Jason Vero

Daily learning and sharing of thoughts around data, personal finance, and investing.

[Graham Stephan @GrahamStephan]

All it took was a few Fed hikes!

[Graham Stephan @GrahamStephan]

The tech industry is going through a terrible churn right now with more than 120,000 layoffs in 2022.

But, what makes it worse is the overall direction.

September ~6,000 layoffs
October ~ 12,500 layoffs
November ~ 45,000 layoffs with still a week to go!

[Jason Vero @jasonvero]

@crispsong @BerkleeCoursera Happy Birthday! 🎉🎂

[Dividend Hero @HeroDividend]

Own companies that own brands

[Jason Vero @jasonvero]

Worked with AG Grid today, which allowed for a more interactive Streamlit dataframe.

This introduced much better sorting and filtering utility.

I especially like the capability to select rows within the main table, to then output those rows into a separate table for review.

[Nikki Siapno @NikkiSiapno]

Git Workflow 👇

[Jason Vero @jasonvero]

Tomorrow, I'll pull a few more companies in related sectors to review P/E across multiple industry peers.

[Jason Vero @jasonvero]

Studying different variations of multiples analysis to evaluate the valuation of a stock.

First up is the P/E ratio: Share price / Earnings Per Share (EPS)

Trailing P/E utilizes historical earnings data for its EPS.

Forward P/E takes expected earnings for its EPS.

[Jason Vero @jasonvero]

And I noticed high coupling in my code was I made adjustments to things such as renaming columns.

I'll need to review and optimize this in another refactoring session.

[Jason Vero @jasonvero]

More and more work with Streamlit and yfinance library.

Pulled out additional attributes of interest like dividend yield or payout ratio of a stock.

Also separated the merged data frame into different table outputs to prevent the need to side scroll.

[Jason Vero @jasonvero]

@crispsong Thanks Jun!

[Jason Vero @jasonvero]

Day 66 | #66daysofdata

And with that, this challenge is complete. Admittedly, the latter half of the challenge saw a steep drop in length of study/practice.

Either way, I've accomplished my primary goal, which was to integrate Python automation to optimize my day-to-day job.

[Jason Vero @jasonvero]

Worked more with my yfinance library. There's just so many different methods to explore, I can't seem to put it down.

After merging a couple of dataframes, I pulled the data into Streamlit with a few conditional statements to denote different colors based on percent change.

[Jason Vero @jasonvero]

More code refactoring.

While building my projects up, I start in Jupyter notebooks.

But D.R.Y principle is typically not followed during this process.

Eventually, I copy the code to a .py file to find areas to optimize design.

#66daysofdata

[Jason Vero @jasonvero]

Having some fun with Streamlit.

Using Pandas to import yfinance data on stocks of interest, exporting that data into a .csv, and then writing it into Streamlit.

Created a few derived columns based on rolling average calculations for different time intervals.

#66daysofdata

[Jason Vero @jasonvero]

@DataWithFay I wouldn't credit a single resource, but I did focus on connecting to SQL data using pyodbc and then Pandas for importing, exporting, and cleaning data.

At my job, I found areas where Excel reports were being manually created/altered and used Python to automate these processes.

[Jason Vero @jasonvero]

@suminohh @panoramicvc That's amazing! Good luck and best wishes Sumin!

[Graham Stephan @GrahamStephan]

well, something's got to give sooner or later!

[Jason Vero @jasonvero]

Day 65 | #66daysofdata

Did more work on my investment portfolio project. Hand picked a few stocks that I would like to track percent changes over different time intervals.

[Jason Vero @jasonvero]

Day 64 | #66daysofdata

Dealing with data being transformed incorrectly. I.e., leading zeros from a .CSV format being dropped when being converted from a .TXT pipe delimited format.