Systematic trading strategies have a long and turbulent history, from Richard Donchian’s simple trend-timing to modern tools like TA-Lib and OLMAR. Yet despite all the sophistication of quantitative finance, many production trading strategies today are still based on some combination of: trend following, mean reversion, value/yield and growth (as noted by Rishi Narang).
This event was entirely devoted to understanding how modern machine learning methods can be applied to the development of systematic trading strategies. We had hands-on workshops of the Quantopian stack (Zipline and Pyfolio), as well as talks by leading practitioners from industry and academia.
For more details, click here.
Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.
Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.
He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.