| |
| """ |
| Test script to verify the portfolio optimizer works correctly with notebook-based methods. |
| """ |
| import sys |
| import os |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
|
|
|
|
| def test_optimizer(): |
| print("Testing portfolio optimizer (Hybrid, QUBO-SA, VQE)...") |
|
|
| try: |
| from services.portfolio_optimizer import run_optimization |
| import numpy as np |
|
|
| print("✓ Successfully imported run_optimization") |
|
|
| n_assets = 5 |
| returns = np.array([0.12, 0.10, 0.08, 0.15, 0.07]) |
| covariance = np.array([ |
| [0.0400, 0.0180, 0.0120, 0.0200, 0.0150], |
| [0.0180, 0.0900, 0.0210, 0.0180, 0.0120], |
| [0.0120, 0.0210, 0.0484, 0.0150, 0.0100], |
| [0.0200, 0.0180, 0.0150, 0.0625, 0.0180], |
| [0.0150, 0.0120, 0.0100, 0.0180, 0.0225] |
| ]) |
|
|
| for objective in ['hybrid', 'qubo_sa', 'vqe', 'markowitz', 'hrp']: |
| result = run_optimization(returns, covariance, objective=objective) |
| assert result.weights is not None |
| assert len(result.weights) == n_assets |
| assert np.abs(np.sum(result.weights) - 1.0) < 1e-5 |
| assert np.all(result.weights >= -1e-6) |
| assert np.isfinite(result.sharpe_ratio) |
| print(f"✓ {objective}: Sharpe={result.sharpe_ratio:.3f}") |
|
|
| print("\n✓ All tests passed! The optimizer is working correctly.") |
| return True |
|
|
| except Exception as e: |
| print(f"✗ Error: {e}") |
| import traceback |
| traceback.print_exc() |
| return False |
|
|
|
|
| if __name__ == "__main__": |
| success = test_optimizer() |
| sys.exit(0 if success else 1) |
|
|
