IPython and Jupyter for Quantitative Finance Analysis





Python 難易度



Quantitative finance is a rapidly growing field. Using mathematics, statistics, and computer science to trade allows for much safer, more reproducible, and comprehensive trading strategies. Traditional traders are rapidly being replaced by 'quants' or quantitative traders, and the finance industry is undergoing changes as money moves from traditional firms to quantitative firms. This is in many ways increasing the already high barrier to entry in finance. Quantopian is a startup in Boston trying to break down some of these barriers. We will give an overview of quantitative finance and some of the ways that Python is used in the industry. We'll also discuss how in many ways quantitative finance is just an application of statistics, or as some know it, data science.

Delaney Granizo-Mackenzie

Delaney Granizo-Mackenzie manages academic outreach at Quantopian. After studying computer science at Princeton, Delaney joined Quantopian in 2014. Since then he has led successful course integrations at schools including Cornell, Stanford, and MIT Sloan. Delaney is using his experience and feedback from professors to build an interactive quantitative finance curriculum focusing on best statistical practices. Delaney’s background includes 7 years of academic research in a computational biology lab, and a strong focus on statistics and data science.