Algorithmic Trading Market by Component (Services, Solution), Deployment (Cloud Based, On Premises), Trading Model, Application, End User - Global Forecast 2025-2032
Description
The Algorithmic Trading Market was valued at USD 13.72 billion in 2024 and is projected to grow to USD 15.24 billion in 2025, with a CAGR of 11.63%, reaching USD 33.09 billion by 2032.
A concise framing of why algorithmic trading now demands integrated technology, disciplined governance, and cloud-aware execution to remain competitive
The convergence of sophisticated quantitative methods, vast data availability, and lower-latency infrastructure has reshaped how institutional and proprietary traders approach markets, creating an environment where algorithmic trading is no longer an edge but a core capability. Against this backdrop, managers must integrate advanced analytics, robust execution venues, and disciplined risk frameworks to capture opportunities across multiple asset classes while maintaining resilience to market microstructure shocks. As technology costs decline and cloud-native services mature, firms at every scale increasingly allocate resources toward automation, model validation, and execution quality rather than manual order placement.
Transitioning from legacy, siloed workflows to integrated algorithmic trading stacks requires simultaneous investment in software engineering, data governance, and latency-aware architecture. The most successful organizations combine rigorous quantitative research with production-grade devops practices, enabling models to move from backtesting to live trading with controlled rollouts and real-time monitoring. Moreover, the regulatory emphasis on best execution and operational resilience amplifies the need for transparent model governance and end-to-end traceability. In short, the modern landscape rewards organizations that fuse scientific rigor with disciplined engineering and proactive compliance.
How the rise of alternative data, cloud-native execution, and real-time compliance is redefining competitive advantage in quantitative trading
Recent years have produced several transformative shifts that recalibrate competitive dynamics across algorithmic trading. The first shift is the proliferation of alternative data and the maturation of machine learning pipelines that convert raw signals into tradable insights. Whereas traditional factor models emphasized handcrafted features, production-ready ML systems now prioritize feature engineering automation, explainability, and continuous retraining to remain robust under regime change. Consequently, research teams are evolving to include data scientists, ML engineers, and quantitative researchers working under unified product and engineering cadences.
A second shift involves the architecture of execution infrastructure. Cloud-native execution platforms and colocated microservices have introduced flexible latency tiers, allowing firms to optimize between cost and speed. Firms can offload non-latency-sensitive workloads to scalable cloud services while reserving colocated hardware for critical execution paths. Third, regulatory scrutiny and market structure complexity have pushed risk and compliance into real time, requiring integrated surveillance, pre-trade controls, and post-trade analytics. Finally, talent and organizational design are in flux: firms that cultivate cross-functional teams and continuous learning cultures accelerate innovation, whereas rigid silos impede responsiveness to rapid market changes.
How changes in trade policy and tariffs in 2025 are reshaping hardware sourcing, data residency, and infrastructure strategies for trading firms
Tariffs and trade policy introduce second-order effects that extend into technology sourcing, hardware procurement, and cross-border data strategies, influencing the operational calculus of algorithmic trading organizations. Beginning in 2025, adjusted tariff regimes have driven firms to reassess supply chains for latency-sensitive hardware such as custom network interface cards, FPGAs, and specialized servers. Some trading firms are responding by diversifying hardware vendors, increasing inventory buffers, or shifting procurement toward regions with more favorable tariff treatments to avoid single points of failure and sudden cost escalation.
Beyond hardware, tariffs have accelerated conversations around software localization, data residency, and vendor consolidation. Firms with global footprints now evaluate whether to centralize compute-intensive workloads in lower-cost jurisdictions or replicate critical systems regionally to mitigate cross-border friction. These shifts are prompting revisions to capital planning, vendor relationship strategies, and total cost of ownership models. Importantly, the most resilient organizations are those that treat tariff risk as part of enterprise risk management, embedding scenario analysis into procurement and infrastructure roadmaps so they can adapt swiftly to policy changes and preserve execution performance.
Detailed segmentation insights showing how components, deployment models, trading strategies, applications, and end users dictate technology and service priorities
Examining component, deployment, trading model, application, and end-user dimensions reveals how distinct needs drive technology choices and service expectations. Based on component, organizations balance investments between services that provide managed data, analytics, and advisory functions and solutions that deliver proprietary execution engines and low-latency connectivity. This division shapes vendor relationships: some firms favor turnkey services for speed to market, while others prioritize in-house solutions for control and differentiation. Based on deployment, cloud-based models offer elastic compute for research and batch processing, enabling rapid experimentation, whereas on-premises deployments continue to serve latency-critical execution paths and environments with strict data residency or regulatory constraints.
Based on trading model, high frequency trading demands ultra-low-latency stacks, deterministic network behavior, and specialized hardware, while market making emphasizes inventory risk controls, adaptive quoting engines, and robust hedging integrations. Statistical arbitrage operations invest heavily in data pipelines, feature stores, and backtesting fidelity; trend following strategies prioritize robust execution during extended market moves and model validation across long horizons. Based on application, commodity and derivatives desks require deep connectivity to clearing venues and margin-aware execution, equities teams focus on fragmented liquidity and smart order routing, and forex desks optimize for global FX pools and prime brokerage relationships. Based on end user, banks seek integration with broader treasury and compliance frameworks, hedge funds demand modular systems for strategy differentiation, and trading firms prioritize operational excellence and tight feedback loops between research and production.
Regional intelligence explaining how market structure, regulation, and talent ecosystems in Americas, EMEA, and Asia-Pacific influence trading strategies
Regional differences shape infrastructure choices, talent availability, regulatory constraints, and market access, creating distinct strategic playbooks for each geography. In the Americas, robust capital markets, deep liquidity pools, and a mature vendor ecosystem make it attractive for both established trading firms and technology startups. Firms here benefit from a dense ecosystem of exchanges, dark pools, and service providers, enabling integrated strategies across equities, derivatives, and FX. Regulatory frameworks emphasize market integrity and best execution, prompting firms to invest in compliance tooling and transparent audit trails while leveraging a strong talent pool for quantitative research and low-latency engineering.
In Europe, the Middle East & Africa region, fragmentation of exchanges and diverse regulatory regimes require tailored approaches to connectivity and venue access. Firms operating here often build hybrid infrastructure to accommodate cross-border trading and multiple clearing arrangements while negotiating regional data protection regimes. Talent depth varies by market, but centers such as London and Frankfurt remain hubs for quantitative finance and infrastructure expertise. In the Asia-Pacific region, rapid market growth, varied market microstructures, and competitive exchange offerings create both opportunity and complexity. Firms prioritize partnerships with regional brokers, localized market intelligence, and architectures that support high-frequency execution alongside scalable research platforms to address disparate trading hours and liquidity dynamics across markets.
Competitive landscape insights highlighting how infrastructure providers, niche specialists, and service models shape vendor selection and platform strategies
The competitive landscape is characterized by established infrastructure providers, niche technology specialists, and nimble boutique vendors that cater to specialized strategy needs. Infrastructure providers continue to evolve by embedding analytics, risk controls, and managed connectivity into their offerings to attract both institutional clients and proprietary trading firms. At the same time, specialized vendors deliver modular components-such as ultra-low-latency messaging layers, advanced order management systems, and ML-driven signal platforms-that enable differentiation for strategy-focused organizations. Partnerships and alliances between technology firms and cloud providers are increasingly common, bridging the gap between elastic computation for research and deterministic execution pathways for production trading.
Firms that sustain competitive advantage invest in productized internal platforms that standardize experimentation and deployment while preserving the flexibility to innovate on alpha generation. Vendor selection therefore hinges on a mix of performance characteristics, integration ease, support SLAs, and the ability to co-develop or customize solutions. Additionally, service models that include hands-on onboarding, ongoing optimization, and transparent benchmarking against peer execution metrics gain preference among buyers who require predictable implementation timelines and measurable impact on execution quality.
Actionable recommendations for leaders to align modular architecture, rigorous governance, diversified procurement, and cross-functional talent development
Industry leaders should pursue a coordinated agenda that aligns technology investments with strategic objectives, operational resilience, and regulatory obligations. First, prioritize modular architectures that separate research workloads from latency-sensitive execution paths, enabling teams to scale compute for experimentation without compromising production determinism. Coupling this with robust CI/CD practices for model deployment reduces time-to-market for new strategies while preserving governance and rollback capabilities. Next, embed continuous validation and explainability into machine learning pipelines so models can be stress-tested against regime shifts and audited for compliance and performance degradation over time.
Additionally, diversify procurement and vendor relationships to mitigate tariff and supply-chain risks; maintain strategic inventories for critical hardware components and negotiate flexible sourcing agreements that allow substitution without disrupting execution. Strengthen cross-functional training programs to blur the lines between research, engineering, and operations, cultivating a culture where deployment, monitoring, and incident response are shared responsibilities. Finally, treat data as a managed asset: invest in feature stores, lineage tracking, and data quality tooling that enforce reproducibility and reduce model drift, while maintaining clear controls around data privacy and residency requirements.
A transparent multi-method research approach combining practitioner interviews, technical benchmarking, and scenario analysis to produce actionable findings
The research underpinning this analysis draws from a multi-method approach to ensure balanced, evidence-based conclusions that reflect both qualitative trends and quantitative operational realities. Primary inputs included structured interviews with industry practitioners spanning trading firms, technology vendors, and exchange operators, complemented by technical reviews of architecture blueprints and non-proprietary implementation case studies. These conversations provided context on real-world constraints such as latency budgets, regulatory requirements, and procurement cycles. Secondary inputs comprised vendor documentation, publicly disclosed technical papers, and peer-reviewed studies that illuminate best practices in model development, execution engineering, and risk management.
Analytic methods integrated thematic synthesis of qualitative interviews with technical benchmarking of common architectures, focusing on latency paths, data flow, and deployment patterns. Scenario analysis explored the operational impact of policy changes, vendor disruptions, and technology shifts, while validation exercises cross-checked assertions with practitioners to reduce bias. Throughout, the methodology prioritized transparency: assumptions, definitions, and limitations were documented to support reproducibility. This combination of grounded practitioner insight and technical evaluation yields recommendations that are both actionable and attuned to operational constraints faced by algorithmic trading organizations.
A strategic synthesis emphasizing modular systems, governance, procurement resilience, and cross-functional talent as the pillars of durable competitive advantage
Algorithmic trading now sits at the intersection of advanced analytics, resilient infrastructure, and disciplined governance, and leaders who align these elements create durable competitive advantage. The most immediate priorities are to build modular, observable systems that allow rapid innovation without compromising execution determinism; to treat data and model governance as enterprise-grade capabilities; and to manage supply-chain and policy risks proactively so that hardware and software procurement do not become single points of failure. Equally important is investing in people and processes that foster collaboration across research, engineering, and operations, ensuring that models transition to production with clear performance guardrails.
Looking ahead, the firms best positioned to thrive will be those that combine flexible architectures with rigorous validation, diversify vendor and regional dependencies, and continuously monitor both market microstructure shifts and regulatory developments. By emphasizing reproducibility, explainability, and operational resilience, organizations can both accelerate innovation and uphold the transparency required by counterparties and regulators. The path forward is not simply technological; it is organizational-requiring intentional design of systems, talent, and governance to sustain high-frequency decision-making in an unpredictable global environment.
Please Note: PDF & Excel + Online Access - 1 Year
A concise framing of why algorithmic trading now demands integrated technology, disciplined governance, and cloud-aware execution to remain competitive
The convergence of sophisticated quantitative methods, vast data availability, and lower-latency infrastructure has reshaped how institutional and proprietary traders approach markets, creating an environment where algorithmic trading is no longer an edge but a core capability. Against this backdrop, managers must integrate advanced analytics, robust execution venues, and disciplined risk frameworks to capture opportunities across multiple asset classes while maintaining resilience to market microstructure shocks. As technology costs decline and cloud-native services mature, firms at every scale increasingly allocate resources toward automation, model validation, and execution quality rather than manual order placement.
Transitioning from legacy, siloed workflows to integrated algorithmic trading stacks requires simultaneous investment in software engineering, data governance, and latency-aware architecture. The most successful organizations combine rigorous quantitative research with production-grade devops practices, enabling models to move from backtesting to live trading with controlled rollouts and real-time monitoring. Moreover, the regulatory emphasis on best execution and operational resilience amplifies the need for transparent model governance and end-to-end traceability. In short, the modern landscape rewards organizations that fuse scientific rigor with disciplined engineering and proactive compliance.
How the rise of alternative data, cloud-native execution, and real-time compliance is redefining competitive advantage in quantitative trading
Recent years have produced several transformative shifts that recalibrate competitive dynamics across algorithmic trading. The first shift is the proliferation of alternative data and the maturation of machine learning pipelines that convert raw signals into tradable insights. Whereas traditional factor models emphasized handcrafted features, production-ready ML systems now prioritize feature engineering automation, explainability, and continuous retraining to remain robust under regime change. Consequently, research teams are evolving to include data scientists, ML engineers, and quantitative researchers working under unified product and engineering cadences.
A second shift involves the architecture of execution infrastructure. Cloud-native execution platforms and colocated microservices have introduced flexible latency tiers, allowing firms to optimize between cost and speed. Firms can offload non-latency-sensitive workloads to scalable cloud services while reserving colocated hardware for critical execution paths. Third, regulatory scrutiny and market structure complexity have pushed risk and compliance into real time, requiring integrated surveillance, pre-trade controls, and post-trade analytics. Finally, talent and organizational design are in flux: firms that cultivate cross-functional teams and continuous learning cultures accelerate innovation, whereas rigid silos impede responsiveness to rapid market changes.
How changes in trade policy and tariffs in 2025 are reshaping hardware sourcing, data residency, and infrastructure strategies for trading firms
Tariffs and trade policy introduce second-order effects that extend into technology sourcing, hardware procurement, and cross-border data strategies, influencing the operational calculus of algorithmic trading organizations. Beginning in 2025, adjusted tariff regimes have driven firms to reassess supply chains for latency-sensitive hardware such as custom network interface cards, FPGAs, and specialized servers. Some trading firms are responding by diversifying hardware vendors, increasing inventory buffers, or shifting procurement toward regions with more favorable tariff treatments to avoid single points of failure and sudden cost escalation.
Beyond hardware, tariffs have accelerated conversations around software localization, data residency, and vendor consolidation. Firms with global footprints now evaluate whether to centralize compute-intensive workloads in lower-cost jurisdictions or replicate critical systems regionally to mitigate cross-border friction. These shifts are prompting revisions to capital planning, vendor relationship strategies, and total cost of ownership models. Importantly, the most resilient organizations are those that treat tariff risk as part of enterprise risk management, embedding scenario analysis into procurement and infrastructure roadmaps so they can adapt swiftly to policy changes and preserve execution performance.
Detailed segmentation insights showing how components, deployment models, trading strategies, applications, and end users dictate technology and service priorities
Examining component, deployment, trading model, application, and end-user dimensions reveals how distinct needs drive technology choices and service expectations. Based on component, organizations balance investments between services that provide managed data, analytics, and advisory functions and solutions that deliver proprietary execution engines and low-latency connectivity. This division shapes vendor relationships: some firms favor turnkey services for speed to market, while others prioritize in-house solutions for control and differentiation. Based on deployment, cloud-based models offer elastic compute for research and batch processing, enabling rapid experimentation, whereas on-premises deployments continue to serve latency-critical execution paths and environments with strict data residency or regulatory constraints.
Based on trading model, high frequency trading demands ultra-low-latency stacks, deterministic network behavior, and specialized hardware, while market making emphasizes inventory risk controls, adaptive quoting engines, and robust hedging integrations. Statistical arbitrage operations invest heavily in data pipelines, feature stores, and backtesting fidelity; trend following strategies prioritize robust execution during extended market moves and model validation across long horizons. Based on application, commodity and derivatives desks require deep connectivity to clearing venues and margin-aware execution, equities teams focus on fragmented liquidity and smart order routing, and forex desks optimize for global FX pools and prime brokerage relationships. Based on end user, banks seek integration with broader treasury and compliance frameworks, hedge funds demand modular systems for strategy differentiation, and trading firms prioritize operational excellence and tight feedback loops between research and production.
Regional intelligence explaining how market structure, regulation, and talent ecosystems in Americas, EMEA, and Asia-Pacific influence trading strategies
Regional differences shape infrastructure choices, talent availability, regulatory constraints, and market access, creating distinct strategic playbooks for each geography. In the Americas, robust capital markets, deep liquidity pools, and a mature vendor ecosystem make it attractive for both established trading firms and technology startups. Firms here benefit from a dense ecosystem of exchanges, dark pools, and service providers, enabling integrated strategies across equities, derivatives, and FX. Regulatory frameworks emphasize market integrity and best execution, prompting firms to invest in compliance tooling and transparent audit trails while leveraging a strong talent pool for quantitative research and low-latency engineering.
In Europe, the Middle East & Africa region, fragmentation of exchanges and diverse regulatory regimes require tailored approaches to connectivity and venue access. Firms operating here often build hybrid infrastructure to accommodate cross-border trading and multiple clearing arrangements while negotiating regional data protection regimes. Talent depth varies by market, but centers such as London and Frankfurt remain hubs for quantitative finance and infrastructure expertise. In the Asia-Pacific region, rapid market growth, varied market microstructures, and competitive exchange offerings create both opportunity and complexity. Firms prioritize partnerships with regional brokers, localized market intelligence, and architectures that support high-frequency execution alongside scalable research platforms to address disparate trading hours and liquidity dynamics across markets.
Competitive landscape insights highlighting how infrastructure providers, niche specialists, and service models shape vendor selection and platform strategies
The competitive landscape is characterized by established infrastructure providers, niche technology specialists, and nimble boutique vendors that cater to specialized strategy needs. Infrastructure providers continue to evolve by embedding analytics, risk controls, and managed connectivity into their offerings to attract both institutional clients and proprietary trading firms. At the same time, specialized vendors deliver modular components-such as ultra-low-latency messaging layers, advanced order management systems, and ML-driven signal platforms-that enable differentiation for strategy-focused organizations. Partnerships and alliances between technology firms and cloud providers are increasingly common, bridging the gap between elastic computation for research and deterministic execution pathways for production trading.
Firms that sustain competitive advantage invest in productized internal platforms that standardize experimentation and deployment while preserving the flexibility to innovate on alpha generation. Vendor selection therefore hinges on a mix of performance characteristics, integration ease, support SLAs, and the ability to co-develop or customize solutions. Additionally, service models that include hands-on onboarding, ongoing optimization, and transparent benchmarking against peer execution metrics gain preference among buyers who require predictable implementation timelines and measurable impact on execution quality.
Actionable recommendations for leaders to align modular architecture, rigorous governance, diversified procurement, and cross-functional talent development
Industry leaders should pursue a coordinated agenda that aligns technology investments with strategic objectives, operational resilience, and regulatory obligations. First, prioritize modular architectures that separate research workloads from latency-sensitive execution paths, enabling teams to scale compute for experimentation without compromising production determinism. Coupling this with robust CI/CD practices for model deployment reduces time-to-market for new strategies while preserving governance and rollback capabilities. Next, embed continuous validation and explainability into machine learning pipelines so models can be stress-tested against regime shifts and audited for compliance and performance degradation over time.
Additionally, diversify procurement and vendor relationships to mitigate tariff and supply-chain risks; maintain strategic inventories for critical hardware components and negotiate flexible sourcing agreements that allow substitution without disrupting execution. Strengthen cross-functional training programs to blur the lines between research, engineering, and operations, cultivating a culture where deployment, monitoring, and incident response are shared responsibilities. Finally, treat data as a managed asset: invest in feature stores, lineage tracking, and data quality tooling that enforce reproducibility and reduce model drift, while maintaining clear controls around data privacy and residency requirements.
A transparent multi-method research approach combining practitioner interviews, technical benchmarking, and scenario analysis to produce actionable findings
The research underpinning this analysis draws from a multi-method approach to ensure balanced, evidence-based conclusions that reflect both qualitative trends and quantitative operational realities. Primary inputs included structured interviews with industry practitioners spanning trading firms, technology vendors, and exchange operators, complemented by technical reviews of architecture blueprints and non-proprietary implementation case studies. These conversations provided context on real-world constraints such as latency budgets, regulatory requirements, and procurement cycles. Secondary inputs comprised vendor documentation, publicly disclosed technical papers, and peer-reviewed studies that illuminate best practices in model development, execution engineering, and risk management.
Analytic methods integrated thematic synthesis of qualitative interviews with technical benchmarking of common architectures, focusing on latency paths, data flow, and deployment patterns. Scenario analysis explored the operational impact of policy changes, vendor disruptions, and technology shifts, while validation exercises cross-checked assertions with practitioners to reduce bias. Throughout, the methodology prioritized transparency: assumptions, definitions, and limitations were documented to support reproducibility. This combination of grounded practitioner insight and technical evaluation yields recommendations that are both actionable and attuned to operational constraints faced by algorithmic trading organizations.
A strategic synthesis emphasizing modular systems, governance, procurement resilience, and cross-functional talent as the pillars of durable competitive advantage
Algorithmic trading now sits at the intersection of advanced analytics, resilient infrastructure, and disciplined governance, and leaders who align these elements create durable competitive advantage. The most immediate priorities are to build modular, observable systems that allow rapid innovation without compromising execution determinism; to treat data and model governance as enterprise-grade capabilities; and to manage supply-chain and policy risks proactively so that hardware and software procurement do not become single points of failure. Equally important is investing in people and processes that foster collaboration across research, engineering, and operations, ensuring that models transition to production with clear performance guardrails.
Looking ahead, the firms best positioned to thrive will be those that combine flexible architectures with rigorous validation, diversify vendor and regional dependencies, and continuously monitor both market microstructure shifts and regulatory developments. By emphasizing reproducibility, explainability, and operational resilience, organizations can both accelerate innovation and uphold the transparency required by counterparties and regulators. The path forward is not simply technological; it is organizational-requiring intentional design of systems, talent, and governance to sustain high-frequency decision-making in an unpredictable global environment.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
189 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of advanced natural language processing models for real-time trading signal extraction and sentiment analysis
- 5.2. Proliferation of decentralized finance arbitrage bots exploiting cross-chain liquidity and pricing inefficiencies
- 5.3. Adoption of quantum-inspired optimization algorithms to enhance high-frequency trading performance and reduce latency
- 5.4. Deployment of machine learning pipelines for dynamic risk management and automated portfolio rebalancing in highly volatile markets
- 5.5. Regulatory-driven implementation of transparent algorithmic audit trails to ensure compliance with emerging global trading standards
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Algorithmic Trading Market, by Component
- 8.1. Services
- 8.2. Solution
- 9. Algorithmic Trading Market, by Deployment
- 9.1. Cloud Based
- 9.2. On Premises
- 10. Algorithmic Trading Market, by Trading Model
- 10.1. High Frequency Trading
- 10.2. Market Making
- 10.3. Statistical Arbitrage
- 10.4. Trend Following
- 11. Algorithmic Trading Market, by Application
- 11.1. Commodities
- 11.2. Derivatives
- 11.3. Equities
- 11.4. Forex
- 12. Algorithmic Trading Market, by End User
- 12.1. Banks
- 12.2. Hedge Funds
- 12.3. Trading Firms
- 13. Algorithmic Trading Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Algorithmic Trading Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Algorithmic Trading Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Akuna Capital
- 16.3.2. AlgoTrader AG
- 16.3.3. AlphaGrep
- 16.3.4. ARGO SE
- 16.3.5. BestEx Research
- 16.3.6. Bluefin Trading
- 16.3.7. Citadel Securities LLC
- 16.3.8. DRW Holdings LLC
- 16.3.9. Estee Advisors
- 16.3.10. Flow Traders B.V.
- 16.3.11. GTS Securities LLC
- 16.3.12. Hudson River Trading LLC
- 16.3.13. IG Group Holdings plc
- 16.3.14. IMC Trading B.V.
- 16.3.15. InfoReach Inc
- 16.3.16. Jane Street Capital LLC
- 16.3.17. Jump Trading LLC
- 16.3.18. Kuberre Systems Inc
- 16.3.19. Optiver Holding B.V.
- 16.3.20. Quantlab Financial LLC
- 16.3.21. Susquehanna International Group LLP
- 16.3.22. Thomson Reuters Corp
- 16.3.23. Tower Research Capital LLC
- 16.3.24. Two Sigma Investments LP
- 16.3.25. Virtu Financial Inc
- 16.3.26. WorldQuant LLC
- 16.3.27. XTX Markets Ltd
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