Backtesting Software Market by Software (Analytics Platforms, Simulation Platforms), End User (Institutional Investors, Retail Investors), Organization Size, Deployment Type, Application - Global Forecast 2025-2032
Description
The Backtesting Software Market was valued at USD 405.06 million in 2024 and is projected to grow to USD 444.16 million in 2025, with a CAGR of 9.44%, reaching USD 833.83 million by 2032.
A forward-looking synopsis of how rigorous backtesting platforms underpin strategy validation, risk control, and competitive differentiation for modern investors
The backtesting software landscape sits at the intersection of financial innovation and rigorous validation practices, serving as the critical engine by which trading strategies, portfolio constructions, and risk frameworks are stress-tested prior to real-world deployment. As investing becomes increasingly algorithmic and data-driven, the role of robust, transparent, and reproducible backtesting systems has evolved from a support function into a strategic differentiator for both institutional and retail market participants.
This introduction outlines the foundational capabilities that define modern backtesting platforms, the evolving expectations of end users across risk and performance disciplines, and the operational constraints that shape vendor selection. It also frames the technical, regulatory, and commercial dynamics that influence adoption, while preparing the reader for deeper analysis of shifts in technology, policy impacts, segmentation insights, and actionable recommendations. By establishing this context up front, the subsequent sections can focus on tangible implications for product roadmaps, procurement strategies, and enterprise risk management practices.
An analysis of accelerating technological, data, and governance shifts reshaping how backtesting systems validate strategies and manage operational risk
The landscape for backtesting software has undergone transformative shifts driven by advances in compute, data, and model sophistication. Machine learning and large-scale simulation have expanded the envelope of what strategies can be validated, enabling scenario generation and feature engineering that were previously impractical. At the same time, cloud-native architectures and containerized deployments have shifted emphasis toward scalable, on-demand compute that supports larger universes and more frequent re-runs of complex simulations.
Concurrently, data availability and granularity have increased materially, with tick-level feeds, alternative datasets, and high-frequency market microstructure data enabling richer validation of execution costs and slippage. This has created pressure for solutions to provide integrated data management, lineage tracking, and reproducibility features. Regulatory scrutiny and governance expectations have also risen; as a result, transparency, auditability, and model governance are now baseline requirements rather than optional features. Taken together, these shifts compel vendors and users to prioritize extensibility, traceability, and collaboration capabilities to maintain credibility and operational resilience.
A focused examination of how cumulative US tariff policies through 2025 have reshaped procurement economics, supply chains, and deployment strategies for backtesting infrastructure
The cumulative policy actions related to tariffs and trade measures implemented by the United States through 2025 have had indirect but consequential effects on the backtesting software ecosystem, particularly through impacts on hardware procurement, cloud economics, and international vendor relationships. Tariffs that increase the effective cost of specialized server hardware, networking equipment, and storage arrays influence the total cost of ownership for on-premise deployments and can make cloud migration relatively more attractive for some organizations. Consequently, procurement cycles lengthen as finance and IT teams reassess capex versus opex trade-offs amid changing import duties.
In addition, tariffs and trade tensions can alter vendor sourcing strategies, prompting both software and data providers to localize infrastructure or adjust contractual terms to preserve service continuity and compliance. This localization trend can improve latency and regulatory alignment for some clients but may also fragment data access and increase integration complexity when multiple regional deployments must be reconciled. For firms that rely on cross-border data pipelines, increased scrutiny at customs or added compliance checks can delay data ingestion and complicate reproducibility efforts. Overall, while tariffs are not a direct driver of product innovation in backtesting software, their cumulative effect reshapes operating models, supplier selection, and cost allocation decisions for both cloud-first and on-premise adopters.
A comprehensive segmentation-driven perspective that links software types, end-user requirements, organization scale, deployment preferences, and application domains into actionable buyer personas and product priorities
Effective segmentation analysis of the backtesting software space requires a clear articulation of how distinct product classes, end-user groups, organization sizes, deployment models, and functional applications create divergent needs and buying behaviors. Based on software classifications, offerings fall into analytics platforms that emphasize model development, feature engineering, and performance analytics, and simulation platforms that specialize in scenario generation, trade replay, and execution cost modeling. Each class requires different integration points with data vendors, execution venues, and risk systems, which in turn determines the engineering effort for adoption.
Based on end user distinctions, institutional investors and retail investors exhibit materially different requirements. Institutional investors encompass asset management firms, brokerages, hedge funds, and pension funds, each with unique mandates around compliance, audit trails, and scale; retail investors prioritize accessibility, cost, and ease of use. Based on organization size, large enterprises demand enterprise-grade security, single-sign-on, role-based access, and professional services, while SMEs prioritize rapid time-to-value and lower total cost of ownership. Based on deployment type, cloud delivery enables elastic compute and managed services, whereas on-premise deployments provide greater control over data residency and latency. Based on application use cases, the domain splits into portfolio optimization, risk management, strategy validation, and trade simulation. Portfolio optimization subdivides into multi-asset and single-asset workflows, requiring distinct optimization engines and constraint handling. Risk management separates into credit risk, market risk, and operational risk, each demanding different scenario sets and stress frameworks. Strategy validation divides into quantitative analysis and technical analysis, reflecting divergent model classes and evaluation metrics. Trade simulation splits into historical simulation and Monte Carlo simulation, each presenting tradeoffs between empirical realism and scenario diversity. Integrating these segmentation axes yields nuanced buyer personas and feature roadmaps, which should drive product prioritization, pricing strategies, and go-to-market motions.
A regional read of demand characteristics and operational imperatives across the Americas, Europe Middle East & Africa, and Asia-Pacific to inform product localization and go-to-market strategies
Regional dynamics shape both demand patterns and the operational considerations for backtesting solutions, with different geographies emphasizing distinct regulatory imperatives, infrastructure maturity, and talent availability. In the Americas, sophistication in algorithmic trading and a dense concentration of institutional capital create high demand for low-latency execution modeling, integrated EMS/OMS connectivity, and advanced risk analytics. As a result, vendors competing in this region must demonstrate enterprise integration capabilities and strong support for compliance workflows.
In Europe, the Middle East & Africa region, regulatory harmonization efforts and prudential requirements place a premium on governance, model explainability, and cross-border data protection. Firms operating here often require multilingual support, regional data hosts, and features that map directly to local reporting standards. In Asia-Pacific, rapid digital adoption, a proliferation of retail trading platforms, and a growing institutional investor base drive interest in scalable cloud deployments, localized data feeds, and mobile-friendly validation tools. Together, these regional considerations inform decisions around localization, partnerships, and product packaging to ensure that solutions align with client priorities across infrastructure, regulation, and user experience.
A strategic assessment of vendor differentiation driven by product modularity, integration ecosystems, service models, and the balance between open-source agility and enterprise-grade support
Key competitive dynamics among companies in the backtesting software space revolve around product depth, integration ecosystem, service models, and pricing flexibility. Leading providers differentiate through specialized modules for transaction cost analysis, execution simulation, and model governance, while others compete on ease of use, rapid onboarding, and prebuilt templates for common strategies. Open-source toolkits and community-driven libraries complement commercial offerings by accelerating prototyping, but vendors that provide hardened, supported implementations and enterprise-grade SLAs maintain an advantage for regulated and capital-intensive users.
Partnerships with cloud providers, data vendors, and execution venues materially influence vendor attractiveness; firms that can offer plug-and-play integrations reduce time-to-insight for clients. Service delivery models also vary, with some suppliers emphasizing professional services and managed backtesting operations, and others focusing on self-service platforms with extensible APIs. For buyers, the choice often hinges on whether they prioritize absolute customization and control or faster deployment and predictable operating costs. Consequently, companies that balance a modular product architecture, transparent pricing, and robust professional services are positioned to capture a broader set of enterprise requirements.
Actionable strategic playbook for product leaders to unify reproducibility, interoperability, flexible deployment, and advisory services into defensible competitive advantage
Industry leaders should adopt an integrated strategy that aligns product development with governance, data strategy, and client workflows to sustain long-term differentiation. First, prioritize capabilities that ensure reproducibility and auditability, such as immutable experiment tracking, data lineage, and standardized reporting templates; these features address regulatory concerns and shorten validation cycles. Second, invest in interoperability through robust APIs, standardized connectors to execution and market data providers, and support for common data formats to lower integration friction for enterprise clients.
Third, adopt flexible deployment options that allow customers to choose between managed cloud services and on-premise installations, thereby accommodating different risk appetites and data residency constraints. Fourth, create tiered support models that combine self-service tooling for nimble teams with high-touch professional services for complex institutional deployments. Fifth, develop a clear roadmap for incorporating advanced simulation techniques, including hybrid historical and synthetic scenario generation, while ensuring explainability and governance controls around any machine learning components. By executing on these priorities, leaders can convert technical strengths into commercial advantage and stronger client retention.
A transparent explanation of the mixed-methods research approach combining stakeholder interviews, product assessments, scenario testing, and regulatory analysis to ensure robust and validated findings
The research methodology underpinning this analysis combined qualitative and quantitative approaches to yield a robust, triangulated view of product, vendor, and user dynamics. Primary research included interviews with technology leads, quant researchers, risk officers, and buying stakeholders to capture real-world priorities, procurement hurdles, and performance expectations. Vendor product reviews and architectural assessments provided a functional baseline across analytics and simulation platforms, focusing on data management, computational models, governance features, and integration patterns.
Secondary inputs included publicly available technical documentation, platform white papers, and regulatory guidance to ground the findings in verifiable practices and compliance norms. Cross-validation steps involved scenario-based testing of representative workflows, including portfolio optimization runs, stress testing exercises, and execution replay, to identify common friction points. Limitations of the approach are acknowledged: rapid product releases and proprietary roadmaps can shift vendor capabilities quickly, and not all user experiences are generalizable across every geography or regulatory regime. To mitigate this, the methodology emphasized representative sampling and iterative validation with domain experts.
A concise synthesis emphasizing the need to balance advanced simulation and data capabilities with governance, integration, and operational rigor to sustain strategic advantage
In conclusion, backtesting software has matured into a mission-critical component of investment infrastructure, where technical capability, governance rigor, and integration breadth jointly determine value. The confluence of richer datasets, cloud-native compute, and advanced simulation approaches has elevated expectations around reproducibility, transparency, and operational resilience. At the same time, policy developments and supply chain factors have reshaped deployment decisions and procurement economics, prompting a careful reassessment of on-premise versus cloud strategies.
For practitioners, the path forward requires balancing innovation with discipline: adopt advanced simulation methods and automation where they materially improve decision quality, while maintaining clear lines of audit and explainability. Vendors and buyers alike should focus on modular architectures, strong integration frameworks, and professional services that translate technical capability into operational impact. By doing so, organizations can strengthen their ability to validate strategies, control risk, and accelerate sustainable deployment of algorithmic and quantitative approaches.
Please Note: PDF & Excel + Online Access - 1 Year
A forward-looking synopsis of how rigorous backtesting platforms underpin strategy validation, risk control, and competitive differentiation for modern investors
The backtesting software landscape sits at the intersection of financial innovation and rigorous validation practices, serving as the critical engine by which trading strategies, portfolio constructions, and risk frameworks are stress-tested prior to real-world deployment. As investing becomes increasingly algorithmic and data-driven, the role of robust, transparent, and reproducible backtesting systems has evolved from a support function into a strategic differentiator for both institutional and retail market participants.
This introduction outlines the foundational capabilities that define modern backtesting platforms, the evolving expectations of end users across risk and performance disciplines, and the operational constraints that shape vendor selection. It also frames the technical, regulatory, and commercial dynamics that influence adoption, while preparing the reader for deeper analysis of shifts in technology, policy impacts, segmentation insights, and actionable recommendations. By establishing this context up front, the subsequent sections can focus on tangible implications for product roadmaps, procurement strategies, and enterprise risk management practices.
An analysis of accelerating technological, data, and governance shifts reshaping how backtesting systems validate strategies and manage operational risk
The landscape for backtesting software has undergone transformative shifts driven by advances in compute, data, and model sophistication. Machine learning and large-scale simulation have expanded the envelope of what strategies can be validated, enabling scenario generation and feature engineering that were previously impractical. At the same time, cloud-native architectures and containerized deployments have shifted emphasis toward scalable, on-demand compute that supports larger universes and more frequent re-runs of complex simulations.
Concurrently, data availability and granularity have increased materially, with tick-level feeds, alternative datasets, and high-frequency market microstructure data enabling richer validation of execution costs and slippage. This has created pressure for solutions to provide integrated data management, lineage tracking, and reproducibility features. Regulatory scrutiny and governance expectations have also risen; as a result, transparency, auditability, and model governance are now baseline requirements rather than optional features. Taken together, these shifts compel vendors and users to prioritize extensibility, traceability, and collaboration capabilities to maintain credibility and operational resilience.
A focused examination of how cumulative US tariff policies through 2025 have reshaped procurement economics, supply chains, and deployment strategies for backtesting infrastructure
The cumulative policy actions related to tariffs and trade measures implemented by the United States through 2025 have had indirect but consequential effects on the backtesting software ecosystem, particularly through impacts on hardware procurement, cloud economics, and international vendor relationships. Tariffs that increase the effective cost of specialized server hardware, networking equipment, and storage arrays influence the total cost of ownership for on-premise deployments and can make cloud migration relatively more attractive for some organizations. Consequently, procurement cycles lengthen as finance and IT teams reassess capex versus opex trade-offs amid changing import duties.
In addition, tariffs and trade tensions can alter vendor sourcing strategies, prompting both software and data providers to localize infrastructure or adjust contractual terms to preserve service continuity and compliance. This localization trend can improve latency and regulatory alignment for some clients but may also fragment data access and increase integration complexity when multiple regional deployments must be reconciled. For firms that rely on cross-border data pipelines, increased scrutiny at customs or added compliance checks can delay data ingestion and complicate reproducibility efforts. Overall, while tariffs are not a direct driver of product innovation in backtesting software, their cumulative effect reshapes operating models, supplier selection, and cost allocation decisions for both cloud-first and on-premise adopters.
A comprehensive segmentation-driven perspective that links software types, end-user requirements, organization scale, deployment preferences, and application domains into actionable buyer personas and product priorities
Effective segmentation analysis of the backtesting software space requires a clear articulation of how distinct product classes, end-user groups, organization sizes, deployment models, and functional applications create divergent needs and buying behaviors. Based on software classifications, offerings fall into analytics platforms that emphasize model development, feature engineering, and performance analytics, and simulation platforms that specialize in scenario generation, trade replay, and execution cost modeling. Each class requires different integration points with data vendors, execution venues, and risk systems, which in turn determines the engineering effort for adoption.
Based on end user distinctions, institutional investors and retail investors exhibit materially different requirements. Institutional investors encompass asset management firms, brokerages, hedge funds, and pension funds, each with unique mandates around compliance, audit trails, and scale; retail investors prioritize accessibility, cost, and ease of use. Based on organization size, large enterprises demand enterprise-grade security, single-sign-on, role-based access, and professional services, while SMEs prioritize rapid time-to-value and lower total cost of ownership. Based on deployment type, cloud delivery enables elastic compute and managed services, whereas on-premise deployments provide greater control over data residency and latency. Based on application use cases, the domain splits into portfolio optimization, risk management, strategy validation, and trade simulation. Portfolio optimization subdivides into multi-asset and single-asset workflows, requiring distinct optimization engines and constraint handling. Risk management separates into credit risk, market risk, and operational risk, each demanding different scenario sets and stress frameworks. Strategy validation divides into quantitative analysis and technical analysis, reflecting divergent model classes and evaluation metrics. Trade simulation splits into historical simulation and Monte Carlo simulation, each presenting tradeoffs between empirical realism and scenario diversity. Integrating these segmentation axes yields nuanced buyer personas and feature roadmaps, which should drive product prioritization, pricing strategies, and go-to-market motions.
A regional read of demand characteristics and operational imperatives across the Americas, Europe Middle East & Africa, and Asia-Pacific to inform product localization and go-to-market strategies
Regional dynamics shape both demand patterns and the operational considerations for backtesting solutions, with different geographies emphasizing distinct regulatory imperatives, infrastructure maturity, and talent availability. In the Americas, sophistication in algorithmic trading and a dense concentration of institutional capital create high demand for low-latency execution modeling, integrated EMS/OMS connectivity, and advanced risk analytics. As a result, vendors competing in this region must demonstrate enterprise integration capabilities and strong support for compliance workflows.
In Europe, the Middle East & Africa region, regulatory harmonization efforts and prudential requirements place a premium on governance, model explainability, and cross-border data protection. Firms operating here often require multilingual support, regional data hosts, and features that map directly to local reporting standards. In Asia-Pacific, rapid digital adoption, a proliferation of retail trading platforms, and a growing institutional investor base drive interest in scalable cloud deployments, localized data feeds, and mobile-friendly validation tools. Together, these regional considerations inform decisions around localization, partnerships, and product packaging to ensure that solutions align with client priorities across infrastructure, regulation, and user experience.
A strategic assessment of vendor differentiation driven by product modularity, integration ecosystems, service models, and the balance between open-source agility and enterprise-grade support
Key competitive dynamics among companies in the backtesting software space revolve around product depth, integration ecosystem, service models, and pricing flexibility. Leading providers differentiate through specialized modules for transaction cost analysis, execution simulation, and model governance, while others compete on ease of use, rapid onboarding, and prebuilt templates for common strategies. Open-source toolkits and community-driven libraries complement commercial offerings by accelerating prototyping, but vendors that provide hardened, supported implementations and enterprise-grade SLAs maintain an advantage for regulated and capital-intensive users.
Partnerships with cloud providers, data vendors, and execution venues materially influence vendor attractiveness; firms that can offer plug-and-play integrations reduce time-to-insight for clients. Service delivery models also vary, with some suppliers emphasizing professional services and managed backtesting operations, and others focusing on self-service platforms with extensible APIs. For buyers, the choice often hinges on whether they prioritize absolute customization and control or faster deployment and predictable operating costs. Consequently, companies that balance a modular product architecture, transparent pricing, and robust professional services are positioned to capture a broader set of enterprise requirements.
Actionable strategic playbook for product leaders to unify reproducibility, interoperability, flexible deployment, and advisory services into defensible competitive advantage
Industry leaders should adopt an integrated strategy that aligns product development with governance, data strategy, and client workflows to sustain long-term differentiation. First, prioritize capabilities that ensure reproducibility and auditability, such as immutable experiment tracking, data lineage, and standardized reporting templates; these features address regulatory concerns and shorten validation cycles. Second, invest in interoperability through robust APIs, standardized connectors to execution and market data providers, and support for common data formats to lower integration friction for enterprise clients.
Third, adopt flexible deployment options that allow customers to choose between managed cloud services and on-premise installations, thereby accommodating different risk appetites and data residency constraints. Fourth, create tiered support models that combine self-service tooling for nimble teams with high-touch professional services for complex institutional deployments. Fifth, develop a clear roadmap for incorporating advanced simulation techniques, including hybrid historical and synthetic scenario generation, while ensuring explainability and governance controls around any machine learning components. By executing on these priorities, leaders can convert technical strengths into commercial advantage and stronger client retention.
A transparent explanation of the mixed-methods research approach combining stakeholder interviews, product assessments, scenario testing, and regulatory analysis to ensure robust and validated findings
The research methodology underpinning this analysis combined qualitative and quantitative approaches to yield a robust, triangulated view of product, vendor, and user dynamics. Primary research included interviews with technology leads, quant researchers, risk officers, and buying stakeholders to capture real-world priorities, procurement hurdles, and performance expectations. Vendor product reviews and architectural assessments provided a functional baseline across analytics and simulation platforms, focusing on data management, computational models, governance features, and integration patterns.
Secondary inputs included publicly available technical documentation, platform white papers, and regulatory guidance to ground the findings in verifiable practices and compliance norms. Cross-validation steps involved scenario-based testing of representative workflows, including portfolio optimization runs, stress testing exercises, and execution replay, to identify common friction points. Limitations of the approach are acknowledged: rapid product releases and proprietary roadmaps can shift vendor capabilities quickly, and not all user experiences are generalizable across every geography or regulatory regime. To mitigate this, the methodology emphasized representative sampling and iterative validation with domain experts.
A concise synthesis emphasizing the need to balance advanced simulation and data capabilities with governance, integration, and operational rigor to sustain strategic advantage
In conclusion, backtesting software has matured into a mission-critical component of investment infrastructure, where technical capability, governance rigor, and integration breadth jointly determine value. The confluence of richer datasets, cloud-native compute, and advanced simulation approaches has elevated expectations around reproducibility, transparency, and operational resilience. At the same time, policy developments and supply chain factors have reshaped deployment decisions and procurement economics, prompting a careful reassessment of on-premise versus cloud strategies.
For practitioners, the path forward requires balancing innovation with discipline: adopt advanced simulation methods and automation where they materially improve decision quality, while maintaining clear lines of audit and explainability. Vendors and buyers alike should focus on modular architectures, strong integration frameworks, and professional services that translate technical capability into operational impact. By doing so, organizations can strengthen their ability to validate strategies, control risk, and accelerate sustainable deployment of algorithmic and quantitative approaches.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
187 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 machine learning algorithms into backtesting platforms for predictive accuracy improvement
- 5.2. Adoption of cloud-native architectures to enable scalable and collaborative backtesting across distributed teams
- 5.3. Emergence of multi-asset class backtesting capabilities including cryptocurrencies, commodities, and alternative investments
- 5.4. Rising demand for real-time data streaming integration to simulate market microstructure effects under live conditions
- 5.5. Increasing focus on regulatory compliance modules to validate backtest outcomes against MiFID II and Dodd-Frank standards
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Backtesting Software Market, by Software
- 8.1. Analytics Platforms
- 8.2. Simulation Platforms
- 9. Backtesting Software Market, by End User
- 9.1. Institutional Investors
- 9.1.1. Asset Management Firms
- 9.1.2. Brokerages
- 9.1.3. Hedge Funds
- 9.1.4. Pension Funds
- 9.2. Retail Investors
- 10. Backtesting Software Market, by Organization Size
- 10.1. Large Enterprises
- 10.2. SMEs
- 11. Backtesting Software Market, by Deployment Type
- 11.1. Cloud
- 11.2. On Premise
- 12. Backtesting Software Market, by Application
- 12.1. Portfolio Optimization
- 12.1.1. Multi Asset
- 12.1.2. Single Asset
- 12.2. Risk Management
- 12.2.1. Credit Risk
- 12.2.2. Market Risk
- 12.2.3. Operational Risk
- 12.3. Strategy Validation
- 12.3.1. Quantitative Analysis
- 12.3.2. Technical Analysis
- 12.4. Trade Simulation
- 12.4.1. Historical Simulation
- 12.4.2. Monte Carlo Simulation
- 13. Backtesting Software 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. Backtesting Software Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Backtesting Software 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. QuantConnect LLC
- 16.3.2. TradingView Inc.
- 16.3.3. MetaQuotes Software Corp.
- 16.3.4. NinjaTrader Group LLC
- 16.3.5. TradeStation Group Inc.
- 16.3.6. MultiCharts LLC
- 16.3.7. AmiBroker Ltd
- 16.3.8. QuantRocket LLC
- 16.3.9. Tickblaze LLC
- 16.3.10. Wealth-Lab LLC
- 16.3.11. AlgoTrader GmbH
- 16.3.12. Sierra Chart Inc
- 16.3.13. IBridgePy Inc
- 16.3.14. Interactive Brokers LLC
- 16.3.15. Bloomberg L.P.
- 16.3.16. Trading Technologies International Inc
- 16.3.17. OneMarketData LLC
- 16.3.18. Portfolio123 LLC
- 16.3.19. QuantInsti Labs Pvt Ltd
- 16.3.20. Quantower LLC
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