Quantitative finance · the complete path
Become interview-ready
in quant — for real.
One guided path from college math to the trading desk — every concept derived, every skill drilled, your readiness tested. Not eight separate courses you stitch together yourself.
✓ Correct — leveling up
Level
Hard
Accuracy
86%
Streak
12
Why QuantLMS
Not another video course
Derived, never hand-waved
Every result proved in full with server-rendered math — the steps a desk actually expects you to reproduce.
One path, not scattered courses
A single 10-module map from college math to the trading desk, so you always know exactly what comes next.
Prove it, don't just watch
Graded problems, an adaptive drill, a mock interview, and a live C++ judge — you're tested, not lectured.
Built by a working quant
Authored from Ibrahim Lanre Adedimeji's research, with rigor adapted from MIT OpenCourseWare.
How it works
Learn → Practice → Interview → Track
Learn
Every concept derived in full, server-rendered KaTeX — not hand-waved.
Practice
An adaptive drill that gets harder when you're right, easier when you miss.
Interview
A graded mock interview that tells you, plainly, if you're ready.
Track
A streak, accuracy, and per-topic mastery — proof you're improving.
One map, not scattered courses
College math → quant desk
Foundations
Calculus & Analysis
Advanced LA & Probability
Differential Equations & Numerics
Stochastic Calculus
Mathematical Finance
Advanced Frontiers
The curriculum
9 more courses
From Diffusion to Jumps: Lévy Models in Finance
Why Brownian motion underprices tail risk, and how Lévy processes fix it.
5 lessonsStochastic Volatility & Measure Change
The Heston model and the Girsanov change of measure behind modern pricing.
2 lessonsHawkes Processes & Market Microstructure
Self-exciting events, volatility clustering, and order-book dynamics.
3 lessonsLinear Algebra & Optimization for Quants
The other half of quant math — vectors, matrices, eigenvalues, SVD, PCA, least-squares regression, and convex optimization — built to where you can extract PCA factors and solve a Markowitz portfolio by hand.
18 lessonsCalculus for Quants
The calculus a quant uses every day — limits, derivatives, Taylor series, integration, multivariable gradients and Lagrange multipliers, and vector calculus — built with full derivations and worked examples.
15 lessonsReal Analysis for Quants
The rigorous foundation under calculus and probability — completeness, sequences and series, topology and continuity, Riemann and Lebesgue integration, and an introduction to functional analysis (Banach/Hilbert/L^p).
16 lessonsMeasure-Theoretic Probability
Probability made rigorous — sigma-algebras and measures, random variables as measurable maps, expectation as a Lebesgue integral, conditional expectation as an L^2 projection, and the limit theorems (LLN, CLT, extreme value) that underpin risk.
13 lessonsBSDE, XVA & Credit Risk
The frontier of derivatives valuation — backward SDEs and nonlinear Feynman–Kac, counterparty credit risk, the full XVA stack (CVA/DVA/FVA/KVA), and the Monte-Carlo and deep-BSDE methods that price them.
14 lessonsC++ & HPC for Quants
The implementation half of quant work — modern C++, numerically-sound pricers, and the high-performance computing (cache, SIMD, threads, GPU) that production pricing engines run on.
15 lessonsOne membership. The whole path.
Every course, the adaptive practice, and the mock interview — for the price of a textbook, not eight separate courses.
Start free →Authored by Ibrahim Lanre Adedimeji · rigor adapted from MIT OpenCourseWare