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QuantDXB

Learn quant finance by building the projects firms look for.

QuantDXB teaches probability, programming and markets through short, rigorous lessons. Each one ends in a project you build and can show an interviewer. It's made in the UAE, where good material for this is hard to find.

Start the option pricing lessonSee what's covered
Each line is one simulated year of a stock's price. The bars count where the paths finished. That lognormal spread is what you integrate over to price an option, the subject of the first lesson.

What you'll learn

Four areas, taught in order, because each one leans on the one before it.

Probability and statisticsAvailable now
Conditional expectation, common distributions, estimation and confidence intervals, Monte Carlo methods.
  1. How sure is an average?
  2. Conditional expectation
  3. Random walks and Brownian motion
  4. Pricing an option with Monte Carlo
  5. Bayesian updating
  6. Hypothesis tests and false discoveries
Programming for quantAvailable now
Python with NumPy and pandas, writing tests for numerical code, then C++ for the parts that need to be fast.
  1. Thinking in arrays with NumPy
  2. Floating point and testing numerical code
  3. Time series with pandas
Markets and tradingAvailable now
Order books and how trades happen, market making, options and their Greeks, execution costs.
  1. The limit order book
  2. Market making
  3. Options and the Greeks
  4. Delta hedging
  5. Execution costs
Data and machine learningAvailable now
Time series, backtesting without look-ahead bias, overfitting, and when machine learning helps.
  1. Returns, volatility and fat tails
  2. Backtesting without look-ahead bias
  3. Overfitting
  4. Pairs trading and cointegration
  5. Validation for financial machine learning

Projects you'll build

Monte Carlo option pricer

Simulate thousands of possible futures for a stock, average the option's payoff, and check your number against the Black–Scholes formula. The lesson walks through the maths and the code, then lists ways to extend it into something worth putting on a CV.

Start this project
The white line is the running Monte Carlo estimate and the shaded band its 95% confidence interval. Each tenfold increase in paths shrinks the band by about √10 ≈ 3.2.

Later in the curriculum

  • Strategy backtester

    Replays historical data event by event, charges realistic costs, and refuses to peek at the future.

  • Market-making simulator

    Quotes around a fair value, manages inventory risk, and measures whether you actually had an edge.

  • Pairs trading strategy

    Finds assets that move together, tests the relationship for cointegration, and trades the spread.

Why QuantDXB exists

Trading and investment firms in the Gulf are growing, but the way into quant roles is still hard to find from here. Most guides assume you're in London or New York, at a university with a quant society down the hall and alumni already on a desk.

QuantDXB is the resource we wanted: structured, honest about the maths, and focused on the kind of work that gets you an interview.