The past several years have seen the automation of financial markets undergo a sea change. The underlying theme for this change has been led by algorithmic trading. Today, almost every investor, from the institutional one to the smallest trader, finds it hard to resist the temptation of using algorithms for speed, precision, and efficiency in trade execution. Indeed, as in any technology, though the benefits are massive, underneath the complexity there lies pitfalls that must be appreciated if the full impact of algo trading on the financial ecosystem and its participants is to be enjoyed.
The pure form of algorithmic trading involves the use of computer algorithms to fully automate the trading process. In other words, it will give way for these algorithms to follow a certain set of rules and parameters in making trades that have to be executed on real-time market data. It may optimize numerous things, from maximization of profits or minimization of risks to executions with minimum market impacts.
The history of algo trading can be traced back to the late 20th century, with its foundations in the early electronic trading systems that started appearing in the 1970s and 1980s. Still, it was not until the early 2000s that algorithmic trading really came to the fore, impelled by improvements in computer power, the growth of high-frequency firms, and increasing accessibility to real-time market data.
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The Rise of Algorithm Trading
The major reason why has grown to be so exponential is that it can process reams of data, turning them into trades at speeds unimaginable to any human trader. Such speed, in financial markets where prices change in milliseconds, might make quite a big difference in an opportunity cost or in a missed profitable trade.
Furthermore, algorithms can run 24/7 without getting tired; therefore, they are most suitable for the global markets that never sleep. They can further rid us of emotional biases of human traders. Fear, greed, overconfidence—these are all emotions that can lead you to act irrationally, but algorithms, being purely logical, nullify such pitfalls.
Another large benefit is the ability to backtest strategies. Before an algorithm is used in live trading, it can be run on historical data to see how well it would perform. This enables the trader to tinker with his or her strategy in a bid to enhance its strength and ability to withstand different market conditions.
The Human Element: Designing and Monitoring Algorithms
While algo trading may seem to be a purely technical affair, the human element is there. After all, algorithms are not written by themselves, but by people who then keep an eye on them. It creates one interesting dichotomy at the very root of this: while algorithms can eliminate some of the human mistakes from trading, in fact, they are still subject to the mistakes and prejudices of the people who create them.
An effective trading algorithm is designed by being well-versed in the financial markets and computer science. It usually starts from developing a trading strategy and then realizing this strategy in a form of rules that an algorithm will employ. Such rules might be derived from technical indicators, statistical models, or anybody's guess at machine learning techniques.
