CFEM Seminar - Title: "Bayesian Deep Learning in Algorithmic Trading" | Thomas Wiecki | Quantopian Inc.
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Deep Learning continues to build out its dominance over other machine learning approaches (as well as humans) on several challenging tasks including image, handwriting, and speech recognition, as well as playing board and computer games. This has generated a lot of interest in the quant finance community to try and mirror Deep Learning's success in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges. Specifically, the risk (i.e. uncertainty) of certain trading decisions as well as the fact that market behavior changes over time (i.e. non-stationarity) is not handled well by deep learning. In this talk, I will show how we can embed Deep Learning in the Probabilistic Programming framework PyMC3 and elegantly solve these issues. Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. This talk is focused on practitioners and will be introductory and hands-on with many code examples.
Dr. Thomas Wiecki is Director of Data Science at Quantopian Inc, where he uses Probabilistic Programming and Machine Learning to solve problems in quantitative finance.
Among other open source projects, he is involved in the development of PyMC3 — a probabilistic programming framework written in Python.
Thomas holds a Ph.D. from Brown University. A recognized international speaker, he has given talks at conferences across the US, Europe, and Asia.