Algorithmic Trading: Definition, How It Works, Pros and Cons
The technique of executing orders using automated and pre-programmed trading instructions that take into consideration factors like price, timing, and volume is known as algorithmic trading. A set of instructions for solving a problem is known as an algorithm. Smaller pieces of the entire order are gradually sent to the market via computer algorithms.
Using intricate formulae, mathematical models, and human monitoring, algorithmic trading makes judgments about whether to buy or sell financial instruments on an exchange. High-frequency trading technology, which allows a company to execute tens of thousands of trades per second, is frequently used by algorithmic traders. Order execution, arbitrage, and trend trading methods are just a few examples of the many instances where algorithmic trading can be applied.
Knowledge of Algorithmic Trading
After computerized trading systems were introduced to American financial markets in the 1970s, the use of algorithms in trading increased. The Designated Order Turnaround (DOT) system, which directs trader orders to specialists on the market floor, was first used by the New York Stock market in 1976. The ability of exchanges to accept electronic trading improved throughout the ensuing decades, and by 2009, upwards of 60% of all deals in the U.S. were carried out by computers.
Author Michael Lewis popularized high-frequency, algorithmic trading when he released the best-selling book Flash Boys, which chronicled the lives of Wall Street traders and businessmen who aided in the establishment of the firms that eventually came to define the framework of electronic trading in America. In his book, he made the case that these firms were competing against one another for market share by developing ever-faster computers that could interface with exchanges at ever-increasing speeds and employing order types that favored them at the expense of regular investors.
Self-Service Algorithmic Trading
Do-it-yourself algorithmic trading has gained popularity in recent years. For instance, hedge funds like Quantopian use algorithms that are crowdsourced from novice programmers who compete to write the most profitable code in order to win commissions. The proliferation of high-speed internet and the creation of increasingly fast computers at affordable rates have made the practice practicable. Day traders who want to try their hand at algorithmic trading can now do so thanks to platforms like Quantiacs.
Machine learning is a further developing technology on Wall Street. Deep learning, an ongoing process that allows for program improvement, has been made possible by recent advancements in artificial intelligence. To increase their profitability, traders are creating deep learning-based algorithms.
The benefits and drawbacks of algorithmic trading
Large brokerage firms and institutional investors primarily utilize algorithmic trading to reduce trading expenses. Research suggests that algorithmic trading is particularly advantageous for big order sizes, which could account for as much as 10% of total trading activity. Market makers typically utilize algorithmic trading to produce liquidity.
Algorithmic trading is appealing to exchanges because it makes order execution quicker and simpler. As a result, traders and investors can swiftly earn a profit off little price movements. Due to the quick buying and selling of stocks at small price increments involved in the scalping trading method, algorithms are frequently used.
When multiple orders are performed simultaneously without human interaction, the speed of order execution, which is typically a benefit, might become a disadvantage. Algorithmic trading has been held responsible for the 2010 flash crash.
Another drawback of algorithmic trades is that liquidity, which is produced by quick buy and sell orders, might vanish in a split second, preventing traders from profiting from price fluctuations. Additionally, it may cause a sudden lack of liquidity. Research has shown that, after the Swiss franc’s Euro peg was terminated in 2015, algorithmic trading played a significant role in the loss of liquidity in the currency markets.
Conclusion
- Algorithms based on processes and rules are used in algorithmic trading to implement trading strategies.
- Since the early 1980s, it has become much more widely employed for a range of uses by institutional investors and big trading companies.
- While algorithmic trading has benefits like quicker execution times and lower costs, it can also accentuate the market’s unfavorable tendencies by resulting in flash crashes and a sudden loss of liquidity.
