Zero → quantitative developer

Everything between your first line of code and building trading systems.

283 lessons in 65 modules, 28 judged coding problems and 12 portfolio projects — one path from “what is a variable” to pricing libraries, order books and low-latency C++. The lessons build on each other, in order, on purpose.

We won’t promise you a job in 90 days. It’s 124+ hours of core lesson time, and the role demands that depth.

Start free — take the diagnostic Explore the path

streaks XP & levels mastery tracking spaced repetition

283: bite-sized lessons
33: interactive labs
28: judged problems
124h+: of core content

What you’ll actually study

The four strands run side by side. The derivative you play with in the math lab is the same delta you hedge in the finance module, and the same number you compute in C++.

Math, from arithmetic up

If your algebra is shaky, we fix that first. The sequence runs from fractions to stochastic calculus, and every formula shows up only after you know why it exists.

C++ and Python, for real

Nine C++ levels that end at lock-free concurrency, plus a Python track through NumPy and friends. You submit code, a real compiler runs it, and you see the result.

Finance you can implement

Not finance trivia. Order books, options pricing, portfolio math — the pieces you can actually put in code, built up from first principles.

Progress you can trust

Quizzes are scored on the server, exercises must be done, and lessons you “pass” come back later to be re-earned through spaced review. No participation trophies.

One foundation, five directions after

Asset management, banking, HFT, research and infrastructure all draw on the same base — but they weight it differently. Pick a track and the planner tilts your schedule accordingly.

Track A

Asset Management & Quantitative Investment Engineering

Portfolio analytics, factor models, backtesting, Python research infrastructure and data platforms.

Track B

Investment Banking Quantitative Development

Derivatives, pricing engines, numerical methods, stochastic calculus, C++ pricing libraries, risk analytics.

Track C

High-Frequency Trading & Low-Latency Systems

Advanced C++, memory, CPU architecture, lock-free concurrency, networking and order-book systems.

Track D

Quantitative Research Engineering

Scientific Python, statistics, ML foundations, research pipelines and reproducible infrastructure.

Track E

Financial Infrastructure & Platform Engineering

Distributed systems, databases, streaming, market-data ingestion, reliability and observability.

Not sure yet?

Take the diagnostic first

28 questions, ~45 minutes — it places you at the right phase and the track picks itself.

The unglamorous parts, done properly

Most course platforms are a video player bolted to a payment form. This one is an engineering project, because that’s the honest way to teach engineering.

Your code actually runs

C++17/C++20 compiled with g++ and Python 3, executed in a separate sandboxed runner with CPU, memory and output limits. Not a toy textarea.

Mental math drills

Timed arithmetic practice with speed and accuracy tracking, so the easy stuff stops slowing you down later.

Flashcards that come back

Every lesson generates cards, scheduled by SM-2 — the same algorithm Anki uses. Miss a quiz question and it joins the queue too.

No inflated numbers

Every metric on your dashboard is computed from what you actually did. Unfinished topics say so. We would rather be boring than fake.

Math connects to algorithms, algorithms to implementations, implementations to the machine they run on. A quant developer who can’t trace that chain end to end is guessing. This platform exists so you never have to guess.

Start with the diagnostic