Thread by @Vertox_DF

Here is what I'd do step by step if I were to start over with quant:
Please retweet so that as many people as possible can find out about those resources.

1/n

First of all I would learn the basics of python so that I can start implementing things asap.
Good Resources:



2/n

Then I'd learn some linear algebra, quant involves working with a lot of data and transforming it in various ways.
You won't get far without knowing lin alg.

Good Resources:
youtube.com/@ritvikmath


math.uwaterloo.ca/~hwolkowi/matr…

3/n

Then I'd learn statistics which I think is self explanatory.

Good Resources:




4/n

After that I'd learn some data science which basically uses lin alg and stats to work with data.

Good Resources:
youtube.com/@statquest
youtube.com/@Datasciencedo…
youtube.com/@ritvikmath


5/n

I think those are the only prerequisites you need in order to get into quant.
95% of the stuff you learn won't stem from quant finance but may come from for example:

-Neuroscience
-Physics
-Engineering

and many more.

6/n

You will learn most of this stuff as you do different projects.
That's what I love most about quant, there is an infinite amount of stuff to learn from all kinds of professions.
Now that you know Lin alg, Stats, Data Science and can code you are ready to start with quant.

7/n

To finally start learning quant I would first read all 3 books written by @chanep in the following order:

Quantitative Trading 2nd ed.
Algorithmic Trading
Machine Trading

Those books will give you an easy overview of different topics in quant that you can explore later on.

8/n

Here are some other general quant books I'd read:

-Finding Alphas: A Quantitative Approach to Building Trading Strategies - Igor Tulchinsky
-Frequently Asked Questions in Quantitative Finance - Paul Wilmott

9/n

Now I'd start reading books on more specific topics.

Options:
-Option Volatility and Pricing - Sheldon Natenberg
-Options, Futures and Other Derivatives - John C. Hull
-All of @SinclairEuan 's books

10/n

ML:
-Machine Learning for Algorithmic Trading - Stefan Jansen
-Advances in Financial Machine Learning - Lopez de Prado

Time Series Analysis:
-Analysis of Financial Time Series - Ruey S. Tsay

Signal Processing:
-Digital Signal Processing: An Introduction - R.Anand

11/n

Portfolio Management:
-Advanced Portfolio Management - Giuseppe A. Paleologo
-Quantitative Portfolio Management - Michael Isichenko
-Advances in Active Portfolio Management - Richard C. Grinold

12/n

This should give you the basic knowledge of the topics you come across the most in quant.
From here you can specialize even more. You mostly do this by reading, implementing and improving research papers and then working on coming up with your own ideas.

13/n

An example from statistical arbitrage:
Implement the SMRP paper in crypto, figure out what works and what doesn't and try to improve it.
How many assets do you want? Is the portfolio mean reverting enough? Is it volatile enough? etc.

14/n

I hope you enjoyed my first thread. Feel free to comment other resources under this post, I will work on updating the thread and adding your suggestions.
If you have any questions feel free to dm me.

15/n