Posts

One of my favorite generative AI/AGI writers is Professor Ethan Mollick, whose latest book is “Co-Intelligence: Living and Working with AI” and who is also the author of the substack One Useful Thing. As someone with early access to the latest frontier models from labs including Open AI and Anthropic, he’s one of my first reads when the latest models are released so that I can quickly be up to speed on the advancements and new capabilities. And while Professor Mollick finds AI remarkable and shares compelling use cases for the technology, he also talks about “the jagged frontier”. The jagged frontier is a concept that describes the uneven and unpredictable boundaries of AI capability. In essence, AI can perform complex, expert-level tasks, such as math and coding, with remarkable efficiency and efficacy, while failing at seemingly simpler tasks that lie outside the capability reach of the models. In his GPT-5.5 post, he mentions that “every few months a new model arrives…[and] the size of the leaps grows each new release cycle. The jagged frontier is still there. It is just much further out than it used to be”. ...

Evaluating home purchase scenarios and associated investment opportunity costs. Photo by Breno Assis on Unsplash. Part 1 of Home Purchase Scenario Model Analyses. Introduction. For those who have previously considered or are considering the purchase of a home, then this post is meant for you to help customize your scenarios and vet your available options. I hope that you find the included financial model, available here, informative as you evaluate potential home purchase scenarios. This post and model should help you understand the full costs associated with home ownership and the inherent opportunity costs of not investing downpayment and ongoing maintenance costs in other assets, e.g., passive market ETFs. ...

Extending Stock Portfolio Analyses and Dash by Plotly to track Robo Advisor-like Portfolios. Photo by Aditya Vyas on Unsplash. Part 3 of Leveraging Python for Stock Portfolio Analyses. Introduction. This post is the third installment in my series on leveraging Python for finance, specifically stock portfolio analyses. In part 1, I reviewed a Jupyter notebook with all of the code needed to extract financial time series data from the Yahoo Finance API and create a rich dataframe for analyzing portfolio performance across individual tickers. The code also included a review of some key portfolio metrics with several visualizations created using the Plotly library. In part 2, I extended Part 1’s analyses and visualizations by providing the code needed to take the data sets generated and visualize them in a Dash by Plotly (Dash) web app. ...

Expanding Jupyter Notebook Stock Portfolio Analyses with Interactive Charting in Dash by Plotly. Part 2 of Leveraging Python for Stock Portfolio Analyses. In part 1 of this series I discussed how, since I’ve become more accustomed to using pandas, that I have signficantly increased my use of Python for financial analyses. During the part 1 post, we reviewed how to largely automate the tracking and benchmarking of a stock portfolio’s performance leveraging pandas and the Yahoo Finance API. At the end of that post you had generated a rich dataset, enabling calculations such as the relative percentage and dollar value returns for portfolio positions versus equally-sized S&P 500 positions during the same holding periods. You could also determine how much each position contributed to your overall portfolio return and, perhaps most importantly, if you would have been better off investing in an S&P 500 ETF or index fund. Finally, you used Plotly for visualizations, which made it much easier to understand which positions drove the most value, what their YTD momentum looked like relative to the S&P 500, and if any had traded down and you might want to consider divesting, aka hit a “Trailing Stop”. ...

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. 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. ...