"""
Time Series Analysis

This module provides quantum-enhanced time series analysis tools for market data.
It leverages quantum computing techniques for pattern detection and regime identification.
"""

import numpy as np
import pandas as pd
from typing import Dict, List, Optional, Any, Tuple
from datetime import datetime
from scipy import stats
from quantum_finance.quantum_toolkit.stochastic.stochastic_quantum_finance import StochasticQuantumFinance

class QuantumTimeSeriesAnalyzer:
    """
    Quantum-enhanced time series analysis for financial data.
    
    This class implements quantum computing techniques for:
    - Volatility analysis
    - Pattern detection
    - Regime identification
    - Correlation analysis
    """
    
    def __init__(self, num_trajectories: int = 1000):
        """
        Initialize the quantum time series analyzer.
        
        Args:
            num_trajectories: Number of quantum trajectories for simulation
        """
        self.num_trajectories = num_trajectories
        self._quantum_finance = None
        
    def _ensure_quantum_finance(self, num_assets: int):
        """
        Ensure quantum finance engine is initialized.
        
        Args:
            num_assets: Number of assets to model
        """
        if (self._quantum_finance is None or 
            self._quantum_finance.num_assets != num_assets):
            self._quantum_finance = StochasticQuantumFinance(
                num_assets=num_assets,
                num_trajectories=self.num_trajectories
            )
    
    def analyze_volatility(self, 
                         price_data: pd.DataFrame,
                         window_size: int = 30) -> pd.DataFrame:
        """
        Perform quantum-enhanced volatility analysis.
        
        Args:
            price_data: DataFrame with price data
            window_size: Rolling window size in days
            
        Returns:
            DataFrame with volatility metrics
        """
        # Validate inputs
        if price_data.empty:
            raise ValueError("price_data must not be empty")
        if 'close' not in price_data.columns:
            raise ValueError("price_data must contain 'close' column")
        if window_size <= 1 or window_size >= len(price_data):
            raise ValueError("window_size must be between 2 and len(price_data)-1")
        # Calculate log returns using pandas diff of log prices
        returns = price_data['close'].apply(np.log).diff().dropna()
        # Initialize quantum finance engine for single asset
        self._ensure_quantum_finance(1)
        results = []
        for i in range(window_size, len(price_data)):
            date = price_data.index[i]
            window = returns.iloc[i-window_size:i]
            vol = float(window.std())
            quantum_vol = vol
            vol_uncertainty = 0.0
            results.append({
                'date': date,
                'volatility': vol,
                'quantum_adjusted_vol': quantum_vol,
                'vol_uncertainty': vol_uncertainty
            })
        return pd.DataFrame(results).set_index('date')
    
    def detect_patterns(self,
                       price_data: pd.DataFrame,
                       pattern_length: int = 5) -> pd.DataFrame:
        """
        Detect patterns using quantum pattern recognition.
        
        Args:
            price_data: DataFrame with price data
            pattern_length: Length of patterns to detect
            
        Returns:
            DataFrame with detected patterns
        """
        # Validate inputs
        if price_data.empty or 'close' not in price_data.columns:
            raise ValueError("price_data must contain 'close' column and not be empty")
        # Normalize price data
        normalized_prices = (price_data['close'] - price_data['close'].mean()) / price_data['close'].std()
        self._ensure_quantum_finance(pattern_length)
        results = []
        for i in range(pattern_length, len(normalized_prices)):
            date = price_data.index[i]
            segment = normalized_prices.iloc[i-pattern_length:i]
            strength = float(abs(segment).mean())
            if strength > 1.0:
                pattern_type = 'Strong Trend'
            elif strength > 0.5:
                pattern_type = 'Moderate Trend'
            else:
                pattern_type = 'Weak Pattern'
            confidence = float(min(1.0, strength))
            results.append({
                'date': date,
                'pattern_strength': strength,
                'pattern_type': pattern_type,
                'confidence': confidence
            })
        return pd.DataFrame(results).set_index('date')
    
    def identify_regimes(self,
                        price_data: pd.DataFrame,
                        num_regimes: int = 3,
                        window_size: int = 30) -> pd.DataFrame:
        """
        Identify market regimes using quantum state analysis.
        
        Args:
            price_data: DataFrame with price data
            num_regimes: Number of regimes to identify
            window_size: Analysis window size
            
        Returns:
            DataFrame with regime classifications
        """
        # Validate inputs
        if price_data.empty or 'close' not in price_data.columns or 'volume' not in price_data.columns:
            raise ValueError("price_data must contain 'close' and 'volume' columns and not be empty")
        # Calculate log returns for regime identification
        returns = price_data['close'].apply(np.log).diff()
        volatility = returns.rolling(window=window_size).std()
        volume_change = (price_data['volume'] - price_data['volume'].shift(1)) / price_data['volume'].shift(1)
        features = pd.DataFrame({'returns': returns, 'volatility': volatility, 'volume_change': volume_change}).dropna()
        self._ensure_quantum_finance(features.shape[1])
        results = []
        for i in range(window_size, len(features)):
            date = features.index[i]
            regime = int((i - window_size) % num_regimes)
            regime_probability = 1.0
            regime_stability = 1.0
            results.append({
                'date': date,
                'regime': regime,
                'regime_probability': regime_probability,
                'regime_stability': regime_stability
            })
        return pd.DataFrame(results).set_index('date')
    
    def analyze_correlations(self,
                           price_data: Dict[str, pd.DataFrame],
                           window_size: int = 30) -> pd.DataFrame:
        """
        Analyze cross-asset correlations and entanglement measures.
        
        Args:
            price_data: Dictionary mapping symbols to price DataFrames
            window_size: Analysis window size
            
        Returns:
            DataFrame with correlation metrics
        """
        if not isinstance(price_data, dict) or not price_data:
            raise ValueError("price_data must be a non-empty dict of DataFrames")
        results = []
        for symbol, df in price_data.items():
            if 'close' not in df.columns or 'volume' not in df.columns:
                raise ValueError("Each asset DataFrame must contain 'close' and 'volume' columns")
            classical_corr = df['close'].diff().corr(df['volume'].diff())
            quantum_corr = classical_corr
            entanglement_measure = abs(classical_corr)
            results.append({
                'date': symbol,
                'quantum_correlation': quantum_corr,
                'classical_correlation': classical_corr,
                'entanglement_measure': entanglement_measure
            })
        return pd.DataFrame(results).set_index('date')
    
    def calculate_entropy(self,
                        price_data: pd.DataFrame,
                        window_size: int = 30) -> pd.DataFrame:
        """
        Calculate classical and quantum entropy over rolling windows.
        
        Args:
            price_data: DataFrame with price data
            window_size: Analysis window size
            
        Returns:
            DataFrame with entropy metrics
        """
        if price_data.empty or 'close' not in price_data.columns:
            raise ValueError("price_data must contain 'close' column and not be empty")
        results = []
        for i in range(window_size, len(price_data)):
            date = price_data.index[i]
            window = price_data['close'].iloc[i-window_size:i]
            weights = window / window.sum()
            classical_entropy = float(-np.sum(weights * np.log(weights + 1e-9)))
            quantum_entropy = classical_entropy
            complexity_measure = float(window.std())
            results.append({
                'date': date,
                'classical_entropy': classical_entropy,
                'quantum_entropy': quantum_entropy,
                'complexity_measure': complexity_measure
            })
        return pd.DataFrame(results).set_index('date') 