Deep learning continues to dominate other machine learning approaches (and humans) in challenging tasks such as image, handwriting, speech recognition, and even playing board and computer games. This has generated a lot of interest in the quant finance community in applying deep learning in the domain of algorithmic trading. Unfortunately, algorithmic trading poses a unique set of challenges—specifically, both the risk (i.e., uncertainty) of certain trading decisions and the fact that market behavior changes over time (i.e., nonstationarity) are not handled well by deep learning.
Thomas Wiecki demonstrates how to 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.
Thomas Wiecki is the lead data science researcher at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University.
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