세계의 알고리즘 트레이딩 시장 규모 : 유형별, 전개별, 최종사용자별, 지역별 범위 및 예측

Algorithmic Trading Market Size And Forecast

Algorithmic Trading Market size was valued at USD 16.37 Billion in 2024 and is projected to reach USD 31.90 Billion by 2032, growing at a CAGR of 10% from 2026 to 2032.

• Algorithmic trading, commonly known as algo trading or automated trading, is a computer algorithms used to execute financial transactions in various markets, utilizing pre-programmed instructions to analyze data, make decisions, and execute orders.
• The technology leverages advanced technological infrastructure like high-speed computers, low-latency data connections, co-location services, and proximity hosting to execute trades quickly and compete in highly competitive markets.
• Algorithmic trading involves the use of mathematical models and computer algorithms to automate trading decisions. These algorithms can be based on various strategies, including statistical analysis, technical indicators, arbitrage opportunities, machine learning, and artificial intelligence.
• It is applied across various financial markets, including stocks, bonds, commodities, currencies, and derivatives. Algorithmic trading has become prevalent in electronic trading platforms and exchanges, where algorithms compete and interact in real-time to capture market opportunities and generate profits.

Global Algorithmic Trading Market Dynamics

The key market dynamics that are shaping the Algorithmic Trading Market include:

Key Market Drivers

• Adoption of Algorithmic Trading by Financial Institutions: Algorithms are significantly lowering trading costs, headcount, and improving sales desk operations. They also help automate order sending to exchanges, eliminating the need for brokers for enhancing liquidity, pricing, and broker commissions. The increasing use of automated trading software by banking organizations is demanding for cloud-based solutions and market monitoring software, driving the market.
• Integration of Artificial Intelligence (AI) and Machine Learning (ML): AI algorithms can react to market changes in milliseconds, executing trades at speeds far exceeding human capabilities. This is crucial for capitalizing on fleeting opportunities and minimizing losses in volatile markets.
• Increasing Complexity in Financial Sector: Algorithms can analyze vast amounts of data and execute trades much faster than humans, allowing them to capitalize on fleeting opportunities and react swiftly to changing market conditions. Thus, algorithmic trading strategies can be rigorously backtested on historical data to assess their effectiveness and then optimized for specific market conditions, creating an established market globally.
• Automating Risk Management Strategies: Implementing pre-trade risk checks to evaluate the potential impact of a trade before it is executed is projected to help upkeep checks for order size limits, position limits, margin requirements, and compliance with regulatory constraints. Hence, automated risk management software, such as algorithmic trading solutions, is projected to analyze trade parameters in real time and reject orders that violate predefined risk thresholds.
• Adoption of Automated Algorithmic Trading Across Diverse Companies: Automated algorithmic trading is becoming more and more popular among top brokerage firms, individual investors, credit unions, and insurance companies. The reason for this is that it helps to reduce the costs associated with trading. By adopting automated algorithmic trading, orders can be executed faster and more easily, making it ideal for exchanges. It is particularly useful in situations where a human trader is unable to handle large volumes of trading.

Key Challenges:

• High Chances of Error and Inconsistency in Data: Inaccurate or inconsistent data can lead to misinformed trading decisions. If trading algorithms are fed with erroneous data, they may generate incorrect signals, resulting in poor trade execution or losses. Errors in market data can increase operational and market risk. For example, if a trading algorithm relies on incorrect pricing data, it may execute trades at unfavorable prices, leading to increased losses or unexpected exposures.
• Market Fragmentation and Liquidity Challenge: Automated trading systems face challenges due to liquidity dispersion across platforms and asset categories, resulting in higher execution costs and limited liquidity. To overcome these issues, market participants should develop advanced order routing algorithms, optimize execution methods, and access various liquidity pools.
• Increase in Time lags in Order and Executions: Time lags in order execution can lead to increased market impact, especially in fast-moving markets or illiquid securities. Delayed order execution may result in slippage, where trades are executed at prices different from the intended price, leading to higher transaction costs and reduced profitability.
• Sudden System Failures and Erroneous Network Connectivity Issues: System failures, such as hardware malfunctions, software glitches, or server crashes, can disrupt automated trading operations, leading to delays or interruptions in order execution. This is likely to result in missed trading opportunities, order queuing, and potential losses for market participants.

Key Trends:

• Expansion of Cryptocurrency Markets: The popularity of cryptocurrencies is on the rise, and as a result, algorithmic trading activities in digital asset markets are expanding. Automated strategies are being used by algorithmic traders to take advantage of price inefficiencies, arbitrage opportunities, and market trends in cryptocurrencies. This is leading to increased liquidity and innovation in the crypto ecosystem.
• Quantum Computing Potential: Although quantum computing is still in its early stages of development, it has the potential to revolutionize algorithmic trading by providing a significant boost in computing power and enabling complex calculations at unprecedented speeds. Market participants are closely monitoring advancements in quantum computing technology and exploring potential applications in algorithmic trading.
• The Evolution of High-Frequency Trading (HFT): HFT firms are continuously refining and developing new algorithms to improve trading strategies, optimize order execution, and capitalize on fleeting market opportunities. These algorithms leverage advanced mathematical models, statistical analysis techniques, and machine learning algorithms to extract alpha from market data with minimal latency.

Global Algorithmic Trading Market Regional Analysis

Here is a more detailed regional analysis of the Algorithmic Trading Market:

Asia Pacific:

• According to Verified Market Research, Asia Pacific is estimated to grow at a faster rate over the forecast period due to the rise in private and public sectors making substantial investments to improve their trading technologies, driving the demand for solutions to automate trading processes.
• In addition, trading companies are increasingly deploying algo trading technology, which is creating lucrative opportunities for market players. Furthermore, the adoption of cloud-based technologies in this region is increasing, contributing to the growth of the regional market.
• Tokyo serves as Asia's primary financial hub and a major center for algorithmic trading. The Tokyo Stock Exchange (TSE) and Osaka Exchange (OSE) are key venues for algorithmic trading in Japanese equities and derivatives markets. Japanese regulators oversee market regulation and infrastructure development.

North America:

• North America currently dominates the Algorithmic Trading Market, holding the largest share. This is due to the high number of market participants, making it a highly competitive industry. Consequently, there have been significant investments in trading technologies and government support for global trade, leading to the development and adoption of algorithmic trading solutions.
• The widespread use of algorithmic trading in financial institutions, along with extensive technology enhancements, is boosting industry expansion, particularly in banks.
• The New York Stock Exchange (NYSE) and NASDAQ are prominent venues for algorithmic trading. High-frequency trading (HFT) is prevalent, driven by advanced technology infrastructure and a regulatory environment conducive to electronic trading.

Europe:

• Europe is expected to exhibit a steady growth rate in the trading industry. The market in Europe is analyzed across various countries, including Germany, France, the U.K., Italy, and others. The use of advanced trading approaches and novel infrastructures has increased due to regulatory platforms, technological advancements, and increased competition among trading participants.
• Additionally, the government has implemented special rules and regulations to promote security and performance, which has further nurtured the market growth.
• For instance, MiFID II, a European Union framework that regulates financial markets, has implemented a comprehensive set of algorithmic and high-frequency trading regulations in 2021. These achievements offer immense opportunities of growth for to the Algorithmic Trading Market in Europe.

Global Algorithmic Trading Market: Segmentation Analysis

The Algorithmic Trading Market is Segmented based on Type, Deployment, End-User, And Geography.

Global Algorithmic Trading Market, By Type

• Stock Market
• Foreign Exchange (FOREX)
• Exchange-Traded Fund (ETF)
• Bonds
• Cryptocurrencies
• Others

Based on Type, the Algorithmic Trading Market is divided into Stock Market, Foreign Exchange, Bonds, Cryptocurrencies, Exchange-Traded Fund (ETF), and Others. The stock market segment is projected to dominate the market. Algorithms are becoming increasingly popular on online trading platforms, creating a large consumer base for stock market. These mathematical algorithms analyze all prices and trades on the stock market, identify liquidity opportunities, and convert the information into intelligent trading results. Algorithmic trading reduces trading costs and enables stock managers to manage their trading processes more efficiently. Algorithm modernization continues to offer returns for firms with the scale to absorb the costs and reap the benefits.

Global Algorithmic Trading Market, By Deployment

• On-Premise
• Cloud-Based

Based on Deployment, the market is divided into On-Premise, and Cloud-Based. The cloud-based segment currently holds the largest market share and is expected to grow at the highest rate during the forecast period. This is due to financial organizations' adoption of cloud-based applications to increase their productivity and efficiency. Moreover, traders are increasingly opting for cloud-based solutions as they ensure effective automation of processes, data maintenance, and cost-friendly management. These factors are likely to fuel the growth of cloud-based algo trading software during the forecast period.

• Global Algorithmic Trading Market, End-User
• Short-term
• Traders
• Long-term Traders
• Retail Investors
• Institutional Investors

Based on End-User, he market is divided into Short-term Traders, Long-term Traders, Retail Investors, and Institutional Investors. The short-term traders segment is expected to grow at the highest CAGR. They focus on price movements to profit from market volatility. The institutional investors segment holds the largest market share and includes mutual fund families, pension funds, exchange-traded funds, and insurance firms. Algorithmic trading benefits significantly from large order sizes.

Key Players

The "Global Algorithmic Trading Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are The major players in the market are 63 Moons Technologies Ltd, Software AG, Virtu Financial, Thomson Reuters, MetaQuotes Software, Symphony Fintech, InfoReach, Argo SE, Kuberre Systems, and Tata Consulting Services, among others.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

• Algorithmic Trading Market Recent Developments
• In August 2020, Non-deliverable forwards algorithms were introduced by Barclays on the BARX electronic trading platform. To give clients a variety of options, this algorithm incorporates large investments in electronic offerings.
• In March 2022, the trading software company Trading Technologies International, Inc. announced that it had acquired RCM, a provider of algorithmic execution methodologies and quantitative trading tools. With its exceptional staff, this acquisition of RCM-X provides best-in-class implementation tools.
• In June 2022, Agency-broker FIS's trading operation will be acquired by Instinet. The acquisition reduces execution costs, minimizes information leakage, and enhances customer execution quality.
• In June 2024, one of the top platforms for automated trading and bot building, Kryll, recently partnered with KuCoin Futures via an API. By incorporating TradingView signal features and Kryll's algorithmic trading bots into the KuCoin Futures platform, this ground-breaking partnership seeks to transform futures trading.
• In June 2024, one of the top software platforms for measuring, analyzing, and data in digital media, DoubleVerify, has partnered with Scibids, a major global provider of artificial intelligence (Al) for digital marketing, to produce DV Algorithmic Optimizer, an advanced measure and optimization tool. With Scibids' AI-powered ad decisioning and DV's proprietary attention signals, advertisers can find the best inventory that maximizes advertising ROI and business outcomes without compromising scalability.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL ALGORITHMIC TRADING MARKET

• 1.1 Overview of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL ALGORITHMIC TRADING MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL ALGORITHMIC TRADING MARKET, BY TYPE

• 5.1 Overview
• 5.2 Stock Market
• 5.3 Foreign Exchange (FOREX)
• 5.4 Exchange-Traded Fund (ETF)
• 5.5 Bonds
• 5.6 Cryptocurrencies
• 5.7 Others

6 GLOBAL ALGORITHMIC TRADING MARKET, BY DEPLOYMENT

• 6.1 Overview
• 6.2 On-Premise
• 6.3 Cloud-Based

7 GLOBAL ALGORITHMIC TRADING MARKET, BY END-USER

• 7.1 Overview
• 7.2 Short-term
• 7.3 Traders
• 7.4 Long-term Traders
• 7.5 Retail Investors
• 7.6 Institutional Investors

8 GLOBAL ALGORITHMIC TRADING MARKET, BY GEOGRAPHY

• 8.1 Overview
• 8.2 North America
  • 8.2.1 U.S.
  • 8.2.2 Canada
  • 8.2.3 Mexico
• 8.3 Europe
  • 8.3.1 Germany
  • 8.3.2 U.K.
  • 8.3.3 France
  • 8.3.4 Rest of Europe
• 8.4 Asia Pacific
  • 8.4.1 China
  • 8.4.2 Japan
  • 8.4.3 India
  • 8.4.4 Rest of Asia Pacific
• 8.5 Rest of the World
  • 8.5.1 Latin America
  • 8.5.2 Middle East & Africa

9 GLOBAL ALGORITHMIC TRADING MARKET COMPETITIVE LANDSCAPE

• 9.1 Overview
• 9.2 Company Market Ranking
• 9.3 Key Development Strategies

10 COMPANY PROFILES

• 10.1 63 Moons Technologies Ltd
  • 10.1.1 Overview
  • 10.1.2 Financial Performance
  • 10.1.3 Product Outlook
  • 10.1.4 Key Developments
• 10.2 Software AG
  • 10.2.1 Overview
  • 10.2.2 Financial Performance
  • 10.2.3 Product Outlook
  • 10.2.4 Key Developments
• 10.3 Virtu Financial
  • 10.3.1 Overview
  • 10.3.2 Financial Performance
  • 10.3.3 Product Outlook
  • 10.3.4 Key Developments
• 10.4 Thomson Reuters
  • 10.4.1 Overview
  • 10.4.2 Financial Performance
  • 10.4.3 Product Outlook
  • 10.4.4 Key Developments
• 10.5 MetaQuotes Software
  • 10.5.1 Overview
  • 10.5.2 Financial Performance
  • 10.5.3 Product Outlook
  • 10.5.4 Key Developments
• 10.6 Symphony Fintech
  • 10.6.1 Overview
  • 10.6.2 Financial Performance
  • 10.6.3 Product Outlook
  • 10.6.4 Key Developments
• 10.7 InfoReach
  • 10.7.1 Overview
  • 10.7.2 Financial Performance
  • 10.7.3 Product Outlook
  • 10.7.4 Key Developments
• 10.8 Argo SE
  • 10.8.1 Overview
  • 10.8.2 Financial Performance
  • 10.8.3 Product Outlook
  • 10.8.4 Key Developments
• 10.9 Kuberre Systems
  • 10.9.1 Overview
  • 10.9.2 Financial Performance
  • 10.9.3 Product Outlook
  • 10.9.4 Key Developments
• 10.10 Tata Consulting Services
  • 10.10.1 Overview
  • 10.10.2 Financial Performance
  • 10.10.3 Product Outlook
  • 10.10.4 Key Developments

11 Appendix

• 11.1 Related Research