Quant Trading Webinar
Please join Bloomberg Specialists on April 4th to gain a better understanding of how to backtest and implement systematic event-driven trading strategies. Our Specialists will walk through a Jupyter notebook to backtest a merger arbitrage strategy and build a machine learning model to enhance the risk/return profile. The relevant data points for other event-driven strategies as well as practical considerations for implementing those strategies in live trading with real-time data feeds will also be covered.
Speakers
Norman Niemer
Global Head of Quant & Data Science
Bloomberg
Norman manages the Quant and Data Science teams within Bloomberg Enterprise Data. Prior to joining Bloomberg LP, Norman was the Chief Data Scientist at UBS Asset Management and he started his career as an investment banking analyst at Morgan Stanley. Outside of investment management, he has led teams that have built data- and AI-based products that won several high profile hackathons. Norman holds a MS in Financial Engineering from Columbia University, a BS in Banking and Finance from City University London and is currently pursuing the program for leadership development at Harvard Business School.
Dominick D'Angelo
Enterprise Data Product Manager
Bloomberg
Dominick oversees product development for Bloomberg's Enterprise Data solutions for Cash Equity and Entity Reference Data, as well as Economics. He joined Bloomberg's Analytics Department in 2009, where he managed Foreign Exchange, Commodities, and Structured Cash Flow Product teams. Dominick holds a Bachelor of Arts in Finance from Muhlenberg College, and a Masters in Business Administration from Fordham University.
Nakul Nair
Data Science Specialist
BloombergNEF
Nakul focuses on quantitative research solutions across the entire Bloomberg Enterprise Data offering. Previously, he lead natural gas market analysis for BloombergNEF. Nakul has an MS in engineering from Columbia University and a BS in Geophysics from Imperial College London.