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CEO CHAT: Justin Lyon, Simudyne
Traders Magazine Online News, March 13, 2019
Simulation is the best way to determine possible outcomes.
That’s the mantra of Simudyne and its Chief Executive Officer Justin Lyon, who believes that computational simulation is the key to radically better decisions both in the trading world and outside it. He created Simudyne with the goal of improving decision making across both private and public sectors by leveraging advances in computational power and complexity science. For him, simulation is the bedrock for training both human and artificial intelligence. Simudyne trains intelligence in high-fidelity simulated environments to make better decisions.
Traders Magazine recently interviewed Lyon about his firm, the application of power and science, modelling and algorithms in the trading universe.
TRADERS MAGAZINE: Tell us a little about your firm? Size? Staff?
Justin Lyon: I built Simudyne because we live in an increasingly complex and connected world where organisations need to better understand how they are impacted by a wide range of potential scenarios.
After several years of consulting with a number of global institutions on virtual reality simulation and risk modelling, I recognized that traditional methods of modelling were no longer fit for purpose. They did not capture the complexity of real-world systems and failed to effectively harness the huge power of technology and big data.
We identified that there was a gap to create a technology solution that could model complex systems and create a virtual environment that could enable dynamic stress-testing. So, we developed the Simudyne platform, which democratizes advanced simulation without the need for expensive hardware infrastructure investment.
Simudyne graduated from the Barclay’s TechStars program in London in 2017 and we have brought together a team of industry experts to create an enterprise-ready platform. We’re now a 30 strong company based in the City of London and our technology is being used by a number of global institutions to allow them to make better decisions.
TM: What is “agent-based modelling”? What is the role of simulation and ‘agent based modelling’ analysis in developing efficient algorithms?
Lyon: Agent-based modelling is universally recognized as the solution to modelling complex, dynamic systems. It’s a model that simulates the actions and interactions of individuals, organizations, groups or other entities (‘agents’) so we can assess their impact.
Until we developed our platform, it was more theory than reality because no enterprise-ready software existed that provided the computational scale necessary to build and execute agent-based simulations. But, now we have made the jump from theory to practice, one of the most receptive audiences to this new technology has been algo execution teams.
They quickly realized we offer them the ability to better train their algos. Rather than looking purely at market data produced by exchanges, agent-based simulations can re-create the data generating process by replicating the low-level interactions of the market, producing synthetic market data that is indistinguishable from a given real-world time series.
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