Hudson River Trading Interview Guide

Understanding HRT helps you frame your answers. It is a multi-asset quantitative trading firm and market maker, providing liquidity across equities, options, futures, fixed income and crypto on more than 200 markets. It employs over a thousand people across New York, Chicago, Austin, Boulder, London, Singapore, Shanghai, Mumbai and Dublin, and at one point accounted for roughly 5% of all US equity trading volume.

The cultural signal that matters for interviews: HRT builds nearly everything in-house, from network switches and FPGA systems to the trading algorithms. This is an engineering-first firm, and if you cannot back your quantitative reasoning with real coding ability you will struggle here in a way you might not at a more trader-focused shop.

A timed coding test, most often on HackerRank. Candidates commonly report 3 problems at LeetCode Medium to Hard, in a language of your choice, with C++ and Python most common.

The flavour is not generic algorithm trivia. HRT leans toward problems that mix coding with quantitative reasoning: implementing a simulation, processing a stream of timestamped trades to calculate profits, coding something numerical against a clock. One frequently reported task is filtering sets of timestamped trades and calculating profits, which rewards clean data handling more than clever tricks.

Drill Medium and Hard problems and Project Euler, but bias toward simulation, combinatorics and data processing. Correctness and speed both count.

One or two screens, splitting into two themes.

Maths and probability

Expected value, conditional probability and brainteasers. Candidates repeatedly note the problems are variations on classics from the green book, A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou, so that material is high-yield. It is on our reading list alongside the other books worth the time.

A representative HRT problem: Romeo and Juliet each arrive at a random time between 0 and 1 hour, uniformly and independently. The first to arrive waits 15 minutes, then leaves. What is the probability they meet? The clean solution is geometric, on the unit square, finding the area where the absolute difference in arrival times is at most 0.25.

Work the probability questions and the cards and coins collection until this class of problem is routine.

Coding

A live round where you write and explain code in real time. Interviewers care as much about how you reason aloud as about the final answer, so narrate while you type. A reported example: live code a poker simulation to find the best hand, in 45 minutes.

A sequence of 1-on-1 interviews, often around five, with quants and algo developers.

• Coding. Deeper than the phone screen, sometimes touching C++ and systems-level thinking, particularly for Algo Developer roles where low-latency performance matters.
• Probability and brainteasers. More expected value, Markov processes and statistics, applied under pressure.
• Expected value and strategy games. A common format is to derive an optimal strategy in a dice or card game, then write code to validate it by simulation. Practise on the dice game and the card game until the reasoning is automatic.
• Data analysis. You may be handed a dataset and asked to explore it in a Python notebook. This is exploratory rather than algorithmic, testing how you reason about real data.

Core topics to have solid: conditional probability, Bayes, Markov chains, expected value, combinatorics, hypothesis testing and regression, plus comfortable Python and, for some tracks, C++.

Two things, and neither is a study list.

The second is how you behave under the pace. HRT interviewers move fast and judge reasoning as much as the answer, so silence reads as being stuck. Narrate while you code, and time yourself on both problems and brainteasers so speed is not what trips you. The maths itself is largely standard, much of it green book variations, which means the differentiator is rarely whether you know the material and usually whether you can apply it quickly and explain it clearly.

One thing candidates neglect: be ready to say why HRT specifically. This is a technology-driven, deliberately secretive firm that builds its own network hardware. Generic enthusiasm for trading is not enough, so tie your answer to the engineering-first culture and the multi-asset liquidity work.

HRT rewards candidates genuinely strong in both quantitative reasoning and coding who can move between them without friction. It is less forgiving than a pure-trader interview for anyone whose programming is shaky, and the pace inside each round is deliberately demanding.

The most effective preparation combines three things: green book style probability and expected value, fast and clean coding on Medium and Hard simulation-style problems, and the specific habit of validating a strategy in code. Candidates who treat the process casually underestimate how high the combined bar is.

Work through everything we have for Hudson River Trading, and read our full interview guide for how these stages compare across firms.