Data Informed Narratives

Economics of Home Ownership Deep Dive

Mon, 10 Aug 2020 12:00:00 -0500

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.

Python for Finance: Robo Advisor Edition

Sat, 06 Apr 2019 12:00:00 -0500

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.

Python for Finance: Dash by Plotly

Fri, 13 Jul 2018 12:00:00 -0500

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”.

Scaling Financial Insights with Python

Sun, 04 Mar 2018 12:00:00 -0500

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.

Scaling Analytical Insights with Python (Part 2)

Wed, 11 Oct 2017 12:00:00 -0500

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.

Scaling Analytical Insights with Python (Part 1)

Sun, 09 Jul 2017 12:00:00 -0500

In recent months, I’ve written about some of the critical undertakings and initiatives which I oversee as VP of Product at FloSports. These have included my efforts to build a data informed culture through product experimentation, our overall approach to our analytics tech stack, and our approach to building and reviewing our rolling financial forecasts.

Data Informed Rolling Forecasts

Sat, 20 May 2017 12:00:00 -0500

As VP of Product at FloSports, I oversee our data warehouse roadmap and manage a team consisting of a senior product manager, product revenue developers, data engineers, and data analysts. Some of the responsibilities that I really enjoy about this work include working at the forefront of business intelligence, leveraging data and product analytics tools such as Periscope Data, Mode Analytics and Segment, and collaborating with very bright people to drive measurable results and accelerate our growth.

Building an Analytics Tech Stack

Wed, 22 Mar 2017 12:00:00 -0500

In an ongoing attempt to be helpful to and learn from others serving in similar job capacities, I am continuing to review my experiences in building out our data infrastructure over the past ~12 months and discuss the most helpful applications which currently sit within our analytics tech stack. In my most recent post, Data Informed Experiments, I discussed the difference between being “data informed” and being “data driven”. I also provided some perspective related to building a “data informed” organizational culture – my intent is to share what we have learned along the way and discuss the applications which enable us to take advantage of all of the work we’ve completed to-date.

Data Informed Experiments

Mon, 27 Feb 2017 12:00:00 -0500

I am presently VP of Product (Revenue and Analytics) at FloSports, a direct-to-consumer provider of live streaming and on-demand digital sports content. In this role, I focus on product innovations which can drive top-line growth while also overseeing analytics (business, product, and data, among others). Recently, our CEO Martin Floreani has spoken about the business intelligence platform we have been building out since last year, which is internally known as Neptune. His interview with Sports Techie, where he discusses the impact Neptune has had on the business, can be found here. I have been fortunate to be one of FloSports’ founding members of Neptune and am responsible for overseeing both our data source and business intelligence reporting roadmaps.

NCAA Must Have Students Best Interests

Thu, 01 Oct 2015 12:00:00 -0500

As an alum of SMU and a staunch follower of the athletics program, particularly men’s football (well, starting again this year) and men’s basketball, I am inherently biased. No matter the decision that the NCAA levied after its 18 month investigation, post announcement I knew that I would immediately rationalize why the punishment was too harsh and plead the case for a lesser penalty.

With that said, I acknowledge that the penalties against Coach Brown and the SMU program are generally fair. Suspending Coach Brown for 30% of this coming season’s games was admitted by SMU’s President Turner to be consistent with the NCAA’s new initiative to hold coaches accountable for program infractions. And, the loss of three scholarships per season over three years also seems consistent with the punishment handed out to Syracuse for its men’s basketball program’s recent transgressions.

Kevin Durant is Someone to Emulate

Tue, 06 May 2014 12:00:00 -0500

Major sporting events, such as the Super Bowl, NCAA March Madness tournament, and the Masters were a huge part of my life while I was growing up in Atlanta – and they certainly continue to be so today. In some respects, these events were similar to holidays in that the entire family would oftentimes get together, with additional friends and relatives, to cheer for their favorite athletes and hope that their respective teams would end up as champions.

About

Mon, 01 Jan 0001 00:00:00 +0000

Kevin Boller

Product, Revenue, Business Intelligence and Analytics Professional

TECHNICAL SKILLS

Programming/Data Science: SQL (Postgres, MySQL), Python (pandas, numpy, scikit-learn), R, Dash by Plotly

Databases/Warehouses/BI: Periscope Data (Cache/Views), Mode Analytics, Segment (warehouse and platform), Redshift, Tableau and AWS QuickSight.

KEY STRENGTHS

Sports Media and Sports Technology Expertise
Business Intelligence and Data Warehouse Product Management
Financial, Operational, Product and Data Analytics
Financial Modeling and Economic Model Optimization
Pricing Strategy Analyses and Advisory
Corporate Strategy and Business Development

My professional experience spans software consulting, investment banking, product management (consumer and enterprise), data warehouse product ownership, and data analytics leadership.

Investing

Mon, 01 Jan 0001 00:00:00 +0000

I’m ramping up my activity in the PNW as an active angel investor and advisor to early-stage companies.

In the spirit of being a founder-friendly investor and advisor, below are some helpful founder resources:

Grubstakes – 20+ individual investors in Seattle that fund and mentor startups in the Pacific Northwest.
Seattle Angel – Seattle Angel is a not for profit organization, focused on educating angel investors, entrepreneurs, and prospective team members about the ins and outs of raising startup capital, scaling business operations, and related legal and financial issues.
UW Comotion – CoMotion® is UW’s collaborative innovation hub dedicated to expanding the economic and societal impact of the UW community and beyond.
WeWork Labs – global community of early-stage startups with membership benefits including workspace, mentorship and education.

I’ve lived in Seattle for a few years after being born in Portland, OR, growing up in Atlanta and living a bit all over – Dallas, Chicago, NYC, and Austin. My background includes co-founding a sports tech start-up (LeagueCast, 2013 - 2015), serving as VP of Product at a direct-to-consumer live sports company (FloSports, 2016 - 2017) and now as a product manager of a greenfield reporting and analytics solution at Amazon (2017 - present).

Resources

Mon, 01 Jan 0001 00:00:00 +0000

My resources page has some helpful starting points for people looking to expand their skill set and learn more about data analytics and data science.

This page also catalogs and organizes the helpful resources I’ve leveraged and/or am in the process of learning from.

Data Warehouse | Business Intelligence Applications

Best Blogging Sources

Greg Reda: Blog posts on web scraping and data analysis using Pandas.
Growth Hackers
David Skok
Coelevate
Python for Business: leveraging Python, in place of traditional tools such as excel, for business tasks

Online Courses