What is Quantitative Finance?
A plain-English guide to the field, the roles, the skills, and how to get started.
What Quants Do
Quantitative finance uses mathematics, statistics, and programming to solve financial problems. Quants build models that price derivatives, manage risk, identify trading signals, and optimise portfolios.
Put simply: if a bank or hedge fund needs to answer a question about money and the answer involves a model, a quant is the person who builds it.
Examples of what quants do every day:
- Price exotic options that have no closed-form solution
- Build risk models that estimate how much a portfolio could lose
- Design systematic trading strategies based on statistical signals
- Backtest strategies against years of historical data
- Optimise execution algorithms to minimise market impact
Where Quants Work
Hedge Funds
Firms like Two Sigma, Citadel, DE Shaw, and Man Group hire quants to find alpha — returns above the market. This is often the most competitive and highest-paying area.
Investment Banks
Goldman Sachs, JPMorgan, Morgan Stanley employ quants for derivatives pricing, risk management, and structuring. Roles are more structured and team-based.
Proprietary Trading Firms
Jane Street, Optiver, Jump Trading trade their own capital. They value speed, both in thinking and code. Quants here often sit right next to traders.
Fintech and Startups
Companies like Stripe, Revolut, and Wise use quantitative techniques for fraud detection, credit scoring, pricing, and algorithmic decision-making.
Skills You Need
You do not need a PhD (though it helps at some firms). Here is what actually matters:
Statistics and Probability
Distributions, hypothesis testing, regression, correlation, Bayesian reasoning. This is the foundation of everything. If you learn one thing well, learn this.
Programming
Python is the minimum. Being able to write clean, testable code that handles real data is essential. C++ or Rust is a bonus for performance-critical roles.
Linear Algebra
Matrices, eigenvalues, covariance matrices. Portfolio optimisation is basically a linear algebra problem.
Some Calculus
Derivatives (the mathematical kind), integrals, partial derivatives. You need this for options pricing and stochastic calculus.
Financial Knowledge
You need to understand what you are modelling. Stocks, bonds, options, futures — know how they work, how they are traded, and what drives their prices.
Career Paths
| Role | What You Do | Key Skills |
|---|---|---|
| Quant Researcher | Find patterns, build models, generate trade ideas | Stats, ML, Python/R |
| Quant Developer | Build the systems that run models in production | C++, Python, systems design |
| Quant Trader | Execute strategies, manage risk, make decisions | Probability, speed, market intuition |
| Risk Quant | Model potential losses, ensure regulatory compliance | VaR, stress testing, regulation |
A Day in the Life
There is no single "quant day" — it depends on the role and firm. But here is a realistic example for a quant researcher at a hedge fund:
07:30 — Check overnight P&L on live strategies. Review any alerts.
08:00 — Morning meeting. Discuss market moves, new data, research progress.
09:00 — Deep work. Clean a new alternative dataset and test whether it predicts returns.
12:00 — Lunch. Often with the team, often talking about markets.
13:00 — Code review a colleague's backtest. Spot a look-ahead bias.
15:00 — Run a parameter sweep on a new momentum signal. Analyse results.
17:00 — Write up findings. Push code to the research repo.
17:30 — Head home. Maybe read a paper on the train.
How to Get Started
- Learn Python properly. Not just syntax — learn pandas, numpy, and matplotlib. Our Python track is designed for this.
- Study statistics. Khan Academy and MIT OpenCourseWare are free and excellent. Focus on probability, distributions, regression, and hypothesis testing.
- Understand financial instruments. Start with stocks and bonds, then move to options and futures. Hull's “Options, Futures, and Other Derivatives” is the standard textbook.
- Build projects. Backtest a strategy. Build a portfolio analyser. Implement Black-Scholes. Employers want to see what you have built.
- Read widely. Follow quant blogs, read research papers, explore our resources page.
Recommended Starting Resources
- Book: “Python for Finance” by Yves Hilpisch — the best bridge between coding and finance.
- Book: “Options, Futures, and Other Derivatives” by John Hull — the quant finance bible.
- Course: MIT 18.S096 (Topics in Mathematics with Applications in Finance) — free on MIT OpenCourseWare.
- Practice: QuantConnect and Alpaca — free platforms to backtest and paper-trade strategies.