Algorithmic Trading Definition

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Algorithmic Trading Definition

What is Algorithmic Trading?

Algorithmic trading is a method of executing orders that uses automated and pre-programmed trading instructions to account for factors including price, time, and volume. A problem-solving algorithm is a collection of instructions. Over time, computer algorithms transmit little chunks of the whole order to the market.

Algorithmic trading makes choices to purchase or sell financial assets on an exchange using complicated calculations, mathematical models, and human monitoring. Algorithmic traders often use high-frequency trading technology, which allows a company to execute tens of thousands of deals per second. Algorithmic trading has a broad range of applications, including order execution, arbitrage, and trend trading methods.

Key Takeaways

  • Algorithmic trading is the use of process- and rule-based algorithms to execute trade strategies.
  • It has increased in popularity greatly since the early 1980s and is used for a number of reasons by institutional investors and major trading businesses.
  • While algorithmic trading has benefits such as quicker execution time and lower costs, it may also accentuate the market’s negative inclinations by triggering flash crashes and sudden lack of liquidity.

Understanding Algorithmic Trading

After computerized trading systems were introduced in American financial markets in the 1970s, the use of algorithms in trading proliferated. The New York Stock Exchange created the Designated Order Turnaround (DOT) system in 1976 to route orders from traders to exchange floor experts. In the decades that followed, exchanges improved their ability to handle electronic trading, and by 2009, computers conducted up to 60% of all deals in the United States.

When author Michael Lewis published the best-selling book Flash Boys, he brought high-frequency, algorithmic trading to the public’s attention. The book 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 contended that these corporations were in an arms race to construct ever faster computers that could interface with exchanges ever quicker in order to obtain a speed edge over rivals, employing order types that benefitted them at the expense of regular investors.

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Do-It-Yourself Algorithmic Trading

Do-it-yourself algorithmic trading has grown in popularity in recent years. Hedge funds, such as Quantopian, crowdsource algorithms from amateur programmers who compete for commissions for designing the most lucrative code. The technique has been made feasible by the widespread availability of high-speed internet and the development of ever-faster computers at low cost. Quantiacs is a platform that caters to day traders who want to experiment with algorithmic trading.

Machine learning is another emerging technology on Wall Street. Artificial intelligence advancements have allowed computer programmers to create systems that can improve themselves via an iterative process known as deep learning. Traders are creating algorithms that depend on deep learning to increase their profits.

Advantages and Disadvantages of Algorithmic Trading

Algorithmic trading is mostly utilized by institutional investors and large brokerage firms to reduce trading expenses. According to studies, algorithmic trading is particularly advantageous for big order sizes, which may account for up to 10% of total trading activity. To generate liquidity, market makers often utilize algorithmic trading.

Algorithmic trading also provides for quicker and simpler order execution, making it appealing to exchanges. As a result, traders and investors may swiftly record gains on modest price movements. Because scalping trading includes quick purchasing and selling of assets at tiny price increments, algorithms are often used.

When numerous commands are performed concurrently without human interaction, the speed of order execution, which is advantageous under normal conditions, might become a problem. Algorithmic trading has been blamed for the 2010 flash collapse.

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Another downside of algorithmic trading is that liquidity, which is produced by quick buy and sell orders, may vanish in an instant, removing the opportunity for traders to benefit from price fluctuations. It might also result in an immediate loss of cash. According to research, algorithmic trading was a key contributor in the lack of liquidity in currency markets when the Swiss franc abandoned its Euro peg in 2015.

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