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app.py

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+"""
+XAUUSD Trading Model Training
+Simple version - generates synthetic data for testing
+"""
+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import RobustScaler
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.metrics import accuracy_score, classification_report
+import warnings
+warnings.filterwarnings('ignore')
+
+def generate_data(n_samples=100000):
+    """Generate synthetic XAUUSD-like data"""
+    np.random.seed(42)
+    
+    # Price-like data
+    prices = 1800 + np.cumsum(np.random.randn(n_samples) * 10)
+    
+    data = pd.DataFrame({
+        'close': prices,
+        'open': prices + np.random.randn(n_samples) * 5,
+        'high': prices + np.abs(np.random.randn(n_samples) * 8),
+        'low': prices - np.abs(np.random.randn(n_samples) * 8),
+        'volume': np.random.randint(1000, 100000, n_samples)
+    })
+    return data
+
+def create_features(df):
+    """Create technical indicators"""
+    features = pd.DataFrame()
+    features['close'] = df['close']
+    features['open'] = df['open']
+    features['high'] = df['high']
+    features['low'] = df['low']
+    features['volume'] = df['volume']
+    
+    # Returns
+    features['returns'] = df['close'].pct_change()
+    
+    # Simple Moving Averages
+    features['sma20'] = df['close'].rolling(20).mean()
+    features['sma50'] = df['close'].rolling(50).mean()
+    
+    # MACD
+    ema12 = df['close'].ewm(span=12).mean()
+    ema26 = df['close'].ewm(span=26).mean()
+    macd = ema12 - ema26
+    features['macd'] = macd
+    features['macd_signal'] = macd.ewm(span=9).mean()
+    features['histogram'] = features['macd'] - features['macd_signal']
+    
+    # RSI
+    delta = df['close'].diff()
+    gain = delta.where(delta > 0, 0).rolling(14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
+    rs = gain / loss
+    features['rsi'] = 100 - (100 / (1 + rs))
+    
+    # Bollinger Bands
+    sma20 = df['close'].rolling(20).mean()
+    std20 = df['close'].rolling(20).std()
+    features['bb_upper'] = sma20 + (std20 * 2)
+    features['bb_lower'] = sma20 - (std20 * 2)
+    
+    # Future returns for labels
+    future_returns = df['close'].shift(-5) / df['close'] - 1
+    labels = pd.Series(0, index=df.index)
+    labels[future_returns > 0.005] = 1   # BUY
+    labels[future_returns < -0.005] = -1  # SELL
+    # HOLD = 0
+    
+    features['label'] = labels
+    return features.dropna()
+
+def train_model(n_estimators, max_depth, learning_rate):
+    import time
+    start = time.time()
+    
+    yield "📊 Loading data..."
+    df = generate_data(100000)
+    
+    yield "🔧 Creating features..."
+    data = create_features(df)
+    
+    yield "⚙️ Preparing..."
+    X = data.drop('label', axis=1).values
+    y = data['label'].values
+    
+    split_idx = int(len(X) * 0.8)
+    X_train, X_test = X[:split_idx], X[split_idx:]
+    y_train, y_test = y[:split_idx], y[split_idx:]
+    
+    scaler = RobustScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    
+    yield f"🚀 Training (n={n_estimators}, depth={max_depth})..."
+    model = GradientBoostingClassifier(
+        n_estimators=int(n_estimators),
+        max_depth=int(max_depth),
+        learning_rate=learning_rate,
+        subsample=0.8,
+        random_state=42
+    )
+    model.fit(X_train_scaled, y_train)
+    
+    yield "📊 Evaluating..."
+    y_pred = model.predict(X_test_scaled)
+    accuracy = accuracy_score(y_test, y_pred)
+    
+    elapsed = time.time() - start
+    
+    yield {
+        "accuracy": f"{accuracy:.2%}",
+        "time": f"{elapsed:.1f}s",
+        "train_samples": f"{len(X_train):,}",
+        "test_samples": f"{len(X_test):,}",
+        "report": classification_report(y_test, y_pred, target_names=['SELL', 'HOLD', 'BUY'])
+    }
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 XAUUSD Trading Model Training")
+    gr.Markdown("**TESTING VERSION** - Uses synthetic data")
+    
+    with gr.Row():
+        with gr.Column():
+            n_estimators = gr.Slider(50, 200, value=100, step=10, label="n_estimators")
+            max_depth = gr.Slider(3, 8, value=5, step=1, label="max_depth")
+            learning_rate = gr.Slider(0.01, 0.3, value=0.1, step=0.01, label="learning_rate")
+            train_btn = gr.Button("🚀 Train Model", variant="primary")
+        
+        with gr.Column():
+            output = gr.JSON(label="Results")
+            report = gr.Textbox(label="Report", lines=8)
+    
+    train_btn.click(
+        fn=train_model,
+        inputs=[n_estimators, max_depth, learning_rate],
+        outputs=[output, report]
+    )
+    
+    gr.Markdown("""
+    ## Test Mode
+    - Synthetic 100k samples
+    - 14 features (price + MACD + RSI + BB)
+    - No external dependencies needed
+    """)
+
+demo.launch()