查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1876669

量化交易平台市场预测至2032年：按策略类型、技术、应用、最终用户和地区分類的全球分析

Quant-Trade Platforms Market Forecasts to 2032 - Global Analysis By Strategy Type, Technology, Application, 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 的一项研究，预计到 2025 年，全球量化交易平台市场规模将达到 22 亿美元，到 2032 年将达到 38 亿美元，预测期内复合年增长率为 8.1%。

量化交易平台是利用量化演算法和统计模型执行投资策略的自动化金融交易系统。它们分析大规模资料集，以识别模式、预测价格走势并优化投资组合表现。这些平台支援多种资产类别，包括股票、外汇和加密货币。它们利用人工智慧、机器学习和即时分析技术，实现快速、数据驱动的决策，并减少金融交易环境中的人为偏见。

根据摩根大通的一项调查，超过 60% 的机构投资者现在使用另类数据和量化策略，这推动了对易于使用的演算法交易基础设施的需求。

演算法交易的激增

演算法交易策略的日益普及是推动量化交易平台市场发展的主要因素。演算法交易基于预设规则自动执行交易，实现高速、高频交易，从而提高市场效率并减少人为错误。这一趋势得益于运算能力、数据分析技术和市场进入的进步，使交易者能够持续掌握多个市场中微小的价格波动。因此，全球对支援无缝演算法部署的先进量化平台的需求日益增长。

高昂的基础设施和延迟成本

高昂的基础设施成本，包括对尖端伺服器、低延迟网路以及接近性资料中心的需求，限制了市场成长。虽然降低延迟对于在高频交易中获得竞争优势至关重要，但必要的投资可能成为中小企业的障碍。维护和升级这些基础设施需要大量支出，限制了其可及性，并设定了准入门槛，儘管技术不断进步，但仍难以实现广泛应用。

整合基于人工智慧的交易引擎

将人工智慧和机器学习技术整合到量化交易平台中蕴藏着巨大的成长机会。基于人工智慧的引擎利用巨量资料和即时市场洞察，提升预测准确度、风险管理能力和交易策略优化水准。这些技术支援自适应决策和持续学习，使交易员能够快速应对市场变化并发现新的套利机会。金融机构和避险基金对人工智慧驱动的自动化技术的日益普及，正在推动对具备人工智慧功能的高阶量化平台的需求。

市场波动与系统性风险

市场波动和系统性风险对量化交易平台市场构成重大威胁。高频交易和演算法交易加剧了市场波动，并可能导致闪崩和其他市场动盪。监管机构正在加强审查，并对演算法交易行为实施更严格的管控。意外的市场波动、网路风险或演算法故障都可能造成重大经济损失、投资者不信任以及监管处罚，这给平台营运商带来了挑战，迫使其确保稳健的风险管理和合规性。

新冠疫情的影响：

新冠疫情加剧了市场波动，导致量化交易平台（尤其是高频交易平台）的交易活动和利润激增。远距办公的广泛普及加速了云端基础交易系统和数位基础设施的采用。儘管部分业务最初受到影响，但总体而言，疫情凸显了自动化交易解决方案在即时回应和风险管理方面的重要性，从而刺激了平台领域的投资和创新。

预计在预测期内，高频交易板块将占据最大的市场份额。

由于高频交易（HFT）在机构投资者中广泛应用，预计在预测期内，高频交易领域将占据最大的市场份额。机构投资者可以透过高交易量获得小规模但稳定的利润。高频交易对速度和自动化的依赖，使其能够很好地应对日益复杂的市场和激烈的竞争压力，这也使得该领域成为推动对具有超低延迟和先进执行能力的量化交易平台需求的主要动力。

预计在预测期内，云端基础的回测引擎细分市场将呈现最高的复合年增长率。

受可扩展、按需运算资源需求不断增长的推动，预计云端基础回测引擎领域在预测期内将实现最高成长率。云端解决方案提供了一个灵活且经济高效的环境，无需对内部基础设施进行大量投资即可运行复杂的模拟模型并检验交易策略。增强的协作能力、数据可用性和快速原型製作能力正在加速避险基金和金融科技公司采用云端解决方案，以期快速优化其策略。

占比最大的地区：

由于中国、日本、韩国和印度等国的数位化加快、金融市场蓬勃发展以及机构投资者参与度不断提高，预计亚太地区将在预测期内占据最大的市场份额。政府支持金融科技创新的倡议、互联网普及率的提高以及新兴经济体对自动化交易解决方案需求的增长，都在推动区域市场扩张，使亚太地区成为量化交易平台发展的关键枢纽。

年复合成长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这与其成熟的金融市场、大型避险基金和投资公司的集中以及人工智慧和云端运算技术的广泛应用密切相关。强而有力的法规结构促进了市场透明度和安全性，加上私部门对金融科技研发的投资，正在推动美国和加拿大持续创新，并不断提升对先进量化交易平台的需求。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 公司概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 主要参与者（最多3家公司）的SWOT分析
  • 区域细分
    • 根据客户要求，对主要国家的市场规模和复合年增长率进行估算和预测（註：可行性需确认）。
  • 竞争基准化分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 原始研究资料
    • 二手研究资料
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球量化交易平台市场（依策略类型划分）

  • 介绍
  • 高频交易策略
  • 演算法动量策略
  • 统计套利
  • 机器学习驱动模型
  • 选择权和衍生性商品演算法
  • 多资产量化策略

6. 全球量化交易平台市场（依技术划分）

  • 介绍
  • 云端基础的回测引擎
  • 人工智慧驱动的交易模型
  • API 连接框架
  • 基于区块链的支付
  • 低延迟基础设施
  • 资料湖和预测分析

7. 全球量化交易平台市场（按应用划分）

  • 介绍
  • 股票交易
  • 加密货币交易
  • 外汇及商品
  • ETF/指数型基金策略
  • 风险套利投资组合
  • 衍生性商品和期货

8. 全球量化交易平台市场（依最终用户划分）

  • 介绍
  • 避险基金
  • 投资银行
  • 资产管理公司
  • 自营交易台
  • 金融科技Start-Ups
  • 机构投资者

9. 全球量化交易平台市场（按地区划分）

  • 介绍
  • 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
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 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
    • 其他中东和非洲地区

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • Numerix
  • QuantConnect
  • Quantopian
  • Two Sigma Investments
  • DE Shaw & Co.
  • Jane Street
  • Citadel LLC
  • AQR Capital Management
  • Renaissance Technologies
  • Susquehanna International Group
  • WorldQuant
  • Millennium Management
  • Hudson River Trading
  • IMC Trading
  • DRW Trading
  • Goldman Sachs
  • JPMorgan Chase
Product Code: SMRC32352

According to Stratistics MRC, the Global Quant-Trade Platforms Market is accounted for $2.2 billion in 2025 and is expected to reach $3.8 billion by 2032 growing at a CAGR of 8.1% during the forecast period. Quant-Trade Platforms are automated financial trading systems that execute investment strategies using quantitative algorithms and statistical models. They analyze large datasets to identify patterns, predict price movements, and optimize portfolio performance. These platforms support multiple asset classes such as equities, forex, and cryptocurrencies. Utilizing AI, machine learning, and real-time analytics, they enable high-speed, data-driven decision-making and reduce human bias in financial trading environments.

According to a J.P. Morgan survey, over 60% of institutional investors now use alternative data and quantitative strategies, increasing demand for accessible algorithmic trading infrastructure.

Market Dynamics:

Driver:

Surging adoption of algorithmic trading

The increasing use of algorithmic trading strategies is a major driver for the quant-trade platforms market. Algorithmic trading automates trade execution based on predefined rules, allowing rapid, high-volume transactions that improve market efficiency and reduce human error. This trend is fueled by advances in computing power, data analytics, and market access, enabling traders to capitalize on small price movements across multiple markets continuously. Consequently, demand for sophisticated quant platforms supporting seamless algorithm deployment is rising globally.

Restraint:

High infrastructure and latency costs

High infrastructure costs, including the need for cutting-edge servers, low-latency networks, and data center proximity, constrain market growth. Reducing latency is critical for gaining competitive advantages in high-frequency trading, but the investments required can be prohibitive for smaller firms. Maintaining and upgrading this infrastructure involves substantial expenditure, limiting accessibility and creating barriers to entry, thereby slowing broader adoption despite technological advances.

Opportunity:

Integration of AI-based trading engines

Integrating AI and machine learning with quant-trade platforms offers significant growth opportunities. AI-based engines enhance predictive accuracy, risk management, and trade strategy optimization by leveraging big data and real-time market insights. These technologies support adaptive decision-making and continuous learning, enabling traders to respond swiftly to market changes and uncover new arbitrage opportunities. Growing adoption of AI-driven automation across financial institutions and hedge funds is driving demand for advanced quant platforms with AI capabilities.

Threat:

Market volatility and systemic risks

Market volatility and systemic risks present substantial threats to the quant-trade platforms market. High-frequency and algorithmic trading can exacerbate volatility, lead to flash crashes, or trigger market disruptions. Regulatory scrutiny is increasing, imposing stricter controls on algorithmic trading practices. Unforeseen market shifts, cyber risks, or flawed algorithms may cause significant financial losses, investor distrust, and regulatory penalties, challenging platform operators to ensure robust risk controls and compliance.

Covid-19 Impact:

The Covid-19 pandemic intensified market volatility, leading to a surge in trading activity and profits for quant-trade platforms, especially in high-frequency segments. Remote work accelerated the adoption of cloud-based trading systems and digital infrastructure. Although initial disruptions affected some operations, overall, the pandemic underscored the importance of automated trading solutions for real-time responsiveness and risk management, boosting platform investment and innovation.

The high-frequency trading segment is expected to be the largest during the forecast period

The high-frequency trading (HFT) segment is expected to account for the largest market share during the forecast period, resulting from its widespread use among institutional investors to derive small but consistent profits from large volumes of trades. HFT's reliance on speed and automation fits well with growing market complexity and competitive pressures, making this segment a dominant force driving demand for quant-trade platforms with ultra-low latency and advanced execution capabilities.

The cloud-based backtesting engines segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based backtesting engines segment is predicted to witness the highest growth rate, propelled by increasing preference for scalable, on-demand computing resources. Cloud solutions offer flexible, cost-efficient environments for running complex simulation models and validating trade strategies without investing heavily in in-house infrastructure. Enhanced collaboration, data availability, and rapid prototyping capabilities accelerate adoption among hedge funds and fintech firms aiming for agile strategy refinement.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to rapid digitization, growing financial markets, and increasing institutional participation across China, Japan, South Korea, and India. Government initiatives supporting fintech innovation, increasing internet penetration, and rising demand for automated trading solutions in emerging economies drive regional market expansion, establishing Asia Pacific as a critical hub for quant-trade platform growth.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR linked to its mature financial markets, concentration of leading hedge funds and investment firms, and extensive adoption of AI and cloud technologies. Strong regulatory frameworks promoting market transparency and security, combined with private-sector investments in fintech R&D, foster continuous innovation and increase demand for sophisticated quant-trade platforms in the United States and Canada.

Key players in the market

Some of the key players in Quant-Trade Platforms Market include Numerix, QuantConnect, Quantopian, Two Sigma Investments, DE Shaw & Co., Jane Street, Citadel LLC, AQR Capital Management, Renaissance Technologies, Susquehanna International Group, WorldQuant, Millennium Management, Hudson River Trading, IMC Trading, DRW Trading, Goldman Sachs and JPMorgan Chase.

Key Developments:

In October 2025, Goldman Sachs unveiled its GS Quant API Suite, a new set of developer tools that allows institutional clients to directly integrate the firm's proprietary pricing models and market data into their own automated trading strategies.

In September 2025, QuantConnect announced the general availability of its LEAN Engine v3, featuring native support for machine learning models and unstructured data analysis, dramatically reducing the backtesting time for complex quantitative strategies.

In August 2025, Two Sigma Investments spun out its Spectrum Platform as a standalone SaaS offering, providing hedge funds with secure, sandboxed access to a curated set of its data science and signal-generation tools.

Strategy Types Covered:

  • High-Frequency Trading Strategies
  • Algorithmic Momentum Strategies
  • Statistical Arbitrage
  • Machine Learning-Driven Models
  • Options & Derivatives Algorithms
  • Multi-Asset Quant Strategies

Technologies Covered:

  • Cloud-Based Backtesting Engines
  • AI-Powered Trading Models
  • API Connectivity Frameworks
  • Blockchain-Based Settlement
  • Low-Latency Infrastructure
  • Data Lake & Predictive Analytics

Applications Covered:

  • Equity Trading
  • Crypto Asset Trading
  • Forex & Commodities
  • ETF & Index Fund Strategies
  • Risk Hedging Portfolios
  • Derivatives & Futures

End Users Covered:

  • Hedge Funds
  • Investment Banks
  • Asset Management Firms
  • Prop Trading Desks
  • Fintech Startups
  • Institutional Traders

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 2024, 2025, 2026, 2028, and 2032
  • 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 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 Quant-Trade Platforms Market, By Strategy Type

  • 5.1 Introduction
  • 5.2 High-Frequency Trading Strategies
  • 5.3 Algorithmic Momentum Strategies
  • 5.4 Statistical Arbitrage
  • 5.5 Machine Learning-Driven Models
  • 5.6 Options & Derivatives Algorithms
  • 5.7 Multi-Asset Quant Strategies

6 Global Quant-Trade Platforms Market, By Technology

  • 6.1 Introduction
  • 6.2 Cloud-Based Backtesting Engines
  • 6.3 AI-Powered Trading Models
  • 6.4 API Connectivity Frameworks
  • 6.5 Blockchain-Based Settlement
  • 6.6 Low-Latency Infrastructure
  • 6.7 Data Lake & Predictive Analytics

7 Global Quant-Trade Platforms Market, By Application

  • 7.1 Introduction
  • 7.2 Equity Trading
  • 7.3 Crypto Asset Trading
  • 7.4 Forex & Commodities
  • 7.5 ETF & Index Fund Strategies
  • 7.6 Risk Hedging Portfolios
  • 7.7 Derivatives & Futures

8 Global Quant-Trade Platforms Market, By End User

  • 8.1 Introduction
  • 8.2 Hedge Funds
  • 8.3 Investment Banks
  • 8.4 Asset Management Firms
  • 8.5 Prop Trading Desks
  • 8.6 Fintech Startups
  • 8.7 Institutional Traders

9 Global Quant-Trade Platforms Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Numerix
  • 11.2 QuantConnect
  • 11.3 Quantopian
  • 11.4 Two Sigma Investments
  • 11.5 DE Shaw & Co.
  • 11.6 Jane Street
  • 11.7 Citadel LLC
  • 11.8 AQR Capital Management
  • 11.9 Renaissance Technologies
  • 11.10 Susquehanna International Group
  • 11.11 WorldQuant
  • 11.12 Millennium Management
  • 11.13 Hudson River Trading
  • 11.14 IMC Trading
  • 11.15 DRW Trading
  • 11.16 Goldman Sachs
  • 11.17 JPMorgan Chase

List of Tables

  • Table 1 Global Quant-Trade Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Quant-Trade Platforms Market Outlook, By Strategy Type (2024-2032) ($MN)
  • Table 3 Global Quant-Trade Platforms Market Outlook, By High-Frequency Trading Strategies (2024-2032) ($MN)
  • Table 4 Global Quant-Trade Platforms Market Outlook, By Algorithmic Momentum Strategies (2024-2032) ($MN)
  • Table 5 Global Quant-Trade Platforms Market Outlook, By Statistical Arbitrage (2024-2032) ($MN)
  • Table 6 Global Quant-Trade Platforms Market Outlook, By Machine Learning-Driven Models (2024-2032) ($MN)
  • Table 7 Global Quant-Trade Platforms Market Outlook, By Options & Derivatives Algorithms (2024-2032) ($MN)
  • Table 8 Global Quant-Trade Platforms Market Outlook, By Multi-Asset Quant Strategies (2024-2032) ($MN)
  • Table 9 Global Quant-Trade Platforms Market Outlook, By Technology (2024-2032) ($MN)
  • Table 10 Global Quant-Trade Platforms Market Outlook, By Cloud-Based Backtesting Engines (2024-2032) ($MN)
  • Table 11 Global Quant-Trade Platforms Market Outlook, By AI-Powered Trading Models (2024-2032) ($MN)
  • Table 12 Global Quant-Trade Platforms Market Outlook, By API Connectivity Frameworks (2024-2032) ($MN)
  • Table 13 Global Quant-Trade Platforms Market Outlook, By Blockchain-Based Settlement (2024-2032) ($MN)
  • Table 14 Global Quant-Trade Platforms Market Outlook, By Low-Latency Infrastructure (2024-2032) ($MN)
  • Table 15 Global Quant-Trade Platforms Market Outlook, By Data Lake & Predictive Analytics (2024-2032) ($MN)
  • Table 16 Global Quant-Trade Platforms Market Outlook, By Application (2024-2032) ($MN)
  • Table 17 Global Quant-Trade Platforms Market Outlook, By Equity Trading (2024-2032) ($MN)
  • Table 18 Global Quant-Trade Platforms Market Outlook, By Crypto Asset Trading (2024-2032) ($MN)
  • Table 19 Global Quant-Trade Platforms Market Outlook, By Forex & Commodities (2024-2032) ($MN)
  • Table 20 Global Quant-Trade Platforms Market Outlook, By ETF & Index Fund Strategies (2024-2032) ($MN)
  • Table 21 Global Quant-Trade Platforms Market Outlook, By Risk Hedging Portfolios (2024-2032) ($MN)
  • Table 22 Global Quant-Trade Platforms Market Outlook, By Derivatives & Futures (2024-2032) ($MN)
  • Table 23 Global Quant-Trade Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 24 Global Quant-Trade Platforms Market Outlook, By Hedge Funds (2024-2032) ($MN)
  • Table 25 Global Quant-Trade Platforms Market Outlook, By Investment Banks (2024-2032) ($MN)
  • Table 26 Global Quant-Trade Platforms Market Outlook, By Asset Management Firms (2024-2032) ($MN)
  • Table 27 Global Quant-Trade Platforms Market Outlook, By Prop Trading Desks (2024-2032) ($MN)
  • Table 28 Global Quant-Trade Platforms Market Outlook, By Fintech Startups (2024-2032) ($MN)
  • Table 29 Global Quant-Trade Platforms Market Outlook, By Institutional Traders (2024-2032) ($MN)

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