My career in the financial services industry has been a relentless pursuit of innovation in financial technology. With over two decades of experience, I've played strategic roles with technology vendors and as a licensed member of the capital markets. My expertise is developing trading algorithms, electronic trading platforms, and managing key institutional relationships.
I began my career at Bloomberg LP in San Francisco, where I excelled in implementing the Trade Order Management System (TOMS). My responsibilities included marketing and deploying TOMS to a diverse client base of regional broker-dealers and banks. This experience laid the foundation for my deep understanding of financial technology and its application in enhancing trading operations and market efficiency.
I transitioned from implementing to operating electronic trading systems in 2010. I served as Vice President at various broker-dealers in Dallas, Texas, including Southwest Securities, Tejas Securities, and Esposito Securities. In these roles, I was instrumental in generating significant revenue through innovative trading strategies and developing electronic trading capabilities.
My international experience as a European Client Relationship Manager
London exemplified my capability to manage and develop high-value institutional relationships. I designed and implemented custom trading solutions, driving the adoption of advanced technology platforms across the financial industry. My work with Algomi highlighted my strategic vision for the future of financial markets and my ability to bridge technology with user needs to foster significant business growth.
In 2019, I founded CatFIX Technology, Inc., demonstrating my entrepreneurial spirit and dedication to innovation within the fixed income sector. My company provides FIX protocol and fixed income marketing consulting to regional banks, showcasing my commitment to enhancing financial technology.
When COVID-19 impacted new projects with regional banks, I took on the role of Sales and Awareness Advisor at
This position allowed me to leverage my marketing expertise to promote a cutting-edge solution for the fixed income institutional investment community. This role honed my skills in strategic marketing and broadened my understanding of advanced financial technology solutions. The service went to market in early 2020 (worst luck ever!) and delivered some very profitable pair trade ideas for the early adopters of the product.
A pivotal moment in my career came when I acquired the intellectual property of Katana Labs. This unexpected move made me the owner of technology that identified bond pair trades with a high probability of mean reversion.
Intellectual Property Assets of Katana Labs Limited
Katana's intellectual property up for sale
If you're unfamiliar with pair trades, find two stocks whose prices have moved together historically. When the spread between them widens, short the winner and buy the loser. If history repeats itself, prices will converge and the arbitrageur will profit. Example using Walmart (WMT) and Costco (COST in the stock market.
Both companies excel at selling products but have different business models. Costco sells in bulk and requires a membership, while Walmart has numerous stores and sells individual items. Their stock prices are highly correlated and usually move together.
Now, imagine Costco starts doing exceptionally well, and people flock to shop there. Their stock price surges, but Walmart's stock price remains relatively unchanged. This scenario is known as a "dislocation," where the stock prices of the two companies no longer move in tandem as they typically do.
A trader or money manager would notice this anomaly and think, "Costco and Walmart are usually priced similarly. Something unusual must be happening." They might decide to "short" Walmart (bet that its stock will go down) and "long" Costco (bet that its stock will go up). This strategy is known as a "pair trade."
The concept is that eventually, Costco and Walmart's stock prices will "revert to the mean," returning to their usual correlated movement. If Costco's stock price decreases and Walmart's increases, the trader or money manager profits from their pair trade.
Now, let's apply this concept to the fixed income market. Walmart and Costco might each have 20 or more active bonds. The correlation metrics for their bonds differ slightly from those for their stock prices due to different factors. Attributes such as industry, currency, and issuer rating remain consistent. While pair trades for highly correlated issuers are well known, Katana has solved the challenge of identifying less well-known highly correlated bond pairs that are predicted to revert to the mean with high confidence.
Good question! When I first acquired the technology, I would have told you, "I have no idea, but these invoices from Google Cloud are not cheap!"
The technology was developed by a multidisciplinary team of experts in quantitative modeling, machine learning, distributed systems engineering, and financial markets trading. The team included a PhD in Astrophysics, a PhD in Statistics from Oxford University with post-doctoral research in next-generation sequencing data analysis, a 10-year veteran cloud systems engineer, and the former Global Head of Credit Trading at ING Bank with 20 years of financial markets experience.
They had a unique combination of quantitative skills, machine learning expertise, distributed systems engineering, and deep domain knowledge that refined an intricate architecture pivotal in pinpointing mean-reverting fixed income pair trades.
In my opinion they developed an ETL tool that was ahead of its time for the fixed income market. Of course I am very biased b/c I own the technology but this is all I have worked on for a long period of time.
If you're familiar with Apache Beam and Dataflow, you'll easily understand the approach. The data pipelines utilize a scalable, distributed processing framework with Apache Beam to analyze vast amounts of bond reference and pricing data. The algorithm employs the Cartesian product to exhaustively compare each bond with every other bond, identifying highly correlated pairs.
A neural network was then used to forecast pairs with the highest probability of mean reversion.
I have been trying to come up with a good acronym, but if you can think of one, let me know. Here are the key components:
Languages and Frameworks: Go, Node.js, JavaScript, TypeScript, React, Python
Databases: PostgresDB
Hosting and Authentication: Firebase
Cloud Platform: Google Cloud Platform (GCP)
Data Processing: Apache Beam
API: GraphQL for connecting to Bloomberg Terminal and App Store
GCP Services: Cloud Functions, BigQuery, GCP data storage, and more, totaling about 25 services
Bonus - Frontend includes an application on the Bloomberg App Store
I've been focusing on cutting costs without touching a line of code by identifying which cloud services can be turned off and which need to be retained for a minimal architecture.
I've had to dive into the deep end to control costs effectively. I am immersed in the complexities of pair trading strategies, data engineering (ETL, ELT, Big Data… you name it), and cloud-based platforms. I'm always actively exploring a range of technologies to deepen my understanding of what I own and how to make it more cost-efficient.
My learning curve has been steep, and I currently consider myself a student. I've been gaining knowledge from The University of Texas, where I completed the Cloud Computing program and am currently enrolled in the Full Stack Development program. It might not be the formal McCombs School of Business, but it's damn good.
My plan for the future is to leverage all that I've learned to build a product and business plan suited for the market. While Katana was originally designed for fixed income, I'm discovering that it has many more potential use cases.