A Machine Learning-Powered Financial Trading Company Doing Things Differently: Meet Tyler Capital
In the ATOM podcast‘s first installment, hosted and produced by GQR’s SVP of Electronic Trading, Olly Thompson, we enjoy an authentic conversation about machine learning with Tyler Capital’s CTO, Chris Donnan. In roughly the same time as a recommended daily dose of cardio, we discover Tyler Trading’s mission – “to industrialize the systematic application of machine learning to global financial markets” – but equally how culture and sociology play a role in the evolution of their model and are critical components to the success of their organization.
Whether you’re in the hedge fund industry, a business leader, a talent acquisition specialist, or just someone interested in machine learning – there is much to take away from the conversation between Chris and Olly.
About Chris Donnan
Graduating with a degree in audio engineering, Chris has a self-proclaimed “different” path to becoming a chief technology officer. Upon leaving school, he was involved in a start-up music business – then in building two of the busiest websites in the late 90s. Around this time, Chris became interested in optimizing payoffs for web traffic, and contacts he had on Wall Street saw parallels in this work and trading challenges they were trying to solve.
This industry transition launched an era of consulting with Investment Banks in New York City and with stand-alone automated trading systems. From there, Chris went on to work for a fund called Polygon and then Barclays. CEO and “best friend,” Mike Bushore, brought Chris to Tyler Capital in 2014 as its CTO, where he gets to “build fun things that are interesting with his friends.”
Tyler Capital & Its Culture
Tyler Capital is a prop trading firm founded in 2003 and is considered a market leader in its space. From the podcast, we learn that this organization champions diversity and doing things a little differently. From a quick gander over to its website, a pioneering and community spirit certainly plays out. Around minute nine, Chris discusses how much the company believes in shaping its machine learning model by the diversity of skills and experiences its people bring to the table.
Chris poses the question, “how can we embody all of the great diversity of skills and experiences from our community in this trading agent that represents the firm?” He goes on to say, “that’s really an important part of what we’re trying to do with machine learning here.”
This sentiment is at the core of Tyler Capital’s culture – with a strong emphasis on community, togetherness, and innovation.
What Is Machine Learning? Tyler Capital’s Take
In contrast to some of its competitors, Chris admits that it’s not their core objective to have a model that uses machine learning. Tyler Capital is obsessed with using artificial intelligence and machine learning to develop a system to generate capacity. When defining what machine learning is to Tyler Capital, Chris breaks it down into three main categories:
- Adaptability – as the environment around them changes, how might they need to generate capacity in new and different ways
- Scale and Innovation
- Efficiency – it’s essential to be able to deploy things rapidly and with a deep understanding of the metrics and measures behind everything they are doing
This definition of machine learning led to an interesting discussion around “building out vs. bolting on” as a critical differentiator from Tyler Capital’s competitors.
To learn more about Tyler Capital and its CTO’s view on leadership and a thriving organizational culture, we encourage you to listen to episode one of The ATOM podcast!
