Financial Computation Workloads

Risk Calculations

def calculate_var(returns, confidence=0.95):
    sorted_returns = sorted(returns)
    index = int(len(sorted_returns) * (1 - confidence))
    return sorted_returns[index]

Pricing Models

def black_scholes(S, K, T, r, sigma):
    from math import log, sqrt, exp, pi
    d1 = (log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * sqrt(T))
    d2 = d1 - sigma * sqrt(T)
    # ... (full implementation)
    return call_price, put_price

Portfolio Analytics

def portfolio_metrics(weights, returns, cov_matrix):
    portfolio_return = sum(w * r for w, r in zip(weights, returns))
    portfolio_variance = 0
    for i in range(len(weights)):
        for j in range(len(weights)):
            portfolio_variance += weights[i] * weights[j] * cov_matrix[i][j]
    return portfolio_return, portfolio_variance ** 0.5

Regulatory Considerations

• Keep audit logs of compilation events for compliance.
• Verify correctness_match on every model release.
• Use pyvorin proof to document performance characteristics.