| 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) |
|
|
| |
| 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] |
|
|
| |
| n_particles = 100 |
| particles = np.random.rand(n_particles, n_assets) |
| particles = particles / particles.sum(axis=1)[:, np.newaxis] |
|
|
| |
| 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))) |
|
|
| |
| for _ in range(self.iterations): |
| |
| phases = 2 * np.pi * (portfolio_returns - portfolio_risks * risk_tolerance) |
| quantum_states = np.exp(1j * phases.reshape(-1, 1)) |
|
|
| |
| interference = np.sum(quantum_states * np.conjugate(quantum_states)) |
| best_idx = np.argmax(np.abs(interference)) |
|
|
| |
| particles = particles * np.exp(1j * phases.reshape(-1, 1)) |
| particles = np.abs(particles) |
| particles = particles / particles.sum(axis=1)[:, np.newaxis] |
|
|
| |
| 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 = [] |
|
|
| |
| 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 = np.abs(interference) |
| phase_coherence = np.angle(interference) |
|
|
| if pattern_strength > 1.5: |
| 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) |
|
|
| |
| interference = np.sum(price_encoded * np.conjugate(volume_encoded)) |
|
|
| |
| 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)) |
| } |
