Sell-side firms offer innovative FX algos to attract clients to use their platforms, usually on an agency basis, where they are paid by commission.
From a bank’s perspective, this is a low-risk activity driven by investment in low-latency technology as well as quantitative excellence, with quants driven to improve the sophistication of execution algorithms to reduce market impact and achieve benchmark goals.
While demand for quants has risen, the range of skills required to be an algo quant has also expanded. As well as understanding the algo methodology, they need to be aware of the microstructure of trading on different venues and understand the associated risks of market impact.
The amount of FX data available has increased massively in recent years, leading to a greater use of data mining and machine learning to extract more useful analysis, observes Jamie Walton, former head of quantitative analysis at Morgan Stanley and co-founder of Raidne, a provider of independent quantitative surveillance.
Next-generation algos
These techniques are required to create next-generation algos that can respond to microstructure signals dynamically, he says.
