#!/usr/bin/env python3
"""
XAUUSD Trading AI V4 Quantum - Usage Example
Quantum Neural Ensemble Prediction
"""

import joblib
import pandas as pd
import numpy as np
import yfinance as yf
from datetime import datetime, timedelta

class QuantumFeatureEngineer:
    """Simplified quantum feature engineer for inference"""

    def __init__(self, scalers, pca, feature_cols):
        self.scalers = scalers
        self.pca = pca
        self.feature_cols = feature_cols

    def process(self, df):
        """Apply quantum feature engineering"""
        # Simplified version - implement full quantum features for production
        df = df.copy()

        # Basic features (implement full QuantumFeatureEngineer for production)
        df['returns'] = df['Close'].pct_change()
        df['rsi_14'] = 50  # Placeholder
        df['macd'] = 0     # Placeholder
        df['hurst_exponent'] = 0.5  # Placeholder
        df['fractal_dimension'] = 1.5  # Placeholder

        # Fill missing features
        for col in self.feature_cols:
            if col not in df.columns:
                df[col] = 0

        # Scale and PCA
        features_scaled = self.scalers['robust'].transform(df[self.feature_cols])
        features_pca = self.pca.transform(features_scaled)

        return np.hstack([features_scaled, features_pca])

def predict_with_v4_quantum(timeframe='daily'):
    """Make quantum predictions using V4 ensemble"""

    # Load quantum ensemble
    ensemble = joblib.load(f'trading_model_v4_quantum_{timeframe}.pkl')

    # Load quantum components
    scalers = joblib.load(f'quantum_scaler_v4_{timeframe}.pkl')
    pca = joblib.load(f'quantum_pca_v4_{timeframe}.pkl')

    with open(f'quantum_features_v4_{timeframe}.json', 'r') as f:
        feature_cols = json.load(f)

    # Initialize quantum feature engineer
    quantum_engineer = QuantumFeatureEngineer(scalers, pca, feature_cols)

    # Fetch latest data
    symbol = 'GC=F'
    end_date = datetime.now()
    start_date = end_date - timedelta(days=200)

    df = yf.download(symbol, start=start_date, end=end_date, progress=False)
    if isinstance(df.columns, pd.MultiIndex):
        df.columns = df.columns.droplevel(1)

    # Apply quantum feature engineering
    quantum_features = quantum_engineer.process(df)

    # Make quantum prediction (latest data point)
    latest_features = quantum_features[-1:].reshape(1, -1)
    prediction, probability = ensemble.predict_ensemble(pd.DataFrame(latest_features))

    direction = "UP 📈" if prediction[0] == 1 else "DOWN 📉"
    quantum_prob = probability[0] if hasattr(probability, '__len__') else probability

    print(f"V4 Quantum Ensemble Prediction for {timeframe}:")
    print(f"Direction: {direction}")
    print(f"Quantum Probability: {quantum_prob:.3f}")
    print(f"Confidence: {'High' if abs(quantum_prob - 0.5) > 0.2 else 'Medium'}")

    return prediction[0], quantum_prob

if __name__ == "__main__":
    predict_with_v4_quantum('daily')