Live Webinar: Buying Happiness: Using LSTMs to Turn Feelings Into Trades
Details
In this webinar, Max Margenot, Academia & Data Science Lead at Quantopian, discusses how to build a model in Python to analyze sentiment from Twitter data. He will cover basic Natural Language Processing (NLP) techniques, providing different ways to extract features from text data for use in modeling. He will also describe a potential use of this sentiment model in developing algorithmic trading signals for factor models. After this webinar, you’ll understand how to use the Word2Vec Python package and long short-term memory networks to analyze Twitter data and turn those insights into trades.
Register for free here: http://bit.ly/2MnskNX
About the Speaker: Max Margenot
Max Margenot is Quantopian’s Academia and Data Science Lead. His 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.
Register for free here: http://bit.ly/2MnskNX