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到 2030 年的演算法交易市場預測：依類型、部署、組件、組織規模、最終用戶和地區進行的全球分析
市場調查報告書
商品編碼
1359010

到 2030 年的演算法交易市場預測：依類型、部署、組件、組織規模、最終用戶和地區進行的全球分析

Algorithmic Trading Market Forecasts to 2030 - Global Analysis By Type (Bonds, Cryptocurrencies, Exchange-Traded Fund and Other Types), Deployment, Component, Organization Size, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，2023 年全球演算法交易市場規模將達到 181.6 億美元，預計 2030 年將達到 429.9 億美元，預測期內年複合成長率為 13.1%。

演算法交易是使用電腦來遵循特定指令進行交易的過程，以便以人類交易者不切實際的速度和頻率賺取利潤。任何演算法交易策略都需要識別獲利機會以增加利潤或降低成本。演算法交易基於價格、時間、數學模型和數量，並遵循既定規則。演算法線上上交易領域變得越來越普遍，許多大客戶都在使用這些技術。這些公式分析股票市場中執行的每個報價和交易，尋找潛在的流動性來源，並使用資訊執行有利可圖的交易。

根據華爾街資料顯示，演算法交易約占美國股票全部交易的 60-73%。根據 Select USA 的數據，美國金融市場是全球最大、流動性最強的市場。

對提高效率和降低成本的需求日益增加

一個關鍵的市場促進因素是金融部門日益關注效率和降低成本。傳統的手動交易方法既耗時又容易出錯。另一方面，演算法交易可以自動化這些步驟，加快執行速度並降低錯誤風險。此外，這種自動化使得在不增加成本的情況下處理大量交易成為可能。此外，它可以快速處理大量資料並在奈秒內做出買賣決策，從而增加市場流動性並降低點差。演算法交易透過巧妙的交易策略最大限度地降低交易成本並最大化利潤，從而提供競爭優勢，從而促進其在金融領域的採用。

設定成本高

如果客戶打算每天下幾個交易訂單，從長遠來看，演算法交易會更實惠。然而，建構演算法交易基礎設施的初始成本很高。為了快速執行交易，演算法交易者需要盡可能快的電腦。這些電腦和必要的硬體的高成本限制了市場的擴展。

技術進步

計算能力和資料處理方面的快速技術進步對該行業的擴張產生了重大影響。這些發展使得複雜的數學模型和演算法的即時執行成為可能。高頻交易平台的可用性顯著減少了延遲，使交易者能夠根據市場狀況快速採取行動。人工智慧和雲端運算的普及也使得針對特定市場環境和個人投資目標的更複雜的交易策略的開拓成為可能。此外，這些技術的可用性的提高和不斷的發展使演算法交易更容易為中小型企業所接受，從而擴大了市場並促進了創新。

缺乏風險評估能力

日內演算法交易存在風險，如果沒有適當的管理，損失可能會迅速增加。違反風險管理閾值的訂單必須立即被投資公司拒絕或取消。使用演算法的高頻交易（HFT）有其自身的問題，包括可能增加系統性風險。因此，預測期內的市場成長可能會因演算法交易系統風險評估能力不足而受到阻礙。

COVID-19 的影響：

COVID-19 的爆發為市場帶來了福音。這場流行病顯著加速了成長，因為人們已經轉向演算法交易，可以更快地做出決策，同時最大限度地減少人為錯誤。在 3 月向歐盟委員會提交的文件中，紐約證券交易所 (NYSE) 表示，由於新冠肺炎 (COVID-19) 在紐約大都會圈的傳播以及對員工安全的擔憂，將關閉其主要現貨交易場所。暫時關閉並轉向完全電子化交易。此外，在疫情期間，許多市場參與企業都實施了尖端的演算法交易解決方案，以應對不斷成長的交易量。

股票市場預計將在預測期內成為最大的市場

股票部門市場預計將佔據最大的市場佔有率。股票市場是最受歡迎的資產類別，可讓您在安全可控的環境中交易各種證券。此外，股票市場也為金融和證券公司提供利潤最大化和風險管理等好處。股票市場提供的好處正在鼓勵交易者和投資者使用演算法交易工具，從而促使市場成長。

預計雲領域在預測期內年複合成長率最高。

隨著金融機構採用雲端基礎的應用程式來提高生產力和效率，雲端領域預計在預測期內將以最高的年複合成長率成長。雲端基礎的解決方案也越來越受到交易者的歡迎，因為它們可確保高效的流程自動化、資料維護和經濟高效的管理。這些因素有助於雲端基礎的演算法交易軟體的預期成長。

佔比最高的地區

預計北美在預測期內將擁有最大的市場佔有率。北美市場結構美國和加拿大。由於其龐大的市場規模和激烈的行業競爭，北美預計將在演算法交易解決方案的採用和開拓方面處於主導。這是政府對國際貿易的大力支持和對貿易技術的巨額投資的結果。此外，該行業的擴張還得到重大技術進步以及銀行和金融機構演算法交易的廣泛使用的支持。

複合年複合成長率最高的地區：

預計亞太地區在預測期內複合年複合成長率最高。在改善交易技術方面的大量公共和私人投資推動了該地區的成長，從而促使對演算法交易平台的需求增加。該地區電腦介導的交易量正在增加。因此，演算法交易解決方案預計將在該地區得到更廣泛的採用。

提供免費客製化：

訂閱此報告的客戶將收到以下免費自訂選項之一：

  • 公司簡介
    • 其他市場參與者的綜合分析（最多 3 家公司）
    • 主要企業SWOT分析（最多3家企業）
  • 區域分割
    • 根據客戶興趣對主要國家的市場估計、預測和年複合成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章 執行摘要

第2章 前言

  • 概述
  • 利害關係人
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 調查來源
    • 主要調查來源
    • 二次調查來源
    • 先決條件

第3章 市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 最終用戶分析
  • 新型冠狀病毒感染疾病（COVID-19）的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第5章 全球演算法交易市場：依類型

  • 紐帶
  • 加密貨幣
  • 交易所交易基金（ETF）
  • 外匯（FOREX）
  • 股市
  • 其他類型

第6章 全球演算法交易市場：依發展分類

  • 雲
  • 本地

第7章 全球演算法交易市場：依組成部分

  • 解決方案
    • 平台
    • 軟體工具
  • 服務
    • 專業的服務
    • 管理服務

第8章 全球演算法交易市場：依組織規模

  • 中小企業
  • 主要企業

第9章 全球演算法交易市場：依最終用戶分類

  • 短期交易者
  • 長期交易者
  • 個人投資者
  • 機構投資者
  • 其他最終用戶

第10章 全球演算法交易市場：依地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲

第11章進展

  • 合約、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章公司簡介

  • Algo Trader AG
  • Argo Software Engineering
  • InfoReach, Inc.
  • Kuberre Systems, Inc.
  • MetaQuotes Ltd.
  • Refinitiv Ltd
  • Symphony
  • Tata Consultancy Services Limited
  • Thomson Reuters
  • Tradetron
  • VIRTU Finance Inc.
  • Wyden
  • 63 Moons Technologies Limited
Product Code: SMRC23882

According to Stratistics MRC, the Global Algorithmic Trading Market is accounted for $18.16 billion in 2023 and is expected to reach $42.99 billion by 2030 growing at a CAGR of 13.1% during the forecast period. Algorithmic trading is the process of using computers created to follow a specific set of instructions for placing a trade in order to earn profits at a pace and frequency that are impractical for a human trader. Any algorithmic trading strategy needs to identify a profitable chance to boost profits or cut expenses. The algorithmic trading methods follow set rules and are based on price, timing, a mathematical model, and quantity. Algorithms are becoming more common in the world of online trading, and many large clients use these technologies. These mathematical formulas analyze each quote and trade executed on the stock market, search for potential liquidity sources, and use the information to execute profitable trades.

According to Wall Street data, algorithmic trading accounts for around 60-73% of the overall US equity trading. As per Select USA, the US financial markets are the largest and most liquid globally.

Market Dynamics:

Driver:

Growing need for efficiency and cost reduction

A significant market driver is the financial sector's growing focus on efficiency and cost-cutting. Traditional manual trading methods take a lot of time and are prone to error. On the other hand, algorithmic trading automates these procedures, resulting in faster execution and a lower risk of errors. Additionally, this automation makes it possible to handle large volumes of trade without correspondingly raising costs. Furthermore, the ability to process enormous amounts of data quickly and make trading decisions in nanoseconds improves market liquidity and reduces spreads. Algorithmic trading provides a competitive edge by minimizing transaction costs and maximizing profits through clever trading strategies, encouraging its adoption throughout the financial sector.

Restraint:

High cost of setup

Algorithmic trading is more affordable in the long run if the customer intends to carry out several trade orders each day. However, the initial cost of building the infrastructure for algorithmic trading is high. For quick trade execution, algorithmic traders need the fastest computers possible. The high cost of these computers and the necessary hardware restricts the market's expansion.

Opportunity:

Technology advancements

Rapid technological advancements in computing power and data processing have had a significant impact on the industry's expansion. These developments have enabled the real-time execution of sophisticated mathematical models and algorithms. The availability of high-frequency trading platforms has significantly decreased latency, allowing traders to act quickly based on market conditions. The development of more sophisticated trading strategies that are adapted to particular market circumstances and personal investment goals has also been made possible by the widespread use of artificial intelligence and cloud computing. Additionally, the accessibility and ongoing development of these technologies have made algorithmic trading available to even smaller companies, thereby expanding the market and encouraging innovation.

Threat:

Lack of risk assessment capabilities

Intraday algorithmic trading is risky, and without adequate controls, losses could grow quickly. Orders that violate risk management thresholds must be immediately rejected or canceled by investment companies. High-frequency trading (HFT) using algorithms raises issues, such as the potential to increase systemic risk. As a result, market growth during the forecast period may be hampered by algorithmic trading systems' insufficient risk valuation capabilities.

COVID-19 Impact:

The COVID-19 pandemic benefited the market. Due to an increased shift toward algorithmic trading, which allows for quick decision-making while minimizing human error, the pandemic has significantly accelerated growth. The New York Stock Exchange (NYSE), in a filing with the Commission in March, stated that due to the spread of COVID-19 in the New York metropolitan area and its employee safety interests, it temporarily closed its main physical trading floor and switched to fully electronic trading. Additionally, during the pandemic, a number of market participants introduced cutting-edge algorithmic trading solutions to better cater to the increased trading volumes.

The stock markets segment is expected to be the largest during the forecast period

The stock markets segment is anticipated to register the largest market share. One of the most popular asset classes for trading a wide variety of securities in a safe, managed, and controlled environment is the stock market. Additionally, stock markets provide financial and brokerage firms with advantages like profit maximization and risk management. The advantages that stock markets provide are encouraging traders and investors to use algorithmic trading tools, which is growing the market.

The cloud segment is expected to have the highest CAGR during the forecast period

Due to financial organizations' adoption of cloud-based applications to boost productivity and efficiency, the cloud segment is anticipated to grow at the highest CAGR during the forecast period. Additionally, cloud-based solutions are becoming more and more popular among traders as they guarantee efficient process automation, data upkeep, and cost-effective management. These elements contribute to the forecasted growth of cloud-based algorithmic trading software.

Region with largest share:

North America's market share is anticipated to be the largest during the forecast period. The North American market is made up of the United States and Canada. North America is expected to take the lead in the adoption and development of algorithmic trading solutions due to its sizable market and competitive industry. This is the result of significant government support for international trade and huge investments in trading technologies. Additionally, the expansion of the industry is aided by significant technological advancements and the widespread use of algorithmic trading in banks and financial institutions.

Region with highest CAGR:

Over the forecast period, the highest CAGR is anticipated in Asia-Pacific. The significant investments made by the public and private sectors to improve their trading technologies are to blame for the regional growth, which has led to a rise in demand for algorithmic trading platforms. The amount of computerized trading has increased in the area. As a result, it is anticipated that algorithmic trading solutions will be adopted more widely in the area.

Key players in the market:

Some of the key players profiled in the Algorithmic Trading Market include: Algo Trader AG, Argo Software Engineering, InfoReach, Inc., Kuberre Systems, Inc., MetaQuotes Ltd., Refinitiv Ltd, Symphony, Tata Consultancy Services Limited, Thomson Reuters, Tradetron, VIRTU Finance Inc., Wyden and 63 Moons Technologies Limited.

Key Developments:

In April 2023, Argo SE announces a new release of Argo Exchange Solution. A new release adds significant latency and scalability improvements. We have implemented of parallel and distributed transactions, federated risk management. There are significant improvements in IOI/RFQ workflow improvements and new reports.

In March 2023, Trading Technologies International Inc. announced the purchase of London-based AxeTrading by the company. With a significant expansion into full coverage of corporate, government, municipal, and emerging market bonds as well as over-the-counter (OTC) interest rate swaps, the acquisition significantly broadens TT's multi-asset capabilities and reinforces TT's dominant position in fixed income derivatives and U.S. Treasury securities.

In September 2022, Refinitiv, an LSEG Business and one of the world's largest providers of financial markets data and infrastructure, today announced a long-term strategic agreement with HDFC Bank, India's largest private sector bank, to support digital transformation and innovation programmes across the whole business in India. Under the multi-year agreement, comprehensive access to Refinitiv's data and products will enable HDFC Bank to realize new customer opportunities and fast-track its innovation agenda while reducing total cost.

Types Covered:

  • Bonds
  • Cryptocurrencies
  • Exchange-Traded Fund (ETF)
  • Foreign Exchange (FOREX)
  • Stock Markets
  • Other Types

Deployments Covered:

  • Cloud
  • On-premise

Components Covered:

  • Solution
  • Services

Organization Sizes Covered:

  • Small and Medium Enterprises
  • Large Enterprises

End Users Covered:

  • Short-term Traders
  • Long-term Traders
  • Retail Investors
  • Institutional Investors
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 End User Analysis
  • 3.7 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Algorithmic Trading Market, By Type

  • 5.1 Introduction
  • 5.2 Bonds
  • 5.3 Cryptocurrencies
  • 5.4 Exchange-Traded Fund (ETF)
  • 5.5 Foreign Exchange (FOREX)
  • 5.6 Stock Markets
  • 5.7 Other Types

6 Global Algorithmic Trading Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-premise

7 Global Algorithmic Trading Market, By Component

  • 7.1 Introduction
  • 7.2 Solution
    • 7.2.1 Platforms
    • 7.2.2 Software Tools
  • 7.3 Services
    • 7.3.1 Professional Services
    • 7.3.2 Managed Services

8 Global Algorithmic Trading Market, By Organization Size

  • 8.1 Introduction
  • 8.2 Small and Medium Enterprises
  • 8.3 Large Enterprises

9 Global Algorithmic Trading Market, By End User

  • 9.1 Introduction
  • 9.2 Short-term Traders
  • 9.3 Long-term Traders
  • 9.4 Retail Investors
  • 9.5 Institutional Investors
  • 9.6 Other End Users

10 Global Algorithmic Trading Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Algo Trader AG
  • 12.2 Argo Software Engineering
  • 12.3 InfoReach, Inc.
  • 12.4 Kuberre Systems, Inc.
  • 12.5 MetaQuotes Ltd.
  • 12.6 Refinitiv Ltd
  • 12.7 Symphony
  • 12.8 Tata Consultancy Services Limited
  • 12.9 Thomson Reuters
  • 12.10 Tradetron
  • 12.11 VIRTU Finance Inc.
  • 12.12 Wyden
  • 12.13 63 Moons Technologies Limited

List of Tables

  • Table 1 Global Algorithmic Trading Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global Algorithmic Trading Market Outlook, By Type (2021-2030) ($MN)
  • Table 3 Global Algorithmic Trading Market Outlook, By Bonds (2021-2030) ($MN)
  • Table 4 Global Algorithmic Trading Market Outlook, By Cryptocurrencies (2021-2030) ($MN)
  • Table 5 Global Algorithmic Trading Market Outlook, By Exchange-Traded Fund (ETF) (2021-2030) ($MN)
  • Table 6 Global Algorithmic Trading Market Outlook, By Foreign Exchange (FOREX) (2021-2030) ($MN)
  • Table 7 Global Algorithmic Trading Market Outlook, By Stock Markets (2021-2030) ($MN)
  • Table 8 Global Algorithmic Trading Market Outlook, By Other Types (2021-2030) ($MN)
  • Table 9 Global Algorithmic Trading Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 10 Global Algorithmic Trading Market Outlook, By Cloud (2021-2030) ($MN)
  • Table 11 Global Algorithmic Trading Market Outlook, By On-premise (2021-2030) ($MN)
  • Table 12 Global Algorithmic Trading Market Outlook, By Component (2021-2030) ($MN)
  • Table 13 Global Algorithmic Trading Market Outlook, By Solution (2021-2030) ($MN)
  • Table 14 Global Algorithmic Trading Market Outlook, By Platforms (2021-2030) ($MN)
  • Table 15 Global Algorithmic Trading Market Outlook, By Software Tools (2021-2030) ($MN)
  • Table 16 Global Algorithmic Trading Market Outlook, By Services (2021-2030) ($MN)
  • Table 17 Global Algorithmic Trading Market Outlook, By Professional Services (2021-2030) ($MN)
  • Table 18 Global Algorithmic Trading Market Outlook, By Managed Services (2021-2030) ($MN)
  • Table 19 Global Algorithmic Trading Market Outlook, By Organization Size (2021-2030) ($MN)
  • Table 20 Global Algorithmic Trading Market Outlook, By Small and Medium Enterprises (2021-2030) ($MN)
  • Table 21 Global Algorithmic Trading Market Outlook, By Large Enterprises (2021-2030) ($MN)
  • Table 22 Global Algorithmic Trading Market Outlook, By End User (2021-2030) ($MN)
  • Table 23 Global Algorithmic Trading Market Outlook, By Short-term Traders (2021-2030) ($MN)
  • Table 24 Global Algorithmic Trading Market Outlook, By Long-term Traders (2021-2030) ($MN)
  • Table 25 Global Algorithmic Trading Market Outlook, By Retail Investors (2021-2030) ($MN)
  • Table 26 Global Algorithmic Trading Market Outlook, By Institutional Investors (2021-2030) ($MN)
  • Table 27 Global Algorithmic Trading Market Outlook, By Other End Users (2021-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.