Applying Deep Reinforcement Learning to Trading

Erez Katz, Lucena Research CEO and Co-founder

An Introduction to Applying Deep Reinforcement Learning to Trading

Deep Reinforcement Learning (DRL) is a combination of two important methods: Deep Learning and Reinforcement Learning that when integrated appropriately can provide a powerful approach to learning stock trading policies.

Deep learning has traditionally been used for image and speech recognition. However, with the growth in alternative data, machine learning technology and accessible computing power are now very desirable for the Financial industry.

To understand Deep Reinforcement Learning, we have to make a distinction between Deep Learning and Reinforcement Learning.

The webinar recording serves as an accessible introduction to reinforcement learning for trading. More specifically, what you need to know about deep learning and why it relevant for traders.

What you can expect from the Deep Learning webinar:

– An accessible introduction to Deep Neural Nets and Deep Reinforcement Learning.

– How deep nets and reinforcement learning can be combined effectively for trading applications.

– An overview as to why hedge funds and proprietary data firms use statistical Machine Learning to find an “edge” in trading securities while leveraging big data.

– Examples of different machine learning algorithms and use case scenarios that demonstrate how stocks can be forecasted.

Whether you are an investment professional looking to understand machine learning or a quant with experience in quantitative investment research, this discussion has something for you. Enjoy!