1. Understand the landscape
‘Quant trading’ is a broad label. Different firms and desks do different things, but a few themes are common.
- Market-making and high-frequency trading (HFT).
- Systematic / statistical arbitrage strategies.
- Options / derivatives trading and risk management.
- Research-heavy quant roles vs more execution-focused trading roles.
2. Core skills firms actually care about
Instead of trying to learn everything, focus on a few pillars that nearly every quant / trading interview will touch.
- Mathematics & probability: random variables, distributions, conditional probability, expectation, variance, basic stochastic thinking.
- Programming: usually Python or C++ (Python is enough to start). Clean code, basic data structures, simple backtests.
- Problem-solving: brain-teaser style questions, algorithmic thinking, mental maths.
- Markets: basic understanding of how an order book works, bid/ask, volume, and what makes prices move.
3. A rough university roadmap
You don’t need to follow this perfectly, but a direction helps. Adapt it to your situation.
- Year 1: strong grades in maths / stats courses, start coding projects in Python, join a trading / quant / finance society.
- Year 2: more serious coding, small backtests, internships or insight programs, start applying for quant / trading spring or summer roles.
- Year 3+: deeper maths (probability, stochastic processes, optimisation), more involved projects, targeted applications to trading firms and banks.
4. Build small but real projects
Projects are one of the best signals you can send. They don’t need to be complex; they need to be real and well-explained.
- Simple backtest of a strategy on equities or ETFs (moving averages, mean reversion, etc.).
- Order-book or tick-data analysis if you can find data.
- Risk or portfolio simulations (e.g. Monte Carlo on returns).
- Clear README: what you tried, what worked, what didn’t, and how you would improve it.
5. Preparing for applications & tests
Recruitment usually involves online coding tests, probability questions, and sometimes game-based or numerical assessments.
- Practise algorithmic questions in Python (arrays, sorting, basic dynamic programming, simple optimisation).
- Review core probability: conditional probability, Bayes’ rule, expected value in betting-style setups, variance, covariance.
- Train mental arithmetic and estimation — many traders are tested on speed & accuracy with numbers.
6. Interviews: how to present yourself
Firms don’t expect you to know everything about markets on day one, but they do expect honesty, curiosity, and clear thinking.
- Be able to walk through your projects: data, assumptions, logic, what went wrong, and what you learned.
- When solving a brain-teaser or probability puzzle, talk through your approach rather than staying silent.
- Admit when you don’t know something, but show how you would approximate or structure the problem.
7. Long-term mindset
Breaking into quant trading is not about being perfect in your first interview. It’s about building a profile that gets steadily stronger over 1–3 years.
- Keep improving on one axis at a time: maths, coding, projects, or market understanding.
- Treat each application cycle as feedback, not as a verdict on your ability.
- Stay curious — read, experiment, and build things you genuinely find interesting.