アルゴリズム取引の世界市場規模：タイプ別、展開別、エンドユーザー別、地域範囲別、予測

アルゴリズム取引の市場規模と予測

アルゴリズム取引の市場規模は、2024年に163億7,000万米ドルと評価され、2026年から2032年にかけて10%のCAGRで成長し、2032年には319億米ドルに達すると予測されます。

• アルゴリズム取引は、一般にアルゴ取引または自動取引として知られ、さまざまな市場で金融取引を実行するために使用されるコンピュータアルゴリズムであり、データを分析し、意思決定を行い、注文を実行するために事前にプログラムされた命令を利用します。
• この技術は、高速コンピュータ、低遅延データ接続、コロケーションサービス、プロキシミティホスティングなどの高度な技術インフラを活用し、取引を迅速に執行し、競争の激しい市場で競争します。
• アルゴリズム取引では、数学的モデルとコンピュータアルゴリズムを使用して、取引の意思決定を自動化します。これらのアルゴリズムは、統計分析、テクニカル指標、裁定取引機会、機械学習、人工知能など、さまざまな戦略に基づいています。
• 株式、債券、コモディティ、通貨、デリバティブなど、さまざまな金融市場に適用されています。アルゴリズム取引は、電子取引プラットフォームや取引所で普及しており、アルゴリズムがリアルタイムで競争し、相互作用することで、市場機会を捉え、利益を生み出しています。

世界のアルゴリズム取引市場力学

アルゴリズム取引市場を形成している主な市場力学は以下の通り：

主な市場促進要因

• 金融機関によるアルゴリズム取引の採用：アルゴリズムにより、トレーディング・コストや人員数が大幅に削減され、セールス・デスク業務が改善されます。また、取引所への注文送信を自動化することで、流動性、価格設定、ブローカー手数料を向上させるためのブローカーの必要性を排除しています。銀行機関による自動取引ソフトウェアの利用が増加しているため、クラウドベースのソリューションや市場監視ソフトウェアに対する需要が高まっており、市場を牽引しています。
• 人工知能（AI）と機械学習（ML）の統合：AIアルゴリズムはミリ秒単位で市場の変化に反応し、人間の能力をはるかに上回るスピードで取引を実行できます。これは、一瞬のチャンスを生かし、不安定な市場での損失を最小限に抑えるために極めて重要です。
• 金融セクターの複雑化：アルゴリズムは膨大な量のデータを分析し、人間よりもはるかに速いスピードで取引を執行することができるため、一瞬のチャンスを生かし、変化する市場環境に迅速に対応することができます。そのため、アルゴリズム取引戦略を過去のデータで厳格にバックテストし、その有効性を評価した上で、特定の市場環境に合わせて最適化することで、世界的に確立された市場を作り出すことができます。
• リスク管理戦略の自動化：取引執行前に取引の潜在的な影響を評価する取引前リスクチェックを導入することで、注文サイズの上限、ポジションの上限、必要証拠金、規制上の制約の遵守のチェックを維持することができると予測されます。したがって、アルゴリズム取引ソリューションのような自動リスク管理ソフトウェアは、リアルタイムで取引パラメータを分析し、事前に定義されたリスクしきい値に違反する注文を拒否すると予測されます。
• 多様な企業における自動アルゴリズム取引の採用：アルゴリズム自動売買は、トップクラスの証券会社、個人投資家、信用組合、保険会社の間でますます普及しています。その理由は、取引に関連するコストを削減できるからです。自動アルゴリズム取引を採用することで、注文をより迅速かつ容易に執行することができ、取引所にとって理想的な取引となります。特に、人間のトレーダーが大量の取引に対応できないような場合に有効です。

主な課題

• データのエラーや不整合の可能性が高い：不正確なデータや一貫性のないデータは、誤った情報に基づく取引決定につながる可能性があります。取引アルゴリズムに誤ったデータが入力されると、誤ったシグナルが生成され、その結果、取引執行が不十分になったり、損失が発生したりする可能性があります。市場データの誤りは、運用リスクや市場リスクを増大させる可能性があります。例えば、取引アルゴリズムが不正確な価格データに依存している場合、不利な価格で取引が執行され、損失の拡大や予期せぬエクスポージャーにつながる可能性があります。
• 市場の断片化と流動性の課題：自動取引システムは、プラットフォームや資産カテゴリー間で流動性が分散しているため、執行コストが高くなり、流動性が制限されるという課題に直面しています。これらの問題を克服するため、市場参入企業は高度な注文ルーティング・アルゴリズムを開発し、執行方法を最適化し、さまざまな流動性プールにアクセスする必要があります。
• 注文と執行のタイムラグの増加：注文執行のタイムラグは、特に動きの速い市場や流動性の低い証券において、マーケットインパクトの増大につながる可能性があります。注文執行の遅延により、意図した価格と異なる価格で取引が執行されるスリッページが発生し、取引コストの上昇や収益性の低下につながる可能性があります。
• 突発的なシステム障害およびネットワーク接続の問題：ハードウェアの故障、ソフトウェアの不具合、サーバーのクラッシュなどのシステム障害は、自動取引オペレーションを混乱させ、注文執行の遅延や中断につながる可能性があります。その結果、取引機会を逃し、注文が滞留し、市場参入企業が損失を被る可能性があります。

主な動向

• 暗号通貨市場の拡大：暗号通貨の人気は上昇傾向にあり、その結果、デジタル資産市場におけるアルゴリズム取引活動は拡大しています。アルゴリズム取引は、暗号通貨の価格非効率性、裁定取引機会、市場動向を利用するために、自動化戦略を利用しています。これにより、暗号エコシステムの流動性と革新性が高まっています。
• 量子コンピューティングの可能性：量子コンピューティングはまだ開発の初期段階にあるが、コンピューティング・パワーを大幅に向上させ、前例のないスピードで複雑な計算を可能にすることで、アルゴリズム取引に革命をもたらす可能性を秘めています。市場参入企業は、量子コンピューティング技術の進歩に注視し、アルゴリズム取引への応用の可能性を探っています。
• 高頻度取引（HFT）の進化：高頻度取引（HFT）の市場開発：高頻度取引（HFT）企業は、取引戦略を改善し、注文執行を最適化し、つかの間の市場機会を活用するため、絶え間なく新しいアルゴリズムを改良・開発しています。これらのアルゴリズムは、高度な数学モデル、統計分析技術、機械学習アルゴリズムを活用し、最小限のレイテンシーで市場データからアルファ値を抽出します。

目次

第1章 世界のアルゴリズム取引市場の導入

• 市場概要
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 世界のアルゴリズム取引市場の展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析

第5章 アルゴリズム取引の世界市場：タイプ別

• 概要
• 株式市場
• 外国為替（FOREX）
• 上場投資信託（ETF）
• 債券
• 暗号通貨
• その他

第6章 アルゴリズム取引の世界市場：展開別

• 概要
• オンプレミス
• クラウドベース

第7章 アルゴリズム取引の世界市場：エンドユーザー別

• 概要
• 短期
• 短期トレーダー
• 長期トレーダー
• 個人投資家
• 機関投資家

第8章 アルゴリズム取引の世界市場、地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他アジア太平洋地域
• 世界のその他の地域
  • ラテンアメリカ
  • 中東・アフリカ

第9章 世界のアルゴリズム取引市場の競合情勢

• 概要
• 各社の市場ランキング
• 主な発展戦略

第10章 企業プロファイル

• 63 Moons Technologies Ltd
• Software AG
• Virtu Financial
• Thomson Reuters
• MetaQuotes Software
• Symphony Fintech
• InfoReach
• Argo SE
• Kuberre Systems
• Tata Consulting Services

第11章 付録

• 関連調査

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