Price derivatives. Implement the solver. Prove it under interview conditions.

Derivations with the assumptions stated. Interactive labs that price in your browser. Reproducible notebooks with fixed seeds. A timed diagnostic that tells you exactly where you stand.

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Built by Benoit Vandevelde

15 years pricing quant at Dymon Asia Capital, Deutsche Bank, Barclays, and BNP Paribas. 10 years teaching derivatives at Master's level in Paris, ex-DEA Lamberton.

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Theory and implementation, side by side.

Every topic starts with the derivation and ends with code judged on execution correctness. This is an excerpt from the Black-Scholes module.

Course derivation

Under risk-neutral measure Q\mathbb{Q}, the asset price follows geometric Brownian motion:

dSt=rStdt+σStdWtQdS_t = r\,S_t\,dt + \sigma S_t\,dW_t^{\mathbb{Q}}

Applying Itô's lemma to V(St,t)V(S_t,\,t), and invoking the no-arbitrage condition:

Vt+12σ2S22VS2+rSVSrV=0\frac{\partial V}{\partial t} + \frac{1}{2}\sigma^2 S^2 \frac{\partial^2 V}{\partial S^2} + rS\frac{\partial V}{\partial S} - rV = 0

Terminal condition for a European call: V(ST,T)=max(STK,  0)V(S_T,\,T) = \max(S_T - K,\;0)

The closed-form solution is:

V=SN(d1)KerTN(d2)V = S\,\mathcal{N}(d_1) - K e^{-rT}\mathcal{N}(d_2)

d1,2=ln(S/K)+(r±12σ2)TσTd_{1,2} = \frac{\ln(S/K) + \bigl(r \pm \tfrac{1}{2}\sigma^2\bigr)T}{\sigma\sqrt{T}}

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C++20 implementation

// compute/pricing-lib — Black-Scholes European pricer
double bs_european(
    double S, double K, double T,
    double r, double sigma, bool is_call)
{
    const double d1 =
        (std::log(S / K) + (r + 0.5*sigma*sigma)*T)
        / (sigma * std::sqrt(T));
    const double d2 = d1 - sigma * std::sqrt(T);

    if (is_call)
        return S*norm_cdf(d1)
             - K*std::exp(-r*T)*norm_cdf(d2);
    return K*std::exp(-r*T)*norm_cdf(-d2)
         - S*norm_cdf(-d1);
}
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