABLE:
+        return 50
+    fd["tgt"]=(fd.close.shift(-3)>fd.close).astype(int)
+    fd=fd.dropna()
+    if len(fd)<40:
+        return 50
+    X=fd[FCOLS].values; y=fd["tgt"].values
+    sc=StandardScaler(); Xs=sc.fit_transform(X)
+    sp=int(len(Xs)*0.8)
+    try:
+        m=XGBClassifier(n_estimators=50,max_depth=3,learning_rate=0.1,
+                        eval_metric="logloss",verbosity=0,n_jobs=1)
+        m.fit(Xs[:sp],y[:sp])
+        lf=featurize(df.tail(50)).tail(1)[FCOLS].values
+        return int(m.predict_proba(sc.transform(lf))[0][1]*100)
+    except:
+        return 50
+
+# ─────────────────────────────────────────────
+# CHART
+# ─────────────────────────────────────────────
+
+def build_chart(df, symbol, signal, sl, tp):
+    p=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)
+    fig.add_trace(go.Candlestick(x=p.index,open=p.open,high=p.high,low=p.low,close=p.close,
+        name="Fiyat",increasing_line_color="#00e676",decreasing_line_color="#ff5252",
+        increasing_fillcolor="#00e676",decreasing_fillcolor="#ff5252"),row=1,col=1)
+    for n,c,w in [(20,"#64b5f6",1.5),(50,"#ffb74d",1.5),(200,"#ce93d8",2)]:
+        fig.add_trace(go.Scatter(x=p.index,y=p[f"ema{n}"],name=f"EMA{n}",
+                                 line=dict(color=c,width=w)),row=1,col=1)
+    fig.add_trace(go.Scatter(x=p.index,y=p.vwap,name="VWAP",
+                             line=dict(color="#f48fb1",width=1.5,dash="dot")),row=1,col=1)
+    # Signal marker
+    li,lc=p.index[-1],p.close.iloc[-1]
+    if "AL" in signal:
+        fig.add_annotation(x=li,y=lc*0.994,text="▲ AL",showarrow=False,
+                          font=dict(color="#00e676",size=16),row=1,col=1)
+    elif "SAT" in signal:
+        fig.add_annotation(x=li,y=lc*1.006,text="▼ SAT",showarrow=False,
+                          font=dict(color="#ff5252",size=16),row=1,col=1)
+    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=p.index,y=p.rsi,name="RSI",
+                             line=dict(color="#80cbc4",width=1.5)),row=2,col=1)
+    for yv,col in [(70,"#ff5252"),(50,"#666"),(30,"#00e676")]:
+        fig.add_hline(y=yv,line_dash="dot",line_color=col,row=2,col=1)
+    # MACD
+    clrs=["#00e676" if v>=0 else "#ff5252" for v in p.macd_hist]
+    fig.add_trace(go.Bar(x=p.index,y=p.macd_hist,name="Hist",
+                         marker_color=clrs,opacity=0.7),row=3,col=1)
+    fig.add_trace(go.Scatter(x=p.index,y=p.macd,name="MACD",
+                             line=dict(color="#64b5f6",width=1.2)),row=3,col=1)
+    fig.add_trace(go.Scatter(x=p.index,y=p.macd_sig,name="Sinyal",
+                             line=dict(color="#ffb74d",width=1.2)),row=3,col=1)
+    fig.update_layout(
+        title=dict(text=f"<b>{symbol}</b> — Teknik Analiz",
+                   font=dict(color="#e0e0e0",size=16,family="Courier New, monospace")),
+        paper_bgcolor="#0d0d0d",plot_bgcolor="#111111",
+        font=dict(color="#b0b0b0",family="Courier New, monospace"),
+        xaxis_rangeslider_visible=False,height=720,
+        legend=dict(bgcolor="rgba(20,20,20,0.8)",bordercolor="#333",borderwidth=1,font=dict(size=10)),
+        margin=dict(l=50,r=60,t=55,b=20),
+    )
+    for i in range(1,4):
+        fig.update_xaxes(gridcolor="#1e1e1e",zeroline=False,row=i,col=1)
+        fig.update_yaxes(gridcolor="#1e1e1e",zeroline=False,row=i,col=1)
+    return fig
+
+# ─────────────────────────────────────────────
+# MAIN
+# ─────────────────────────────────────────────
+
+def analyze(symbol, interval):
+    try:
+        df_raw = fetch_klines(symbol, interval, 300)
+    except Exception as e:
+        return None, f"<p style='color:#ff5252'>❌ Veri alınamadı: {e}</p>"
+
+    df = add_indicators(df_raw)
+    if len(df) < 50:
+        return None, "<p style='color:#ff5252'>❌ Yeterli veri yok.</p>"
+
+    signal, tech_conf, sl, tp = generate_signal(df)
+    ai_conf = train_ai(df)
+    final = int(0.5*tech_conf + 0.5*ai_conf)
+
+    price = df.close.iloc[-1]
+    rsi_v = df.rsi.iloc[-1]
+    atr_v = df.atr.iloc[-1]
+    rr    = round(abs(tp-price)/abs(sl-price),2) if abs(sl-price)>0 else 0
+
+    if "AL" in signal:   dir_txt,dir_col = "🟢 YUKARI","#00e676"
+    elif "SAT" in signal: dir_txt,dir_col = "🔴 AŞAĞI","#ff5252"
+    else:                dir_txt,dir_col = "🟡 YATAY","#ffb74d"
+
+    bar_color = "#238636" if final>=60 else ("#f0883e" if final>=40 else "#da3633")
+
+    html = f"""
+<div style="background:linear-gradient(135deg,#0d1117,#161b22);border:1px solid #30363d;
+     border-radius:12px;padding:20px;font-family:'Courier New',monospace;color:#e6edf3;">
+  <h2 style="margin:0 0 16px;color:#58a6ff;font-size:1em;letter-spacing:2px;">
+    ⚡ SİNYAL — {symbol} / {interval}
+  </h2>
+  <table style="width:100%;border-collapse:collapse;">
+    <tr><td style="padding:7px 12px;color:#8b949e;width:42%">📌 Sinyal</td>
+        <td style="padding:7px 12px;font-size:1.15em;font-weight:bold">{signal}</td></tr>
+    <tr style="background:rgba(255,255,255,.03)">
+      <td style="padding:7px 12px;color:#8b949e">📊 Güven</td>
+      <td style="padding:7px 12px">
+        <div style="background:#21262d;border-radius:6px;overflow:hidden;height:18px">
+          <div style="background:linear-gradient(90deg,{bar_color},{bar_color}cc);width:{final}%;
+               height:100%;display:flex;align-items:center;justify-content:center;
+               font-size:11px;font-weight:bold">{final}%</div>
+        </div>
+      </td>
+    </tr>
+    <tr><td style="padding:7px 12px;color:#8b949e">🔬 Teknik / 🤖 AI</td>
+        <td style="padding:7px 12px">{tech_conf}% &nbsp;/&nbsp; {ai_conf}%</td></tr>
+    <tr style="background:rgba(255,255,255,.03)">
+        <td style="padding:7px 12px;color:#8b949e">📈 Yön</td>
+        <td style="padding:7px 12px;color:{dir_col};font-weight:bold">{dir_txt}</td></tr>
+    <tr><td style="padding:7px 12px;color:#8b949e">💰 Fiyat</td>
+        <td style="padding:7px 12px">${price:,.4f}</td></tr>
+    <tr style="background:rgba(255,255,255,.03)">
+        <td style="padding:7px 12px;color:#ff5252">🛑 Stop Loss</td>
+        <td style="padding:7px 12px;color:#ff5252;font-weight:bold">${sl:,.4f}</td></tr>
+    <tr><td style="padding:7px 12px;color:#00e676">✅ Take Profit</td>
+        <td style="padding:7px 12px;color:#00e676;font-weight:bold">${tp:,.4f}</td></tr>
+    <tr style="background:rgba(255,255,255,.03)">
+        <td style="padding:7px 12px;color:#8b949e">⚖️ Risk/Ödül</td>
+        <td style="padding:7px 12px">{rr}x</td></tr>
+    <tr><td style="padding:7px 12px;color:#8b949e">📉 RSI</td>
+        <td style="padding:7px 12px">{rsi_v:.1f}</td></tr>
+    <tr style="background:rgba(255,255,255,.03)">
+        <td style="padding:7px 12px;color:#8b949e">📏 ATR</td>
+        <td style="padding:7px 12px">${atr_v:,.4f}</td></tr>
+  </table>
+  <p style="margin:14px 0 0;font-size:.7em;color:#484f58;text-align:center">
+    ⚠️ Eğitim amaçlıdır — finansal tavsiye değildir. DYOR.
+  </p>
+</div>"""
+
+    fig = build_chart(df, symbol, signal, sl, tp)
+    return fig, html
+
+# ─────────────────────────────────────────────
+# GRADIO UI
+# ─────────────────────────────────────────────
+
+CSS = """
+.gradio-container { background:#0a0a0f !important; }
+footer { display:none !important; }
+"""
+
+HEADER = """
+<div style="background:linear-gradient(135deg,#0d0d1a,#0a0a0f,#1a0a1a);
+     border:1px solid #1e1e3f;border-radius:16px;padding:28px 32px;
+     text-align:center;font-family:'Courier New',monospace;margin-bottom:4px;position:relative;">
+  <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;font-size:1.9em;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:#444;letter-spacing:3px;font-size:.72em;text-transform:uppercase">
+    AI-Powered Technical Analysis &amp; Trading Signals</p>
+  <div style="display:flex;justify-content:center;gap:20px;margin-top:12px;
+       font-size:.68em;color:#333;letter-spacing:2px">
+    <span>EMA · RSI · MACD</span><span style="color:#222">|</span>
+    <span>VWAP · ATR · Volume</span><span style="color:#222">|</span>
+    <span>XGBoost AI</span>
+  </div>
+</div>"""
+
+def run(symbol, interval):
+    return analyze(symbol, interval)
+
+with gr.Blocks(css=CSS, title="⚡ Crypto Signal Pro") as demo:
+    gr.HTML(HEADER)
+    with gr.Row():
+        sym = gr.Dropdown(choices=SYMBOLS, value="BTCUSDT", label="📊 Parite", scale=2)
+        ivl = gr.Dropdown(choices=INTERVALS, value="1h",    label="⏱ Zaman Dilimi", scale=1)
+        btn = gr.Button("🔍  ANALİZ ET", variant="primary", scale=1)
+    with gr.Row():
+        chart = gr.Plot(label="Grafik")
+        info  = gr.HTML(label="Sinyal Özeti")
+    btn.click(fn=run, inputs=[sym, ivl], outputs=[chart, info])
+    gr.HTML("""<p style="text-align:center;color:#2a2a2a;font-size:.65em;
+               font-family:'Courier New',monospace;padding:12px;letter-spacing:1px">
+               CRYPTO SIGNAL PRO · Binance API · XGBoost AI · Plotly</p>""")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

requirements-22.txt

@@ -0,0 +1,7 @@
+requests
+pandas
+numpy
+plotly
+xgboost
+scikit-learn
+ta