Intro to FinTech Using R
What you'll learn
- Learn how to time stock market using probability theory
- Learn the qualitative and quantitative nature about stock market timing
- Learn the basics of asset pricing theory
- Learn the intermediate and advanced asset pricing practices
- No programming experience needed. You will learn everything from the class.
This course provides basic introductory guidance to FinTech. We cover three sections: (i) basic statistics in money management, (ii) stock market timing, and (iii) asset pricing. This course is for financial and technology enthusiasts. We will learn a few fundamentals and I am really happy to share all of my knowledge to you for FREE! Most of my friends know me as the guy who has been both retail traders and institutional traders on Wall Street. They consider me the go-to guy when it comes to financial problems. What most people do not know is how I get myself started. This is why I put together this course that is uniquely based on my personal passion and career choices. You will be using an easy programming language R to learn some basic statistics in money management. Then I will teach you to time stock market and build trade-able factor-based algorithms from scratch. Each lecture is a live coding round in R directly and I will walk you through the code block by block to explain the functionality. This course is not designed for you to master R or to becoming a professional trader. However, this course provides some of the most basic rules of thumb and intuition that every successful trader and institutional hedge fund managers know. I hope you will enjoy the content as much as I do!
Remark: Udemy charges when contents have more than 2 hours. Message me for additional materials and I will make it up for you to ensure it is FREE for you!
Who this course is for:
- This is an introductory course to teach you to use basic R language.
- This course teaches you the fundamentals of stock market timing.
- This course teaches you the fundamentals of asset pricing and build a trade-able factor-based algorithm from scratch.
I was a PhD student in Statistics at Columbia University from September of 2020 to December of 2021. I had a B.A. in Mathematics, and an M.S. in Finance from University of Rochester. I have a wide range of research interests in representation learning: Feature Learning, Deep Learning, Computer Vision (CV), and Natural Language Processing (NLP).
I am currently a Senior Data Scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. Prior, I have held professional positions such as enterprise-level Data Scientist at a EURO STOXX 50 company Bayer, quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street. I supervise a small fund specializing in algorithmic trading (since 2011, performance is here) and real estate investment. I also run my own monetarized YouTube Channel.