Quant Finance, As It Is Actually Practised
Derivations done properly, code you can run, and the judgement in between.
Practitioner-led modules on derivatives pricing, XVA, stochastic calculus and model validation — each one from first principles to working, validated Python.
The Gap
Most quantitative finance education stops at the formula.
You derive Black-Scholes, you see the closed form, the course moves on. But on a desk the hard part starts after the formula: choosing the right measure, discounting on the right curve, calibrating to the market you actually trade in, and being able to defend a number when a reviewer asks where it came from.
Textbooks give rigour without implementation. Tutorials give code without understanding.
We do the whole path.
Who Is This For
- Aspiring and working quants
- Quant developers
- Risk and model-validation professionals
- Engineers and graduate students moving toward a pricing role
If you can read a derivation and write Python, you’re ready.
What Every Module Gives You
Full derivations
Nothing skipped, nothing assumed. The derivation is where the assumptions live — and the assumptions are what break.
Working Python
Executable notebooks structured as they would be in production: calibration, simulation, validation. You run them, break them, keep them.
Exercises with solutions
From mechanical drills to full applications, with worked answers.
A validation suite
Parity checks, martingale tests, convergence, benchmarks. Every module ends with tests a reviewer could re-run.
The practitioner’s view
Where textbooks and desks disagree — and they disagree more than you’d think.
What We Cover
- Derivatives pricing and risk-neutral valuation
- Stochastic calculus and the machinery underneath
- Monte Carlo simulation, discretisation and variance reduction
- Volatility modelling and calibration
- XVA, counterparty risk and exposure simulation
- Interest rate and credit modelling
- Model validation
New modules are released continuously. Each is independent — take what you need.
Start Here, Free
The Quant Reference Card
Four pages covering stochastic calculus, measure change, the models you’ll meet, discretisation, rates, credit and XVA — with the practitioner notes that don’t make it into textbooks.
From The Blog
Most of what we teach shows up here first, in shorter form.
→ What is a risk-neutral measure, and why is it important?
→ Black-Scholes from scratch: the derivation that actually makes sense
→ What CVA actually is — explained like on a desk, not in a paper
→ Why doesn’t my Monte Carlo match Black-Scholes?
→ What is a martingale? Explained for quant interviews