StockAnalyzer: Resuming Transmission

After focusing on global pandemics and relocations for the last months, I am able to resume focusing on my pet projects, but at a slower pace than before. I expect to post at least once per month.

I'll continue coding for the machine learning project StockAnalyzer.

Earlier this year, I explored NodeJS. I'll switch to Python instead, since it is more flexible and since I'm more familiar with it.

StockAnalyzer
I'll document the project in a separate page on the blog. The source code is available on my GitHub page.

StockAnalyzer will both present a number of graphs of how some key numbers are evolving over time.

StockAnalyzer will also perform an automated scan of all key numbers for all records over time in order to detect flawed data, where the data formats are OK, but the numeric values appear to be invalid. This will be done later.

Here, some key numbers are shown over time.
The graphs will help me find outliers and understand the data.

The first step is to present the data graphically - the vast amount of data will make it impossible to just look at the numbers.

Several of the curves appear to have identical shapes. The curves that relates the price to the earnings and capital will be similar to the price curve. This because the earnings and capital per share doesn't change very often. The yield is the dividend divided by the price and that is inversely correlated to the stock price.

In the next blog posts, I'll keep exploring the data.

In the following blog posts, I'll explore the data