Algorithmic trading statistics in 2024

Algorithmic trading statistics Research

Algorithmic trading simplifies the trading of financial instruments in all markets. The TradingWebsite team has gathered key statistical data on algorithmic trading that will be useful for traders, financial analysts, journalists, and bloggers.

Algorithmic trading, or algo trading, is a system where trades are executed using pre-programmed algorithms. There is no human intervention in such transactions. When using algo trading, trades are executed according to pre-written instructions.

Need more facts about financial markets? Read other articles on our blog: stock market statistics, forex statistics, and cryptocurrency statistics. These are fresh data for 2024.

Key algorithmic trading statistics for 2024

  • By 2027, the volume of algorithmic trading will reach 8.61 billion dollars
  • From 2021 to 2026, the algo trading market will grow by 11.23%
  • 60-73% of stock transactions were made using algorithmic trading in 2018
  • The 12 largest investment banks earned about 2 billion dollars on algo trading in 2020
  • About 50% of transactions with a volume of more than 10 million dollars were made using algo trading in 2019
  • 10% of European and American hedge funds used algo trading for transactions in 2020.

Algorithmic Trading Statistics in Financial Markets

It is expected that by 2024, the volume of transactions involving algorithmic trading globally will grow to 18.8 billion dollars (in 2019, the volume of algo-trades was 11.1 billion dollars). The increase in the number of algorithmic transactions is most likely due to the increased demand from investors for quick, reliable, and efficient order execution. Reduction in transaction costs and increased government regulation are also major reasons for the growing popularity of algo trading.

  • By 2024, the total volume of algorithmic transactions globally will reach 18.8 billion dollars
  • According to JPMorgan, in March 2020, over 60% of transactions over 10 million dollars were made using algo trading (in 2019, the volume of transactions involving algo trading was 50%)
  • In 2018, the volume of trades involving algo trading amounted to approximately 66.7 billion dollars per day (compared to 2017, the volume of electronic trading increased by 87%)
  • In December 2018, the average daily volume of algorithmic trading amounted to 135 billion dollars
  • In January 2021, the average daily volume of algorithmic trading was 10.6 billion dollars
  • In 2018, between 60 to 73% of stock transactions were made using algo trading
  • The volume of algorithmic trading in North America in 2018 was 3.89 billion dollars
  • By 2027, the total volume of algorithmic stock trading will reach 8.61 billion dollars
  • In 2018, the share of algo trading in commodities was estimated at 1.38 billion dollars
  • Investor expenses on algo trading amount to up to 5 billion dollars per year
  • From 2005 to 2009, the volume of transactions involving algo trading increased by 164%
  • In February 2018, the Dow Jones stock index fell by 1600 points in just 15 minutes due to an automatic stock sell-off executed through algo trading
  • In August 2012, the American company Knight Capital lost 440 million dollars in less than an hour due to algo trading
  • The 12 largest investment banks earned nearly 2 billion dollars with the help of algo trading in 2020
  • In 2019, about 11% of high-yield bonds were traded using algo trading
  • 50% of stock trading in the US is conducted using algo trading
  • In 2020, investor revenues from high-frequency trading increased by 26.1%
  • In 2010, the share of algo trading in European markets ranged from 19% to 40%, in the US from 40% to 70%, and in Australia it was almost 10%
  • According to Wells Fargo, robots will replace 200,000 jobs in the banking sector over the next decade
  • 57% of institutional investors (banks, credit organizations, government companies, pension funds, and others) believe that artificial intelligence and machine learning will define the future of trading in financial markets over the next three years
  • 72% of institutional investors believe that artificial intelligence analyzes the market very deeply
  • 23% of institutional investors increased their trading volume involving algo trading during the COVID-19 pandemic
  • In 2010, the volume of algo trading in Asia was 20%, in Europe — 30%, and in the US — 50%.

Trading Volume of Financial Instruments Involving Algorithmic Trading

Algo trading can be used to trade a variety of financial instruments in different markets. This allows the investor to create very flexible trading strategies. Algo trading is used in trading stocks, currencies, futures, options, and fixed income securities. But the majority of algorithmic trades are in stock and futures trading.

  • 35%-50% of commodity trades are carried out using algo-trading
  • In 2016, algo-trading accounted for 60%-70% of stock trades, 40%-50% of futures trades, and almost 40% of options trades
  • In November 2019, about 34.4% of investment-grade bonds were traded using algorithmic trading
  • In 2021, 92% of listed options and 57.6% of index options were traded using algo-trading
  • In the third quarter of 2018, about 26% of corporate bonds were traded using algorithmic trading (the average daily trading volume was then $31.2 billion)
  • The largest transaction of $1 million was opened in automatic mode in the corporate bond market in 2018.

Salaries of in-house algo-traders in the UK and the US

To become a successful algo-trader, one must possess a mixed set of skills in programming, financial analysis, mathematics, and of course, the ability to develop complex trading strategies.

  • Currently, there are more than 87,000 algo-trading-related job openings in the UK
  • The average salary of an in-house algo-trader in the UK is £90,000 per year
  • The average salary of an in-house algo-trader in the US is $48,000-$53,000 per year.

Hedge funds actively use algo-trading

Hedge funds managing large assets often employ algorithmic trading to manage their investment portfolios. In addition to convenience and speed, hedge funds use algo-trading to reduce market volatility. Access to dark pools and alternative trading systems is another reason why hedge funds have become more active in using algorithmic trading.

  • In 2020, 10% of European and American hedge funds engaged over 80% of their capital in algo-trading
  • In 2020, investors from Europe and the US used algo-trading 7% more often than in 2019
  • In 2019, 46% of European and American hedge funds used 5 or more algo-trading providers for equity market investments.

Forex market algo-trading statistics

  • About 92% of transactions in the Forex market are executed by trading robots, not humans
  • About 70% of world’s FX spot deals are carried out using algo-trading
  • 7% of buy-side forex traders in the US and Europe use algorithmic trading (Note: buy-side traders are «wealthy» market participants, typically banks, funds, insurance companies, etc.)
  • 46% of institutional trader transactions are executed through direct market access (DMA), smart order routing (SOR), or algo-trading
  • The volume of algo-trading in the foreign exchange market via mobile devices has increased by almost 54%
  • 15% of forex traders gain access to algo-trading primarily through multi-dealer platforms
  • 14% of forex traders believe that algo-trading will be controlled via voice chat in the near future.

High-Frequency Algo-Trading Statistics

High-frequency algo-trading (HFT) depends entirely on the speed at which trading organizations or private traders can execute orders. Since thousands of transactions are made every second, a microsecond delay in transaction execution can lead to huge profits or losses. This delay is known as latency.

High-frequency trading is very time-sensitive. Most market participants spend large amounts of money to reduce order processing latency.

  • In 2016, 36% of trading organizations measured order processing latency using dummy trades, using the «Round-trip» method (this is so-called round-trip trading, where a trader buys and then sells a financial asset, thereby not making a profit or a loss, but creating a false appearance of trading volume in the market).
  • 58.43% of trading organizations use software to monitor trading infrastructure.
  • 49.16% of trading organizations use virtual trading to create a test load on the market.
  • A 1-millisecond delay in order processing in the financial market can cost over $100 million per year.
  • 20% of trading volume in financial markets is due to latency arbitrage (this is the opportunity to learn a financial asset’s quote fractions of a second before the quote appears in the market). According to FCA data, excluding latency arbitrage from the market could reduce trading costs by 17%.

Algo-Trading Statistics

Algorithmic trading operates using robots or so-called trading advisors. Trading advisors do not place market orders for traders automatically. They only provide trading signals to traders who decide whether to buy or sell a financial asset. Things are different in the Forex market, where trading using algo-trading is carried out automatically.

  • In April 2021, the daily volume of the financial company Tradeweb’s algo-trades was $896.8 billion.
  • In January 2021, the monthly volume of the financial company MarketAxess Holdings’ algo-trades was $575.3 billion.
  • In 2019, UBS (the largest Swiss financial holding) accounted for 19.3% of algorithmic trades.
  • In 2019, 9.8% of the Crossfinder ATS’s stock volume was algo-trades.

What is algorithmic trading?

Algorithmic trading is an investment method in which financial operations in the market are carried out automatically using algorithms. Instead of relying on traders’ decisions, algorithms analyze market data based on predefined criteria and execute trades independently. This approach significantly increases the speed of reaction to changes in the market situation and the execution of trading strategies.

Examples of algorithmic trading:

  1. High-Frequency Trading (HFT) — a strategy in which algorithms conduct numerous short-term trades over very short periods, sometimes within fractions of a second. Such operations can be executed at high speed thanks to specialized algorithms and fast data transmission.
  2. Statistical Arbitrage — a strategy based on statistical analysis of market data to identify undervalued or overvalued assets. Algorithms use statistical models to determine moments for buying and selling assets to profit from price differences.

Algorithmic trading is an effective tool for trading in financial markets, providing fast execution of trades and automated decision-making based on predefined criteria.

Data Sources:

Analyzing Alpha, Mordor Intelligence, Financial Times, Thetradenew, Business Wire, Transparency Market Research, Coherent Market Insights, CNBC, The New York Times, MarketWatch, Investopedia.

Mark Rubezhny

Senior Staff Writer at TradingWebsite, specializing in the development of forex websites and marketing. Mark has extensive experience in creating web projects related to trading in financial markets, including websites, widgets, trader cabinets, and integration with MT4.