Algorithmic trading, or algo trading, is a strategy by which to use complex computer algorithms to execute stock trades at an extremely fast pace. Using a combination of computer code and mathematical modeling, algorithmic trading allows for the analysis of vast amounts of data to complete highly informed trades based on user-determined parameters. While public attention over algo trading is a recent phenomenon, algo trading actually dates back to the 1970s. Over the past three decades, entities ranging from individuals to financial institutions have begun to utilize algorithmic trading only contributing to the rise in popularity in recent years.

Algo trading consists of anything ranging from the execution of single trades to the use of technical indicators to the exploitation of market inefficiencies; as long as computer algorithms are used somewhere in the process. To put a definition to what might be an unfamiliar term, at a basic level, an algorithm is defined as an ordered set of rules put in place to meet some goal. In life, algorithms are everywhere; for example, a certain set of rules must be followed to tie your shoes. There may be more than one way to meet the end goal (i.e. multiple kinds of knots), but in the end, ordered rules are followed in order to meet some goal. 

Specifically in reference to computers, an algorithm is a mathematical framework by which to meet some end goal. A very simple example of a computer algorithm would be using a coding language to compute the average value of a list of numbers; the elements in the list are added together, the length of the list is determined, then the sum is divided by the length and the value is returned. While the algorithms used in algo trading are a lot more complex than the averaging algorithm, the general principle is the same: mathematical models used to meet some end goal. With practice, building and analyzing complex algorithms will even feel like tying your shoes. 

One of the major benefits of algo trading as opposed to regular trading is the ability to perform High Frequency Trading (HFT). As the name suggests, HFT is the low-latent relative of normal trading; in other words, trades are executed extremely fast in the millisecond environment. Given successful trading conditions for an algorithm that have been tested for success through either backtesting or forward performance testing, an algorithm can be set to run once every few milliseconds to expose sometimes microscopic changes in markets. These aforementioned successful conditions act as the parameters of a given trading strategy which is effectively up to the individual to decide. For example, in a trading algorithm involving a technical indicator, we must decide what critical values will alert the computer to buy, sell, or neither. In any case, correct parameters alongside HFT can yield significant profits.

Unfortunately, this is not a guide on what exactly these trading conditions should be. This determination can be made only through tests. While the testing process can be time-consuming, it proves to be incredibly beneficial in the end, as it essentially eliminates human error when the trades are actually being executed. By letting the human element of trading fall completely on accounts containing theoretical money, errors are much less likely to be made while trading using real money. This is another general benefit of algo trading. There are so many adverse factors when trades are executed manually, including even the inability to initiate a trade at the millisecond level. Algo trading seeks to ease some of these challenges. 

Another significant benefit to the use of computer algorithms in trading is the ability to execute many different trades at the exact same time. In terms of asset diversification, this is an incredibly useful tool. In normal trading, it is impossible to execute more than a few hundred trades during the six and a half time span that is market hours. Say, we have an algorithm that is set to run every 5 milliseconds; that is 4.68 million trades a day. With an algorithm that is set to trade the top performing stock during a given period, 4.68 million trades during a day will lead to a large and diverse portfolio.

With these various benefits to algo trading, there are also drawbacks. One of the prominent advantages in algo trading can also technically be thought of as a downside: low-latency. While the reason behind this is not immediately obvious, there is a simple explanation as to why this is the case. Because algo trading is executed at such high speeds, the difference between the person who actually makes the money on a trade and someone who doesn’t can come down to physical distance from the market provider. All data is transmitted either through fiber optic cables or waves; in both cases, the person closest to a market provider will be able to execute the trade first (because the data is traveling a shorter distance), thus cashing in on the best possible price for a transaction. With this being said, the combination of input parameters makes this phenomenon not as likely, as there are essentially infinite choices for an algorithm’s run conditions.

Another potential downside is the capital cost for getting started trading algorithmically. Software supporting algo trading can sometimes be expensive: monthly fees, fees on trades, fees on data, and even the opportunity cost of learning how to effectively trade algorithmically. Especially when a large financial corporation is hoping to fully integrate algorithmic trading using an external service, costs can add up relatively quickly. MachineTrader.io is great for this reason; at only $99 a month, you receive access to all of the features listed here, as well as the ability to be creative and make your own trading algorithms. With this $99, you receive access to both Polygon and Alpaca, which would otherwise total to $300 a month. Perhaps the best benefit to MachineTrader, however, is the access to pre-written algorithms to get you, as a new or even experienced, algorithmic trader started. 

While at a basic level it is just a means of trading stocks, algorithmic trading is unique in the sense that it can happen extremely fast, allows for the elimination of human error, plus a variety of other benefits not covered in this short article. While this strategy, alongside normal trading, is not a fail-safe for generating consistent profits, the automation behind algo trading eases a lot of the time sensitivity of normal trading. It is for this reason that it has become a sizable part of large financial institutions’ portfolios. As of right now algo trading is projected to grow at a steady yearly rate of eight percent; the question is, who will generate the winning algorithm to defy cyclic macroeconomic theory?

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