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 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 to 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!
Here’s the full list of Q&A we received during the webinar.
Still have questions? Feel free to reach out!
Liked this post? Read more about similar topics:
Erez Katz, CEO and Co-founder of Lucena Research. The rapid growth of big data has resulted in a technology and AI arms race. In the past, being an AI player would typically earn you a new level of professional esteem but big data, data science and machine learning...
Erez Katz, CEO and Co-founder of Lucena Research. At Lucena, our mission is to bridge the gap between validated data and data-driven professionals. Portfolio managers seek reliable ways to efficiently assess and deploy alternative data for investment decision...
Erez Katz, CEO and Co-founder of Lucena Research. Investors often look at Sharpe ratio to determine a portfolio’s strength. (Sharpe ratio measures a portfolio’s risk adjusted return.) The goal of Sharpe ratio is to assess a portfolio’s returns discounted against...
Erez Katz, CEO and Co-founder of Lucena Research At Lucena, we always try to understand the root cause of unexpected results and pull actionable insights from the data. Sometimes it's easy to blame the machine when actually it did exactly what it was...
Erez Katz, CEO and Co-founder of Lucena Research How A Deep Neural Net Model Predicts An Outcome First step: Recognition Deep neural networks are able to classify complex relationships between characteristics of an image and its corresponding classification....