List of resources for aspiring quants

A few useful resources for Quantitative Finance

In this article I will be going through some of my recommendations on Quant resources for the following topics:

• Guides on how to become a Quant

• Derivatives

• Trading

• Machine Learning / Statistics

• Github

• Data Science competitions

• IDEs

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Guides on how to become a Quant

A few online guides on how to become a quant

• On becoming a quant - Mark Joshi

• G-Research Quantitative Researcher Assessment Process Guidance

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Derivatives

These are my book recommendations for derivatives. They cover a broad range of derivatives such as: options, forwards, futures, bonds, swaps, cap floors, swaptions.

• Options, Futures and Other Derivatives - John C Hull

• Options Pricing and Volatility - Sheldon Natenberg

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Trading

These are my book recommendations for quantitative / algorithmic trading. You can use these books to give you ideas for your own trading strategies or they can be topics of discussion with your interviewer.

• Algorithmic Trading - Ernest Chan

• High Frequency Trading - Irene Aldridge

• Inside the Black Box - Rishi K Narang

  

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Machine Learning / Statistics

Here are some of my recommendations for Machine Learning and Statistics. These resources will be particularly helpful for Quantitative Researcher roles which involve a lot of statistics and machine learning.

• Advances in Financial Machine Learning - Marcos Lopez de Prado

• Elements of Statistical Learning - Hastie, Tibshirani, Friedman

• StatQuest by Josh Starmer on YouTube

• Machine Learning Specialisation by Andrew Ng on Coursera

• Deep Learning Specialisation by Andrew Ng on Coursera

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Github Repositories

Here’s a few good Github repositories which contain some interesting quant projects. You can use these for inspiration for you own project ideas or as a resource for learning.

• https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading

• https://github.com/edarchimbaud/awesome-systematic-trading

• https://github.com/EliteQuant/EliteQuant

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Data Science Competitions

There are plenty of online data science competitions to participate in. Working up to Kaggle would be a good idea: they have become an industry standard as their prediction problems are close to a lot of quant work.

• Kaggle: Many top firms such as Optiver and G-Research often host competitions on Kaggle

• Numerai: Predict equity relative returns for a long-short portfolio using machine learning

• Crunch DAO: Predict equity relative returns for a long-short portfolio using machine learning

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Integrated Development Environments (IDEs)

My IDE recommendations:

• PyCharm (All the Jetbrains IDEs are great but some features require payment)

• VS Code: even supports Jupyter Notebook

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Interview Guide

I’m 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

Other Media

• Twitter: @quant_prep

• LinkedIn: Quant Prep

• Medium: @quant_prep

• Free Stoikov Market Making code: here

• Free BTC Options Scenario Analysis code: here