What is 'Algorithmic Trading'

Algorithmic trading is a type of trading done with the use of mathematical formulas run by powerful computers. An algorithm, in mathematics, is a set of directions for solving a problem. An example of an algorithm is an algebraic equation, combined with the formal rules of algebra. With the these two elements, a computer could derive the answer to that equation every time.

Algorithmic trading makes use of much more complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second.

BREAKING DOWN 'Algorithmic Trading'

Algorithmic trading grew in use following the computerization of trading that took root in American financial markets in the 1970s. In 1976, the New York Stock Exchange introduced the Designated Order Turnaround (DOT) system for routing orders from traders to specialists on the exchange floor. In the following decades, exchanges enhanced their abilities to accept electronic trading, and by 2010, upwards of 60 percent of all trades were executed by computers.

Author Michael Lewis brought high-frequency, algorithmic trading to the public’s attention when he published the best-selling book Flash Boys, which documented the lives of Wall Street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in America. His book argued that these companies were engaged in an arms race to build ever faster computers, which could communicate with exchanges ever more quickly, to gain advantage on competitors with speed, using order types which benefited them to the detriment of average investors.

Do it Yourself Algorithmic Trading

In recent years, the practice of do-it-yourself algorithmic trading has become widespread. Hedge funds like Quantopian, for instance, crowd source algorithms from amateur programmers who compete to win commissions for writing the most profitable code. The practice has been made possible by the spread of high speed Internet and the development of ever-faster computers at relatively cheap prices. Platforms like Quantiacs have sprung up in order to serve day traders who wish to try their hand at algorithmic trading.

Another emergent technology on Wall Street is machine learning. New developments in artificial intelligence have enabled computer programmers to develop programs which can improve themselves through an iterative process called deep learning. Traders are developing algorithms that rely on deep learning to make themselves more profitable.

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