Strategy families

How quant strategies divide the market

What are the main quant trading strategies?

Quant strategies split into five families: high-frequency trading, which competes on speed; market making, which earns the spread while managing inventory; statistical arbitrage, which trades relative value across baskets; systematic macro, which takes rules-based macro positions; and machine-learning alpha, which finds non-linear structure in large datasets.

High-frequency trading

Microseconds to seconds

High-frequency trading captures very short-lived pricing edges by being faster than competitors to observe and act. The edge is largely engineering: network path, kernel bypass, and increasingly FPGA implementation of the decision hot path.

Hires: HFT / Low-Latency Engineer · Quant Trader · Quant Researcher

Concentrated in: Chicago · Amsterdam · New York

Market making

Continuous

Market makers quote continuous two-sided prices and earn the spread while managing inventory risk. Options market making is the largest and most quantitative variant, requiring volatility modelling alongside fast systems.

Hires: Quant Trader · HFT / Low-Latency Engineer · Quant Developer

Concentrated in: Amsterdam · Chicago · Hong Kong · Singapore

Statistical arbitrage

Intraday to weeks

Statistical arbitrage trades large baskets of related instruments on mean-reversion and relative-value relationships identified statistically. It is the classic quant research discipline and the deepest employer of doctoral researchers.

Hires: Quant Researcher · Quant Developer · Systematic Portfolio Manager

Concentrated in: New York · London · Hong Kong

Systematic macro

Weeks to months

Systematic macro takes rules-based positions across currencies, rates, commodities and equity indices based on macroeconomic and trend signals. Horizons are long, capacity is high, and London is the global centre.

Hires: Quant Researcher · Systematic Portfolio Manager · Quant Developer

Concentrated in: London · New York · Singapore

Machine-learning alpha

Intraday to weeks

Machine-learning driven strategies use modern statistical learning and alternative data to find non-linear structure in markets. It is the fastest-growing research discipline and competes directly with AI labs for talent.

Hires: Quant Researcher · Quant Developer

Concentrated in: New York · San Francisco · London

Methodology

Figures are expressed as annual total compensation (base salary plus expected performance bonus) in the reference market's local currency, converted from a US dollar base. They describe typical market ranges rather than any individual offer, and exclude sign-on payments, deferred equity and carried interest. Portfolio-manager figures reflect formulaic profit-share arrangements and are therefore far more dispersed than any other role.

KnowQaunt 2026.1 · Updated September 2026

These are indicative ranges pending first-party verification. They will be replaced by verified submissions as the Quant Salary Index dataset builds.