What is Algorithmic Trading?

Historically, investment decisions were made by humans who weighed up the pros and cons of a buy or sell, considered the risks and rewards, and decided at the moment whether to go ahead. 

Today, many trades are carried out automatically by machines. Investors and developers tell their automated trading systems what to do, but their computers have taken over the job of monitoring prices and other data, spotting patterns, and pulling the trigger. 

It’s estimated that on modern financial markets up to 80 percent of trades are generated by computerized systems engaged in algorithmic trading. 

Algorithmic Trading: The Basics

An algorithm is a set of instructions for carrying out a sequence of actions. A developer gives a computer instructions in the form of a program, and it carries them out, often in response to a specific input. In the case of algorithmic trading, the inputs are typically market and security-related data and the actions are to buy or sell securities such as stocks and options. 

In the simplest case, the algorithm monitors the price of a security and, when it falls below a predefined threshold, buys however much of that security its code tells it to. As you can imagine, algorithmic trading gets much more complicated than that, often implementing complex mathematical models and trading strategies. 

The Relationship Between Algorithmic Trading and High-Frequency Trading

One of the key benefits of algorithmic trading is that machines react more quickly than humans. The fastest trader takes seconds to react, whereas a well-programmed computer with a fast data connection reacts orders of magnitude faster. By the time a trader’s finger clicks a single key on their keyboard, an automated trading system has placed thousands of buy and sell orders. 

The Benefits of Algorithmic Trading

In addition to raw speed, algorithmic trading has several advantages:

• Because trades are executed almost instantaneously, traders can buy and sell before price changes, increasing the likelihood of orders being processed while desirable conditions are met. 
• Automated trading systems make fewer data entry mistakes. 
• Machine-powered trading reduces transaction costs. 
• Traders can be sure that algorithmic trading systems adhere to their predefined system, whereas human traders may be influenced by emotional and psychological factors. This doesn’t mean algorithms always do the right thing; they’re only as good as the available data and the programmed strategy.

The previous bullet point raises an interesting question about algorithmic trading. What evidence do the algorithm’s developers have that it works? There are several methods used to validate algorithmic trading strategies, but one of the most important is backtesting. 

Backtesting Trading Algorithms

Backtesting is the process of testing a trading strategy on historical data. Backtesting is useful because the creators of an algorithmic trading system can feed the historical data to their algorithm and see if it makes the right decisions. It helps them to understand whether they would have made money in the past with the system they plan to use in the future.  

Backtesting isn’t perfect: it can be difficult to find high-quality historical data for backtesting, and the future isn’t the past—there’s no guarantee that markets and trading patterns will repeat themselves in a meaningful way. 

Nevertheless, in 2021 algorithmic trading dominates markets across the globe. Traders who don’t take advantage of risk being left behind by agile algorithmic trading systems. 

About the Author: Craig Iseli is the Chief Operating Officer of SpiderRock, a SaaS solution for institutional portfolio managers to implement the trading and risk management of systematic, multi-asset strategies. Their data products are used by many institutional portfolio managers to enhance trading strategies.

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