While some brokers do trade stocks on stock exchange floors, much of today’s trading is now automated by technology. One of the most important technological developments in trading in recent years is algorithmic trading.
- Is trading designed and executed by a computer algorithm.
- Can generate larger profits more rapidly than human traders.
In this article, we’ll summarize everything you need to know about the basics of algorithmic trading, the benefits of algorithmic trading, and introductory trading algorithms you might encounter.
What Is Algorithmic Trading?
Algorithmic trading is also called algo-trading, black box trading, or automated trading. The term refers to using an algorithm or computer program to place trades. The trade algorithm consists of a set of instructions around quantity, price, timing, or any other variable the trader believes is significant. The algorithm then automatically places trades based on these instructions, which means it can, at least in theory, place trades and reap the benefits faster than a human trader can.
An algorithm can have simple instructions for trade entry and exit, such as a moving average crossover. The moving average is the market average over a particular period, which traders use to identify trends. But an algorithm could also generate a more complex set of instructions that require complicated calculations and code written in a programming language compatible with a trader’s platform. Automated trading systems usually require software to be connected to a direct access broker.
(A moving average is an average of past data points that smooths out day-to-day price fluctuations and thereby identifies trends.)
How Does Algorithmic Trading Work? An Example of Algorithmic Trading in Practice
Here’s a simple example to illustrate the concept of algo-trading in practice:
A trader could decide to follow a simple set of trade criteria. This might be to buy 100 shares of a certain stock when the 50-day moving average rises over the 100-day moving average and to sell shares in that stock when the 50-day moving average falls below the 100-day moving average.
Once the trader has put these instructions into his or her software system, the computer program will track the stock price and the relevant moving averages and place trades automatically when those particular conditions are met. This means the trader does not need to track these statistics or place trading orders manually.
A Short History of Algorithmic Trading
The use of algorithms in trading started trending in the 1970s when computerized trading systems first entered U.S. markets. In 1976, for example, the New York Stock Exchange introduced its first automated system for routing orders on to the exchange floor, the Designated Order Turnaround (DOT) system. Over subsequent years, stock exchanges continued to add to their ability to accept electronic trading. By 2010, more than 60% of all stock trades were executed by computers rather than live brokers.
The American public became more aware of algorithmic trading when bestselling author Michael Lewis published the book “Flash Boys,” which profiled the entrepreneurs and Wall Street traders who built the companies that came to dominate electronic trading. Lewis’ book indicated that these companies were involved in a veritable arms race to see who could acquire the most cutting-edge technology to one-up their competitors.
However, more recently, algorithmic trading has become far more accessible to the average trader. Some hedge funds crowdsource trading algorithms from amateur programmer-traders, for example. High-speed internet and computers that can process data faster and are available at attainable prices have made it possible for many people to learn to write code and study the market.
Machine learning is another emerging technology on Wall Street. New technical strides in artificial intelligence have enabled computers to engage in deep learning, or improving algorithms on their own via iteration. Machine learning can help traders hone their algorithms and make trading even more profitable.
The Advantages of Algo-Trading
Algorithmic trading provides a number of benefits, including:
- Cutting trading costs for large brokerage houses.
- Making it possible to execute orders faster.
- Minimizing the emotions of trading.
- Allowing for more precise backtesting.
- Preserving discipline.
Algorithmic trading allows major brokerage firms and institutional investors to cut down on their costs. It creates liquidity by executing trades faster and is especially beneficial for large orders.
Algorithmic trading is also attractive because it allows traders to execute orders faster and more easily. This makes it simpler and more lucrative for day traders to quickly book profits off small, rapid changes in stock prices. Many day trading strategies, such as the scalping trading strategy, often employ algorithms because they require purchases and sales to be made very rapidly, at a pace a human trader simply can’t keep up with.
Algorithmic trading software also minimizes the emotions of trading. A human trader might create his or her own rules for buying and selling but might have trouble sticking to that plan in the high-paced trading environment. Algorithms ensure a trader doesn’t hesitate or take a beat before placing a trade.
Computers also help traders improve their backtesting. Backtesting means applying trading rules to historic market data to see how lucrative a particular trading strategy could be. It’s a useful way to try different approaches without putting actual cash on the line. Computers make it possible to test precise rules on historical data and hone their ideas and methods.
Finally, algorithms help traders remain disciplined. Emotional factors such as fear of loss or the rush of making just a bit more profit can drive traders to make nonsensical trading moves. Algorithmic trading follows a precise trade plan because the computer will never deviate from the plan or make an error, such as entering the wrong number of shares to buy or sell.
Algorithmic Trading in Different Types of Trading
Most algorithmic trading facilitates high-frequency trading, in which traders try to capitalize on rapidly placing large orders to reap profits over short periods. But algorithmic trading can be useful in several types of investment activities.
Day traders and other types of short-term traders benefit from the frequency, liquidity, and tempo of algo-trading. Mid- to long-term investors, such as mutual funds, insurance companies, and pension funds, also use algorithmic trading to place large-scale purchases when they do not want to rock the markets by making massive trades at once. The algorithm allows them to buy and sell more discretely.
Finally, systematic traders, including hedge funds and individuals who do their own analysis and trading, often find it more efficient to trade via algorithm instead of manually placing orders themselves.
Algorithmic Trading Strategies
A number of trading strategies take advantage of algorithmic trading — indeed, some even require the use of computers and algorithms. The easiest and most straightforward algo-trading strategies follow trends such as price level movements, channel breakouts, or moving averages and execute trades based on rules built around these or other technical indicators.
Arbitrage opportunities involve buying a dual-listed stock at a lower price in one market while selling it simultaneously for a higher price in a different market. Algorithmic trading allows traders to make those sorts of trades at the same time.
Index fund rebalancing allows traders to take advantage of profitable opportunities created by moments when index funds rebalance their holdings to bring them in line with their respective benchmark indices.
Mathematical model-based strategies allow traders to make trades on a combination of options and their underlying security. Mean reversion strategy is based around the idea that unusually low or high prices will eventually revert to their mean, or average, value. Implementing an algorithm helps traders identify when prices break out of their typical range and what level they are likely to revert to.
Finally, the volume-weighted average price (VWAP) involves breaking up a large trade order and implementing it in smaller batches. The goal is to execute the order close to the VWAP.
Do you want to learn more about how algorithmic trading works and how you can take advantage of this and other methods to make money on the stock market? RagingBull is the premier destination for new traders and experts alike who are looking to hone their craft. Sign up for a free training session with one of our expert traders on your own schedule. For a limited time, you can also pick up a free e-book copy of RagingBull’s own Jeff Bishops book on options trading.