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"""
Quantum Integration Example
Demonstrates integration of portfolio optimization methods using
the unified run_optimization service:
1. Hybrid pipeline (3-stage quantum-inspired)
2. QUBO + Simulated Annealing
3. Hierarchical Risk Parity (HRP)
4. Classical methods (Markowitz, min-variance, risk parity)
This example shows how to use the quantum hybrid portfolio system
for real-world portfolio optimization tasks.
"""
import numpy as np
import pandas as pd
from typing import Dict, Any
import json
from services.portfolio_optimizer import run_optimization
def generate_sample_portfolio(n_assets: int = 10, seed: int = 42) -> Dict[str, np.ndarray]:
"""
Generate sample portfolio data for demonstration.
Args:
n_assets: Number of assets
seed: Random seed for reproducibility
Returns:
Dictionary with 'returns', 'covariance', and 'asset_names'
"""
np.random.seed(seed)
# Generate realistic returns (5-15% annualized)
returns = np.random.uniform(0.05, 0.15, n_assets)
# Generate realistic covariance matrix
volatilities = np.random.uniform(0.10, 0.30, n_assets)
random_matrix = np.random.randn(n_assets, n_assets)
correlation = np.corrcoef(random_matrix)
correlation = (correlation + correlation.T) / 2
np.fill_diagonal(correlation, 1.0)
covariance = np.outer(volatilities, volatilities) * correlation
asset_names = [f"ASSET_{i:02d}" for i in range(n_assets)]
return {
'returns': returns,
'covariance': covariance,
'asset_names': asset_names,
}
def example_hybrid_optimization(data: Dict[str, np.ndarray]) -> Dict[str, Any]:
"""
Example 1: Hybrid Pipeline Optimization
The 3-stage hybrid pipeline combines screening, quantum-inspired
selection, and optimization for robust portfolios.
"""
print("\n" + "="*70)
print("EXAMPLE 1: HYBRID PIPELINE OPTIMIZATION")
print("="*70)
returns = data['returns']
covariance = data['covariance']
result = run_optimization(returns, covariance, objective='hybrid')
print(f"\nHybrid Results:")
print(f" Sharpe Ratio: {result.sharpe_ratio:.3f}")
print(f" Expected Return: {result.expected_return*100:.2f}%")
print(f" Volatility: {result.volatility*100:.2f}%")
print(f" Active Assets: {result.n_active}")
return {
'sharpe_ratio': result.sharpe_ratio,
'expected_return': result.expected_return,
'volatility': result.volatility,
'n_active': result.n_active,
}
def example_qubo_sa_optimization(data: Dict[str, np.ndarray]) -> Dict[str, Any]:
"""
Example 2: QUBO + Simulated Annealing
QUBO-based portfolio optimization with classical simulated annealing.
"""
print("\n" + "="*70)
print("EXAMPLE 2: QUBO + SIMULATED ANNEALING")
print("="*70)
returns = data['returns']
covariance = data['covariance']
result = run_optimization(returns, covariance, objective='qubo_sa')
print(f"\nQUBO-SA Results:")
print(f" Sharpe Ratio: {result.sharpe_ratio:.3f}")
print(f" Expected Return: {result.expected_return*100:.2f}%")
print(f" Volatility: {result.volatility*100:.2f}%")
print(f" Active Assets: {result.n_active}")
return {
'sharpe_ratio': result.sharpe_ratio,
'expected_return': result.expected_return,
'volatility': result.volatility,
'n_active': result.n_active,
}
def example_hrp_optimization(data: Dict[str, np.ndarray]) -> Dict[str, Any]:
"""
Example 3: Hierarchical Risk Parity
HRP based on López de Prado (2016). Uses clustering to allocate
risk across hierarchical asset groups.
"""
print("\n" + "="*70)
print("EXAMPLE 3: HIERARCHICAL RISK PARITY")
print("="*70)
returns = data['returns']
covariance = data['covariance']
result = run_optimization(returns, covariance, objective='hrp')
print(f"\nHRP Results:")
print(f" Sharpe Ratio: {result.sharpe_ratio:.3f}")
print(f" Expected Return: {result.expected_return*100:.2f}%")
print(f" Volatility: {result.volatility*100:.2f}%")
print(f" Active Assets: {result.n_active}")
weights = result.weights
print(f"\nWeight Statistics:")
active = weights[weights > 0]
if len(active) > 0:
print(f" Max Weight: {np.max(active)*100:.2f}%")
print(f" Min Weight: {np.min(active)*100:.2f}%")
print(f" Mean Weight: {np.mean(active)*100:.2f}%")
return {
'sharpe_ratio': result.sharpe_ratio,
'expected_return': result.expected_return,
'volatility': result.volatility,
'n_active': result.n_active,
'weights': weights,
}
def example_unified_service(data: Dict[str, np.ndarray]) -> None:
"""
Example 4: Unified Optimization Service
Demonstrates the unified service interface supporting multiple objectives.
"""
print("\n" + "="*70)
print("EXAMPLE 4: UNIFIED OPTIMIZATION SERVICE")
print("="*70)
returns = data['returns']
covariance = data['covariance']
objectives = ['markowitz', 'min_variance', 'hrp', 'hybrid', 'qubo_sa', 'vqe']
print(f"\nComparing optimization objectives:")
print(f"{'Objective':<20} {'Sharpe':>10} {'Return':>10} {'Volatility':>12} {'Active':>8}")
print("-" * 62)
for objective in objectives:
result = run_optimization(returns, covariance, objective=objective)
print(f"{objective:<20} {result.sharpe_ratio:>10.3f} "
f"{result.expected_return*100:>9.2f}% "
f"{result.volatility*100:>11.2f}% "
f"{result.n_active:>8}")
def compare_all_methods(data: Dict[str, np.ndarray]) -> None:
"""
Compare all optimization methods side-by-side.
"""
print("\n" + "="*70)
print("COMPARISON: ALL OPTIMIZATION METHODS")
print("="*70)
returns = data['returns']
covariance = data['covariance']
methods = [
('Hybrid', 'hybrid'),
('QUBO-SA', 'qubo_sa'),
('VQE', 'vqe'),
('HRP', 'hrp'),
('Markowitz', 'markowitz'),
('Min Variance', 'min_variance'),
]
results = []
for name, obj in methods:
r = run_optimization(returns, covariance, objective=obj)
results.append((name, r.sharpe_ratio, r.expected_return, r.volatility))
# Equal Weight baseline
n = len(returns)
ew_weights = np.ones(n) / n
ew_return = np.dot(ew_weights, returns)
ew_vol = np.sqrt(ew_weights @ covariance @ ew_weights)
ew_sharpe = ew_return / ew_vol if ew_vol > 0 else 0
results.append(('Equal Weight', ew_sharpe, ew_return, ew_vol))
print(f"\n{'Method':<20} {'Sharpe Ratio':>12} {'Return':>12} {'Volatility':>12}")
print("-" * 58)
for name, sharpe, ret, vol in sorted(results, key=lambda x: -x[1]):
print(f"{name:<20} {sharpe:>12.3f} {ret*100:>11.2f}% {vol*100:>11.2f}%")
def main():
"""Run all integration examples."""
print("\n" + "="*70)
print("QUANTUM HYBRID PORTFOLIO - INTEGRATION EXAMPLES")
print("="*70)
print("\nGenerating sample portfolio data...")
data = generate_sample_portfolio(n_assets=15, seed=42)
print(f"Created portfolio with {len(data['asset_names'])} assets")
print(f"Expected returns: {data['returns'].mean()*100:.2f}% (mean)")
print(f"Volatility: {np.sqrt(np.diag(data['covariance'])).mean()*100:.2f}% (mean)")
example_hybrid_optimization(data)
example_qubo_sa_optimization(data)
example_hrp_optimization(data)
example_unified_service(data)
compare_all_methods(data)
print("\n" + "="*70)
print("INTEGRATION EXAMPLES COMPLETE")
print("="*70)
print("\nAll portfolio optimization methods are working correctly!")
print("\nNext steps:")
print(" 1. Try the interactive dashboard: cd frontend && npm start")
print(" 2. Start the API server: python middleware/api.py")
print(" 3. Explore more examples in examples/")
if __name__ == "__main__":
main()
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