세계의 알고리즘 트레이딩 시장 평가, 컴포넌트별, 방식별, 기능별, 유형별, 최종사용자별, 지역별, 기회, 예측(2017-2031년)

Global algorithmic trading market is projected to witness a CAGR of 10.68% during the forecast period 2024-2031, growing from USD 15.76 billion in 2023 to USD 35.49 billion in 2031. With the help of various factors, the global algorithmic trading market is experiencing dramatic growth. Increased market volatility forces traders to use algorithms for quicker executions and better risk management, where a trader can take advantage of price movements. Technological progress in computing power and data processing capabilities has been improved to enhance the efficiency and effectiveness of algorithmic trading strategies. Big data analytics enable traders to process huge amounts of data in real-time, thus creating more sophisticated trading techniques.

Additionally, less transaction cost and fewer human errors with algorithmic trading make it an attractive trading option. Regulatory compliance calls for better means, along with the increased adoption of high-frequency trading strategies. Increased participation of retail investors, which has been made possible by developed trading platforms, is accessible to most people, along with the infusion of artificial intelligence and machine learning.

Algorithmic trading employs computer algorithms, automatically generating and executing trading decisions and orders within the financial markets. By analyzing enormous amounts of market data while executing trades at highly increased speeds, these algorithms can take advantage of price differences and maximize the profitability of trading strategies. Lately, it has become trendy among institutional traders and retailers, increasing efficiency with minimal transaction costs and human error. In October 2024, Broker ATFX successfully launches the MetaTrader 5 platform. This is an important step in the development of its mission to provide investors with the best possible trading environment. MetaTrader 5 provides operational functionality improvement and increased general user experiences, offering innovative solutions for further successful trading in global financial markets to clients.

Growing Demand for Effective Algorithmic Trading Solutions to Boost Market Growth

The increasing need for effective algorithmic trading solutions is a major driver for the growth of the market. Traders and institutions seek alternatives to enhance their trading strategy, and hence, it is pivotal to have automation in executing precise trades. Algorithmic trading could include analyzing information about the market in real time, allowing traders to make swift decisions or take full advantage of fleeting opportunities. It leads to higher productivity, ruling out the possibility of human error, especially when dealing with a fast-moving market, reducing transaction costs. In October 2024, LIST, a subsidiary of ION Capital UK Limited, enhanced its FastTrade trading solution to provide customers with access to the direct equity trading mechanism of Cboe Europe. As a result, it became possible for LIST's customers to be connected directly to the largest available Dark and Periodic Auction Books of Cboe, along with their Lit Order Books. The upgrade brings sweep functionality that allows access to multiple Cboe order books using a single order so that users can maximize potential size and price improvement opportunities.

Also, with increased market volatility, algorithmic solutions have gained popularity among traders for optimal risk management and overall performance enhancement. Advanced technologies, such as AI and ML, have increased the demand as they support the development of sophisticated trading algorithms. Financial firms are thus investing heavily in these technologies in the pursuit of competitive advantages, a situation that is likely to propel growth in the algorithmic trading market.

Increasing Market Liquidity to Drive Market Growth

One of the main factors driving the growth of the algorithmic trading market is increased market liquidity. Market liquidity refers to the ease with which it is possible to buy and sell assets without affecting the level of market prices. Indeed, as algorithms percolate into markets, they increase market liquidity due to faster and more efficient transactions. With algorithms trading extremely fast, adding more institutional and retail participants is possible. Increasing liquidity attracts higher liquidity, benefitting traders with lower spreads and other transaction costs and stabilizing markets against extreme price changes. In April 2024, a capital markets technology platform provider headquartered in Chicago, Trading Technologies International Inc. (TT) announced the release of TT Splicer, a new TT Premium Order Type that brings industry-first functionality for synthetic multi-leg spread trading.

As more traders use algorithmic strategies, interconnectivity across global financial markets rises, and hence, overall liquidity increases. The birth of new financial instruments and asset classes works as an encouragement as algorithms easily switch to different trading environments. Per se, the whole synergy between algorithmic trading and market liquidity creates an ever-growing virtuous cycle that thrusts more players into adopting automated solutions so that it fosters a more dynamic and resilient trading landscape that will reward every participant in the market.

Stock Market Segment to Dominate the Global Algorithmic Trading Market Share

The high liquidity, diversified trading opportunities, and growing participation of institutional and retail investors make the stock market segment dominant in the algorithmic trading market. With advanced technologies being deployed in stock exchanges across the world, more transactions are taking place, including algorithmic trading in their list of services. The algorithms allow the processing of enormous quantities of data and the execution of trades at lightning-fast speeds, making them very effective in a highly volatile market with rapidly moving prices. This efficiency is enhancing trading strategies and slashing transaction costs, appealing to a wide spectrum of traders. In October 2024, Bloomberg Finance L.P., a financial software company, launched its fully customizable intraday quant pricing solution for Investment Research, the Open-High-Low-Close (OHLC) Bar product. The new product simplifies workflows in the quant arena, allowing customers to quickly build intraday pricing datasets, using either pre-set templates through Bloomberg or customizing fully-tailored pricing with their choice of trade condition codes.

Another factor that gives rise to algorithmic trading is the inflating usage of exchange-traded funds and new financial products. Investors look to optimize their portfolios and control the amount of risk involved, and the demand for algorithms making automated stock market trades is expected to rise. Altogether, when the stock market segment is put in the equation of technological advancements, market dynamics and the behavior of investors will be molding the force of algorithmic trading in the forecast years.

North America to Dominate the Algorithmic Trading Market Share

North America will lead the share of the global algorithmic trading market, driven by a strong financial infrastructure, technological innovation, and a high population of institutional investors. Some of the world's largest exchanges are located in this region, such as the National Association of Securities Dealers Automated Quotations (NASDAQ) and the New York Stock Exchange. Furthermore, the presence of leading fintech companies and investment firms creates an environment of competition, accelerating the development of sophisticated trading algorithms. Additionally, increased market volatility and high demand for much faster execution speeds impel traders in North America to have more algorithmic solutions to improve their strategies and reduce risks.

Also, various regulatory frames within the region continue to change in ways that support algorithmic trading practices. This is leading to an increase in the market. North America will continue to be a significant player within the global sphere of algorithmic trading, impelling trends and innovations across all regions. Institutional and retail investors are focused on exploiting their opportunities through technology. In July 2024, Trading Technologies International Inc. (TT), a Chicago-based provider of capital markets technology solutions, announced that it is offering its clients access to Abaxx Exchange, a global commodity futures exchange and clearinghouse located in Singapore.

With rapid economic growth, Asia-Pacific is emerging as the most rapidly growing market for algorithmic trading. Countries such as China, India, and Japan are seeing growing retail investors and hosting increasingly developed technological hubs. Algorithmic trading is, therefore, gaining prominence through advanced trading infrastructure and ideal regulatory support that saves firms from teething difficulties in using automated strategies for trading. Hence, Asia-Pacific will prove to be a more important hub for future algorithmic trading as a response to volatility exploitation and further implementation efficiency.

Future Market Scenario (2024 - 2031F)

Enhanced algorithms that include artificial intelligence and machine learning will further enhance increasingly sophisticated trading strategies and predictive analytics.

Algorithmic trading will bring tighter regulations in the trading style, which will change and stabilize the market to ensure fair trade.

Growing demand for customized trading algorithms used by the platforms will tailor the needs of traders in the forecast period.

Key Players Landscape and Outlook

The top market players in the algorithmic trading market are engaged with strategies to broaden their geographical footprint through region-specific and industry-specific solutions. By working together and buying local firms, they are establishing a regional stronghold and responding to the nuances of different markets. Innovations and new products are at the heart of their strategy, considering that these developments attract diverse groups of customers and improve revenue margins.

Companies look forward to effective marketing strategies to increase brand awareness, along with customer contact, while developing new solutions to maintain and gain higher market share. The growing global trade volume creates new opportunities for profitable business, and thus, market participants take it as an opportunity to grow in the global algorithmic trading market. In a quest to remain competitive, firms engage in strategic initiatives, such as mergers, acquisitions, and partnerships, that enable them to exploit synergies and upgrade their capabilities of offering cutting-edge trading technologies and solutions.

In October 2024, London-based trading automation software company ION Capital UK Limited announced that Instantia had selected ION Foreign Exchange for trade execution, trade management, risk management, and settlement services for its FX operations. By leveraging ION APIs, Instantia created customized user interfaces for clients and dealers, bringing fundamental differences in overall user experience.

Table of Contents

1. Project Scope and Definitions

2. Research Methodology

3. Executive Summary

4. Voice of Customer

• 4.1. Product and Market Intelligence
• 4.2. Mode of Brand Awareness
• 4.3. Factors Considered in Purchase Decisions
  • 4.3.1. Software Name
  • 4.3.2. Computer Programming
  • 4.3.3. Price
  • 4.3.4. Execution Speed
  • 4.3.5. Functions
  • 4.3.6. Average Trade
  • 4.3.7. Promotional Offers and Discounts
• 4.4. Customer Support
• 4.5. Frequency of Algorithmic Trading
• 4.6. Consideration of Privacy and Regulations

5. Global Algorithmic Trading Market Outlook, 2017-2031F

• 5.1. Market Size Analysis & Forecast
  • 5.1.1. By Value
• 5.2. Market Share Analysis & Forecast
  • 5.2.1. By Component
    • 5.2.1.1. Solution
      • 5.2.1.1.1. Platform
      • 5.2.1.1.2. Software Tools
    • 5.2.1.2. Services
  • 5.2.2. By Mode
    • 5.2.2.1. Cloud
    • 5.2.2.2. On-premises
  • 5.2.3. By Function
    • 5.2.3.1. Programming
    • 5.2.3.2. Debugging
    • 5.2.3.3. Data Extraction
    • 5.2.3.4. Back-testing and Optimization
    • 5.2.3.5. Risk Management
  • 5.2.4. By Type
    • 5.2.4.1. Stock Market
    • 5.2.4.2. Foreign Exchange Market
    • 5.2.4.3. Exchange-traded Funds
    • 5.2.4.4. Bonds
    • 5.2.4.5. Cryptocurrencies
    • 5.2.4.6. Others
  • 5.2.5. By End-user
    • 5.2.5.1. Short-term Traders
    • 5.2.5.2. Long-term Traders
    • 5.2.5.3. Retail Investors
    • 5.2.5.4. Institutional Investors
  • 5.2.6. By Region
    • 5.2.6.1. North America
    • 5.2.6.2. Europe
    • 5.2.6.3. Asia-Pacific
    • 5.2.6.4. South America
    • 5.2.6.5. Middle East and Africa
  • 5.2.7. By Company Market Share Analysis (Top 5 Companies and Others - By Value, 2023)
• 5.3. Market Map Analysis, 2023
  • 5.3.1. By Component
  • 5.3.2. By Mode
  • 5.3.3. By Function
  • 5.3.4. By Type
  • 5.3.5. By End-user
  • 5.3.6. By Region

6. North America Algorithmic Trading Market Outlook, 2017-2031F*

• 6.1. Market Size Analysis & Forecast
  • 6.1.1. By Value
• 6.2. Market Share Analysis & Forecast
  • 6.2.1. By Component
    • 6.2.1.1. Solution
      • 6.2.1.1.1. Platform
      • 6.2.1.1.2. Software Tools
    • 6.2.1.2. Services
  • 6.2.2. By Mode
    • 6.2.2.1. Cloud
    • 6.2.2.2. On-premises
  • 6.2.3. By Function
    • 6.2.3.1. Programming
    • 6.2.3.2. Debugging
    • 6.2.3.3. Data Extraction
    • 6.2.3.4. Back-testing and Optimization
    • 6.2.3.5. Risk Management
  • 6.2.4. By Type
    • 6.2.4.1. Stock Market
    • 6.2.4.2. Foreign Exchange Market
    • 6.2.4.3. Exchange-traded Funds
    • 6.2.4.4. Bonds
    • 6.2.4.5. Cryptocurrencies
    • 6.2.4.6. Others
  • 6.2.5. By End-user
    • 6.2.5.1. Short-term Traders
    • 6.2.5.2. Long-term Traders
    • 6.2.5.3. Retail Investors
    • 6.2.5.4. Institutional Investors
  • 6.2.6. By Country Share
    • 6.2.6.1. United States
    • 6.2.6.2. Canada
    • 6.2.6.3. Mexico
• 6.3. Country Market Assessment
  • 6.3.1. United States Algorithmic Trading Market Outlook, 2017-2031F*
    • 6.3.1.1. Market Size Analysis & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share Analysis & Forecast
      • 6.3.1.2.1. By Component
        • 6.3.1.2.1.1. Solution
        • 6.3.1.2.1.1.1. Platform
        • 6.3.1.2.1.1.2. Software Tools
        • 6.3.1.2.1.2. Services
      • 6.3.1.2.2. By Mode
        • 6.3.1.2.2.1. Cloud
        • 6.3.1.2.2.2. On-premises
      • 6.3.1.2.3. By Function
        • 6.3.1.2.3.1. Programming
        • 6.3.1.2.3.2. Debugging
        • 6.3.1.2.3.3. Data Extraction
        • 6.3.1.2.3.4. Back-testing and Optimization
        • 6.3.1.2.3.5. Risk Management
      • 6.3.1.2.4. By Type
        • 6.3.1.2.4.1. Stock Market
        • 6.3.1.2.4.2. Foreign Exchange Market
        • 6.3.1.2.4.3. Exchange-traded Funds
        • 6.3.1.2.4.4. Bonds
        • 6.3.1.2.4.5. Cryptocurrencies
        • 6.3.1.2.4.6. Others
      • 6.3.1.2.5. By End-user
        • 6.3.1.2.5.1. Short-term Traders
        • 6.3.1.2.5.2. Long-term Traders
        • 6.3.1.2.5.3. Retail Investors
        • 6.3.1.2.5.4. Institutional Investors
  • 6.3.2. Canada
  • 6.3.3. Mexico

All segments will be provided for all regions and countries covered

7. Europe Algorithmic Trading Market Outlook, 2017-2031F

• 7.1. Germany
• 7.2. France
• 7.3. Italy
• 7.4. United Kingdom
• 7.5. Russia
• 7.6. Netherlands
• 7.7. Spain
• 7.8. Turkey
• 7.9. Poland

8. Asia-Pacific Algorithmic Trading Market Outlook, 2017-2031F

• 8.1. India
• 8.2. China
• 8.3. Japan
• 8.4. Australia
• 8.5. Vietnam
• 8.6. South Korea
• 8.7. Indonesia
• 8.8. Philippines

9. South America Algorithmic Trading Market Outlook, 2017-2031F

• 9.1. Brazil
• 9.2. Argentina

10. Middle East and Africa Algorithmic Trading Market Outlook, 2017-2031F

• 10.1. Saudi Arabia
• 10.2. UAE
• 10.3. South Africa

11. Demand Supply Analysis

12. Regulatory Framework and Compliance

• 12.1. Securities & Exchange Board of India Guidelines and Policies
• 12.2. RBI Guidelines and Policies
• 12.3. Taxation Policies

13. Value Chain Analysis

14. Porter's Five Forces Analysis

15. PESTLE Analysis

16. Software Price Analysis

17. Market Dynamics

• 17.1. Market Drivers
• 17.2. Market Challenges

18. Market Trends and Developments

19. Case Studies

20. Competitive Landscape

• 20.1. Competition Matrix of Top 5 Market Leaders
• 20.2. SWOT Analysis for Top 5 Players
• 20.3. Key Players Landscape for Top 10 Market Players
  • 20.3.1. Bloomberg Finance L.P.
    • 20.3.1.1. Company Details
    • 20.3.1.2. Key Management Personnel
    • 20.3.1.3. Products and Services
    • 20.3.1.4. Financials (As Reported)
    • 20.3.1.5. Key Market Focus and Geographical Presence
    • 20.3.1.6. Recent Developments/Collaborations/Partnerships/Mergers and Acquisition
  • 20.3.2. London Stock Exchange Group PLC (Refinitiv)
  • 20.3.3. ION Capital UK Limited (Fidessa)
  • 20.3.4. Charles River Systems Inc.
  • 20.3.5. Trading Technologies International Inc.
  • 20.3.6. QuantConnect Corporation
  • 20.3.7. Algo Trader AG
  • 20.3.8. Interactive Brokers Group Inc.
  • 20.3.9. Metaquotes Software Corporation
  • 20.3.10. Jump Trading LLC

Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.

21. Strategic Recommendations

22. About Us and Disclaimer