首頁 > 市場調查報告書 > 通訊

投資金融科技

市場調查報告書

商品編碼

1735621

全球演算法交易市場規模（按類型、部署、最終用戶、區域覆蓋和預測）

Global Algorithmic Trading Market Size By Type, By Deployment, By End-User, By Geographic Scope And Forecast

出版日期: 2025年05月02日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

演算法交易市場規模及預測

預計演算法交易市場規模在 2024 年將達到 163.7 億美元，到 2032 年將達到 319 億美元，2026 年至 2032 年的複合年成長率為 10%。

演算法交易，通常稱為演算法交易或自動交易，是一種用於在各個市場執行金融交易的電腦演算法，利用預先編程的指示來分析數據、做出決策和執行訂單。
該技術利用先進的技術基礎設施，包括高速電腦、低延遲數據連接、主機託管服務和近距離託管，以快速執行交易並在競爭激烈的市場中競爭。
演算法交易使用數學模型和電腦演算法來自動化交易決策。這些演算法基於各種策略，包括統計分析、技術指標、套利機會、機器學習和人工智慧。
演算法交易廣泛應用於股票、債券、商品、貨幣及衍生性商品等各種金融市場。演算法交易在電子交易平台和交易所中廣泛應用，演算法透過即時競爭和互動，捕捉市場機會並創造利潤。

全球演算法交易市場動態

影響演算法交易市場的主要市場動態是：

關鍵市場促進因素

金融機構採用演算法交易：演算法顯著降低了交易成本和員工數量，改善了銷售部門的業務。它們還能自動向交易所提交訂單，從而消除了仲介為了獲得更好的流動性、定價和仲介費用而需要的依賴。銀行機構擴大採用自動交易軟體，這推動了對雲端基礎的解決方案和市場監控軟體的需求，從而推動了市場的發展。
人工智慧 (AI) 與機器學習 (ML) 的融合：人工智慧演算法可以在幾毫秒內對市場變化做出反應，並且比人類更快地執行交易，這對於利用瞬間機會並在動蕩的市場中最大限度地減少損失至關重要。
金融領域日益複雜：演算法能夠分析大量數據，並以遠超人類的速度執行交易，從而能夠抓住瞬息萬變的市場機遇，快速應對不斷變化的市場環境。因此，演算法交易策略需要基於歷史數據進行嚴格的回測測試，以評估其有效性，並根據特定的市場環境進行最佳化，最終打造一個全球化的市場。
自動化風險管理策略：引入交易前風險檢查，在交易執行前評估其潛在影響，有助於檢查是否符合訂單規模限制、持股限制、保證金要求和監管限制。因此，自動化風險管理軟體（例如演算法交易解決方案）可以即時分析交易參數，並拒絕違反預設風險閾值的訂單。
自動演算法交易在各類企業的應用：演算法交易在頂級券商、散戶投資者、信用合作社和保險公司中越來越受歡迎，因為它降低了交易成本。自動演算法交易可以更快、更輕鬆地執行訂單，使其成為交易所的理想選擇，尤其是在人工交易員無法應對高交易量的情況下。

主要挑戰

數據錯誤或不一致的可能性很高：不準確或不一致的數據可能導致錯誤的交易決策。輸入交易演算法的錯誤資料會產生錯誤訊號，從而導致執行不足和/或損失。市場數據錯誤會增加營運和市場風險。例如，如果交易演算法依賴不準確的價格數據，交易可能會以不利的價格執行，從而導致更大的損失和意外風險敞口。
市場碎片化與流動性挑戰：由於流動性在不同平台和資產類別之間碎片化，自動化交易系統面臨執行成本高昂且流動性有限的挑戰。為了克服這些問題，市場參與企業必須開發複雜的訂單路由演算法，以最佳化執行方式並連接不同的流動性池。
訂單和執行延遲增加：訂單執行延遲可能會增加市場影響，尤其是在快速波動的市場和流動性較低的證券中。訂單執行延遲可能導致滑點，即交易以與預期價格不同的價格執行，從而導致交易成本增加和盈利下降。
突然的系統故障和網路連線問題：系統故障，包括硬體故障、軟體故障和伺服器崩潰，可能會擾亂自動交易操作並導致訂單執行延遲或中斷，從而導致錯失交易機會、訂單積壓以及市場參與企業的潛在損失。

主要趨勢

加密貨幣市場擴張：加密貨幣的普及度不斷提升，導致數位資產市場中的演算法交易活動日益活躍。演算法交易利用自動化策略，利用加密貨幣價格低效、套利機會和市場趨勢。這正在提升加密生態系統的流動性和創新力。
量子運算的潛力：量子運算仍處於早期發展階段，但它有可能透過大幅提升運算能力並以前所未有的速度實現複雜運算，從而徹底改變演算法交易。市場參與企業正在密切關注量子計算技術的進展，以探索其在演算法交易中的潛在應用。
高頻交易 (HFT) 的演變：高頻交易 (HFT) 市場發展：高頻交易 (HFT) 公司不斷改進和開發新演算法，以改善交易策略、最佳化訂單執行並利用短暫的市場機會。這些演算法利用先進的數學模型、統計分析技術和機器學習演算法，以最小的延遲從市場數據中提取 alpha。

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.

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