Past Meetup

DIY Quant Strategies featuring Minimum Variance Portfolio

This Meetup is past

19 people went

Details

Quantopian is happy to attend and cross post the next meeting of Boston's chapter of QWAFAFEW (Quantitative Work Alliance for Applied Finance, Education and Wisdom).

QWAFAFEW meetings are free to members of that group who are current on their dues. Guests are welcome space provided. Based on demand, the $30 guest fee is requested to be paid online AHEAD of arrival here: http://qwafafew.org/BostonPayDues.html

Please also RSVP directly to Hugh Crowther, meeting organizer at: [masked]

Abstract: This talk will cover a practical overview of the breadth and characteristics of quant strategies built, backtested and shared in an open source community on Quantopian. We will cover basics of mean reversion, price momentum, value, sentiment and seasonality strategies as they can be built in python using freely available tools and data. We will also cover a brief progress report on live trading, from backtest to forward test to putting real money on the line, all in a browser-based open-source framework.

Discussant Wayne Nilsen of Northfield Information Services will cover his implementation of a Markowitz minimum variance portfolio on Quantopian. In order to get diversification benefits without the curse of dimensionality, Spyder Sector ETFs were utilized to create a minimum variance portfolio which is recalibrated in time. The Quantopian platform allowed for proper testing and demonstration of the implementation of this simple, yet effective algorithm.

https://www.quantopian.com/posts/modern-portfolio-theory-minimum-variance-portfolio

Speaker bio: Dr. Jess Stauth is currently VP of Quant Strategy at Quantopian. Formerly Jess worked as a quant research analyst at StarMine where she built equity stock selection models including the StarMine Short Interest model. Following Thomson Reuters' acquisition of Starmine, she worked as director of quant product strategy for Thomson Reuters where she worked closely with large buyside firms, integrating new data and analytics to drive more profitable and efficient investment processes. Jess received her PhD from UC Berkeley in 2007 in the field of biophysics with thesis research in computational and systems neuroscience.