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Resources for Preparing for Quantitative Finance Interviews

Article Quant Q&A · Author: user29945

Summary

The document recommends several resources for preparing for quantitative finance interviews, spanning puzzle solving and broader quantitative finance topics. It points readers to a collection of interview questions and answers, a set of automated trading notes with practice questions, and books covering interview puzzles and quantitative finance more widely.

The examples describe question areas such as probability, market making, betting, pattern recognition, mathematics, and logic. One sample probability problem asks how to choose when to stop a card-dealing game whose next-card payoff depends on color, illustrating the need to reason about optimal stopping and prove a result. The answers are recommendations rather than a complete syllabus: they do not compare the resources in detail or provide coverage of all the areas named in the original question, such as programming, machine learning, or portfolio management. Readers would need to consult the cited materials to assess their fit and depth.

Key ideas

  • Interview preparation can combine dedicated question collections with broader quantitative finance references.
  • Practice material may cover probability, logic, pattern recognition, and market making.
  • Optimal stopping problems test both strategic reasoning and the ability to justify an answer.
  • The recommendations do not establish that any single resource covers every interview topic.

Tags

Full text
# Quant Interview Course


# Quant Interview Course












I there any course on for quant that covers all the factors such as logical reasoning, puzzles, statistics, probability, time series analysis, portfolio management, options, machine learning, and Python that are covered during Interview.

## Answer by Theodore (score 3)

https://quant.stackexchange.com/a/43496

Please consult What are the quantitative finance books that we should all have in our shelves? first prior to posting questions regarding resources for quantitative finance as there is a plethora of books mentioned there, two of which are about quant interview questions and ways to approach them.

Quant Job Interview Questions and Answers by Mark Joshi is an extensive compilation of 225 quant interview questions.

I have found the collection of interview questions mentioned in Max Dama on Automated Trading (see http://isomorphisms.sdf.org/maxdama.pdf) to be useful for learning about how to think about quant interview questions.

He introduces it with the following:

> I never wanted to do brainteasers. I thought I should spend my time learning something useful and not try to “game” job interviews. Then I talked to a trader at Deutche Bank that hated brainteasers but he told me that he, his boss, and all his co-workers did brainteasers with each other everyday because if DB ever failed or they somehow found themselves out of a job (even the boss), then they knew they would have to do brainteasers to get another. So I bit the bullet and found out it does not take a genius to be good at brainteasers, just a lot of practice.

There are questions about probability theory, market making / betting, pattern recognition, and basic math / logic.

Here’s one of the probability theory questions mentioned:

> Normal 52 card deck. Cards are dealt one-by-one. You get to say when to stop. After you say stop you win a dollar if the next card is red, lose a dollar if the next is black. Assuming you use the optimal stopping strategy, how much would you be willing to pay to play? Proof?

## Answer by Lucas Morin (score 0)

https://quant.stackexchange.com/a/43486

I'd say Heard on the Street: Quantitative Questions from Wall Street Job Interviews by Timothy Crack is the reference for logical reasoning, puzzle and a bit of the rest too. Frequently Asked Questions in Quantitative Finance by Paul Wilmott cover broader topics and provide further reading lists.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.