Algorithmic Trading and Finance Models with Python, R, and Stata Essential Training

Many stock market trades are conducted with algorithms, or “algos,” computer programs that buy or sell stocks according to mathematical formulas. These equity trades happen at a speed and frequency that humans cannot replicate. It's important for finance professionals and everyone who invests in the stock market to understand how these algorithms work. In this course, Professor Michael McDonald shows you how to develop a back-tested, rules-based trading strategy and program a simple trading algorithm of your own. Professor McDonald goes over the basics of securities markets, from stocks, bonds, and derivatives to predicting values with regressions, before turning to investing and securities fundamentals. Find out what it takes to build your own algorithms, as well as buy, sell, and expand them to other securities. Plus, Professor McDonald covers practical examples and case studies of trading with algorithms.

Learning objectives
Demonstrate an understanding of the fundamental concepts and applications of algorithmic trading, including market making, proprietary trading, textual analysis, and qualitative data analysis.
Develop proficiency in using Python for various tasks related to algorithmic trading, such as importing and analyzing financial data, building databases, and implementing trading strategies.
Employ R for bond trading tasks, including importing data from different sources, using the QuantMod package, performing data analysis, and conducting regressions.
Utilize Stata for investment analysis tasks, such as retrieving and cleaning currency data, developing and testing trading strategies, and performing regression analysis.
Evaluate different types of financial market data sources, including broad market data, CRSP, Compustat, and other specialized data sources, and apply appropriate strategies and techniques for data acquisition and analysis.