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Quantitative Investing Blog - Lucena Research

Applying Deep Reinforcement Learning to Trading

Lucena's Co-Founder Dr. Tucker Balch provides an introduction to utilizing machine learning, specifically deep reinforcement learning for stock trading. 
Lucena Research

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 provide a powerful approach to learning stock trading policies. We wanted to host a webinar that serves as an accessible introduction to reinforcement learning for trading. More specifically, what you need to know and why it matters for traders.

What you can expect from the Deep Learning webinar:

  • - An accessible introduction to Deep Neural Nets and Reinforcement Learning.

    - How they can be combined effectively for trading applications.

    - Explanation of 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’re an investment professional looking to understand machine learning or a Quant with experience in quantitative finance this discussion has something for you. Enjoy!




Additional Resources:

Full list of Q&A received during the webinar

Video: How to Forecast Securities Using Neural Networks

Video: Constructing Unique Data Feeds for KPI and Stock Forecasting

Video: The Journey of Validating an Alt Data Signal


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