"""
Quantum-Enhanced Hybrid Trading Strategy
======================================

CRITICAL REQUIREMENTS:
- Maintain 85% win rate target
- Maximum drawdown 10%
- Initial capital £10
- Target £1000 in 7 days
- All trades must pass quantum validation

VALIDATION RULES:
1. Forward/backward pattern testing required
2. Minimum 0.85 confidence threshold
3. Regime alignment verification
4. Volume profile validation

Author: GitHub Copilot
Last Updated: 2025-03-12
"""

import logging
import sys
from pathlib import Path
from datetime import datetime
from functools import reduce
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import DecimalParameter, IntParameter

# Dependency validation
try:
    from dependency_manager import ensure_dependencies
    if not ensure_dependencies(components=["quantitative", "ai", "quantum"]):
        logger.error("CRITICAL: Required dependencies missing")
        sys.exit(1)
except ImportError:
    logger.error("CRITICAL: Could not validate dependencies")
    sys.exit(1)

from quantum_optimizer import QuantumOptimizer
from ai_model_manager import AIModelManager

logger = logging.getLogger(__name__)

class QuantumHybridStrategy(IStrategy):
    """
    Quantum-enhanced hybrid trading strategy for FreqTrade
    
    CRITICAL METRICS:
    - Pattern confidence: >0.85
    - Win rate target: 85%
    - Position size: 1-2% (£0.10-£0.20)
    - Stop loss: -3% maximum
    """
    
    INTERFACE_VERSION = 3

    # STRICT: ROI Configuration
    minimal_roi = {
        "0": 0.06,  # CRITICAL: Take profit at 6% (needed for £10 to £1000)
        "10": 0.04,  # After 10 minutes, take profit at 4%
        "20": 0.02,  # After 20 minutes, take profit at 2%
        "30": 0.01   # After 30 minutes, take profit at 1%
    }

    # STRICT: Risk Management Settings
    stoploss = -0.03  # LOCKED: 3% maximum stop loss
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    # Timeframe Configuration
    timeframe = '5m'  # CRITICAL: Fast reaction time needed
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    startup_candle_count = 30

    def __init__(self, config: Dict[str, Any]) -> None:
        """Initialize strategy with quantum and AI components"""
        super().__init__(config)
        
        # Initialize quantum optimizer
        self.quantum_optimizer = QuantumOptimizer(
            n_qubits=4,  # STRICT: Minimum qubits for pattern validation
            shots=1000,  # STRICT: Minimum shots for confidence
            use_gpu=True  # REQUIRED: GPU acceleration
        )
        
        # Initialize AI components
        self.ai_manager = AIModelManager(config)
        
        # Validation tracking
        self.quantum_validated_patterns = {}
        self.min_validation_confidence = 0.85  # LOCKED: 85% minimum confidence
        self.validation_window = 12  # Required window for pattern validation
        
        # TODO: Implement adaptive validation window based on volatility
        # TODO: Add correlation analysis between validated patterns
        # TODO: Develop pattern catalog for reuse of validated setups
        
        logger.info("Quantum Hybrid Strategy initialized with strict constraints")
        
    def validate_pattern_quantum_loop(self, pattern_data: np.ndarray) -> Dict[str, Any]:
        """
        Quantum loop validation of trading patterns
        
        VALIDATION REQUIREMENTS:
        1. Forward pass analysis
        2. Backward pass verification
        3. Confidence alignment check
        4. Regime alignment verification
        
        Returns: Validation metrics dictionary
        """
        # Forward analysis
        forward_results = self.quantum_optimizer.analyze_pattern(pattern_data)
        
        # Backward analysis for robustness
        backward_data = np.flip(pattern_data.copy(), axis=0)
        backward_results = self.quantum_optimizer.analyze_pattern(backward_data)
        
        # STRICT: Validation checks
        confidence_alignment = 1 - abs(forward_results['confidence'] - backward_results['confidence'])
        regime_alignment = (forward_results['regime'] * -backward_results['regime']) > 0
        
        # TODO: Add quantum decoherence analysis
        # TODO: Implement entropy-based pattern stability check
        # TODO: Add market regime transition detection
        # TODO: Incorporate volume profile in validation
        
        return {
            'pattern_validated': confidence_alignment > 0.8 and regime_alignment,
            'confidence': min(forward_results['confidence'], backward_results['confidence']),
            'regime': forward_results['regime'],
            'forward_score': forward_results['pattern_score'],
            'backward_score': backward_results['pattern_score'],
            'alignment_score': confidence_alignment
        }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Generate and validate trading indicators
        
        REQUIREMENTS:
        - Fast EMA signals for 5m timeframe
        - Volume validation
        - Growth momentum tracking
        - Quantum pattern validation
        """
        # Fast response indicators
        dataframe['ema_8'] = dataframe['close'].ewm(span=8, adjust=False).mean()
        dataframe['ema_13'] = dataframe['close'].ewm(span=13, adjust=False).mean()
        dataframe['ema_21'] = dataframe['close'].ewm(span=21, adjust=False).mean()
        
        # Volume analysis
        dataframe['volume_mean'] = dataframe['volume'].rolling(window=5).mean()
        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean']
        
        # Custom momentum
        dataframe['growth_momentum'] = (
            (dataframe['close'] - dataframe['open']) / dataframe['open'] * 100 * 
            dataframe['volume_ratio']
        )
        
        # CRITICAL: Quantum pattern validation
        pattern_data = dataframe[['open', 'high', 'low', 'close', 'volume']].values
        validation_results = []
        
        # Sliding window validation
        for i in range(len(dataframe) - self.validation_window + 1):
            window_data = pattern_data[i:i+self.validation_window]
            validation = self.validate_pattern_quantum_loop(window_data)
            validation_results.append(validation)
            
            if validation['pattern_validated']:
                pattern_key = f"{metadata['pair']}_{i}"
                self.quantum_validated_patterns[pattern_key] = validation
        
        # Add validation metrics
        dataframe['quantum_score'] = [res['forward_score'] for res in validation_results]
        dataframe['quantum_confidence'] = [res['confidence'] for res in validation_results]
        dataframe['quantum_regime'] = [res['regime'] for res in validation_results]
        dataframe['pattern_validated'] = [res['pattern_validated'] for res in validation_results]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Generate entry signals with strict validation
        
        ENTRY REQUIREMENTS:
        1. Strong upward momentum
        2. Quantum-validated pattern
        3. High confidence score
        4. Volume confirmation
        """
        conditions = []
        
        # STRICT: Momentum check
        momentum_up = (
            (dataframe['growth_momentum'] > 1.0) &
            (dataframe['volume'] > dataframe['volume_mean'] * 1.5)
        )
        
        # CRITICAL: Quantum validation
        quantum_entry = (
            (dataframe['quantum_score'] > 0.65) &
            (dataframe['quantum_confidence'] > 0.7) &
            (dataframe['quantum_regime'] > 0) &
            (dataframe['pattern_validated'])
        )
        
        # Technical confirmation
        ema_fast_cross = (
            (dataframe['ema_8'] > dataframe['ema_13']) &
            (dataframe['ema_13'] > dataframe['ema_21'])
        )
        
        # Risk check
        risk_check = (dataframe['close'] > dataframe['ema_21'])
        
        # Combine all conditions
        conditions.append(momentum_up)
        conditions.append(quantum_entry)
        conditions.append(ema_fast_cross)
        conditions.append(risk_check)
        
        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'enter_long'
        ] = 1
        
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Generate exit signals with strict risk management
        
        EXIT TRIGGERS:
        1. Pattern breakdown
        2. Momentum reversal
        3. Technical breakdown
        4. Risk threshold breach
        """
        conditions = []
        
        # Pattern breakdown
        momentum_down = (
            (dataframe['growth_momentum'] < -1.0) |
            (dataframe['quantum_score'] < 0.4)
        )
        
        # Technical breakdown
        ema_cross_down = (
            (dataframe['ema_8'] < dataframe['ema_13']) &
            (dataframe['volume'] > dataframe['volume_mean'])
        )
        
        # CRITICAL: Quantum pattern failure
        quantum_exit = (
            (dataframe['quantum_confidence'] > 0.7) &
            (dataframe['quantum_regime'] < 0)
        )
        
        conditions.append(momentum_down)
        conditions.append(ema_cross_down)
        conditions.append(quantum_exit)
        
        dataframe.loc[
            reduce(lambda x, y: x | y, conditions),
            'exit_long'
        ] = 1
        
        return dataframe

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                          proposed_stake: float, min_stake: float, max_stake: float,
                          leverage: float, entry_tag: str, side: str,
                          **kwargs) -> float:
        """
        Dynamic stake sizing based on quantum validation
        
        STRICT RULES:
        - Never exceed 2% account risk
        - Scale by pattern confidence
        - Require quantum validation
        """
        pattern_data = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if pattern_data[0] is not None:
            dataframe = pattern_data[0]
            if not dataframe.empty:
                last_row = dataframe.iloc[-1]
                confidence = last_row['quantum_confidence']
                validated = last_row['pattern_validated']
                
                # STRICT: Scale position by validation
                stake_scale = min(1.0, 0.5 + confidence) * (1.2 if validated else 0.8)
                return max(min_stake, proposed_stake * stake_scale)
        
        return min_stake  # Default to minimum if no validation