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

Adaptive practice calibrated by topic rating. Coding challenges judged on execution. A skill profile built from what you actually solve — not what you claim to know.

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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.

What the content looks like

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}}

Read the full derivation →

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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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.

MSc / PhD students

Theory you can defend in an interview.

Turn advanced coursework into a portfolio of implementations scored on correctness and reproducibility — before the first phone screen.

Explore the curriculum
SWE → Quant

Fill the financial mathematics gap.

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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Quant Bootcamp

12 weeks. First principles
to production C++20.

A structured curriculum designed around the depth a senior quant interviewer actually applies — built by someone who has been on both sides of that conversation for 15 years.

  • C++20 pricing engines from scratch
  • Calibration, Greeks, Monte Carlo, and Finite Difference methods
  • Interview-grade derivations with working code
  • Verified skill signal visible to employers

3 months

Interview Ready

6 months

Quant Professional

What you leave with

28+

Modules

C++20

Primary language

3

Production engines

3

Phases

[Placeholder — Marco's quote goes here. 2–3 sentences about what the platform gave him as a software engineer pivoting into quant finance: what gap it filled, what he was able to do that he couldn't before, how the content compared to what he had tried previously.]

M

Marco

Software Engineer → Quant Finance

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