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.
Free to start · No credit card required
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. Prove it.
Every topic follows the same loop: understand the derivation, reproduce the computation, then demonstrate you can implement it under evaluation.
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.
Prove
Submit C++17/20 or Python. Judged on execution correctness by an automated judge. XP and topic rating updated on first accept.
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.
Prove
Submit C++17/20 or Python. Judged on execution correctness by an automated judge. XP and topic rating updated on first accept.
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);
}Try the challenge →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.
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 →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.
View bootcamp programs →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.]
Marco
Software Engineer → Quant Finance
Ready to close the gap?
Join the beta — free to start. Problems designed by someone who has been on both sides of the quant interview table for over a decade.
Free to start · No credit card required
