Application of Generative Adversarial Networks (GANs) in Algorithmic Trading and Aggregation of Low-Alpha Strategies
Machine learning is attracting a lot of attention recently due to the promising capabilities of adapting, learning and even self-teaching algorithms. Application of machine learning in algorithmic trading offers a variety of opportunities to extract greater alpha and improve order execution. In this session, I am going to present the application of Generative Adversarial Networks (GANs) in algorithmic trading and share insights on aggregation of strategies.I would highlight the key functions of a strategy aggregator; categorisation of strategies, classification of market behaviour and application of ensemble learning. I would then briefly discuss about the design and development of controls for the aggregator framework that allows ease in monitoring and optimisation of the solution.
Mohammad Yousuf Hussain, CFA is a Senior Technology and Innovation Specialist at HSBC. Working in the Applied Innovation and Strategic Investments team, he has designed and delivered a number of artificial intelligence based electronic trading solutions. Previously, he was a Senior Consultant at GreySpark Partners where he delivered projects for UBS, HSBC, Mizuho Securities, Nomura-Instinet, Interactive Brokers and SFC. He developed expertise in assessing trading algorithms by investigating the market abuse incidents for the regulator.