Last Updated on 10 February, 2024 by Trading System

How much trading is done by algorithms? What trading volume is made by algorithmic trading? The idea of creating computer programs to trade one’s trading strategies is not just fascinating but has become the ideal trading approach in recent times. Since the turn of the century, algorithmic trading has come to dominate the financial trading world, but what percentage of trading is algorithmic?

In the U.S. stock market and many other developed financial markets, about 60-75 percent of overall trading volume is generated through algorithmic trading according to Select USA. However, in emerging economies like India, the overall trading volume of algorithmic trading is estimated to be around 40 percent.

The acceptance of algorithmic trading seems to differ from one financial center to another. We will explore all that in this post about
financial trading algorithms, and you will learn the following:

• What Percentage Of Trading Is Algorithmic?
• What algorithmic trading means
• The percentage of trading that is algorithmic
• Stock market algorithms and their reach today
• Why the use of algorithmic trading on the rise
• The common strategies used in algorithmic trading
• Concerns about algorithmic trading and market algorithms

How much of trading is algorithmic?

The use of computer algorithms for trading has been on the rise in the U.S. equity markets since the turn of the century but seems to have plateaued around 70-80 percent in the last 5 to 10 years. As of 2003, algo trading accounted for only about 15 percent of the market volume, but between 2009 and 2010, more than 70 percent of U.S. trading was attributed to trading algos. The foreign exchange markets also have active algorithmic trading, which is measured at about 80 percent of orders in 2016 — up from about 25 percent of orders in 2006.

The story is not so different in the European Union and the UK, as only about a third of all E.U. and U.K. stock trades in 2006 were driven by computer trading algorithms, but by 2009, studies suggested algorithmic trading accounted for 60-73 percent of all equity trading volume. However, the number fell to approximately 50 percent in 2012.

For instance, in 2006, about 40 percent of all orders were entered by algorithmic traders at the London Stock Exchange, and the number was expected to be 60 percent by 2007. Generally, American markets and European markets tend to have a higher proportion of algorithmic trades than other markets, and the estimate for 2012 was as high as an 80 percent proportion in some markets.

In Japan, algorithmic trading also constitutes a great percentage of the entire market volume. The market share contributed by trading algos went up to approximately 70-80 percent in 2019 in the FX spot market transacted on the EBS2, which is one of the most commonly used electronic broking systems in the interbank market. In the equity market, more than 70 percent of orders on Tokyo’s stock exchange are now made by algorithmic traders.

The value is much lower in emerging economies. For example, in India, the overall trading volume of algorithmic trading estimated is roughly 40 percent.

What does algorithmic trading mean?

Algorithmic trading is an automated trading approach that uses computer algorithms to trade the markets. These algorithms create buy and sell orders (when the right conditions are met in each case) and automatically send the orders to the market via the brokerage platform. For the trading algorithm to place an order, the market conditions must match the predefined criteria for an order entry per the trading strategy the algo is based on.

What really happens is that the trading algos search the markets for qualifying trade setups, and once they encounter the right setups, they execute the trades and manages them in accordance with the instructions written in the codes regarding the right position size and trade management. From spotting the trade setups to executing and managing the trades, the entire process is automated.

Algorithmic trading systems shouldn’t just be launched to trade a live account after coding. The algorithm has to be backtested using historical price action of up to 10 years. The essence of the backtesting is to know how well the strategy can perform and whether to optimize the strategy. However, there is a risk of over-optimization or curve-fitting since the data is already known. The effect is a misleading backtesting result, and the system won’t be able to perform as well in live trading.

So, if you buy a trading algorithm from a bot vendor, don’t just trust the backtesting result. You have to test the algo system for robustness using the walk-forward methodology or forward testing. In forward testing, you actually test the performance of the system with real-time market data. A strategy that performs well in the forward testing will do well when used to trade a live account, provided there is no drastic change in the market.

Learning how to create an algorithmic trading system on your own might take you many years, but if you enroll in a good algo trading course, you can learn it in a few months. After that, you may wish to diversify your risk by creating multiple systems to trade multiple markets at the same time.

Why is the use of algorithmic trading on the rise?

There is no doubt that technology makes things a lot easier, and trading is not left out. Trading has become a lot easier with the availability of computer algorithms that trade on your behalf. There are many reasons why algorithmic trading is on the rise, and these are some of them:

• Fully automated: Algorithmic trading automates the entire process of asset selection, trade setup identification, order execution, trade management, and trade exit. Thus, it makes trading systematic. With an algo trading system, trading becomes only a step-by-step execution of instructions, which makes it objective and rule based.
• Known probability of success: Trading algorithms are backtested using historical market data. This shows you how well the strategy performs and the odds of your trades, which you can use to plan your capital allocation better.
• Trading all the time: The computer doesn’t need sleep, so your trading algorithms can trade all the time, as long as the market is open. Unlike in the traditional trading form where the trader may miss some trades for not being around when the trade setup formed, there is no missing any qualified trade setup with algorithmic trading because the algos are scanning the markets and taking trades all the time, even while you sleep.
• Fast trade execution: With algo trading, trades are identified and executed very fast. The algorithms can analyze a variety of parameters and technical indicators in a split second and execute the trade immediately, which ensures good order entry and minimal slippage. The speedy execution is very important in fast-moving markets or intraday trading styles where any delay can lead to a poor entry price, reducing the potential profits.
• Better accuracy and fewer mistakes: In algorithmic trading, there is minimum human intervention, so there is a lower chance of making dangerous trading mistakes, such as entering abnormally large position sizes or unknowingly entering trades you wouldn’t normally take. These mistakes are quite common in discretionary trading, but people are now using trading algorithms to execute trades with accuracy.
• Less impact of human emotions on trade execution: Perhaps, the most important benefit of algorithmic trading is that it reduces the impact of human emotions, such as fear, greed, and anger, on the trading process and outcome. Since the entire process is automated, your emotions cannot interfere in the execution of individual trades. However, you may still have your emotions, but as long as you leave the system alone, your emotions won’t affect the outcome of your trades.
• Ease of diversification: With algo trading, it is easy to diversify your risk across different strategies and markets. Trading algorithms can execute many trades using multiple strategies in different markets and even across different timeframes, all at the same time — something that would be very hard to achieve in discretionary trading. Since the algo system can easily scan a range of markets, assets, and instruments and place orders simultaneously, diversification is easy to achieve.
• Better market liquidity: With algorithmic trading, large volumes of shares to be bought and sold within a fraction of a second, which increases the overall volume and liquidity of the market, making the market better for everyone.
• Trading consistency: When you are using the traditional form of trading, it is quite difficult to plan your trade and execute your plan effectively, even when you have the best strategies. Adhering to your plan can be quite difficult due to market fluctuations. However, since algo trading is automated, trades are executed with consistency, which preserves the trading edge of your strategy.

The common strategies used in algorithmic trading

Many strategies can be coded into trading algorithms, and different traders use different strategies. While there are many strategies out there for both discretionary and automated trading, the common strategies for the algorithmic trading category are the following:

• Mean-reversion strategies
• Trend-following strategies
• Breakout strategies
• Biased strategy

Mean-reversion strategies

The mean-reversion strategies are based on the theory that the price of an asset has a long-term mean despite the up and down fluctuations. While the price swings above and below the mean, it tends to always revert to the mean anytime it moves significantly away from it.

Thus, when the price moves significantly below the mean, it is expected to rise toward the mean, and this presents a buying opportunity. On the other hand, if the price rises significantly above the mean, it is expected to fall back to the mean, creating a selling opportunity.

Algo trades exploit this mean-reversion concept in different ways. One common method is to use the 2-period RSI method. In this method, when the indicator falls below 10, it signals an oversold market with a high probability of an upward reversal, which is a buy signal. Another common mean-reversion method is the use of the Bollinger Bands: here, when the price falls below the lower band, it signifies an oversold market, which implies a buying opportunity, and when the price rises above the upper band, it signifies an overbought market — a sell signal.

Trend-following strategies

Tren-following strategies aim to profit from individual impulse swings in a trending market. The idea is to know when a pullback is about to end and a new impulse wave about to start, which is when the trade is entered to ride the impulse wave. There many different ways to identify the turning point, and some of the tools used are support and resistance levels, oscillators, and trend lines.

Breakout strategy

A breakout strategy aims to benefit from the increase in price momentum that often follows breakouts. To trade this strategy, there must be a way to identify the right price level. The instruction would be to buy when the price closes above that level. The entry strategy may be to place a buy order on the open of the next candlestick after the breakout candlestick or to place a buy stop order a little above the high of the breakout candlestick.

Biased strategy

We coined the name ‘biased strategy’ to express the tendency of some markets to behave in a certain way that cannot be easily categorized under any of the trading strategies we have here. An example of this is a market that tends to move in a certain direction at a specific time of the trading day.

Concerns about algorithmic trading

There have been different concerns about algorithmic trading, especially as regards the volume of trades and the possibility of using it for insider trading. Because they send the great majority of orders coming into the markets, trading algorithms are often blamed for the increased propensity for flash clashes in the markets in recent times.

A flash crash is a sudden and very rapid sell-off in a financial market, resulting in a dramatic price decline within a very short time. It is believed that the situation results from computer trading algorithms reacting to aberrations in the market by automatically selling large volumes of the security at an incredible speed.

A recent example of a flash crash is the October 7, 2016 flash crash in the GBP/USD where the GDP fell 6 percent against the USD in 2 minutes. Another example is the May 6, 2010 flash crash in the Dow — a United States trillion-dollar stock market crash that happened in minutes, making it the greatest fall in the Dow in decades. To prevent flash crashes in the equity market, the regulators introduced circuit breakers in major stock exchanges like the NYSE.

Here is further reading that might be of interest.

Candlestick Guide: How to Read Candlesticks and Chart Patterns

Swing Trading Guide – How to Start and learn to be a Swing Trader

Algorithmic trading -The COMPLETE guide

Futures Market Guide- Information about all major markets

FAQ

What percentage of trading is algorithmic in different markets?

Algorithmic trading has seen significant growth, reaching around 70-80% in the U.S. equity markets, 80% in foreign exchange markets, and 70-80% in the Japanese FX spot market. However, the percentage varies in different regions, with emerging economies like India having a lower estimated volume of around 40%.

Why is algorithmic trading on the rise?

Algorithmic trading is gaining popularity due to its fully automated nature, known probability of success through backtesting, ability to trade continuously, fast execution, better accuracy, reduced impact of human emotions, ease of diversification, enhanced market liquidity, and trading consistency.

How can I test the reliability of a trading algorithm I purchase?

Backtesting is essential, but to ensure reliability, use forward testing or the walk-forward methodology. Forward testing involves evaluating the algorithm’s performance with real-time market data, reducing the risk of over-optimization seen in backtesting with historical data.