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RBC ' s Aiden VWAP : A new era of AI trading in Europe , leveraging advanced AI and deep reinforcement learning to achieve optimised execution
AI is not merely a tool , but a catalyst for change setting new standards and expectations . Amidst this transformation , the need to stay agile is paramount , writes RBC Capital Markets ’ European head of multi-asset agency solutions , James Hilton .
In competitive financial markets , where every trading action can make or break a trade , advances in AI and deep real-time reinforcement learning are revolutionising algorithmic trading strategies . The accelerating impact of these technologies on lowtouch trading has been profound , as market dynamics drive demand for precise execution , adaptability and efficiency .
To address trading execution issues such as slippage and alpha erosion through periods of volatility that could upend the best historical models , RBC and its Borealis AI research institute developed Aiden – an electronic trading platform that uses AI and deep reinforcement learning to adjust to market conditions in real-time . Making its debut in North America in 2020 , Aiden proved its mettle by swiftly adapting to volatile market changes while preserving performance , without the need for frequent manual re-coding . Now launched in UK and European markets , the platform continues to adeptly navigate the complexities of dynamic market conditions , with the goal of delivering improved trading execution quality and insights for clients .
Solving for VWAP with advanced AI One key area of focus is optimising execution performance while minimising slippage against the volume-weighted average price ( VWAP ) benchmark , a critical reference point for traders . Aiden VWAP is the first algorithm on the Aiden platform in Europe - a volume-weighted average price strategy that uses the power of AI and deep reinforcement learning to reward optimal outcomes , aiming to minimise the slippage against the benchmark . Unlike traditional VWAP algorithms , Aiden VWAP considers a variety of different market data with more than 200 inputs that it processes through a deep neural network . Within guardrails , it is given discretion to optimise its behaviour , learning from its performance both during and after the trade .
RBC has also developed Aiden Insights , an initial version of an explanation system for the Aiden algorithms . The RBC coverage team utilises Aiden Insights to provide real-time trading insights and explain to our clients how Aiden is adapting and making decisions to optimise its execution benchmark .
Beyond pre-set trading algorithms By analysing both historical data and real-time shifts in market dynamics and liquidity patterns , Aiden learns autonomously , seeking to minimise slippage and execute orders at optimal prices . Unlike traditional pre-set algorithms , Aiden proactively ex-
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