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.
Learn it. Build it. Check yourself.
Every topic follows the same loop: understand the derivation, reproduce the computation with a fixed seed, then find out what you actually retained.
Learn
Rigorous derivation with assumptions stated and notation defined. Interactive lab to explore model behaviour before touching code. Interview angle included.
Build
Reproduce the result end-to-end in a notebook with a fixed seed. Convergence analysis included. Every numerical result is traceable.
Check yourself
Take the quiz and see the diagnostic breakdown — cognitive level, topic, and the misconception behind each wrong answer. Then sit the timed readiness diagnostic.
Learn
Rigorous derivation with assumptions stated and notation defined. Interactive lab to explore model behaviour before touching code. Interview angle included.
Build
Reproduce the result end-to-end in a notebook with a fixed seed. Convergence analysis included. Every numerical result is traceable.
Check yourself
Take the quiz and see the diagnostic breakdown — cognitive level, topic, and the misconception behind each wrong answer. Then sit the timed readiness diagnostic.
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 , the asset price follows geometric Brownian motion:
Applying Itô's lemma to , and invoking the no-arbitrage condition:
Terminal condition for a European call:
The closed-form solution is:
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);
}Read the derivation →Built for two distinct problems.
Whether you have the maths and need the implementation, or the engineering and need the domain layer — the platform is built around the actual gap, not a generic curriculum.
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You write production code. Brownian Bridge adds the domain layer: SDEs, derivatives pricing, calibration, and Greeks — with working C++ and Python, not textbook notation.
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