Google uses them when you search. Facebook employs these to show you ads you might be interested in. And Amazon utilizes them to predict which products will make you a happy consumer.
What are these mysterious tools that the big tech companies use? Algos, or algorithms. Algos are simply computer codes that automate tasks, make predictions and generally make life easier. Traders in financial markets also use algos, in the form of algorithmic trading strategies.
Traders create different types of algorithmic trading strategies, depending on their needs. For example, professional traders at hedge funds use algo trading systems to make markets, to perform high-speed profitable scalping trades, and to control entry and exit of a big position without adversely affecting the price. Usually, these professionals are dealing in very high-speed trading, requiring expensive hardware located right at the trading exchange.
Retail traders – traders who may not trade for a living, but instead trade to supplement their income or build their retirement nest egg – have different needs. Retail traders may not have a need for nanosecond speed, but they do require solid algo trading strategies that generate profit.
For the typical trader, there are a few different types of algorithmic trading strategies. A good trader will utilize all of them, and balance their use in accordance with their personal risk profile. Each of these strategies is discussed below.
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Day Trading Algorithms
Most retail traders have two desires: to be in and out of the market quickly and to have little or no overnight risk. For these requirements, day trading algorithmic strategies are ideal. These trading systems can be tailored to have short holding times, and therefore very small losses and gains. These algos typically trade 5-50 times per day and have instructions to exit all trades by the end of the day.
While most traders prefer this day trading algos, in reality, they are the most difficult to create and manage. Many traders find that the slippage and commission costs with these strategies overshadow the profit the algos generate. Because of these factors, many failed day traders then move on to swing trading strategies.
Swing Trading Algorithms
For traders willing to hold positions overnight, swing trading algos can be ideal. These strategies typically have trades lasting 1-10 days, allowing the algo to catch significant trends, and reduce trading costs at the same time.
Many traders who swing trade use daily bars, ignoring the twists and turns that occur during the trading day. This can be very agreeable to the trader since intraday price movements are generally very noisy.
Long Term Algorithms
For traders who do not want to be bothered with daily trading activities, a long term trading algorithm can be a good solution. A simple example of this would be to buy when the price is above the 200-day moving average, and sell only if the price falls below that average. Such a strategy may hold for years, allowing the algo trader to benefit from long term trends.
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As you may have noticed, the discussion above focused primarily on the holding period of the trade. This is an essential part of any trading algorithm but is not the only area of importance. The style of trading is also critical in algorithm development. Let’s discuss few types of algorithmic trading strategies based on this
Trend Following Algorithms
Any trader will tell you the way to make money is to buy low and sell high. In other words, the market price has to trend from a low price to a higher price. This is trend following. Trend following is one of the primary types of algorithmic trading strategies and is undoubtedly the most popular.
A simple trend following algorithm would be to buy if today’s price moves above the highest price of the past 5 days, and exit if the price falls below the 5 day low. This is known as a breakout algo strategy. It “predicts” that a new high will lead to more new highs, thus continuing the upward trend and providing the trader profit.
Mean Reversion Algorithms
Sometimes during a trend, there are points where the price moves too far, too quickly, and is due for a correction. An example could be a price that has been respecting a trend line for a while, and then suddenly moves away from the trend line. Chances are eventually the price will return to that trend line.
Algos that exploit this tendency are known as mean-reverting trading algorithms. They are usually of short duration and can be useful in capturing small movements opposite to the main trend.
Mean reversion strategies are popular with traders searching for a “bargain.” They do not buy at the peak, but rather wait until the price falls, hoping for a more favorable entry price.
Hedging Algorithms
For traders who do not like the risk of outright positions, for example, being long the stock market, there are algorithms that can satisfy their risk profile. For instance, an algorithm can be designed to be long Coca-Cola stock while being short Pepsi stock. Such trading algorithms allow the trader to hedge their risk – in this case, the trader is not fully exposed to general market movement, but instead is concerned only with the relative movement of the two stocks. If the stock market crashed, Coca-Cola would surely decline, but so would Pepsi. A loss in Coca-Cola could be offset by a gain in Pepsi.
There are also hedging algo trading strategies in the forex and futures market. In general, these algos profit from the relative movement of two or more instruments, rather than the outright movement of one. When designed correctly, these algos will provide a better reward/risk profile than other strategies.
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Wrapping It Up
Retail traders have many different options when dealing with algo trading. There are many different types of algorithmic strategies, and they can be combined to create a portfolio of trading systems that satisfies the trader’s risk and return requirements. The key is for the trader to examine the categories of algo trading techniques, and pursue the ones he or she likes the best.
About the Author
Kevin Davey is an international trading contest winner and the author of three best selling trading books. He can be reached at kjtradingsystems.com.