How I Started Building My Quant Finance Knowledge 📚🚀

When I first got into quant finance, I quickly realized that structured learning made a huge difference. Instead of jumping randomly from topic to topic, I built a roadmap — starting from math foundations all the way to machine learning and portfolio management.

Here are some of the books that really helped me along the way:

Math:

• Calculus: Any standard textbook + Stochastic Calculus for Finance I & II by Steven Shreve

• Differential Equations: (Partial & Ordinary) — more suggestions in the full list

• Linear Algebra: Introduction to Linear Algebra by Gilbert Strang

Probability & Statistics:

• Probability: A First Course in Probability by Sheldon Ross

• Econometrics: Introductory Econometrics by Jeff Wooldridge, then Greene

• Time Series: Analysis of Financial Time Series by Ruey Tsay

Programming & AI:

• Python: (covered in the full list)

• Machine Learning: Advances in Financial Machine Learning by Marcos López de Prado

Finance:

• Fixed Income: The Handbook of Fixed Income Securities by Frank Fabozzi

• Derivatives: Options, Futures, and Other Derivatives by Hull & Basu

• Portfolio Management: Active Portfolio Management by Kahn and Grinold

Miscellaneous:

• Quant Overview: Paul Wilmott Introduces Quantitative Finance (3 volumes)

• Career Inspiration: My Life as a Quant by Emanuel Derman

If you’re just starting out or looking to deepen your knowledge, these books are a great place to begin.