QAOA portfolio optimisation and quantum risk simulation

import os, requests

API_KEY = os.environ["QCOS_API_KEY"]
BASE    = "https://api.softquantus.com"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# 5-asset portfolio optimisation
assets = ["AAPL", "MSFT", "NVDA", "GOOG", "AMZN"]
returns = [0.12, 0.15, 0.22, 0.10, 0.18]
risk_matrix = [
    [0.04, 0.01, 0.02, 0.01, 0.01],
    [0.01, 0.05, 0.02, 0.01, 0.02],
    [0.02, 0.02, 0.08, 0.01, 0.02],
    [0.01, 0.01, 0.01, 0.03, 0.01],
    [0.01, 0.02, 0.02, 0.01, 0.04],
]

# Build QUBO from Markowitz model
# (QCOS handles this mapping internally via QAOA problem_type="portfolio")
resp = requests.post(f"{BASE}/api/v1/algorithms/qaoa/run", headers=HEADERS, json={
    "problem_type": "portfolio",
    "assets": assets,
    "expected_returns": returns,
    "covariance_matrix": risk_matrix,
    "risk_aversion": 0.5,
    "budget": 3,          # select 3 of 5 assets
    "p_layers": 3,
    "shots": 4096,
    "backend": "aer_simulator",
})

result = resp.json()
print("Selected assets:", result["selected_assets"])
print("Expected return:", result["expected_return"])
print("Portfolio risk:", result["portfolio_risk"])
print("Sharpe ratio:", result["sharpe_ratio"])

resp = requests.post(f"{BASE}/api/v1/algorithms/hybrid/run", headers=HEADERS, json={
    "workflow": "monte_carlo_var",
    "portfolio_value": 1_000_000,
    "confidence_level": 0.95,
    "time_horizon_days": 10,
    "n_assets": 5,
    "returns_distribution": "normal",
    "quantum_backend": "aer_simulator",
    "classical_backend": "azure_cpu_worker",
    "n_qubits_precision": 6,
})

result = resp.json()
print(f"VaR (95%, 10d): ${result['var_95']:.2f}")
print(f"CVaR (95%, 10d): ${result['cvar_95']:.2f}")

curl -X POST https://api.softquantus.com/api/v2/cost-estimate \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "backend": "aws_braket_ionq_aria1",
    "n_qubits": 20,
    "shots": 10000,
    "circuit_depth": 50
  }'