JimmyChill VS seaborn

Cramer 6 month percent returns, post pandemic Cramer 6 month percent returns, pre pandemic SPY 6 month percent returns, post pandemic SPY 6 month percent returns, pre pandemic

Jim Cramer. The man, the myth, the meme. He is a perennial presence on CNBC and a favorite of the boomers. There are a wide range of opinions on the man, from shill to lunatic to conman. Before I started this project, I personally fell in to the "Cramer is a lunatic with a button budget larger than North Korea's" camp. But how good is he at picking stocks? Looking to the past, there are many articles that show that he is not great at picking stocks, but with the recent pandemic and the wide range of opinions, I wanted to take a statistical and data driven look at his performance of his recommended stocks from his show, Mad Money, to understand if he was underperforming, and if so, in what way. So to that end, I extracted the data from his website, wrote a few python scripts to analyze the data to generate probability density function approximations for 1, 3 and 6 month percent returns for his stock picks (n = ~5500), and then compared those picks to probability density function approximations from the same time period of the $SPY and $QQQ. Again, I have put those things up at the GitHub Repo, so feel free to take a look at that.

I spent some time compiling the buy recommendation data on Jim Cramer's Stock Picker and have compiled all the data into a text file and created a GitHub repo for the project, which can be found here. The text file buyStocksCramerFormatted.txt on the repo has the data in CSV format along with source text files and source which can be easily processed and used by anyone here interested in doing the same.

If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.

I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.

I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.

For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.

Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).

The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.

It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).

While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.

https://seaborn.pydata.org/

Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.

Matplotlib Seaborn Example data sets

Seaborn - Statistical data visualization using Matplotlib.

Github: https://github.com/mwaskom/seaborn

Seaborn Documentation

You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts

I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340