"Thrifting Alpha: Using Ensemble Learning To Revitalize Tired Alpha Factors" by Max Margenot

This talk was given by Max Margenot at the Quantopian Meetup in San Francisco on July 18th, 2017.

Video work was done by Matt Fisher, http://www.precipitate.media/.

Max’s background is in applied mathematics, statistics, and quantitative finance. He runs the online lecture series at Quantopian and is responsible for workshop curriculums and educational content. In addition to having experimented with algorithmic trading of cryptocurrencies and Bayesian estimation of covariance matrices, Max has published work in theoretical mathematics. He works with top universities including Columbia, U Chicago, and Cornell and holds a MS in Mathematical Finance from Boston University.

"Thrifting Alpha: Using Ensemble Learning To Revitalize Tired Alpha Factors"

Finding alpha is a constant search in algorithmic trading. New alpha factors are always exciting, but sometimes you can come up with new trading signals simply by applying novel aggregation techniques to familiar factors. In this talk we will discuss using ensemble learning methods to combine individual weak signals into stronger factors and assessing their predictive power for long-short equity strategies.

To learn more about Quantopian, visit us at https://www.quantopian.com.

Disclaimer
Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice.

More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian.

In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.