What Is Quantitative Finance?

Quantitative finance is the intersection of mathematics, statistics, and programming — applied to financial markets. If you have ever wondered what those people at hedge funds actually do all day, this guide explains it.

Quants in One Sentence

Quants use data, maths, and code to find patterns in financial markets, manage risk, price derivatives, and build trading systems. They replace gut feeling with statistical evidence.

Where Quants Work

Hedge funds — Renaissance Technologies, Two Sigma, Citadel, DE Shaw. Build trading strategies.
Investment banks — Goldman Sachs, JP Morgan, Morgan Stanley. Price derivatives, manage risk.
Proprietary trading firms — Jane Street, Optiver, Jump Trading. Trade the firm's own capital.
Asset managers — BlackRock, AQR, Man Group. Quantitative portfolio management.
Fintech — Stripe, Revolut, Robinhood. Fraud detection, credit scoring, algorithmic pricing.

Skills You Need

You do not need all of these on day one. Most quants build skills over years, often starting strong in one area and learning the rest on the job.

Statistics & probability — distributions, hypothesis testing, regression, Bayesian inference
Programming — Python (research), C++/Rust (production systems), SQL (data)
Linear algebra — matrices, eigenvalues (portfolio optimisation, PCA)
Calculus — derivatives (the maths kind), stochastic calculus for options pricing
Machine learning — increasingly important for signal generation and alternative data
Financial knowledge — how markets work, asset classes, derivatives

A Day in the Life

The daily routine depends on the role, but a typical quant researcher's day might look something like this:

07:00 — Check overnight P&L, review any positions that moved significantly
08:00 — Morning meeting: discuss market events, strategy performance, new ideas
09:00 — Research: explore new data sources, test hypotheses, build models
12:00 — Lunch (yes, quants eat too)
13:00 — Code review, backtest analysis, statistical validation of results
16:00 — End of US market. Review daily performance.
17:00 — Write up research, document findings, plan tomorrow

Famous Quants

Jim Simons — Mathematician who founded Renaissance Technologies. Their Medallion Fund returned ~66% per year before fees for over 30 years. Arguably the most successful trader in history.
Ed Thorp — The original quant. Beat blackjack with card counting, then applied the same mathematical thinking to options markets.
David Shaw — Founded DE Shaw using computational approaches to trading. Jeff Bezos worked there before starting Amazon.
Emanuel Derman — Physicist who moved to Goldman Sachs and became one of the most influential quants in derivatives pricing.

Career Paths

Quant Researcher — Develop new trading strategies and signals. Heavy statistics and data science.
Quant Developer — Build the systems that run strategies in production. Heavy software engineering.
Quant Trader — Execute strategies and manage risk in real-time. Blend of research and market intuition.
Risk Quant — Model and measure risk across the firm. Banks especially need risk quants for regulatory compliance.
Derivatives Quant — Price complex financial instruments. Requires stochastic calculus and PDE knowledge.