The Tradetech Daily



UBS has pursued a strategy of developing model-based rather than rule-based algorithms, such as UBS Swoop, both to negate predictability and ensure more subtle market interaction. 


The firm has sought to enhance the sophistication
of the UBS smart order router engine in Australia, India and Japan and to
implement complex event processing alert systems, which inherently understand
and adapt orders to current market dynamics.

UBS continues to invest and maintain its service
differentiation to help clients balance the allocation of resources between
development and complying with an ever-changing regulatory environment.


Last year, UBS launched the Quant on Demand
platform in Asia, which lets clients create efficient workflow programmes and
tailored algo solutions in days or weeks as opposed to months.

UBS’ algorithmic platform is designed to cater to a
diverse client base and meet changing user demand for greater functionality
with ease of use.

Continual research and refinement of statistical learning
models lets the firm apply nuanced behavior in lit and dark venues to enhance
transparency in transaction cost analysis and the trading experience. 


The UBS team includes quants and information
technologists with backgrounds in computer science, maths and physics. On
average, team members have at least eight years experience.

Significant differences in Asian markets require a high
degree of adaptability. UBS develops and maintains its algo engine in the
Asia-Pacific region to ensure that local requirements are accommodated.