Below is a collection of resources relevant to quant finance that I’ve collected or personally found useful. Quant finance can often seem like a field shrouded in mystery. Hopefully, this list can help illuminate certain aspects of the industry you may be interested in learning more about.
If you have any suggestions or comments on the contents of this article, especially if you think something should be added to this list, feel free to email me at email@example.com.
Good Introductory Articles
Max Dama’s blog has some great readings on Quantitative Finance. I would recommend starting with some of the articles I’ve referenced in this list from his collection.
Taken from Max’s blog, this article gives a dense but thorough overview of how a more research-focused quantitative finance firm is structured, along with the tasks and expectations assigned to the different groups within the firm. This analysis applies well to more systematic firms like Jump Trading, HRT, IMC, and some teams within Citadel, amongst others.
Useful Articles and References
An article by Max Dama which covers which chapters from Elements of Statistical Learning (ESL) are the most useful for a career in quantitative finance. ESL is often considered a go-to text and reference for practitioners of machine learning and statistical modeling, so I would recommend going over the textbook if you’re interested in these topics. If you’re also focusing on quant finance, pay particular attention to the chapters outlined by Max. You can usually find a free copy of ESL online pretty easily.
This article does a great job of explaining the statistical intuition behind the assumptions of Linear Regression, an important interview topic for quantitative research roles and an extremely important tool on the job as well.
Another recommendation from Max Dama, Google’s Rules of ML guide serves as a practical primer on ML engineering. When it comes to the job, traders, devs, and quant researchers often find themselves spending large chunks of time engaging in ancillary tasks such as data cleaning, pipelining, and other work adjacent to ML Engineering. This makes knowledge of these skills especially important to increase productivity when doing more relevant tasks such as research or exploring data.
Interview and Recruiting Preperation
If you’re looking for a primer on probability and regression, look no further. Berkeley’s Data 140 is the best introductory course on applied probability in a data science context, with a leaning towards framing, intuition, and practice problems that strongly aligns with what one can expect during a trading or research interview. Whether you’re a beginner looking to deeply learn probability or an experienced CLT enjoyer looking to brush up, the course’s textbook and practice problems and perfect for you.
Kaggle is your one-stop shop for practical data science. I recommend going through a couple of tutorials and giving ur own hand at some beginner competitions using classicial models such as Linear/Ridge/Lasso/Logistic Regression and Trees/Forests as a way to practice for the data science portion of trading and research interviews.
While leaning on the easier side for interviews with top firms, Quantguide serves as a decent repository of extra practice questions. I wouldn’t recommend starting here though, and instead focusing on learning the fundamentals of probability/statistics first and understanding relevant questions from the Green Book.
It’s often said that while books on options theory and trading are great in, well, theory, they tend to break down in application. It’s important to remember that any strategy or wisdom within these books has already been tried and most likely used up by the big players in the space. As a consequence, most traders don’t recommend prioritizing these readings. However, If you’re interested in broad self-learning, these can be interesting guides to get started in their respective subjects.