RBC Launches AI e-Trading Platform for Equities

RBC Capital Markets has launched Aiden, an AI-based electronic trading platform for clients trading U.S. and Canadian equities. There are plans to introduce the platform to additional clients across geographical markets
October 15, 2020 - Editor
Category: Technology

RBC Capital Markets has launched Aiden, an AI-based electronic trading platform for clients trading U.S. and Canadian equities. There are plans to introduce the platform to additional clients across geographical markets in the coming months.

The platform was developed jointly by RBC Capital Markets and Borealis AI, a research centre created by RBC. The firms applied deep reinforcement learning into the constantly changing equities trading environment.

"Aiden speaks to our long-term commitment to leverage next-generation AI technology to create differentiated solutions for our clients,"said Bobby Grubert, Co-Head of Global Equities, RBC Capital Markets. "Aiden is the result of a massive collaborative effort between RBC employees, including traders and AI scientists, and our partners at Borealis AI. Aiden is at the beginning of its journey and we see great potential to further expand its capabilities and redefine execution, while remaining true to its client-centric foundation."

"After several years of comprehensive research, testing and fine-tuning, we are proud to bring a new standard of execution excellence to clients," said Dr. Foteini Agrafioti, Chief Science Officer at RBC and Head of Borealis AI. "Together, we are continuing our exploration of deep reinforcement learning, while advancing the development of responsible AI for our clients."

The new platform has shown the ability to navigate the challenges of fluid and dynamic market conditions in real-time, without the need for continuous re-coding. Utilising hundreds of pre-programmed data inputs and able to make more than 32 million calculations per order, it is able to execute trading decisions based on live market data, dynamically adjust to new information and learn from each of its previous actions.

The first live solution, a VWAP algorithm, was designed to help improve clients' trading performance by reducing slippage.


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