Sometimes you want to store your model output in a dataframe, for example when running grouped regressions so you’ll have multiple regression outputs to review and sort etc. But while using pmdarima’s auto_arima() function I was surprised to learn there’s no summary2() as you’ll find in statsmodels. Instead you’ll get that same retro block of text which has been familiar to users of Python scikit-learn and R’s glm() for two

Using Daloopa API to pull SIVB balance sheet data. Code base here: https://github.com/jed-gore/bank_assets_chart I was curious whether it was easy to SEE what drove the issues at SIVB. I noticed a huge ramp in the balance sheet in late 2021 thru mid-2022. Turns out https://www.svb.com/news/company-news/svb-financial-group-completes-acquisition-of-boston-private2 this was in PART the acquisition of Boston Private. The deal closed 7/1/2021 so the actual reporting impact I believe would have been the quarter

ML Ops See notebook arima_forecast.ipynb in the MLOps repo. Codebase here: https://github.com/jed-gore/MLOps A test case to begin to develop a reusable, scalable ARIMA module. from statsmodels.tsa.statespace.sarimax import SARIMAX PROCESS: Using Daloopa to pull data for AMZN (you will need to get your own API key): We isolate Net Sales And difference it twice to remove the trend and seasonality: our ARIMA looks ok: The ARIMA model looks better than naive

A base class for use in inheriting. Codebase here: https://github.com/jed-gore/webcrawler_class Something to save searches because I always forget the syntax for Beautiful Soup. I’ll be adding to this to handle encoding and error handling, and specific tags. Remember: always scrape responsibly according to Terms of Service. Includes a class WebCrawler with 3 simple methods: get_html_document() get_links() and get_tables() Usage: Output: Also includes a c.py file to handle certification updates for

The goal of this project is to develop skills around serializing Python classes to and from both SQL and JSON. Codebase here: https://github.com/jed-gore/serlialized_python_objects/blob/main/README.md Reference:https://marshmallow-sqlalchemy.readthedocs.io/en/latest/ User Story:AS A financial analyst I WANT TO map my companies across various things like KPIS and SECTORS SO THAT I can visualize mappings easily in an app with JSON (and share with Javascript front end apps) but also maintain a second form database structure on the back end

Canalyst’s blog has some relevant examples of my work doing multiple stock analysis using the Candas data science library I wrote which is still in use available on PyPI.  The library is called canalyst-candas as an intentional pun on the name of the python library Pandas.

One of the most interesting challenges I faced as a portfolio manager post the 2008 GFC was a renewed interest in pairs trading as a risk mitigation strategy. And while we often paired off stocks by region or business type, I was never really sure a pair was an actual mean-reverting pair. Fortunately there’s a pretty simple mechanism for this. Cointegration is a measure of the linear relationship between two

Codebase here: https://github.com/jed-gore/stock_betas Monitoring cross-correlations inside a sector, to see if there’s enough differentiation for pair trading. As a former PM, I would avoid pair trading a group when correlations were high. This group of processors is sitting right on the mean. The chart shows a 60 day rolling window, the function takes days as a parameter.

Codebase: https://github.com/jed-gore/stock_betas/blob/main/README.md I recall a couple years ago I was looking at SQ and how much the stock traded with bitcoin. Apparently they clear bitcoin trades? Page 11 of the 2022 10K: “Customers can also use Cash App to invest their funds in U.S. listed stocks and exchange-traded funds (“ETFs”) or buy and sell bitcoin” Now that bitcoin has pulled back a bit (?) I was curious what SQ (Block)