6 min read · Jan 28, 2024
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In this article, I will be sharing tips and the list of resources I’d use if I had to start over with becoming a Quant again.
- Skills needed to become a Quant
- Types of Quant jobs
- What to study at University
- Quant Interview Books
- Programming Interviews
- Personal Projects to add to your CV
First, let’s talk about the general skillset for becoming a quant. It’s better to have a broad knowledge base and know a little bit about a lot of different things. You’ll never know what the interviewer’s background might be and what might come up in the interviews.
In general, the skills needed for a quant job are:
- Maths: Linear Algebra, Calculus, Stochastic Calculus
- Statistics: Probability, Statistics, Time Series, Machine Learning
- Programming: Python, C++
- Finance/Derivatives: Options, Bonds, Futures, Greeks, Risk
Next, you need to know the different types of quant jobs. You will need to focus your preparation on different topics for different types of quant jobs.
You should note that the same job title can do vastly different things at different firms. You should make sure you understand what the job is actually about and do not only look at the job title. To generalise, I will group the different types of quant jobs as follows:
Quantitative Researcher
Quantitative Researchers are the quants researching for “alpha”. These people are typically the best paid quants as they work closely to Portfolio Managers and will take home a percentage of the PnL.
The role is mainly about using statistics and machine learning to try to predict market returns and they typcally require a PhD or a strong Masters.
- Mainly on the buy-side (Two Sigma, Citadel, Optiver, etc)
- What they do: Research quantitative trading strategies
- Skillset: Statistics, Machine learning, Derivatives, Programming
Trader / Quant Trader (Market Making)
A trader is typically someone who trades by market making and takes responsibility of their trading pnl. They will typically quote bid-ask prices to clients and their inter-dealer broker. If their quote is filled, they then try to hedge their position. If they are able to successfully dynamically hedge, then they will realise the profit of the bid-ask spread. The trader is responsible for the dynamic hedging and hence is responsible for their trading PnL.
- Can be on the buy-side (e.g. Optiver, IMC) or sell-side (any trading desk at an investment bank)
- What they do: Quote bid-ask prices, manage their risk / hedge their positions
- Skillset: Quick decision-making, Quick mental maths, Derivatives
Quantatitive Analyst
A quantitative analyst is someone responsible for creating the pricing and risk tools used by the traders. Their main responsibility is to model derivative products and calculate their risk.
- Can be on the buy-side or sell-side (sometimes this role can be called Quantitative Researcher on the sell-side)
- What they do: Responsible for the modelling and creating pricing and risk tools for traders
- Skillset: Derivatives, Programming, Stochastic Calculus
Quantitative Developer
A quantitative developer is a developer but with an element of finance. Typically, the quantitative developer role will require a bit of knowledge about quantitative finance whereas a software engineer does not.
- Can be on the buy-side or sell-side
- What they do: productionise trading strategies, develop the trading platform or develop pricing and risk tools (the role varies greatly depending on the team and company)
- Skillset: Programming, some Quant Finance/Derivatives knowledge
If you want the highest chances to get a quant job, make sure to take a STEM university degree: Maths, Physics, Engineering are best. A masters is also highly favourable. In the employer’s view, it’s much easier for them to teach a maths student finance than it is to teach a finance student maths. Within your STEM degree, make sure to focus on:
- Statistics: Time series, Statistical modelling, Data science/Machine learning, Stochastic processes
- Finance: Black-Scholes/Options, Derivatives, Quant Finance
- Programming: Python/C++. (You can do most interviews in Python these days)
I would also recommend taking coursework modules where you complete projects which involve data / building something. These projects can be mentioned on you CV and can make good conversation in interviews — especially if you don’t have any work experience yet.
Quant interviews usually involve a mixture between Maths, Finance, Statistics, Programming & Brainteasers. You need to make sure you at least have a basic understanding in all of these areas so that you can at least start answering an interview question. If you manage to get started, the interviewer can give you hints if you get stuck.
Here are some of the best books/resources which can help prepare you for Quant interviews:
- A Practical Guide to Quantitative Finance Interviews
- Heard on the Street: Quantitative Questions from Wall Street Job Interviews
- Quant Job Interview Questions and Answers
- 150 Most Frequently Asked Questions on Quant Interviews
- An Interview Primer for Quantitative Finance on Github
A significant part of quant interviews will be programming. These interview will consist of Algorithms and Data Structure questions — which are very different from actual programming. Make sure you do a lot of practice to learn the patterns of solving these questions.
The best resources to practice programming interview questions are:
- Leetcode (my personal favourite)
- HackerRank
- Cracking the Coding Interview: 189 Programming Questions and Solutions
If you find that you cannot answer a question after 10 mins of thinking, I recommend you to read the solution and try to understand what it’s doing.
I highly recommend Neetcode on YouTube for Leetcode solutions. He’s by far the best resource for Leetcode solutions, explained in an simple and easy to understand way.
Also, there are thousands of questions on Leetcode which can make it overwhelming to choose which questions to work on. I recommend you start with the Blind 75 questions which is a list of the most common types of interviews questions. Completing these questions will give you the foundations of answering most other interview questions.
Personal projects is a great way to apply the knowledge you have acquired to real-life problems. They give you things to talk about with your interviewer and can help fill up space on your CV when you don’t have much work experience.
It also shows that you are genuinely interested in Quantitative Finance and that you’ve spent some of your free time learning and working on Quant projects.
Here are some ideas which can help you get started:
- Backtest some trading strategies which you found
- Participate in a data science competition: Kaggle, Numerai, Crunch DAO
- Connect to a broker API (such as Alpaca) and create a trading algorithm
In this article, I have shared tips and given the list of resources I’d use if I had to start over with becoming a Quant again. I have gone through:
- Skills needed to become a Quant
- Types of Quant jobs
- What to study at University
- Quant Interview Books
- Programming Interviews
- Personal Projects to add to your CV
If you liked this article and would like to see more, please give it a like and subscribe support my content.
I’m considering compiling a guide for Quantitative Finance interviews. It will include:
- Valuable tips on securing interviews efficiently and avoiding the time-consuming process of applications
- A detailed overview of the interview process, including best practices and strategies for success.
- A comprehensive interview preparation roadmap
- Technical practice questions including: Probability & Statistics, Finance & Derivatives, Python, Algorithms, Linear Algebra, and more.
- Insightful questions to ask your interviewer to show your interest.
- Proven ways to excel in HR interviews.
- My real-life interview experiences + questions that were asked.
If you’d like to ace your next Quant interview, join the waiting list: here
- Twitter: @quant_prep
- LinkedIn: Quant Prep
- Substack: Quant Prep
- Free Stoikov Market Making Code: Here
- Free BTC Options Notebook: Here
