#!/usr/bin/env python3
"""
ML SIGNAL PREDICTOR INTEGRATION
Einfache Integration des ML Predictors in den Trading Bot

VERWENDUNG IM NOTEBOOK:
    from ml_integration import (
        ml_predictor,
        check_ml_signal,
        train_ml_model,
        get_ml_status,
        enable_ml_predictor,
        disable_ml_predictor,
        ML_CONFIG
    )

    # Status anzeigen
    print(get_ml_status())

    # Model trainieren (falls genug Daten)
    train_ml_model()

    # In enhanced_trading_check_wrapper:
    ml_result = check_ml_signal(signal_info)
    if not ml_result['should_trade']:
        print(f"TRADE BLOCKIERT durch ML Predictor!")
        return None
"""

import logging
from typing import Dict

logger = logging.getLogger(__name__)

# ==========================================
# IMPORT ML PREDICTOR
# ==========================================

try:
    from ml_signal_predictor import (
        MLSignalPredictor,
        ML_CONFIG,
        get_ml_predictor,
        check_ml_signal,
        train_ml_model,
        get_ml_status,
        enable_ml_predictor,
        disable_ml_predictor
    )

    # Initialize global predictor
    ml_predictor = get_ml_predictor()

    ML_AVAILABLE = True
    print("ML Signal Predictor loaded successfully")

except ImportError as e:
    logger.warning(f"ML Signal Predictor not available: {e}")
    ML_AVAILABLE = False

    # Fallback functions
    ML_CONFIG = {'enabled': False}
    ml_predictor = None

    def check_ml_signal(signal_info: Dict) -> Dict:
        return {
            'win_probability': 0.5,
            'should_trade': True,
            'lot_multiplier': 1.0,
            'action': 'ALLOW',
            'reason': 'ML not available'
        }

    def train_ml_model(force: bool = False) -> Dict:
        return {'error': 'ML not available'}

    def get_ml_status() -> str:
        return "ML Signal Predictor: NOT AVAILABLE (install xgboost)"

    def enable_ml_predictor():
        print("ML Signal Predictor not available")

    def disable_ml_predictor():
        print("ML Signal Predictor not available")


# ==========================================
# INTEGRATION HELPER
# ==========================================

def check_ml_and_get_multiplier(signal_info: Dict, debug: bool = True) -> tuple:
    """
    Prüft ML Signal und gibt (should_trade, lot_multiplier, result) zurück

    Für einfache Integration in enhanced_trading_check_wrapper

    Returns:
        (should_trade: bool, lot_multiplier: float, ml_result: Dict)
    """
    if not ML_AVAILABLE or not ML_CONFIG.get('enabled', False):
        return True, 1.0, {'action': 'SKIP', 'reason': 'ML disabled'}

    ml_result = check_ml_signal(signal_info)

    if debug:
        action_emoji = {
            'ALLOW': '',
            'CAUTION': '',
            'REDUCE': '',
            'BLOCK': ''
        }.get(ml_result['action'], '')

        print(f"\n{action_emoji} ML SIGNAL CHECK:")
        print("-" * 50)
        print(f"   Win Probability: {ml_result['win_probability']:.1%}")
        print(f"   Action: {ml_result['action']}")
        print(f"   Lot Multiplier: {ml_result['lot_multiplier']:.0%}")
        print(f"   Reason: {ml_result['reason']}")
        print("-" * 50)

    return ml_result['should_trade'], ml_result['lot_multiplier'], ml_result


# ==========================================
# AUTO-TRAINING CHECK
# ==========================================

def auto_train_if_needed():
    """
    Prüft ob genug neue Trades für Retraining vorhanden sind
    und trainiert automatisch falls nötig
    """
    if not ML_AVAILABLE or ml_predictor is None:
        return

    if ml_predictor.should_retrain():
        print("\n Auto-Retraining triggered...")
        train_ml_model()


# ==========================================
# QUICK STATUS
# ==========================================

def ml_quick_status() -> str:
    """Kurzer Status für Startup-Output"""
    if not ML_AVAILABLE:
        return " ML Predictor: NOT INSTALLED (pip install xgboost)"

    if not ML_CONFIG.get('enabled', False):
        return " ML Predictor: DISABLED"

    if ml_predictor and ml_predictor.is_trained:
        stats = ml_predictor.training_stats
        auc = stats.get('cv_auc_mean', 0)
        trades = stats.get('total_trades', 0)
        return f" ML Predictor: ACTIVE (AUC: {auc:.2f}, trained on {trades} trades)"
    else:
        return " ML Predictor: NOT TRAINED (run train_ml_model())"


# ==========================================
# STANDALONE TEST
# ==========================================

if __name__ == "__main__":
    print(ml_quick_status())
    print()
    print(get_ml_status())