What is this Course About?
Wall Street needs more quants and data scientists.
This course will allow you to build the essential initial programming skills and tool belt of statistical techniques required for quantitative analysis.
First, we'll teach you how to program with financial timeseries before diving deep into multivariate regressions using factor analysis to explain Berkshire Hathaway's performance.
Next, we'll examine the performance of 9 different hedge fund strategies and compare the risk and return characteristics of each type of fund.
Finally, we'll construct our own fund strategy using quadratic optimization to track a benchmark on a rolling basis, and we'll build our own backtesting engine in R to analyze our strategy.
Am I Ready for this Course?
Whether you're a hedge fund manager or a business student, this course is for you if you're looking to upgrade your game and begin investing intelligently.
We'll provide you with commented source code, guided video tutorials and high quality animations to help you understand every line of code and concept.
Become a Quant.
How Does Warren Buffet Do It?
Use multivariate rolling regression techniques on market factors to explain Warren Buffet's returns
Create Your Own Indexing Strategy
Use quadratic optimization to create an indexing strategy and then build a rolling backtesting engine to compare your results to the benchmark
Dakota is a Quantitative Analyst at an AI startup, where he built out the first few financial products and sells complex signals and factors on global securities to the most sophisticated hedge funds in the world, and helps to construct advanced network-based products and create specialized indexes for leading index providers, such as the STOXX AI powered AI Index and the NASDAQ Yewno Global Disruptive Technology Benchmark Index. Dakota previously worked in NYC for Mizuho Bank as a front office investment banking quant on the corporate finance advisory team as well as in San Francisco for Charles Schwab's Investment Risk quant group.
He has an M.Sc. in Financial Analytics with high honors sponsored by Accenture, and a B.Sc. in Quantitative Finance from the Stevens Institute of Technology, where Dakota's work included research on risk averse two-stage stochastic programming problems for portfolio optimization.