Many modern apps and businesses have capitalized on algorithmic trading — an automated process of buying and selling assets like stocks, in the hopes of maximizing potential returns. But what exactly is algorithmic trading, and what should you know about it as a consumer?
To understand how algorithmic trading works, you first need to understand how algorithms work. An algorithm is essentially a set of rules which a machine can follow to reach an expected result. Google's search engine algorithm, for example, chooses which content is listed in its search engine results pages (SERPs) based on each page's relevance to the search query and overall trustworthiness.
Algorithms can be simple or complex, and they rely on a steady stream of real-time information about the stock market, usually with the help of an integrated stock API. Stock APIs feed countless data points to an application, including individual stock prices, index metrics, and more. Using that information, and the set of rules defined by the algorithm, a trading algorithm can choose when and how to buy and sell stocks.
Let's use a simple trading algorithm as an example. Suppose you create an algorithm that buys 10 shares of a stock whenever its 30-day moving average increases beyond its 200-day moving average, which would indicate an upward trajectory. You can also set the algorithm that sells 10 shares of stock when its 30-day moving average falls below its 200-day moving average, which would indicate a downward trajectory. Effectively, your algorithm will execute trading decisions based on the parameters you set, automating a decision you might have made anyway.
Most modern trading algorithms are exceptionally more complicated than this. They take dozens of factors into consideration when making a decision, and sometimes execute thousands of decisions in the span of time it takes a human to make one. Some algorithms can even be tweaked to adhere to a certain risk tolerance level, or to mimic a certain investment style.
Beyond that, many trading algorithms are based on machine learning, and are constantly improving. They not only execute buying and selling decisions, but also self-reflect to evaluate whether those decisions were effective. They then "learn" from their past actions, and improve themselves to do better next time.
Algorithmic trading has a handful of advantages:
- Impartiality. First, algorithms are completely impartial. They're not influenced by human impulses or emotions, which almost always do more harm than good. A trading algorithm can't sell in a mad panic, nor can it buy in a greedy feeding frenzy. Instead, it can only observe the data and follow the rules, often leading to better — or at least more consistent results.
- Accuracy. Human beings make mistakes. They enter the wrong quantities of a stock to buy or sell, they hit buttons unintentionally, and they mistype stock symbols when trying to make a trade. But with a trading algorithm, you don't have to worry about these kinds of mistakes; the algorithm handles it for you, with machine-like precision.
- Delegation. Trading algorithms can also save you time, especially if you're issuing dozens or hundreds of trades per day. Rather than spending hours of time researching, and even more time manually issuing trades, you can let your algorithm handle everything. If you trust your entire account to algorithmic trading, it can save you a ton of time.
- Better data analysis. As a human investor, it can be tough to determine whether your trading decisions were "good." But trading algorithms often have built-in mechanisms designed to help them evaluate the strength of each decision. It ultimately leads to better data analytics, and better overall feedback.
However, there are some drawbacks to consider:
- Additional fees. While the transaction costs for algorithmic trading are lower, most services that offer algorithmic trading do so at a premium. They charge more for the use of their algorithms, which took months, or even years to develop. While this is certainly understandable, it can hurt your bottom-line returns.
- Lack of subjective context. Machines aren't great at evaluating context. They can't get a "feel" for the new leadership of a given company, and they don't have intuition guiding them.
- Flash crashes and volatility. The high trading volume and numbers-based approach of trading algorithms can lead to sudden spikes in volatility — like the flash crash of 2010. Fortunately, new preventive measures have been instated to reduce this risk.
Algorithmic trading has come to define a new era of stock investing, and even experienced investors are taking part. While it's not a foolproof system, the combination of constant real-time information and continually self-updated algorithms with machine learning is providing new investment opportunities for people of all walks of life — and it's only going to get more advanced from here.
Larry Alton is a professional blogger, writer, and researcher. A graduate of Iowa State University, he's now a full-time freelance writer and business consultant. Currently, Larry writes for Entrepreneur.com, Inc.com, and Forbes.com, among others. In addition to journalism, technical writing and in-depth research, he's also active in his community and spends weekends volunteering with a local non-profit literacy organization and rock climbing. Follow him on Twitter (@LarryAlton3), at LinkedIn.com/in/larryalton, and on his website, LarryAlton.com. Read Larry Alton's Reports — More Here.
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