Algorithmic Trading Market - Growth, Trends, and Forecasts (2020 - 2025)

The Algorithmic Trading Market is segmented by Types of Traders (Institutional Investors, Retail Investors, Long-Term Traders, Short-Term Traders), Component (Solutions, Services), Deployment (On-Cloud, On-Premise), Organization Size (Small and Medium Enterprises, Large Enterprise), and Geography.

Market Snapshot

Study Period:

2019-2025

Base Year:

2019

Fastest Growing Market:

Asia Pacific

Largest Market:

North America

CAGR:

11.23 %

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Market Overview

The algorithmic trading market is expected to register a CAGR of 11.23% in the forecast period from 2020 to 2025. Traditionally, traders keep track of their trading activities and investment portfolio by using market surveillance technology. Applications, such as algorithmic trading, have built-in intelligence to search for the opportunities that exist in the market, as per the yield and other criteria defined by the user. Factors, such as favorable government regulations, increasing demand for fast, reliable, and effective order execution, growing demand for market surveillance, and reducing transaction costs, are expected to spearhead the need for the algorithmic trading market. Institutional investors and big brokerage houses use algorithmic trading to cut down on costs associated with bulk trading.

  • Factors, such as favorable government regulations, increasing demand for fast, reliable, and effective order execution, growing demand for market surveillance, and reducing transaction costs, are expected to spearhead the need for the algorithmic trading market. Institutional investors and big brokerage houses use algorithmic trading to cut down on costs associated with bulk trading. Furthermore, the emergence of AI in the financial service sector is expected to be a major factor aiding in the growth of the algorithmic trading market. Regulators are also starting to take note of the ways by which individuals interact with the market due to advances in artificial intelligence. For instance, in May 2010, the House of Representatives announced the creation of a Task Force on Financial Technology and a Task Force on Artificial Intelligence.
  • Further, Algos, with the help of pre-defined rules, backtested, and placed at pre-defined levels, can also analyze every quote and trade in the stock market. Thus, these programs identify liquidity opportunities and turn such information into intelligent trading decisions. For instance, in April 2020, Bitcoin suddenly jumped by 20%. Some observers speculated that algo trading might have been the factor behind the sudden move in the world's most popular cryptocurrency. Liquidity plays a vital role in the financial market. Thus, algorithmic trading creates a situation to maintain the liquidity, exceptionally high liquidity, in demand, due to rapid buy and sell orders, without any human intervention. Algorithmic trading works when big trades are fed into computers running relevant programs.
  • In the age of cloud deployment, the cloud-based algorithmic trading platforms are expected to play a significant role in the growth of the market, owing to various benefits, like a gain of maximum profits, as cloud-based trading solutions help traders to automate their trading process, easy trade data maintenance, cost-effectiveness, scalability, and effective management. Cloud-based trading works on the cloud computing model that uses networks of remote servers usually accessed over the internet to store, manage, and process data. Attributed to the convenience of the cloud, traditional traders can deploy algorithmic trading in the cloud to check new trading strategies, backtest, and run-time series analysis, while executing trades.
  • Due to the spread of coronavirus, stock markets plunged in March 2020, triggering circuit breakers that halted market-wide trading several times. Algo trading has been contributing to the market rebound after the March lows. Thus, algorithmic execution tools in foreign exchange increased significantly since March 2020. As per the latest Survey by JPMorgan, more than 60% of trades for ticket sizes bigger than USD 10 million were executed in March via an algorithm. This was compared to less than 50% a year ago. Hedge funds and real money accounts are leading the end-user industry. Additionally, a report on algorithmic trading by the National Institute of Financial Management, submitted to the Department of Economic Affairs in May 2010, found that algorithms accounted for half the orders on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE).

Scope of the Report

Algorithmic trading (also known as algo-trading, black-box trading, or automated trading) is defined as a method of executing trade orders with the help of automated pre-programmed trading instructions. Considering several variables, such as time, price, and volume, the programs send small slices of the order to the market over time. The study characterizes the algorithmic trading industry based on the type of traders, by component, including solutions and services, by deployment, and organization size.

By Types of Traders
Institutional Investors
Retail Investors
Long-term Traders
Short-term Traders
By Component
Solutions
Platforms
Software Tools
Services
By Deployment
On-Cloud
On-Premise
By Organization Size
Small and Medium Enterprises
Large Enterprises
Geography
North America
Europe
Asia-Pacific
Latin America
Middle-East & Africa

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Key Market Trends

Institutional Investors are Expected to Hold Major Share

  • Institutional investors are banks, credit unions, insurance companies, hedge funds, investment advisors, and mutual funds companies, which pool in their money to purchase securities, real estate, or any other kind of investment assets. Institutional investors, daily, use multiple computer-driven algorithmic strategies in the volatile trading markets, which succumb to the trade influence and the market makers. These techniques enable the traders to cut down the costs of trades and improve their profitability. 
  • These investors need to execute high-frequency numbers, which are not possible every time. It helps them to break the whole amount into small parts and continue to perform in particular time intervals or according to any dedicated strategies. For instance, instead of placing 1,00,000 shares at a time, an algo-trading technique may push 1,000 shares out every 15 seconds and incrementally put small amounts into the market over the period or the entire day. 
  • With high-frequency traders making a large number of trades per day, automated trading using computer programs and artificial intelligence is required, primarily to speed up the execution of trades. Therefore, only institutional investors can afford this technology, and thus, they get an unfair advantage to profit off from value, which is based around millisecond arbitrage. Moreover, when the institutional-based investors want to take advantage of various occasional tiny market price discrepancies that arise in the security’s market price trading available on two different exchanges, they incorporate the algorithmic trading to follow up on arbitrage strategy. 
  • Trend following is also one of the most popular techniques used by traders, which is based on algorithms. The approach identifies specified patterns used to carry out the buying and selling of assets. Many traders use the programming trend following algorithmic trading platforms, as they are simple to implement. Also, there is high adoption of algorithmic trading because traders do not need to make any predictions or price forecasts to become profitable, as the algorithmic trading strategy relies on the occurrence of the desired trends rather than predictive analysis.

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North America Expected to Dominate the Market

  • North America is expected to hold the largest market size in the global algorithmic trading market in adopting and developing algorithmic trading. The rising investments in trading technologies such as blockchain, increasing presence of algorithmic trading vendors, and growing government support for global trading are the major factors that contribute to the market growth during the forecast period. Also, due to the substantial technological advancements and considerable application of algorithm trading in various applications such as banks and financial institutions across the region is expected to stimulate market growth.
  • Algorithmic trading is accounted for around 60-73% of the overall United States equity trading. According to Select USA, the United States financial markets are the largest and most liquid in the world. Sentient Technologies, an A.I. company, based in the United States, which operates a hedge fund, developed an algorithm that processes millions of data points to find trading patterns and forecast trends. Based on trillions of simulated trading scenarios, Sentient’s algorithms use those scenarios to identify and blend successful trading patterns and devise new strategies. Not only does this reduce human labor, but it also allows for optimum accuracy.
  • Activision, an American video game publisher, based in Santa Monica, California, recorded 100 million mobile downloads of COD: Mobile, its new first-person shooter game, owing to the booming video game industry. This provided the traders with the opportunity to mine data masses of web and app data, which is produced by the players and fans, for market insights. Chris Schon tried to build a monthly trading signal using the SimilarWeb’s data for the company’s stock based on the following four key metrics: total visits, daily active users, total installs, and full downloads. The trading algorithm took a long/short position on the company’s stock by comparing each of the metrics mentioned above with the previous month’s data. 
  • In May 2020, Streak, the strategy development and algorithmic trading provider for retail investors, announced its Streak application in the United States. The company, which already counts 300,000 retail investors as clients and has handled over half a billion dollars in trading turnover, will now provide American users access to a wide range of advanced trading capabilities for multiple asset classes, allowing them to find new trading inspiration, build strategies, and rapidly capture new opportunities as they arise. Streak breaks the barriers for algo trading, which generally require traders and investors to learn code or pay for expensive, slow, and clunky legacy software.

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Competitive Landscape

The global algorithmic trading market is moderately fragmented due to the presence of various market players globally, including Virtu Financial, Inc., Algo Trader AG, MetaQuotes Software Corp., Refinitiv Ltd, etc. Key players focus on developing new solutions and creating effective marketing strategies for market surveillance to maintain and increase their market share.

  • February 2020 - The listed German Fintech firm, NAGA, announced that it had enhanced its overall trading experience with the integration of the MetaTrader 5 platform. The brand has completely expanded its multi-asset offering to provide its growing network of clients with direct market access to stocks listed on nine global exchanges, including NASDAQ, NYSE, London Stock Exchange, HKE, Börse Frankfurt and BME, among others.
  • March 2020 - Algo Trader announced the release of AlgoTrader 6.0. In addition to the existing crypto adapters, AlgoTrader 6.0 now includes the following crypto exchange adapters, including Deribit, Huobi, Kraken, and Bithumb. AlgoTrader 6.0 offers full support for Level II Order Book data for all market data adapters. The new AlgoTrader UI Order Book widget shows the user all BUY and SELL orders available at each price level.

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Table Of Contents

  1. 1. INTRODUCTION

    1. 1.1 Study Assumptions and Market Definition

    2. 1.2 Scope of the Study

  2. 2. RESEARCH METHODOLOGY

  3. 3. EXECUTIVE SUMMARY

  4. 4. MARKET INSIGHTS

    1. 4.1 Market Overview

    2. 4.2 Industry Attractiveness - Porter's Five Force Analysis

      1. 4.2.1 Bargaining Power of Suppliers

      2. 4.2.2 Bargaining Power of Buyers/Consumers

      3. 4.2.3 Threat of New Entrants

      4. 4.2.4 Threat of Substitute Products

      5. 4.2.5 Intensity of Competitive Rivalry

    3. 4.3 Technology Snapshot

      1. 4.3.1 Algorithmic Trading Strategies

        1. 4.3.1.1 Momentum Trading

        2. 4.3.1.2 Arbitrage Trading

        3. 4.3.1.3 Trend Following

        4. 4.3.1.4 Execution-Based Strategies

        5. 4.3.1.5 Sentiment Analysis

        6. 4.3.1.6 Index-Fund Rebalancing

        7. 4.3.1.7 Mathematical Model-Based Strategies

        8. 4.3.1.8 Other Algorithmic Trading Strategies

  5. 5. MARKET DYNAMICS

    1. 5.1 Market Drivers

      1. 5.1.1 Rising Demand for Fast, Reliable, and Effective Order Execution

      2. 5.1.2 Growing Demand for Market Surveillance Augmented by Reduced Transaction Costs

    2. 5.2 Market Restraints

      1. 5.2.1 Instant Loss of Liquidity

  6. 6. MARKET SEGMENTATION

    1. 6.1 By Types of Traders

      1. 6.1.1 Institutional Investors

      2. 6.1.2 Retail Investors

      3. 6.1.3 Long-term Traders

      4. 6.1.4 Short-term Traders

    2. 6.2 By Component

      1. 6.2.1 Solutions

        1. 6.2.1.1 Platforms

        2. 6.2.1.2 Software Tools

      2. 6.2.2 Services

    3. 6.3 By Deployment

      1. 6.3.1 On-Cloud

      2. 6.3.2 On-Premise

    4. 6.4 By Organization Size

      1. 6.4.1 Small and Medium Enterprises

      2. 6.4.2 Large Enterprises

    5. 6.5 Geography

      1. 6.5.1 North America

      2. 6.5.2 Europe

      3. 6.5.3 Asia-Pacific

      4. 6.5.4 Latin America

      5. 6.5.5 Middle-East & Africa

  7. 7. COMPETITIVE LANDSCAPE

    1. 7.1 Company Profiles

      1. 7.1.1 Jump Trading LLC

      2. 7.1.2 Refinitiv Ltd

      3. 7.1.3 63 Moons Technologies Limited

      4. 7.1.4 Virtu Financial, Inc.

      5. 7.1.5 MetaQuotes Software Corp.

      6. 7.1.6 Symphony Fintech Solutions Pvt Ltd

      7. 7.1.7 Info Reach, Inc.

      8. 7.1.8 ARGO SE

      9. 7.1.9 Algo Trader AG

      10. 7.1.10 Kuberre Systems, Inc.

  8. *List Not Exhaustive
  9. 8. INVESTMENT ANALYSIS

  10. 9. FUTURE OF THE MARKET

** Subject to Availability

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