Quant Study Resources

These are resources I compiled and used while preparing primarily for quant research roles. The list is geared toward systematic equities and equity alpha research (statistics, econometrics, time series, optimization, and market microstructure), so it includes less on derivatives pricing and C++ focused quant dev material.

The resources below are unordered. I recommend starting with the guides ("On Becoming a Quant," "The Green Book," and Keigo Hayashi's workshop) to map your gaps, then going deeper on the areas that need the most work for your target role. There are many more resources out there, and I did not study through every item here in full. Treat this as a reference list and prioritize the most important lectures or chapters for your needs.

Video playlists

• Econometrics (Ben Lambert)
• Machine Learning (mathematicalmonk)
• MIT OCW Financial Math
• Numerical Linear Algebra (Computational Linear Algebra)
• Probability and Stochastics for Finance Part I
• Probability and Stochastics for Finance Part II
• Convex Optimization (Stephen Boyd, 2023)

Articles and guides

• On Becoming a Quant (Mark Joshi)
• Max Dama on quant trading (summary)
• QuantConnect Introduction to Financial Python
• UChicago FinMath Quantitative Research Workshop (Keigo Hayashi)
• Econ 424 Lecture Notes (Eric Zivot) - financial economics and portfolio math

Repos

• QuantResearch (letianzj)

Books

• Active Portfolio Management - Richard C. Grinold, Ronald N. Kahn
• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, Jerome Friedman
  • A guide on important ESL chapters
• Practical Guide to Quantitative Finance Interviews (Green Book) - Xinfeng Zhou
• Trading and Exchanges - Larry Harris
• Analysis of Financial Time Series - Ruey S. Tsay
• All of Statistics - Larry Wasserman
• Introductory Econometrics - Jeffrey M. Wooldridge

Side note: These are often covered in coursework, but they are foundations you cannot skip. If you feel shaky on any of them, work through these first.

• Stats 110 (Harvard)
• Linear Algebra (MIT, Gilbert Strang)