A Guide to Quantitative Finance
About the Writer
Hello! I'm Horstann, a proud Malaysian and Data Science & AI undergrad at NTU (Batch 2025), where I developed my love for quantitative finance. I've successfully published a machine learning in quant finance research paper as part of a book on Springer Nature.
To experience how quant finance is like in the industry, I've also interned at numerous firms ranging from hedge funds, systematic prop trading shops to investment banks, from which I received multiple full-time return offers. These internships allowed me to get my hands dirty with mathematical modelling and machine learning applied in sub-areas like alpha research, derivative pricing and more. Upon graduation, I'll be returning to Goldman Sachs as a full-time quant strategist/researcher.
I hope my write-up will help you decide if quant finance is right for you, and if so, get you a head-start in the field like I had when I started!
What do Quants Do?
Quantitative Finance combines financial theory, mathematics, and computer science to solve complex financial problems and develop sophisticated trading strategies.
Overview about Quant Researchers
Quantitative researchers/strategists/analysts use or develop mathematical/statistical models/algorithms to analyze data and derive insights about the financial markets, which in turn can help drive trading or investment decisions.
What firms hire Quants?
These types of quants are often found in hedge funds, proprietary trading shops, banks or even in advanced academia. They can be working with any major asset class – from equities (stocks), commodities, currencies, fixed income, cryptocurrencies to futures, options and other exotic financial derivatives.
What are some available roles within this field?
Below are 3 types of quant researchers/strategists/analysts (general and non-exhaustive):
- Alpha/Strategy Researchers – They research new or maintain existing trading/market-making strategies/models, which helps the firm generate revenue.
- Derivatives Specialists – They work on pricing or structuring financial derivatives, as well as help traders or portfolio managers risk-manage or hedge positions. This often requires knowledge in stochastic calculus and derivative instruments.
- Quant Portfolio Managers – They work on portfolio optimization problems (how money is allocated across their portfolio of assets or strategies/alphas) and may often lead the general research direction of their team. Though these roles are often reserved for senior quants.
How is this field important in today's industry?
The growing availability of advanced computing power and big data technologies have transformed the finance industry, and quants are a big part of that arms race. They are hired to leverage these nascent technologies to drive advanced research that was not possible before, which are essential in helping financial firms innovate and stay competitive with their peers.
Personal Journey
How I got started in Quantitative Finance
I started my first year in uni with 0 idea about quant, but an undying passion for data science and AI, which happens to be my major! The core of that passion was about the synergies between mathematics and computing, alongside how it can help us uncover deep insights from data in ways I, at the time, could not imagine.
At the end of my first year, I noticed a job posting by a quant hedge fund hiring quant research interns, read its job description, and realized it has been what I wanted to do all this while. Since then, it has been quant all the way, even up until now, my final year in uni. I believe the idea of quant enthralled me in the first place because of its multidisciplinary aspect – combining applied mathematics, data science, coding and finance. It was a field that I felt I could never get bored of, constantly having so many new things to learn; so many new things I had 0 idea about. Even on the job, quant allows you to collaborate with a diverse array of people – Bachelors, Masters and PhD graduates from varying backgrounds like CS, math, engineering, theoretical physics, economics, operations research and so on. The level of breadth and depth (and in turn, learning opportunities) felt unparalleled compared to any other field I knew.
How did your internships/research experiences shape your understanding?
Through my various quant and data science internships at hedge funds, prop firms and banks, I got to understand how different quant roles function and how even the same type of role could have varying responsibilities in different types of firm. Several SPMS modules I undertook at NTU and my exchange at NUS also allowed me to further reinforce my technical knowledge for quant. I even conducted and published my research under an SPMS prof who specialized in computational finance and ML, which allowed me to apply my knowledge to the most recent cutting-edge research areas within quant finance. On the side, I also connected with very helpful peers and seniors from my major and beyond who have landed amazing quant finance roles. Through them, I have learnt so much about possible quant career options and each firm's specific practices/interview styles.
What are the key lessons you learned along the way?
Be humble, passionate and curious. Quant is a relatively niche field that few people know a lot about, so it's natural to feel daunted at first. What I'd recommend is to be unafraid, and let your passion and curiosity guide you. Be unafraid to reach out to people of similar interests – that's the best way to learn. As long as you're kind and considerate, people are usually always willing to share knowledge with you. Always assume that people will know more than you (it was definitely true in my case).
Besides that, it is important to start as early as possible, if you're certain that quant is what you hope to pursue. Quant hiring is known to be competitive and selective, so having an early head start in preparing for interviews and building your resume will definitely give you an edge. Of course, I would never recommend rushing into anything you are unsure of, please take the time to explore the different CS fields until you find one that truly aligns with you.
Recommended Learning Paths
Core Prerequisites
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Probability and Statistics. It goes without saying that this is the most basic prerequisite for any quantitative role. Concepts like probability distributions , hypothesis testing, conditional probability and more will give you a fundamental framework to think and reason like a quant. These will also come in handy when you're faced with games and puzzles in interviews.
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Linear Algebra and Calculus. This is also crucial in understanding many things related to machine learning and high-dimensional statistics, which arise often in quant. Concepts in these areas are also heavily tested in interviews.
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Data Structures and Algorithms (DSA). Common in many CS fields, DSA helps you understand how your programming language handles data and how you can optimize your code to run faster with less memory. Quant interviews may also have a DSA component, so practice your leetcode often.
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Puzzles and games. Similar to DSA, puzzles and games may be used in interviews to assess your ability to think outside the box. So practice often too. Such puzzels and games can be easily found in online resources and books.
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Machine Learning (ML). Whether you've done ML projects or studied ML in school, if you're pursuing a career in quant research, ML is unavoidable in interviews and on-the-job. Models like regression, principal component analysis, trees, support vector machines, neural networks and more, are used very frequently in quant.
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Financial Mathematics. This includes understanding of options and derivatives, option pricing theory, as well as stochastic calculus and simulation techniques for finance. This may be very crucial for roles involving financial derivatives.
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General Programming Concepts. This includes areas like object-oriented programming, clean code, memory management, parallel programming and more. Having knowledge in these areas will help you compare between different programming languages and understand how they work behind the scenes, as well as their strengths and weaknesses. Having such in-depth understanding in your programming language can also help you better optimize your code.
Essential Technologies and Concepts
If you're not a math major, chances are, doing some math BDEs from SPMS will definitely help you, like it did me. After all, the core of all quantitative roles is math. There are also many books and YouTube videos online that can further help you understand math, statistical analysis and finance. Whatever new concepts you learn, I recommend:
- Understanding things not just at an applied level, but also at a mathematical/theoretical level – how and why things work. Interviewers will expect this level of rigor.
- Taking notes, unless you can remember very well
- Apply what you've learnt in a project, and record what you did and any newfound insights in your CV. This displays your interest to recruiters within the field.
The common programming languages in quant are Python and C/C++. Python is usually used for research and data analysis, while C/C++ is used for implementing and executing strategies or models. However, these are very general guidelines and different firms very likely use different languages, so please take this with a grain of salt and do your own research too.
The Bottom Line
From my experience, interviewers typically look for 3 things in a candidate:
- Passion/Interest – via personal projects or knowledge about the firm itself
- Knowledge – whether you deeply and intuitively understand concepts you've learnt in school and in projects
- Intelligence – whether you can think outside the box, adopt a systematic approach to solving new problems, and communicate your thought process clearly
That's about it for now. My final advice: have fun!