FX’s OTC nature will limit machine learning
Almost two decades after the principles of machine learning were applied to FX trading, challenges remain to be addressed for the technology to become ubiquitous.
A machine’s capacity to analyze market data in a quantity and speed that no human trader could hope to match is undeniable.
However, that machine also has to deal with data characteristics that are constantly moving, autocorrelation – where today’s price depends heavily on yesterday’s price – and low signal to noise ratios, which require it to sift through large volumes of meaningless data to find a meaningful result.
The over-the-counter (OTC) nature of the FX market also means that data might only be applicable to specific brokers, says Kris Longmore, co-founder and head of quantitative research at Quantify Partners.
“Ideally, any forecasting algorithm would be robust to small differences in broker feeds, but still it is an issue,” he says. “Finally, the 24-hour nature of the market means that sampling time of market data will be a consideration.”
Celent senior analyst Joséphine de Chazournes refers to problems around latency, saying: “FX markets are a high-frequency asset class and machine learning does not support that kind of speed yet.
“Strategies that work right now are long-short or event driven that learn overnight or at T+2 but never in real time, as putting a machine-learning algorithm within a data centre would be too costly.”