Reinforcement Learning for Trading Practical Examples and Lessons Learned by Dr. Tom Starke

This talk, titled, “Reinforcement Learning for Trading Practical Examples and Lessons Learned” was given by Dr. Tom Starke at QuantCon 2018. Description: Since AlphaGo beat the world Go champion, reinforcement learning has received considerable attention and seems like an attractive choice for completely autonomous trading systems. This talk shows practical aspects and examples of deep reinforcement learning applied to trading and discusses the pros and cons of this technology. The slides for this talk can be viewed at: https://www.slideshare.net/secret/1qo.... About the Speaker: Dr. Tom Starke has a Ph.D. in Physics and works as an algorithmic trader at a proprietary trading company in Sydney. He has a keen interest in mathematical modeling and machine learning in the financial markets. He has previously lectured computer simulation at Oxford University and lead strategic research projects for Rolls-Royce Plc. Tom is very active in the quantitative trading community, running workshops for Quantopian, teaching people quantitative analysis techniques, and organizing algorithmic trading meetup groups such as Cybertraders Syd. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.