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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))
} |