Data-driven stock quant investment with Python Part 1
In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.
1,849 learners
Level Basic
Course period Unlimited
[20% discount on all classes] Celebrating 4,000 cumulative students
Hello. This is Deepingsauce, Infraon knowledge sharer.
The cumulative number of students we announced at the start of summer, 3,000, has increased to 4,000 as summer draws to a close in the last month of August!
To thank you for your warm support, we have prepared a 20% discount event for all lectures to celebrate the 4,000th person milestone, just like last time (until 23:59 on August 31, 2021)
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1. Python that anyone can learn, whether they are a liberal arts student or a non-major!
2. Python Web Crawling & Automation to Replace My Work (feat. Stock, Real Estate Data / Instagram)
3. Data-based stock quant investment with Python Part 1
Through this opportunity, I hope to provide students with a chance to further improve their quality of life at a slightly lower price.
Also, there have been many inquiries about the second class of the existing 'Data-based Stock Quant Investment with Python' course, 'Part 2', and it will be opened in September at the earliest or October at the latest.
We would like to once again express our gratitude to the students who have been waiting for a long time. We ask for your interest and anticipation as we have prepared better quality content than the previous lectures to compensate for the delay.
thank you