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Tuesday, December 24, 2024

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The Algorithmic Traders' Association prides itself on providing a forum for the publication and dissemination of its members' white papers, research, reflections, works in progress, and other contributions. Please Note that archive searches and some of our members' publications are reserved for members only, so please log in or sign up to gain the most from our members' contributions.

Deep Learning in Finance: Learning to Trade with Q-RL and DQNS

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 Charlotte Magnall, Summit Creator at RE•WORK

 Wednesday, March 22, 2017

'Reinforcement Learning provides a potential framework for learning how to trade but traditional methods, when presented with a relatively small amount of noisy market data, are plagued by various complexities that make the approach difficult to tackle. However, Q-Learning (a form of Reinforcement Learning) applied to specially designed trading simulation games do provide some promising results. Generating synthetic data that largely mimics the random behaviour of markets but also contains the salient features that will result in exploits presenting themselves is an important part of the process of getting Q-RL to successfully learn how to trade.'


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1 comments on article "Deep Learning in Finance: Learning to Trade with Q-RL and DQNS"

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 Hashir Sherwani, PhD Researcher at University College London

 Sunday, March 26, 2017



Seems more like a promotion for a conference rather than anything informative.

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