For years, I would download historical prices and load the data into the financial model - while online brokers calculate realized and unrealized returns, as well as income and dividends, I like to have historical data in the model as I conduct my own analyses to evaluate positions. Overview of PME and benchmarking individual stock performanceĪs a quick background, I have been investing in my own stock portfolio since 2002 and developed a financial model for my portfolio a number of years ago.
In this post, I will share how I leveraged some very helpful online resources, the Yahoo Finance API (requires a work around and may require a future data source replacement), and Jupyter notebook to largely automate the tracking and benchmarking of a stock portfolio’s performance.
While I left early on in that implementation, over the past several months I’ve continued using Python extensively for financial analyses, particularly pandas. Prior to my departure at my former company, FloSports, we were in the process of overhauling our analytics reporting across the organization (data, marketing, product et al), and part of this overhaul included our financial reporting. It has been several months since I wrote those, largely due to the fact that I relocated my family to Seattle to join Amazon in November I’ve spent most of the time on my primary project determining our global rollout plan and related business intelligence roadmap. My two most recent blog posts were about Scaling Analytical Insights with Python part 1 can be found here and part 2 can be found here.
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