Algorithmic trading, often referred to as “algo” trading by those in the industry, has become a hot topic for retail traders and small investment firms. In the 1970s, large financial institutions invented and started computer-based trading to handle buying and selling financial securities. Banks and insurance companies dominated markets for centuries; in more recent times, hedge funds have claimed a significant place in the financial markets. Then, the digital revolution removed barriers to entry into the market. High-speed internet, computing power, and data science tools are now available and affordable for the broad public. With the emergence of online trading platforms/apps, trading in financial products has never been easier. Today, it requires only a few mouse clicks to trade stocks, futures, and currencies.

In this article, I’d like to give you an overview of algorithmic trading and provide a practical guide on how to start your algorithmic trading business.

Today, more than 75% of US stock trades are placed by computer algorithms, not humans. This figure has been expanding over time and will continue to do so. There is no single definition of “algorithmic trading.” Depending on their background, different people mean different things. At its most basic, an algorithm is a “sequence of steps to achieve a goal.” The CFA Institute defines algorithmic trading as “using a computer to automate a trading strategy.”. Computer programmers have created many different algorithmic trading strategies used by traders every day. Irrespective of the specific strategy, algorithmic trading has two major aspects:

Algorithms are initiated by humans and follow a clear strategy to reach specific goals. Initially, algorithms had been pre-programmed rules. These rules, developed by programmers, are based on mathematical and statistical models. The emergence of artificial intelligence and machine learning introduced data-driven and self-learning algorithms. Consequently, the job profile for algorithmic traders has changed. Data science and data engineering skills have become much more relevant.

Trades are automated. Computers place and execute orders, not humans. Unlike the human brain, computers can process large amounts of data with ease. They can make thousands of trading decisions within microseconds.

For years, algorithmic trading had been synonymous with execution-only algorithms (broker algorithms). Large institutions use execution algorithms to break down large orders. These smaller orders are then executed over time. The goal is to reduce the impact that a large order has on the market. With this, traders can achieve a benchmarked price at low trading costs. Examples of execution algorithms are “volume-weighted average price (VWAP)” and “implementation shortfall” algorithms. Execution algorithms are standard tools for brokers and large institutions. They play a minor part for retail traders.

Institutional investors have target weights for assets and asset classes. As time goes by and markets move, weights of portfolio constituents slip away. That’s why portfolio rebalancing is a critical workflow. In simple words, rebalancing algorithms sell “winners” and buy “losers” to restate their target weights. Performance goals and regulatory constraints are the driving factors for target weights. Insurance and pension plans are regulated investors. They need to comply with strict limits. One example could be having no more than 40% stock investments at any time. Automated monitoring and automated trading systems play a pivotal role in achieving this.