Hey, I just recently heard about QuantEcon. I am currently an Engineering undergrad at BITS Pilani, at India, with a lot of free time.
QuantEcon essentially brings about the best of two worlds that I am interested in - Machine Learning and Data Science, and Quantitative finance.
Aspiring to become something in this world of growing computational finance, I want to contribute to this awesome initiative and learn more about an organization that does exactly what I’m looking for.
I am quite well versed with popular machine learning and deep learning algorithms and their implementations on Python (and by extension, Julia) and also, well versed in quantitative strategies, both basic (like mean reversion) and advanced (like the ones using deep learning on a time-series).
As such, what areas of QuantEcon must I look into and get started with? Sorry if this post sounds too direct or crass. Any help would be greatly appreciated!
Hi @sauradefy99, thanks for your interest.
If you look at the org site there are a bunch of projects with GH repositories that you can consider submitting pull requests to.
You can also submit PRs directly to the lecture source via this repo:
There’s also a Julia equivalent.
If you’re not sure of some of my terminology, please invest some time learning about GitHub, and find out how to make contributions to open source projects. Small contributions such as fixing documentation are a great way to get started.
Good luck with your studies.
John.