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FX: Different testing no guarantee of better outcomes

While the ability to run simulations based on hypothetical future as well as historic data is appealing, there is no guarantee that testing algos against both types would make them operate more efficiently in stressed market environments.

Advanced simulation models are not a recent phenomenon – they have been used for many years in disciplines such as climatology and physics to study 'what if' scenarios. But increased availability and affordability of computing power have brought these techniques within the reach of banks and other FX market participants.

Justin Lyon 160x186
Justin Lyon, Simudyne

According to Justin Lyon, CEO of Simudyne, advanced simulation models based on synthetic future data are able to prepare algos to deal with unprecedented market conditions rather than simply shutting down in the face of volatility, which can mean immediately exiting or hedging all current positions or simply not sending any orders at all until the unprecedented condition passes. "Historical data can quickly lose relevance as market conditions and structures evolve, but execution is increasingly being undertaken by trading algorithms which can only learn from the historical data they have been fed," he says. "Agent-based modelling and simulation allows key aspects of the data-generating process to be captured, thereby enabling the creation of synthetic data."

Lyon reckons that testing algos against this data would improve market participants' understanding of how their strategies could impact the broader market.

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