Algorithmic trading, also known as automated trading or “algo trading,” is the use of computers and high-speed internet connections to execute large volumes of trading in financial markets much faster than would be possible for human traders. “Algos” leverage machine learning algorithms, typically created using reinforcement learning techniques in Python, to build high-frequency trading strategies that can make orders based on electronically-received information on variables like time, share price, and volume.

Understanding algorithmic trading is critically important to understanding financial markets today. It is estimated that algorithms are responsible for 80% of trading on U.S. stock markets, and it is widely used by investment banks, hedge funds, and other institutional investors. There are debates over the impacts of this rapid change in the market; some argue that it has benefitted traders by increasing liquidity, while others fear the speed of trading has created more volatility.

However, there is no question that algo trading is here to stay, and day traders as well as finance professionals need to understand how they work at a minimum - and, ideally, be able to make use of these powerful tools themselves.

Because of their ubiquity in today’s financial markets, a baseline familiarity with algorithmic trading is increasingly essential for careers as a trader, analyst, portfolio manager, or other finance jobs. These highly-paid professionals may work at institutions such as banks, asset management firms, and hedge funds, and they are increasingly adding courses in algorithms, machine learning, and other related areas to their education in order to understand this critical topic.

Career opportunities in this field are also attracting professionals with high-level computer science skills, who have gained nearly as high of a profile in the finance industry as algorithmic trading itself. Quantitative analysts, or “quants,” are highly prized for their ability to apply their programming skills to massive datasets, statistics, and other high-velocity market inputs to create the mathematical models required for algorithmic trading and other financial engineering techniques.

In a sense, then, algorithmic trading is where finance and programming meet, giving professionals with the ability to span these worlds the opportunity to create enormous value for their firms.

Absolutely. Coursera offers a wealth of courses and Specializations about relevant topics in both finance and computer science, including opportunities to learn specifically about algorithmic trading. These courses are offered by top-ranked schools from around the world such as New York University and the Indian School of Business, as well as leading companies like Google Cloud.

In addition to being able to access a high-quality education remotely from anywhere in the world, learning online through Coursera offers other advantages. The ability to virtually attend lectures and complete coursework on a flexible schedule makes online courses ideal for working professionals in finance or computer programming that want to add algorithmic trading to their skillset. And the lower cost of online courses compared to on-campus alternatives means that this high-value education can be surprisingly affordable.