Everything inside Quantt, laid out in full. 96 lessons across 3 streams, grouped into modules with difficulty ratings.
Stream
The quantitative backbone underneath every pricing model and risk system.
6 modules · 21 lessons
Stream
The software-engineering foundation that turns ideas into shipped code.
12 modules · 52 lessons
Stream
How modern markets work, and the models we use to price and risk-manage them.
7 modules · 23 lessons
The quantitative backbone that underpins every pricing model, risk system, and trading strategy. Calculus, linear algebra, probability, statistics, and stochastic processes, taught in the order you actually need them for finance.
Quants live and die by clean notation. Before you touch a single model, you need to read formulas fluently, manipulate exponentials and logs without thinking, and recognise the series expansions that show up everywhere from interest accrual to option pricing.
Pricing is local. Greeks, hedging, optimisation of portfolios — all of it lives in derivatives and integrals. This module gets you fluent with limits, differentiation, optimisation, and integration so the rest of the curriculum can lean on them silently.
Portfolio theory, factor models, PCA, neural networks — they all collapse to matrix algebra. This module builds the vector and matrix intuition you need to reason about covariance, eigenstructure, and high-dimensional risk in code.
Markets are random. To reason about hedging error, drawdowns, or any risk metric, you need a working command of distributions, expectation, conditional probability, and the limit theorems that justify the models we ship to production.
Data is the raw material of every quant strategy. Estimation, hypothesis testing, and regression are how you tell signal from noise — and how you justify a backtest to anyone who matters.
Black–Scholes, interest-rate models, Monte Carlo pricing — all of them assume Brownian motion under the hood. This module gives you the random-walk intuition that makes derivatives modelling click instead of feeling like incantation.
The software-engineering foundation that makes the difference between a quant who can prototype and a quant who can ship. Python from refresher to advanced, then CS fundamentals, databases, networking, cloud, systems programming, and data engineering.
If you've never written code before, this is your zero-to-one ramp. Optional for everyone else — but doing it gives you the muscle memory to read every Python snippet in the rest of the curriculum without friction.
Working as a quant means living inside a terminal, a debugger, and a git repo. This module gives you the developer toolbox — Python, the command line, version control, packaging, and how to read errors — so the rest of tech doesn't fight you at every step.
Quant systems are distributed, networked, and stateful. You don't need to be a systems engineer, but you do need to know how networks, operating systems, data formats, and databases behave — because every model eventually meets one of them.
Python is the lingua franca of quant work. This module takes you from competent scripter to confident library author — comfortable with advanced techniques, OOP vs functional patterns, and the NumPy/Pandas stack that backs every research notebook in the industry.
A model that breaks silently in production is worse than no model at all. Testing, debugging, environment isolation, and a clean dev workflow are the disciplines that turn experimental code into something a desk can rely on.
Big-O isn't an interview gimmick — it's why your backtest takes 30 seconds or 30 hours. This module covers the CS fundamentals, OS internals, data formats, and version-control workflows that every senior quant draws on weekly.
Tick data, position state, trade history — quants live on top of databases. This module takes you from SQL essentials to schema design, advanced queries, ORMs, and the time-series stores built specifically for market data.
Trading systems live and die on latency and integration. This module covers the network internals, REST APIs, and authentication patterns that connect a research model to an exchange or counterparty.
Reproducibility is non-negotiable in regulated finance. Environments, SDLC discipline, CI/CD, and containers are the practices that let a small quant team ship code with confidence and roll it back when something breaks.
Modern quant infrastructure is cloud-first. AWS and Azure power the data lakes, model training, and risk runs at most hedge funds and investment banks. This module orients you so you can navigate either provider.
Latency-sensitive code, low-level systems work, and disciplined design patterns are how quant developers cross the gap between research and execution. This module is the bridge into systems-programming territory.
Quant signals are only as good as the pipelines feeding them. This module covers the big-data tools and platform thinking that keep a research desk fed with clean, timely data at scale.
How modern markets actually work, the instruments traded on them, and the models used to price and risk-manage those instruments. From financial markets and time value of money through to derivatives pricing, structured notes, and market microstructure.
Before models, you need a clear mental map of markets — who participates, why prices move, and how a single dollar today relates to a dollar next year. Without these foundations, every later concept floats untethered.
Quants are paid to model instruments. You can't model what you don't understand — bonds, equities, and the full derivatives zoo each have economics that drive how their prices behave under stress.
Every quant decision is a trade-off between risk and reward. Portfolio theory and factor models give you the language to express that trade-off precisely — and the maths that lets you optimise it.
This is where quants really earn their keep. From simple forwards through to Black–Scholes, you'll learn to price contingent claims, understand replication, and read the Greeks like a trader reads a screen.
Pricing without risk management is gambling with extra steps. Volatility models, VaR, and credit risk frameworks are how firms decide what they can stomach losing — and how they explain it to regulators.
Structured notes are where pricing, risk, and product design collide. Understanding them is a fast track to seeing how multiple parts of the curriculum compose into a real, sellable product on a bank's balance sheet.
The frontier of quant finance: how prices are made on the order book, how strategies are systematised, and the risk-neutral framework that ties pricing theory together. This is the material that separates analysts from quants.
Reading a syllabus only gets you so far. The lessons themselves include interactive Python in the browser, exercises with auto-graded tests, and quizzes after every module.
