TTU99: The Good and Bad of Machine Learning ft. Robert Sinnott of AlphaSimplex – 1of2
From the very beginning, Robert Sinnott has studied the movement of the markets and how to anticipate their changes. Robert earned both an A.B. and an A.M. in Statistics from Harvard University, where he focused on statistical machine learning, capital markets, and time series analysis.
In our conversation, my co-host Katy Kaminski and I talk with Robert about how he has used these skills to redesign AlphaSimplex’s performance analytics infrastructure and develop smarter machine learning processes, and how Robert employs this data with such success.
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In This Episode, You’ll Learn:
- The history behind AlphaSimplex and how it began
- How Robert got involved with AlphaSimplex
- How the surrounding technology industry affects Robert’s work
- What lessons Robert has learned from his time at AlphaSimplex
- How Robert spends his time outside of his work
- How adaptive markets shape the way Robert works
- Why Robert utilizes machine learning and how it benefits AlphaSimplex
- The dangers of relying too much on machine learning
- What makes AlphaSimplex’s machine learning so easy to understand
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Resources & Links Mentioned in this Episode:
- Learn more about Joe Blitzstein’s “Introduction to Probability”
- Find out more about my co-host Katy Kaminski’s “Trend Following with Managed Futures: The Search for Crisis Alpha”
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And you can get a free copy of my latest book “The Many Flavors of Trend Following” here.
Learn more about the Trend Barometer here.
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