查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
到 2030 年的演算法交易市场预测：按类型、部署、组件、组织规模、最终用户和地区进行的全球分析
市场调查报告书
商品编码
1359010

到 2030 年的演算法交易市场预测：按类型、部署、组件、组织规模、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，2023 年全球演算法交易市场规模将达到 181.6 亿美元，预计 2030 年将达到 429.9 亿美元，预测期内年复合成长率为 13.1%。

演算法交易是使用电脑来遵循特定指令进行交易的过程，以便以人类交易者不切实际的速度和频率赚取利润。任何演算法交易策略都需要识别获利机会以增加利润或降低成本。演算法交易基于价格、时间、数学模型和数量，并遵循既定规则。演算法在线上交易领域变得越来越普遍，许多大客户都在使用这些技术。这些公式分析股票市场中执行的每个报价和交易，寻找潜在的流动性来源，并使用资讯执行有利可图的交易。

根据华尔街资料显示，演算法交易约占美国股票全部交易的 60-73%。根据 Select USA 的数据，美国金融市场是全球最大、流动性最强的市场。

对提高效率和降低成本的需求日益增加

一个关键的市场促进因素是金融部门日益关注效率和降低成本。传统的手动交易方法既耗时又容易出错。另一方面，演算法交易可以自动化这些步骤，加快执行速度并降低错误风险。此外，这种自动化使得在不增加成本的情况下处理大量交易成为可能。此外，它可以快速处理大量资料并在奈秒内做出买卖决策，从而增加市场流动性并降低点差。演算法交易透过巧妙的交易策略最大限度地降低交易成本并最大化利润，从而提供竞争优势，从而促进其在金融领域的采用。

设定成本高

如果客户打算每天下几个交易订单，从长远来看，演算法交易会更实惠。然而，建构演算法交易基础设施的初始成本很高。为了快速执行交易，演算法交易者需要尽可能快的计算机。这些计算机和必要的硬体的高成本限制了市场的扩展。

技术进步

计算能力和资料处理方面的快速技术进步对该行业的扩张产生了重大影响。这些发展使得复杂的数学模型和演算法的即时执行成为可能。高频交易平台的可用性显着减少了延迟，使交易者能够根据市场状况快速采取行动。人工智慧和云端运算的普及也使得针对特定市场环境和个人投资目标的更复杂的交易策略的开拓成为可能。此外，这些技术的可用性的提高和不断的发展使演算法交易更容易为中小型企业所接受，从而扩大了市场并促进了创新。

缺乏风险评估能力

日内演算法交易存在风险，如果没有适当的管理，损失可能会迅速增加。违反风险管理阈值的订单必须立即被投资公司拒绝或取消。使用演算法的高频交易（HFT）有其自身的问题，包括可能增加系统性风险。因此，预测期内的市场成长可能会因演算法交易系统风险评估能力不足而受到阻碍。

COVID-19 的影响：

COVID-19 的爆发为市场带来了福音。这场流行病显着加速了成长，因为人们已经转向演算法交易，可以更快地做出决策，同时最大限度地减少人为错误。在 3 月向欧盟委员会提交的文件中，纽约证券交易所 (NYSE) 表示，由于新冠肺炎 (COVID-19) 在纽约大都会圈的传播以及对员工安全的担忧，将关闭其主要现货交易场所。暂时关闭并转向完全电子化交易。此外，在疫情期间，许多市场参与企业都实施了尖端的演算法交易解决方案，以应对不断增长的交易量。

股票市场预计将在预测期内成为最大的市场

股票细分市场预计将占据最大的市场占有率。股票市场是最受欢迎的资产类别，可让您在安全可控的环境中交易各种证券。此外，股票市场也为金融和证券公司提供利润最大化和风险管理等好处。股票市场提供的好处正在鼓励交易者和投资者使用演算法交易工具，从而导致市场成长。

预计云领域在预测期内年复合成长率最高。

随着金融机构采用云端基础的应用程式来提高生产力和效率，云端领域预计在预测期内将以最高的年复合成长率成长。云端基础的解决方案也越来越受到交易者的欢迎，因为它们可确保高效的流程自动化、资料维护和经济高效的管理。这些因素有助于云端基础的演算法交易软体的预期成长。

占比最高的地区

预计北美在预测期内将拥有最大的市场占有率。北美市场结构美国和加拿大。由于其庞大的市场规模和激烈的行业竞争，北美预计将在演算法交易解决方案的采用和开拓方面处于主导。这是政府对国际贸易的大力支持和对贸易技术的巨额投资的结果。此外，该行业的扩张还得到重大技术进步以及银行和金融机构演算法交易的广泛使用的支持。

复合年复合成长率最高的地区：

预计亚太地区在预测期内复合年复合成长率最高。在改善交易技术方面的大量公共和私人投资推动了该地区的成长，从而导致对演算法交易平台的需求增加。该地区电脑介导的交易量正在增加。因此，演算法交易解决方案预计将在该地区得到更广泛的采用。

提供免费客製化：

订阅此报告的客户将收到以下免费自订选项之一：

  • 公司简介
    • 其他市场参与者的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和年复合成长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第1章执行摘要

第2章前言

  • 概述
  • 利害关係人
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 调查来源
    • 主要调查来源
    • 二次调查来源
    • 先决条件

第3章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 最终用户分析
  • 新型冠状病毒感染疾病（COVID-19）的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第5章全球演算法交易市场：按类型

  • 纽带
  • 加密货币
  • 交易所交易基金（ETF）
  • 外汇（FOREX）
  • 股市
  • 其他类型

第6章全球演算法交易市场：依发展划分

  • 云
  • 本地

第7章全球演算法交易市场：按组成部分

  • 解决方案
    • 平台
    • 软体工具
  • 服务
    • 专业的服务
    • 管理服务

第8章全球演算法交易市场：依组织规模

  • 中小企业
  • 大公司

第9章全球演算法交易市场：依最终用户划分

  • 短期交易者
  • 长期交易者
  • 个人投资者
  • 机构投资者
  • 其他最终用户

第10章全球演算法交易市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第11章进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第12章公司简介

  • 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.