Quantopian Summer Lecture: You Don't Know How Wrong You Are Part 2

You’ve constructed a model and are getting significant p-values. Everything looks good, but then your algorithm starts losing money. Sometimes the ways that models are constructed can lead to a complete breakdown in the statistics used to evaluate them. We will show some common cases of this and discuss warning signs.

This talk is part of Quantopian’s Summer Lecture Series. We are currently developing a quant finance curriculum and will be releasing clone-able notebooks and algorithms to go along with this lecture.

Pizza and beer will be served.

Speaker Details

Delaney Granizo-Mackenzie will be presenting. Delaney is an engineer at Quantopian whose focus is on how Quantopian can be used as a teaching tool. After studying computer science at Princeton, Delaney joined Quantopian in 2014. Since then he has led successful course integrations at MIT Sloan and Stanford, and is planning on expanding to many more schools this fall. Delaney’s background includes 7 years of academic research at a bioinformatics lab, and a strong focus on statistics and machine learning.

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