About Aiden
RBC Capital Markets and RBC Borealis have developed an AI-powered electronic trading platform with the goal of delivering improved execution quality and insights for our clients globally. Aiden uses Deep Reinforcement Learning to learn from its experiences in the market and adjust to changing trading conditions in real-time.
How it works
As a reinforcement learning (RL) algorithm, Aiden develops an order execution strategy dynamically, by adapting to constantly shifting market conditions and by balancing long-term goals against short-term opportunities. As modern financial markets become increasingly more sophisticated, this adaptability allows Aiden to adjust within a constantly shifting landscape.
The potential of RL in the real world was demonstrated in 2016 when it was applied in gaming through Google’s AlphaGo program that beat the human world champion in the ancient game of Go.
In RL approaches, an agent is trained to take actions that maximize their expected reward (often during long horizons) instead of training a system to match recorded answers. For example, in the game of chess, a reward might be the immediate removal of an enemy piece, or the final game state of victory or loss via a checkmate. In the case of Aiden, this methodology allows us to optimize for the actions which the agent believes will result in the best possible order execution.
RBC Capital Markets’ Aiden VWAP algorithm is the first foundational step in Aiden’s evolution. Current research at RBC Borealis aims to keep Aiden on the forefront of ML advances.
In 2022, RBC Capital Markets announced the launch of Aiden® Arrival, the second algorithm on the Aiden® platform, building on the success of the Aiden® platform’s first solution. We believe there are many possibilities for how we can expand Aiden’s application to other trading strategies and asset classes.
Please read more about the research behind Aiden and about how we use Aiden and Aiden Arrival in RBC Capital Markets.
RBC Capital Markets announces the launch of Aiden® Arrival, the second algorithm on the Aiden® platform
Traders and AI scientists at RBC and RBC Borealis collaborate to deliver a real-world AI solution to help improve trading results and insights for clients in a measurable and explainable way.
Anchored in
Research
Aiden, developed jointly by RBC Capital Markets and RBC Borealis aims to solve the following problem: a customer indicates that they wish to buy or sell a certain number of units of an asset and it is the broker’s responsibility to execute this order within a specified time window, seeking prices that minimize loss relative to a specified benchmark.
To the savvy machine learning researcher, it will be obvious that this problem lends itself to a reinforcement learning formulation. The execution algorithm must make a series of sequential decisions about how many shares to buy at each time step and receives rewards in the form of low execution prices.
Aiden – Reinforcement Learning for Order Execution
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Multi Type Mean Field Reinforcement Learning
Multi Type Mean Field Reinforcement Learning
S. Subramanian, P. Poupart, M. E. Taylor, and N. Hegde. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020
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Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents
F. L. Da Silva, P. Hernandez-Leal, B. Kartal, and M. E. Taylor. Association for the Advancement of Artificial Intelligence (AAAI), 2020
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Aiden – Reinforcement learning for order execution
Aiden – Reinforcement learning for order execution
H. Burhani, G. W. Ding, P. Hernandez-Leal, S. Prince, D. Shi, and S. Szeto.
Meet Aiden, an AI-powered electronic trading platform.
Aiden is an AI-powered electronic trading platform that uses deep reinforcement learning and adjusts to changing trading conditions in real-time. Watch this short video to learn more.