Vertex Macro | Financial Cloud Cloud · AWS Re:cap
AWS Re:cap 01: Modern Trade Lifecycle: Trading to Settlement
@ AWS Financial Services Symposium 2025
Introduction by Brian Cassen:
● Brian leads market development in capital markets and data at AWS.
● He has been with AWS for seven years, assisting customers in cloud transformation and innovation.
● The session will focus on modernizing the trade life cycle in capital markets.
Mark Murphy from NASDAQ:
● Works in the fintech division, providing critical market and financial services software.
● Uses the products sold by NASDAQ.
● Collaborated with AWS over the past decade to modernize tech stack and share technology with clients like ASX.
John Foley from Fidelity Investments:
● Works in the market data group, processing real-time quotes, trades, and disseminating data to clients.
● Has been with Fidelity for 20 years, heading site reliability engineering during the migration of the ticker plant to the cloud.
Tim Whitley from Australian Securities Exchange (ASX):
● Chief Information Officer at ASX.
● Oversees a comprehensive technology program at ASX, which is significant for Australia but small in the global context.
Discussion Topics:
● Learnings and challenges faced during the modernization process.
Mark Murphy's Defining Moment:
● Curiosity of engineers at NASDAQ led to early experimentation with AWS over a decade ago.
● The critical moment was during COVID-19 when exchange volumes doubled, and on-prem servers struggled to keep up.
● Realization of the necessity of cloud infrastructure for resiliency and continuous market service.
● Accelerated modernization efforts across NASDAQ, focusing on modern technology and cloud capabilities.
John Foley's Introduction:
● John works with Fidelity Investments, a wealth management company with over 77,000 employees and managing over $15 trillion in assets.
● He has been with the company for 20 years, primarily in the market data group.
● His role involves processing real-time quotes, trades, historical content, news, and social market sentiment, and disseminating this data to clients and business partners.
● He was heading up site reliability engineering during the migration of the ticker plant to the cloud.
Tim Whitley's Introduction:
● Tim is the Chief Information Officer at the Australian Securities Exchange (ASX).
● He has been at the exchange for a couple of years.
● The ASX operates two trading venues and three clearing services, making it significant for Australia but small in the global context.
● Tim is currently overseeing a large technology program at ASX, which is a major focus for the exchange.
The defining moment or impetus for each panelist to start using AWS.
Mark Murphy's Defining Moment:
● Curiosity of engineers at NASDAQ led to early experimentation with AWS over a decade ago.
● The critical moment was during COVID-19 when exchange volumes doubled, and on-prem servers struggled to keep up.
● Realization of the necessity of cloud infrastructure for resiliency and continuous market service.
● Accelerated modernization efforts across NASDAQ, focusing on modern technology and cloud capabilities.
John Foley's Defining Moment:
● John's journey with AWS began when he was heading up site reliability engineering during the migration of Fidelity's ticker plant to the cloud.
● The migration highlighted the importance of cloud infrastructure for processing real-time data and ensuring service reliability.
● This experience solidified the need for continued investment in cloud technology at Fidelity.
Turning Point and Strategy Development
● Initial challenge: Need to replace all five core systems at ASX within 3-4 years.
● Concern: Replacing systems as it would result in a "shiny new legacy".
● Strategy development: Spent time working out a platform strategy to underpin the system renewal.
● Key component: Implementation of a data platform for both operational and analytical purposes.
● Objective: Use the data platform to support core systems and enable innovation outside the systems.
● Additional focus: Examine hosting options for a couple of back office systems.
● Main focus: Data platform and its capabilities outside of the core systems.
Future Outlook and Cloud Strategy
● Current market volatility and anticipated changes in how people access markets and consume data.
● Firm's long-term positioning: Leading edge of cloud technology for capital markets.
● All products are cloud-ready.
● Collaboration with AWS to enhance infrastructure and product optimization.
● Offering more choices to clients for various systems (ultra low latency trading, clearing, data platform).
● AWS as a key partner in this initiative.
● Emphasis on cloud’s resiliency and ability to handle high volumes.
● Cloud’s role in compressing time to market and enabling risky innovations.
Benefits of an AWS Data Platform
● Avoids the need for significant capital investment on data platforms.
● Enables innovation on top of the platform.
● Democratizing access to the data platform and providing great tooling stimulates organic innovation within the organization.
● Goal: NASDAQ aims to provide this data platform to exchanges worldwide to promote their exchanges, increase liquidity, and enable organizational innovation.
Impact of AI on Market Data
● AI is rapidly advancing and disrupting various facets of technology, including code writing, documentation, test code, code refactoring, chatbots, customer service, personalization, automation, DevOps, and SRE (Site Reliability Engineering).
● In market data, AI's role is to provide insights to clients for profitable decision-making.
● Challenges: AI struggles with large datasets and numerous parameters.
● Solution: Requires cloud infrastructure and significant compute power to combine real-time trading activity, historical trends, news, and social media sentiments.
● Future focus: Scaling up and down in the cloud to perform calculations and provide insights quickly.
Transformation Driven by Data and AI
● The platform concept should place the data platform at the top of the stack.
● Matching engines and clearing systems are important commodities but not the core business.
● Data will drive overall business transformation and customer experience.
● The ability to leverage data for better outcomes is crucial.
● Preparing data and having the right architecture is essential to stay ahead in the AI-driven future.
Key Learnings and Insights
● Challenges of Modernization: Launching the NASDAQ Eqlipse intelligence platform highlighted the difficulties in helping clients modernize their data platforms and strategies. Despite understanding the need for modernization, executing and delivering on these goals is very challenging.
● Talent Acquisition and Retention: It is hard to acquire and retain the talent needed to successfully modernize data platforms in an exchange environment.
● AWS Products and Modernization: AWS offers amazing products that continue to modernize and improve, making them easier to use. However, implementing these at scale in an exchange environment remains challenging.
● Client Support and Handholding: Most clients know what they want to achieve but need significant help and support to get there. Simply spinning up AWS services may not be sufficient for large-scale, day-to-day operations.
● Regulatory Compliance: Convincing regulators to allow national exchanges to move to public cloud, especially in different nation-states, requires demonstrating the safety, security, and resilience of the cloud solutions.
● Lesson Learned: The need for comprehensive support and handholding for clients across the planet to successfully navigate the complexities of modernization and regulatory compliance.
Key Learnings from Moving Fidelity Market Data Ticker Plant to the Cloud
● Novel Experience: Moving a real-time market data ticker plant to the cloud was a novel experience for both AWS and Fidelity, presenting unique challenges.
● Super Bowl Model vs. Market Data: Unlike the Super Bowl model of sudden, high traffic, market data experiences a solitary, intense burst of traffic at market open, requiring different handling.
● Critical Partnership with AWS: The partnership with AWS was crucial, involving OEM processes where AWS specialists understood Fidelity’s infrastructure and architecture. This allowed for real-time feedback and immediate action when issues arose.
● Anomaly Detection and Alarms: AWS helped build out anomaly detection and alarms, enabling rapid response to issues without the need for escalation.
● Refactoring Applications: A key lesson was the need to refactor applications when moving to the cloud. Simply lifting and shifting monolithic applications would not work and would lead to issues.
● Service-Oriented Architecture: Applications needed to be broken into services, protected, and scaled appropriately. Implementing failure modes like safe modes, bulkheads, and circuit breakers was essential.
● Health Checks and Alarms: Sensitive health checks could tear down infrastructure if not managed properly. Ensuring containers were in good health was critical to avoid sending good traffic after bad.
● Long-Term Benefits: While refactoring applications for the cloud required initial effort, it paid dividends in terms of scalability, optimization, and performance.
Additional Learnings and Insights
● Two-Speed Change Model: The goal is to implement a two-speed change model within the exchange, allowing different teams to operate at different speeds to foster innovation while maintaining compliance.
● Focus on Data Platforms: Continuing to focus on data platforms to enable this two-speed change model and help the ASX navigate its journey.
● Appreciation and Future Collaboration: Thanking the panelists for their valuable insights and expressing excitement for continued partnership with AWS over the next three to five years.
Two-speed change model
● Enterprise architecture approach with two distinct operating speeds
● Integration issues and organizational friction due to reliance of fast front-end systems on slower back-end data
● Modern approaches like DevOps and "all-agile" mindset have largely overtaken this model
"Fast Speed" (Pace Layer 1)
● Customer-facing applications and systems
● Frequent, rapid updates using agile methodologies
● Examples: e-commerce platforms, mobile apps
"Slow Speed" (Pace Layer 2)
● Stable, mission-critical back-end legacy systems
● Reliability and stability are paramount
● Changes are made more methodically
● Examples: core banking, credit card processing
Bulkheads
● Implementation of the Bulkhead Pattern in software architecture
● Isolates elements of an application into pools to prevent cascading failures
Code-Level (Resource Segregation)
● Thread Pool Isolation: Separate thread pools for different types of operations
● Connection Pool Segregation: Separate database connection pools for different operations
System-Level (Architectural Isolation)
● Service-Level Isolation: Independently deploying services in microservices architecture
● Infrastructure Isolation: Using cloud-native platform limits as natural bulkheads
A "ticker plant"
● System in the financial industry for aggregating and distributing real-time market data
● Aggregates raw feeds from exchanges, normalizes them, and publishes processed data to clients
Key functions
● Data Aggregation: Collects raw data from multiple trading venues
● Data Normalization: Converts disparate data formats into a standardized format
● Data Distribution: Publishes normalized market data to various clients
● Data Logging: Writes incoming and processed data to a log file for recovery
● Subscription Management: Manages requests from downstream subscribers