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.

KnowQuantIndex 2026.1 · Updated September 2026

Modelled bands describe the market, not an individual offer. Where an employer states pay, the stated figure is used and labelled as such.

Strategy coverage

The desks behind the strategies

Which firms run each strategy, in which market, and what the seats pay.