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79+ Amazing Algorithmic Trading Statistics (2021) [Fact Checked]

Algorithmic trading facilitates automated trading across all asset classes and market segments. The following are the most important historical and current algorithmic trading statistics for those researching the markets or industry.

Key Statistics

  • Equities are likely to contribute $8.61 billion in the algo trading market share in 2027.7
  • The algorithmic trading market is growing at a CAGR of 11.23% between 2021-2026.2
  • Algorithmic trading contributed nearly 60-73% of all U.S. equity trading in 2018.5
  • Leading 12 investment banks earned about $2 billion from the portfolio and algorithmic trading in 2020, according to Coalition Greenwich.14
  • 52% of the institutional investors feel workflow efficiency is most instrumental in supporting best execution in algo trading.24
  • Less than 50% of trades for ticket sizes over $10 million were executed through algo trading in 2019.2
  • In Europe and the US, 10% of the hedge funds used algos to trade over 80% of their value in 2020.30

Table of Contents

Automation has permeated most of the areas of finance, and securities trading is no exception. Algorithms are here to automate trading, and stock exchanges swear by it. Institutional investors and large fund houses choose algorithmic trading due to the speed of execution and lower operational cost.

Algorithmic trading or algo trading is a system in which pre-programmed algorithms are utilized to execute trades. There is zero human intervention in such transactions. Instead, these trades are executed upon pre-written instructions.

A specialized type of algorithmic trading is High-frequency Trading or HFT that involves trades with a greater degree of complexity and speed. Robust market conditions, widespread digital transformation, and the quest for efficiency have collectively created a favorable environment for algorithmic trading.

I have curated a list of incredible statistics on the algorithmic trading market. These statistics also illustrate the impact of Algorithmic trading on various market segments and asset classes.

Market Statistics

The global algorithmic trading market is expected to grow from $11.1 billion in 2019 to $18.8 billion by 2024. The growth is likely to be driven by rising demand for quick, reliable, and effective order execution. Lowered transactional costs, heightened government regulations, and increased demand for market surveillance are also few of the major catalysts for the growth of the Algo Trading market.

Algorithmic Trading Market Stats

  • The total algorithmic trading market is likely to touch $18.8 billion by 2024.1
  • More than 60% of trades for ticket sizes over $10 million were executed in March 2020 via an algorithm, stated a JPMorgan survey.2
  • Less than 50% of trades for ticket sizes over $10 million were executed through algo trading in 2019.2
  • Electronic trades clocked in at a daily average of 66.7 billion in 2018, as per the Financial Industry Regulatory Authority (FINRA). This indicated an 87% year-over-year surge.3
  • The daily average of electronic trading was 135 billion In December 2018.3
  • $10.6 billion was the average daily e-trading volume in January 2021.4
  • Algorithmic trading contributed nearly 60-73% of all U.S. equity trading in 2018.5
  • $3.89 billion was the algorithmic trading market in North America in 2018.6
  • Equities are likely to contribute $8.61 billion in the algo trading market share in 2027.7
  • Commodities accounted for $1.38 billion algo trading market share in 2018.7
  • The algorithmic trading market is growing at a CAGR of 11.23% between 2021-2026.2
  • High-frequency trading costs retail investors up to $5 billion per year.8
  • High-frequency trading volume grew by 164% between 2005 and 2009.9
  • The May Flash Crash in 2010 led the Dow Jones to plummet 1000 points in a single trading day.11
  • The Dow Jones Industrial Average fell nearly 1,600 points in just fifteen minutes during another computer-driven selling in February 2018.10
  • Nearly $1 trillion was wiped off the market value within a 5-minutes time frame before recovering moments later.12
  • Knight Capital, an American global financial services firm, lost $440 million in less than an hour on August 1, 2012, due to HF trading.13
  • The top 12 investment banks earned nearly $2 billion from the portfolio and algorithmic trading in 2020, according to Coalition Greenwich.14
  • Morgan Stanley’s algo and portfolio trading account for nearly 80% of its corporate bond tickets and 20% of volumes.14
  • Nearly 11% of high-yield bonds traded electronically on average in 2019, according to Greenwich.16
  • 50% of stock trading volume in the US is currently driven by computer-backed high-frequency trading.18
  • Revenue for the High-Frequency Trading industry was expected to increase 26.1% in 2020, primarily due to the COVID-19.19
  • 19%-40% was the range of market share of high-frequency trading in Europe in 2010.20
  • 40%-70% was the range of market share of high-frequency trading in the US in 2010.20
  • Nearly 10% was the range of market share of high-frequency trading in Australia in 2010.20
  • According to Wells Fargo, robots will replace 200,000 banking jobs over the next ten years.22
  • In 2016, HFT on average initiated 10-40% of trading volume in equities and 10–15% of volume in foreign exchange and commodities.
  • In 2019, thirty-three ATSs executed trades in NMS stocks.15
  • Alternative Trading Systems (ATS) executed approximately 10.2% of share volume in NMS stocks in 2009.15
  • The top two ATSs each executed nearly 1-2% of share volume in NMS stocks, with most ATSs running under 1% of share volume.15
  • 56% of buy-side volume in U.S. investment-grade corporate bonds is with the five most prominent dealers.15
  • 57% of institutional investors believe that AI and Machine Learning would shape the future of trading over the next three years.24
  • 6% of institutional investors believe that mobile trading applications would shape the future of trading over the next three years.24
  • 52% of the institutional investors feel workflow efficiency is most instrumental in supporting best execution in algo trading.24
  • 72% of institutional investors think that AI and Machine Learning provides deep data analytics.24
  • 23% of the institutional investors reported an increase in algo trading during the COVID-19 phase.25
  • 50% of the institutional investors expect growth in algo trading in 2021.25
  • 10.35% of the traders of APAC chose ‘Ease of use’ as the reason for algo trading in 2018.26
  • 9.55% of the traders of APAC chose ‘Consistency in execution’ as the reason for algo trading in 2018.26
  • Traders of buy-side firms in APAC with more than $50 billion AUM had nearly 2-3 algo traders in 2018.26
  • Traders of Buy-side firms in APAC with AUM of between $1 billion $10 billion had nearly 3-4 algo traders in 2018.26
  • 75% of the buy-side firm in APAC use VWAP as a strategy for algo-trading in 2018.26
  • 61.98% of the buy-side firm in APAC use percentage volume as a strategy for algo-trading in 2018.26
  • 20% of the total trades in 2010 in Asia were algorithmic trades.27
  • 30% of the total trades in Europe in 2010 were algorithmic trades.27
  • 50% of the total trades in the US were algorithmic trades in 2010.27

Asset Class Statistics

Algo trading supports multiple asset classes and markets to facilitate a wide array of trading strategies. Available types and asset classes which leverage algo trading are equities, commodities, futures, options, and fixed income. Out of these, equities have the maximum share in algo trading, followed by futures.

Algorithmic Trading Asset Class Stats

  • Nearly 35%-50% of the commodity trading volume is generated by algorithmic trading.17
  • 60%-70% of algorithmic trading was contributed by equities in 2016.1
  • 40%-50% of the algorithmic trading was contributed by futures in 2016.1
  • Nearly 40% of the algorithmic trading was contributed by options in 2016.1
  • 20%-30% of algorithmic trading was contributed by forex in 2016.1
  • Nearly 10% of algorithmic trading was contributed by fixed income in 2016.1
  • 34.4% of investment-grade bonds traded electronically in November 2019.14
  • 12-15% of municipal bond trading in 2018 was electronic.15
  • 92% of the multiple listed options (equities & ETPs) were electronically traded to date in 2021.25
  • 57.6% of the index options were electronically traded to date in 2021.25
  • 26% of the corporate bond volume was traded electronically in the third quarter of 2018.1
  • $31.2 billion was the average daily volume traded in corporate bonds in 2018.1
  • $1 million was a rough upper bound for automatically executed trades In the corporate bond market in 2018.1

Job Market Statistics

Algorithmic trading can be quite a rewarding career. The work is less mundane and intellectually stimulating. To be a successful algo trader, one needs to possess a blended skillset that includes programming, analytical and mathematical skills, as well as a strategy development process. The proliferation of algorithm trading also calls for analyzing massive volumes of data for the quant traders foraying into this field.

  • 87,560 are permanent vacancies in the UK with a requirement for process and methodology skills such as algorithmic trading year-to-date.28
  • 0.15% of the job postings in the UK cite algorithmic trading as a proportion of all IT jobs advertised till May 2021 year-to-date.28
  • GBP 90,000 is the median annual salary for jobs citing algo trading in the UK year-to-date.28
  • $48,570 and $53,845 is the range of annual salary of an algorithmic trader in the US.29
  • $52,037 was the average algorithmic trader salary in the United States as of April 27, 2021.29

Hedge Funds Statistics

Hedge Funds with massive assets under management are increasingly turning to algorithmic trading to handle their portfolio strategically. Besides convenience and speed, hedge funds use algo trading to reduce market volatility and gain price efficiency. Access to dark pools and alternative trading systems is another reason hedge funds are going for algorithmic trading.

  • In Europe and the US, hedge funds managing funds between $0.5 million and $10 billion have posted a rise in their average number of algo providers in 2020.30
  • In Europe and the US, hedge funds managing over $10 billion and those managing less than $500 million posted a decline in the average number of algo providers in 2020.30
  • In Europe and the US, hedge funds managing between $500 million and $1 billion reported an average of 4.0 providers in 2020.30
  • In Europe and the US, 10% of the hedge funds used algos to trade over 80% of their value in 2020.30
  • In Europe and the US, 25% of the hedge funds used algos to trade over 80% of their value in 2019.30
  • In Europe and the US, 16.1% of the hedge funds used algos to trade around 50%-60% of their value in 2020.30
  • There was a 7% year-over-year increase in dark liquidity algo usage in 2020 in Europe and the US.30
  • There was nearly a 6% year-over-year increase in the implementation shortfall (single stock) usage in 2020 in Europe and the US.30
  • The usage of the percentage of volume algo strategy fell 10.58%, while volume-weighted average price (VWAP) algo dropped 0.86% in 2020.30
  • The usage Time-weighted average price (TWAP), rising by nearly 13% from its 2019 score.30
  • 46% of the hedge funds in Europe and the US used 5 or more algo providers. This number increased from 33% in 2019.30

Forex Statistics

The introduction of algorithmic trading in forex trading has improved its functioning and efficiency. Not just institutional but even retail traders are also widely using algo trading. The high-end algorithmic trading programs automate forex trading using various available strategies. While algorithmic trading gives an edge to forex traders in terms of speed and execution, it has some inherent risks. Only a handful of influential traders can acquire such sophisticated trading, leading to imbalances and liquidity issues.

  • Around 92% of trading in the Forex market was performed by trading algorithms instead of humans.39
  • 70% of total spot FX turnover across the globe is executed electronically in 2019.32
  • Only 37% of the buy-side forex traders In the US and Europe use algorithmic trading.33
  • Institutional traders believe that an additional 15% of FX trading is likely to be done via algos over the next two years.24
  • 46% of all institutional trading volume is now executed through either direct market access (DMA), intelligent order routing, or algorithmic trades.33
  • There has been nearly 54% growth in trading FX algorithmically using mobile devices.34
  • 15% of forex traders believe that execution algorithms are most frequently accessed through multi-dealer platforms.32
  • 14% of forex traders believe that execution algorithms will be distributed through voice chat.32

High-Frequency Statistics

High-frequency algo-trading is totally dependent on the super speed at which institutions can execute the orders. As thousands of trades are executed every second, a differential of even a microsecond could make a massive difference in profits or losses. This differential or delay is known as latency.

HFT is a time-sensitive activity. Many institutional traders or fund houses are making significant investments to reduce latency. These investments aim to reduce the travel distance of data between physical and virtual nodes.

  • 36% of the organizations measure latency through one-way and round-trip methods in 2016.35
  • 26% of the organizations measure latency through Round-trip methods in 2016.35
  • 58.43% of the organizations use infrastructure monitoring software and manual troubleshooting.35
  • 49.16% of the organizations leverage running virtualized workloads on dedicated clusters.35
  • A 1-millisecond advantage in latency can cost more than $100 million per year.36
  • The UK FCA study found 20% of trading volume was from latency arbitrages.21
  • According to the FCA, that latency eliminating latency arbitrage would lower the cost of trading by 17%.21

E-Trading Statistics

Algorithmic trading is a subset of electronic trading, wherein the strategies are executed automatically through pre-programmed software. Automated trading can be executed through robots or Expert Advisors, or EAs. EAs don’t place market orders on the trader’s behalf automatically. They only provide trading signals to the users who take a call on taking a position. On the other hand, Forex robots conduct the complete process automatically.

  • The average daily volume for April 2021 for Tradeweb was $896.8 billion, indicating a 17.5% increase year-over-year.37
  • $575.3 billion is the monthly electronic trading volume of MarketAxess Holdings for January 2021.38

Algo Trading ATS Market Share

  • In 2019, trading system. UBS accounted for 19.3% of the algorithmic trades.15
  • 9.8% of the Crossfinder ATS share volume was algo trading in 2019.15
  • Trading system, JPM-X, garnered a market share of 7.1% in algo trading in 2019.15

Sources: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39

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Leo Smigel

Based in Pittsburgh, Analyzing Alpha is a blog by Leo Smigel exploring what works in the markets.