Advisor, operator, investor, and sports fanatic. Deep financial and analytical background, working at the intersection of data, analytics, and AI for several years.
My focus is on building the data and AI infrastructure that grows revenue, generates operational efficiency, and drives enterprise-wide adoption.
Having executed at the intersection of finance, product, and business intelligence, I understand what it takes to move from insight to action at scale.
Migrated personal website from Jekyll to Hugo, replacing a stale Jekyll 3.6.3 + Minima stack (last updated ~2021, Ruby 2.3.1) with Hugo 0.160.1 + PaperMod theme, that is now deployed via GitHub Actions to GitHub Pages.
Key technical work:
<details>/<summary> (no JS), thumbnail support with full-width banner and lightbox, and a reusable partial as a single source of truth across the home page and Projects page
I recently collapsed several days of work into roughly three hours, and the end result was better than what I would have produced the traditional data science way.
The project followed a familiar path: read files into a Jupyter notebook, work through EDA and data transformations to understand what I have and figure out how to best synthesize and present my findings. The key difference for this project is that I used Claude Code throughout.
Python syntax I used to search for on Stack Overflow, Claude handled directly. When I ran into issues with the synthetic dataset, I asked Claude to review it and tell me if it saw the same problems that I did. Once we agreed on fixes, Claude normalized the data and kept a running record of the adjustments. Last, what would have been an intractable challenge for my front-end skills took about 20 minutes of iteration to produce a finished dashboard.
I’ve been using AI daily for analysis for several years, but the past few weeks ramping on Claude Code and Claude in Excel have felt much different. The ability to move from concept to working product quickly and spend most of my time on insights rather than implementation is here.
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. ...
If you would like to read Part 1 of this Series, please find it at this link. A fair amount has happened since my Scaling Analytical Insights with Python (Part 1) post back in August. Since that time, I decided to resign from FloSports in order to join Amazon’s Kindle Content Acquisition team as a Sr. Product Manager -- this obviously includes moving my family from Austin, TX to Seattle. While this was taking place behind the scenes, I had every intention to return to my write up of Part 2 of this series. Note that I’ve decided to put Part 3 on hold, and I may potentially not revisit the topic of using Python for financial analysis for a decent while. ...