Hudson River Trading quant interview preparation: a role-specific study guide
Prepare algorithms, implementation and research reasoning for a Hudson River Trading application, with the role title as your starting point. HRT publishes separate engineering preparation and role descriptions. The examples below are original SelfMadeQuant practice. They do not establish HRT’s exact questions, interview frequency or a common process for algorithm developers and software engineers.
Algorithm development and software engineering are different starting points
HRT's role descriptions describe algorithm development and engineering responsibilities. Its software interview preparation article is specifically aimed at software engineering. Read both in the context of your vacancy; do not treat a software preparation article as a published specification for every quantitative role.
The firm's engineering and interviewing discussion also emphasises communication alongside implementation. Our practical interpretation is to rehearse how you arrive at a design, test it and revise it after a counterexample. That is editorial preparation advice, not a claim about a fixed interview scoring rubric.
Software: build the smallest correct solution first
Start from an explicit input contract and a simple solution whose correctness you can explain. Then identify the workload that makes it too slow. Improve the data structure or algorithm and state the new complexity. A clever optimisation that silently changes semantics is a regression, not a better solution.
Practise duplicate events, out-of-order timestamps, empty inputs and resource limits. For a trading-related stream, distinguish the time a message arrives from the time of the event it describes. Explain whether your system buffers, rejects, reorders or revises earlier output.
Research: keep the evidence boundary intact
For an algorithm-development application, add probability, statistics and experiment design to coding. Explain a model's target, the information available at prediction time and how you would detect a misleading result. If your data overlap in time, discuss why an ordinary random split may overstate performance.
A reviewable practice project
Build a small event replay with a cash and position ledger. Keep the input fixed, add a reference implementation and compare the output after each change. Describe which property your tests establish and which behaviour remains unmodelled. This creates a useful technical discussion without pretending to reproduce a low-latency production trading stack.
Original practice questions with worked answers
1. Detecting a duplicate event
A feed assigns a unique integer ID to each event. How can you prevent processing an ID twice, and what is the memory trade-off?
Answer. Store processed IDs in a hash set and check before applying each event. Expected lookup is O(1), with O(n) memory for n unique IDs. A bounded retention policy changes the guarantee; it needs assumptions about how late duplicates can arrive.
2. A rolling statistic
How can you compute the sum of the last k values in a stream efficiently?
Answer. Keep a queue of at most k values and a running sum. Add the new value; if the queue exceeds k, subtract and remove the oldest. Each update is O(1), with O(k) space. Define the output before k observations exist.
3. An event-time ambiguity
An event stamped 10:00:01 arrives after one stamped 10:00:02. What should a backtest do?
Answer. First specify whether the strategy acts on arrival time or reconstructed event time. Do not silently reorder data into information that was unavailable at the decision. A deterministic replay must record the chosen policy and handle late messages consistently.
Topic practice behind this plan
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Free practice tools
Primary sources and scope
Employer information was checked on the review date. Our study activities and original questions are editorial suggestions. Recruiting requirements, regional processes and vacancies can change; the employer’s current instructions take priority.