Traders Magazine recently interviewed Simudyne CEO Justin Lyon about his firm, the application of power and science, modeling and algorithms in the trading universe.
Check out the full article here.
Excerpt
TM: What is “agent-based modeling”? What is the role of simulation and ‘agent based modeling’ analysis in developing efficient algorithms?
Lyon: Agent-based modeling is universally recognized as the solution to modeling 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.
The virtual market’s parameters can then be adjusted to construct any potential future scenario, or large numbers of random simulations can be run, to generate synthetic data that the algorithms can be tested against. By introducing multiple potential market dynamics, sell-side brokers can ensure their algos perform under the broadest range of stressed scenarios and eventualities.
