import numpy as np
from sklearn.preprocessing import MinMaxScaler
from typing import List, Dict, Tuple
import pandas as pd

class QuantumInspiredOptimizer:
    def __init__(self, n_qubits=4, iterations=100):
        self.n_qubits = n_qubits
        self.iterations = iterations
        self.scaler = MinMaxScaler()

    def quantum_inspired_encoding(self, data):
        """Convert classical data into quantum-inspired representation"""
        scaled_data = self.scaler.fit_transform(data.reshape(-1, 1))
        phase = 2 * np.pi * scaled_data
        return np.exp(1j * phase)

    def quantum_pattern_detection(self, prices):
        """Detect patterns using quantum-inspired interference"""
        encoded_data = self.quantum_inspired_encoding(prices)
        amplitudes = np.abs(encoded_data)
        phases = np.angle(encoded_data)

        # Pattern detection through interference
        interference_pattern = np.convolve(amplitudes.flatten(), np.exp(1j * phases.flatten()))
        return np.abs(interference_pattern[:len(prices)])

    def quantum_momentum_indicator(self, prices, window=14):
        """Calculate quantum-inspired momentum indicator"""
        encoded_data = self.quantum_inspired_encoding(prices)
        momentum = np.zeros(len(prices))

        for i in range(window, len(prices)):
            quantum_state = encoded_data[i-window:i]
            interference = np.sum(quantum_state * np.conjugate(quantum_state))
            momentum[i] = np.abs(interference)

        return momentum

    def quantum_trend_prediction(self, prices, lookback=5):
        """Predict trend using quantum-inspired algorithm"""
        encoded_data = self.quantum_inspired_encoding(prices)
        predictions = np.zeros(len(prices))

        for i in range(lookback, len(prices)):
            quantum_state = encoded_data[i-lookback:i]
            superposition = np.sum(quantum_state) / np.sqrt(lookback)
            predictions[i] = np.abs(superposition)

        return self.scaler.inverse_transform(predictions.reshape(-1, 1)).flatten()

    def optimize_portfolio(self, returns: pd.DataFrame, risk_tolerance: float = 0.5) -> Dict[str, float]:
        """Quantum-inspired portfolio optimization"""
        n_assets = returns.shape[1]

        # Initialize quantum-inspired particles
        n_particles = 100
        particles = np.random.rand(n_particles, n_assets)
        particles = particles / particles.sum(axis=1)[:, np.newaxis]

        # Calculate returns and risks
        portfolio_returns = np.dot(particles, returns.mean().values)
        covariance = returns.cov().values
        portfolio_risks = np.sqrt(np.diagonal(np.dot(np.dot(particles, covariance), particles.T)))

        # Quantum interference optimization
        for _ in range(self.iterations):
            # Quantum phase estimation
            phases = 2 * np.pi * (portfolio_returns - portfolio_risks * risk_tolerance)
            quantum_states = np.exp(1j * phases.reshape(-1, 1))

            # Interference effect
            interference = np.sum(quantum_states * np.conjugate(quantum_states))
            best_idx = np.argmax(np.abs(interference))

            # Update particles
            particles = particles * np.exp(1j * phases.reshape(-1, 1))
            particles = np.abs(particles)
            particles = particles / particles.sum(axis=1)[:, np.newaxis]

        # Select best portfolio weights
        best_weights = particles[best_idx]

        return {
            asset: weight for asset, weight in zip(returns.columns, best_weights)
        }

    def detect_quantum_patterns(self, prices: np.ndarray) -> List[Dict[str, any]]:
        """Advanced pattern detection using quantum interference"""
        encoded_data = self.quantum_inspired_encoding(prices)
        patterns = []

        # Sliding window analysis
        window_sizes = [5, 10, 20]
        for window in window_sizes:
            for i in range(window, len(prices)):
                quantum_state = encoded_data[i-window:i]
                interference = np.sum(quantum_state * np.conjugate(quantum_state))

                # Pattern strength and type detection
                pattern_strength = np.abs(interference)
                phase_coherence = np.angle(interference)

                if pattern_strength > 1.5:  # Significant pattern threshold
                    pattern_type = "Bullish" if phase_coherence > 0 else "Bearish"
                    patterns.append({
                        'type': pattern_type,
                        'strength': float(pattern_strength),
                        'position': i,
                        'window': window
                    })

        return patterns

    def quantum_risk_assessment(self, prices: np.ndarray, volumes: np.ndarray) -> Dict[str, float]:
        """Quantum-inspired risk assessment"""
        price_encoded = self.quantum_inspired_encoding(prices)
        volume_encoded = self.quantum_inspired_encoding(volumes)

        # Quantum interference between price and volume
        interference = np.sum(price_encoded * np.conjugate(volume_encoded))

        # Calculate risk metrics
        volatility = np.std(prices) / np.mean(prices)
        volume_impact = np.abs(interference) / len(prices)

        return {
            'volatility': float(volatility),
            'volume_impact': float(volume_impact),
            'risk_score': float(np.sqrt(volatility * volume_impact))
        }