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Update app.py
Browse files
app.py
CHANGED
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@@ -1,8 +1,5 @@
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import os
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import time
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import requests
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import numpy as np
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import pandas as pd
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import gradio as gr
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import xgboost as xgb
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from groq import Groq
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@@ -16,191 +13,44 @@ if api_key:
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client = Groq(api_key=api_key)
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# ==========================================
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#
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# ==========================================
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"
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OKX_BASE = "https://www.okx.com/api/v5"
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BINANCE_BASE = "https://api.binance.com/api/v3"
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# ==========================================
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# 📡 3. VERİ ÇEKME FONKSİYONLARI (Retry + Fallback)
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# ==========================================
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def fetch_okx_ticker(inst_id="BTC-USDT"):
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"""OKX'ten anlık fiyat çeker. 3 kez dener."""
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for attempt in range(3):
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try:
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r = requests.get(f"{OKX_BASE}/market/ticker",
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params={"instId": inst_id},
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headers=HEADERS, timeout=15)
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r.raise_for_status()
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d = r.json()["data"][0]
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return {
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"source": "OKX",
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"last": float(d["last"]),
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"open24h": float(d["open24h"]),
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"high24h": float(d["high24h"]),
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"low24h": float(d["low24h"]),
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"vol24h": float(d["vol24h"]),
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"change_pct": (float(d["last"]) / float(d["open24h"]) - 1) * 100
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}
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except Exception as e:
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print(f"OKX ticker attempt {attempt+1} failed: {e}")
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if attempt < 2:
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time.sleep(2)
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return None
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def fetch_binance_ticker(symbol="BTCUSDT"):
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"""OKX başarısız olursa Binance'ten fiyat çeker (yedek)."""
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try:
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r = requests.get(f"{BINANCE_BASE}/ticker/24hr",
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params={"symbol": symbol},
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headers=HEADERS, timeout=15)
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r.raise_for_status()
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d = r.json()
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return {
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"source": "Binance",
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"last": float(d["lastPrice"]),
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"open24h": float(d["openPrice"]),
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"high24h": float(d["highPrice"]),
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"low24h": float(d["lowPrice"]),
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"vol24h": float(d["volume"]),
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"change_pct": float(d["priceChangePercent"])
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}
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except Exception as e:
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print(f"Binance ticker failed: {e}")
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return None
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def fetch_okx_candles(inst_id="BTC-USDT", bar="1H", limit=100):
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"""OKX'ten mum verisi çeker."""
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for attempt in range(3):
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try:
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r = requests.get(f"{OKX_BASE}/market/candles",
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params={"instId": inst_id, "bar": bar, "limit": limit},
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headers=HEADERS, timeout=15)
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r.raise_for_status()
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raw = r.json()["data"]
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df = pd.DataFrame(raw, columns=["ts", "open", "high", "low", "close", "vol"])
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for col in ["open", "high", "low", "close", "vol"]:
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df[col] = df[col].astype(float)
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df["ts"] = pd.to_datetime(df["ts"].astype(int), unit="ms")
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df = df.sort_values("ts").reset_index(drop=True)
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return df
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except Exception as e:
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print(f"OKX candles attempt {attempt+1} failed: {e}")
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if attempt < 2:
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time.sleep(2)
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return None
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def fetch_binance_candles(symbol="BTCUSDT", interval="1h", limit=100):
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"""OKX başarısız olursa Binance'ten mum verisi çeker."""
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try:
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r = requests.get(f"{BINANCE_BASE}/klines",
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params={"symbol": symbol, "interval": interval, "limit": limit},
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headers=HEADERS, timeout=15)
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r.raise_for_status()
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raw = r.json()
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df = pd.DataFrame(raw, columns=[
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"ts", "open", "high", "low", "close", "vol",
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"close_time", "quote_vol", "trades", "buy_base", "buy_quote", "ignore"
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])
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for col in ["open", "high", "low", "close", "vol"]:
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df[col] = df[col].astype(float)
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df["ts"] = pd.to_datetime(df["ts"].astype(int), unit="ms")
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return df[["ts", "open", "high", "low", "close", "vol"]]
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except Exception as e:
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print(f"Binance candles failed: {e}")
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return None
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def fetch_market_data():
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"""Önce OKX'i dener, başarısız olursa Binance'e geçer."""
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ticker = fetch_okx_ticker()
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candles = fetch_okx_candles()
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if ticker is not None and candles is not None:
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return ticker, candles
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print("OKX failed, switching to Binance fallback...")
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ticker = fetch_binance_ticker()
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candles = fetch_binance_candles()
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return ticker, candles
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def compute_rsi(series, period=14):
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delta = series.diff()
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gain = delta.clip(lower=0).rolling(period).mean()
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loss = -delta.clip(upper=0).rolling(period).mean()
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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# ==========================================
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# 🤖 4. XGBOOST MODEL EĞİTİMİ
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# ==========================================
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def train_model():
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"""Çekilen mum verisiyle XGBoost modelini eğitir."""
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df = fetch_okx_candles(limit=500)
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if df is None:
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df = fetch_binance_candles(limit=500)
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if df is None or len(df) < 50:
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return None
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df["ma_5"] = df["close"].rolling(5).mean()
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df["ma_20"] = df["close"].rolling(20).mean()
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df["rsi"] = compute_rsi(df["close"], 14)
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df["macd"] = df["close"].ewm(span=12).mean() - df["close"].ewm(span=26).mean()
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df["target"] = (df["close"].shift(-1) > df["close"]).astype(int)
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df = df.dropna().reset_index(drop=True)
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features = ["ma_5", "ma_20", "rsi", "macd", "vol"]
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model = xgb.XGBClassifier(
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n_estimators=50, max_depth=3, learning_rate=0.1,
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objective="binary:logistic", eval_metric="logloss", random_state=42
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)
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model.fit(
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return model
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# Uygulama başlarken modeli eğit
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xgb_model = train_model()
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if xgb_model is None:
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# Fallback: API hiç çalışmazsa dummy model
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xgb_model = xgb.XGBClassifier(n_estimators=10, max_depth=2)
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xgb_model.fit(np.random.rand(20, 5), np.random.randint(0, 2, 20))
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# ==========================================
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# 🧠
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# ==========================================
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def predict_and_analyze():
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if ticker is None or candles is None:
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return "❌ Hem OKX hem de Binance API'sine erişilemedi. Lütfen daha sonra tekrar deneyin.", {}
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last_price = ticker["last"]
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change_pct = ticker["change_pct"]
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vol24h = ticker["vol24h"]
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source = ticker["source"]
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close = candles["close"]
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ma5 = close.rolling(5).mean().iloc[-1]
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ma20 = close.rolling(20).mean().iloc[-1]
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rsi = compute_rsi(close, 14).iloc[-1]
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macd = (close.ewm(span=12).mean() - close.ewm(span=26).mean()).iloc[-1]
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# XGBoost tahmini
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features = np.array([[ma5, ma20, rsi, macd,
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prob_up = float(xgb_model.predict_proba(features)[0][1])
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prob_down = 1 - prob_up
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direction = "YUKARI 📈" if prob_up > 0.5 else "AŞAĞI 📉"
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confidence = max(prob_up, prob_down) * 100
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prompt = f"""
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Sen uzman bir kripto para analisti ve teknik analiz uzmanısın.
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- Son Fiyat: ${last_price:,.2f}
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- 24s Değişim: %{change_pct:+.2f}
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- 24s Hacim: {vol24h:,.0f} BTC
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- 24s Yüksek/Düşük: ${ticker['high24h']:,.2f} / ${ticker['low24h']:,.2f}
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📊 Teknik Göstergeler (1H):
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- RSI (14): {rsi:.2f}
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- MA5: ${ma5:,.2f} | MA20: ${ma20:,.2f}
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- MACD: {macd:.4f}
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🤖 XGBoost Model Sonucu:
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- Yükseliş Olasılığı: %{prob_up*100:.1f}
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Türkçe bir teknik analiz raporu hazırla. Şu başlıklar bulunsun:
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1. 🎯 Model Yorumu (XGBoost + teknik göstergelerin mantığı)
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2. 📈 Teknik Analiz (RSI, MA, MACD; destek/direnç)
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3. 💡 İşlem Stratejisi (Long/Short, stop-loss, take-profit, risk yönetimi)
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Yatırım tavsiyesi olmadığını vurgula. Rasyonel ve profesyonel ol.
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analysis_result = "GROQ_API_KEY bulunamadı! Hugging Face Secrets'a ekleyin."
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header = (
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f"**
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f"RSI: {rsi:.1f} | MA5: ${ma5:,.0f} | MA20: ${ma20:,.0f}\n\n---\n\n"
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)
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return header + analysis_result, prob_dict
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# ==========================================
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# 🎨
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# ==========================================
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo:
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gr.Markdown(
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"""
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# ₿ BTC/USD XGBoost & Groq AI Tahmin Radarı (
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-
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"""
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)
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with gr.Row():
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with gr.Column():
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-
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gr.
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with gr.Column():
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-
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-
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fn=predict_and_analyze,
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inputs=[],
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outputs=[analysis_output, prob_output]
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)
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gr.Markdown(
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"""
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---
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⚠️ **Yatırım Tavsiyesi Değildir**: Bu uygulama eğitim amaçlıdır.
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"""
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)
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import os
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import numpy as np
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import gradio as gr
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import xgboost as xgb
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from groq import Groq
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client = Groq(api_key=api_key)
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# ==========================================
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# 🤖 2. XGBOOST MODEL EĞİTİMİ (Sanal Veri)
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# ==========================================
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def train_dummy_model():
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"""API olmadan çalışabilmesi için sanal veriyle hızlı eğitim."""
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np.random.seed(42)
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n = 300
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# Sanal BTC verisi (gerçekçi aralıkta)
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ma5 = np.random.uniform(55000, 75000, n)
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ma20 = ma5 + np.random.normal(0, 500, n)
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rsi = np.random.uniform(20, 80, n)
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macd = np.random.normal(0, 200, n)
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vol = np.random.uniform(100, 1000, n)
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X = np.column_stack([ma5, ma20, rsi, macd, vol])
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# Hedef: MA5 > MA20 ve RSI < 70 ise yukarı eğilim (sanal mantık)
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y = ((ma5 > ma20) & (rsi < 70)).astype(int)
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model = xgb.XGBClassifier(
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n_estimators=50, max_depth=3, learning_rate=0.1,
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objective="binary:logistic", eval_metric="logloss", random_state=42
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)
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model.fit(X, y)
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return model
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xgb_model = train_dummy_model()
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# ==========================================
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+
# 🧠 3. ANALİZ MOTORU (Manuel Giriş)
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# ==========================================
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def predict_and_analyze(last_price, rsi, ma5, ma20, macd, vol):
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if not last_price or last_price <= 0:
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return "Lütfen geçerli bir BTC/USD fiyatı giriniz.", {}
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# XGBoost tahmini
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features = np.array([[ma5, ma20, rsi, macd, vol]])
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prob_up = float(xgb_model.predict_proba(features)[0][1])
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prob_down = 1 - prob_up
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+
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direction = "YUKARI 📈" if prob_up > 0.5 else "AŞAĞI 📉"
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confidence = max(prob_up, prob_down) * 100
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prompt = f"""
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Sen uzman bir kripto para analisti ve teknik analiz uzmanısın.
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📊 BTC/USD Piyasa Verileri (Manuel Giriş):
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- Son Fiyat: ${last_price:,.2f}
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- RSI (14): {rsi:.2f}
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- MA5: ${ma5:,.2f} | MA20: ${ma20:,.2f}
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- MACD: {macd:.4f}
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- Hacim: {vol:,.2f}
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🤖 XGBoost Model Sonucu:
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- Yükseliş Olasılığı: %{prob_up*100:.1f}
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Türkçe bir teknik analiz raporu hazırla. Şu başlıklar bulunsun:
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1. 🎯 Model Yorumu (XGBoost + teknik göstergelerin mantığı)
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2. 📈 Teknik Analiz (RSI, MA, MACD; destek/direnç seviyeleri)
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3. 💡 İşlem Stratejisi (Long/Short, stop-loss, take-profit, risk yönetimi)
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Yatırım tavsiyesi olmadığını vurgula. Rasyonel ve profesyonel ol.
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analysis_result = "GROQ_API_KEY bulunamadı! Hugging Face Secrets'a ekleyin."
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header = (
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f"**Manuel Veri** | **Fiyat: ${last_price:,.2f}** | "
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f"RSI: {rsi:.1f} | MA5: ${ma5:,.0f} | MA20: ${ma20:,.0f}\n\n---\n\n"
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)
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return header + analysis_result, prob_dict
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# ==========================================
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# 🎨 4. GRADIO ARAYÜZÜ
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# ==========================================
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo:
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gr.Markdown(
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"""
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+
# ₿ BTC/USD XGBoost & Groq AI Tahmin Radarı (Manuel Giriş)
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API erişim sorunları nedeniyle verileri **manuel** giriyorsunuz. **XGBoost** yön tahmini yapar, **Groq** analiz raporu üretir.
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"""
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)
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with gr.Row():
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with gr.Column():
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gr.Markdown("### 📊 Piyasa Verileri")
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price_input = gr.Number(label="Güncel BTC/USD Fiyatı ($)", value=65000.0)
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vol_input = gr.Number(label="24s Hacim (BTC)", value=500.0)
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+
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gr.Markdown("### 📈 Teknik Göstergeler")
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rsi_input = gr.Slider(label="RSI (14)", minimum=0, maximum=100, value=55.0, step=0.1)
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with gr.Row():
|
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ma5_input = gr.Number(label="MA5 ($)", value=64800.0)
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ma20_input = gr.Number(label="MA20 ($)", value=64000.0)
|
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macd_input = gr.Number(label="MACD Değeri", value=150.0)
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+
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submit_btn = gr.Button("Analiz Et ve Tahmin Üret", variant="primary")
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with gr.Column():
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gr.Markdown("### 📈 XGBoost Yön Tahmini")
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prob_output = gr.Label(label="Olasılık Dağılımı")
|
| 134 |
+
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gr.Markdown("### 🧠 Groq Yapay Zeka Analiz Raporu")
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analysis_output = gr.Markdown(value="Verileri girdikten sonra butona basarak analizi başlatabilirsiniz.")
|
| 137 |
|
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submit_btn.click(
|
| 139 |
fn=predict_and_analyze,
|
| 140 |
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inputs=[price_input, rsi_input, ma5_input, ma20_input, macd_input, vol_input],
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outputs=[analysis_output, prob_output]
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)
|
| 143 |
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| 144 |
gr.Markdown(
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"""
|
| 146 |
---
|
| 147 |
+
⚠️ **Yatırım Tavsiyesi Değildir**: Bu uygulama eğitim amaçlıdır.
|
| 148 |
"""
|
| 149 |
)
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