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Resources for developers using Python for scientific computing and quantitative analysis

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Archives for February 2021

Finance Database github.com

under Investing

As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being millions of companies amd derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found simply because they are known to the public (for example, Microsoft, Tesla, S&P500 ETF or an All-World ETF). However, what else is out there is often unknown.

under Data Science

In this article the author uses Reddit sentiment data to inform trading strategies. He derives market sentiment in two ways using the wallstreetbets subreddit:

  1. Collecting comments from daily discussion submissions then running the VADER sentiment model to assess overall daily positive/negative sentiment.
  2. Collecting all submission titles per day then assessing daily bullish/bearish sentiment using keyword analysis.

under Python

For the most part, this book follows the standard material taught at the University of California, Berkeley, in the class E7: Introduction to computer programming for scientists and engineers. This class is taken by most science and engineering freshmen in the College of Engineering, and by undergraduate students from other disciplines, including physics, biology, Earth, and cognitive sciences. The course was originally taught in Matlab, but with the recent trend of the data science movement at Berkeley, the Division of Data Sciences agreed on and supported the transform of this course into a Pythonoriented course to prepare students from different fields for further data science courses.