Introduction
Trading infrastructure has become increasingly distributed as financial institutions combine on-premises data centres, cloud environments and multiple geographic locations. The expansion creates opportunities to improve capacity and resilience, but it also introduces a fundamental engineering challenge: ensuring that interconnected systems interpret the same market events in the same way.
For institutional trading platforms, differences of only small amounts of time can affect risk calculations, profit-and-loss reporting and regulatory processes. At the same time, the systems supporting execution, order management, risk controls and post-trade processing must continue operating when individual components fail or markets become unusually volatile.
Gaurav Tiwari, CFA, FRM, vice president of digital assets and a trading-technology leader at a global investment management firm, argues that predictable behaviour under stress is more important than performance measured only during normal market conditions. His approach emphasizes deterministic event sequencing, common data models, replicated application states and platform-wide observability.
Why Trading Platforms Need Consistent Data
A modern institutional trading environment can contain numerous systems handling different stages of the same transaction.
An execution engine may receive and act on an order before a risk platform or downstream application has processed the same event. When systems operate across different infrastructure locations, even small propagation delays can create temporary differences in their understanding of market activity.
Tiwari identifies consistency as a core architectural requirement. An order, position or trade needs to retain the same meaning across execution, risk and downstream systems.
Without that consistency, differences in system state can affect:
- Risk calculations
- Profit-and-loss reporting
- Order processing
- Regulatory records
- Post-trade reconciliation
- Operational decision-making
The challenge becomes more pronounced as institutions add multiple application instances to improve availability.
Deterministic Event Sequencing Can Create a Common Record
Tiwari’s approach uses deterministic event sequencing to give connected systems a common ordered representation of trading activity.
The principle is relatively straightforward: when several systems generate or receive messages, a sequencing layer can establish the order in which those events occurred and distribute a consistent stream to downstream applications.
The architecture can also address duplicate processing.
Running multiple instances of an order-management system reduces dependence on a single application instance, but it can introduce the possibility that the same event will be processed more than once. A sequencing layer can receive messages from multiple instances, identify duplicates and produce one ordered stream.
That creates a common reference point for systems consuming the events.
However, sequencing alone does not resolve every consistency problem. Tiwari identifies three additional components as important to maintaining a coherent platform.
Common Data Models Reduce Interpretation Gaps
The first is a common data model.
Financial institutions often have several applications representing the same underlying financial object. If one system interprets a trade, position or order differently from another, discrepancies can emerge even when both systems have received the same event.
A common data model establishes consistent definitions across applications.
This becomes particularly important when institutions integrate legacy systems with newer cloud or distributed technologies. A technically successful integration can still produce inconsistent outcomes if the underlying systems do not share the same definitions.
State Machine Replication Supports Consistent Transitions
The second component is state machine replication.
Financial entities move through defined states during their lifecycle. An order can be submitted, accepted, executed, amended or cancelled, while other financial objects have their own sequences of state changes.
Tiwari argues that applications should recognize those transitions in the same order.
If one system processes an event that another system has not yet recognized, the resulting state can become inconsistent. In a trading environment, that can create downstream operational and risk-management complications.
State-machine approaches therefore complement event sequencing by helping applications maintain consistent representations of how individual entities evolve.
Observability Must Cover the Entire Platform
The third component is observability.
Traditional monitoring often focuses on individual applications. Distributed trading infrastructure requires a broader view because failures can emerge from interactions between otherwise functioning systems.
Tiwari advocates standardized logging, metrics and telemetry across the platform.
This enables engineering teams to investigate how an event moved through multiple systems rather than examining each application independently.
Observability also becomes increasingly important after deployment. Once a trading platform enters production, engineering work shifts substantially toward operating, supporting and maintaining the system.
Without sufficient telemetry, diagnosing a production incident can require reconstructing events after the fact, increasing the time required to identify and isolate the problem.
Resilience Requires Controlled Failure
Performance is only one measure of a trading platform’s effectiveness.
Tiwari emphasizes graceful degradation, in which non-critical services can absorb failures without disrupting execution and risk-management functions.
Reporting and analytics, for example, may be less operationally critical than order execution. Separating these functions can prevent an issue in a peripheral service from spreading into the core trading environment.
The architectural objective is therefore not necessarily to prevent every failure. Instead, it is to prevent individual failures from becoming systemic failures.
This distinction is particularly important during periods of market stress, when trading volumes and infrastructure demands can increase simultaneously.
Technology Choices Should Follow the Trading Requirement
Financial institutions have access to a wide range of cloud, distributed-processing, messaging and automation technologies. Tiwari argues that technology selection should begin with the business and operational problem rather than with the popularity of a particular platform.
One example involves latency-sensitive infrastructure.
For a sequencing platform requiring microsecond-level end-to-end latency, Tiwari’s team retained the system on-premises rather than moving it to the cloud. The decision sacrificed some cloud benefits, including elasticity and managed services, in exchange for meeting the application’s latency requirements.
The same decision might not apply to another workload.
This reflects a broader architectural trade-off in financial technology: the appropriate infrastructure depends on requirements such as latency, resilience, scalability, data retention, operational complexity and cost.
Structured Decision-Making Helps Resolve Architecture Trade-Offs
Tiwari uses the MoSCoW framework to structure technology decisions, dividing requirements into:
- Must Have
- Should Have
- Could Have
The approach allows engineering teams to compare technologies against explicit requirements instead of relying on generalized claims about one platform being superior to another.
He described a similar lesson from an earlier project involving a data warehouse for a large asset manager.
The initial inclination was to use a distributed processing framework that was attracting significant industry attention. The actual users, however, required fast and reliable analytical queries rather than large-scale distributed processing.
The team selected a columnar analytical database and introduced distributed processing later as data volumes increased.
The example illustrates an important principle for financial technology architecture: infrastructure decisions can change as requirements change, and an architecture designed around the actual workload may be more appropriate than one designed around a prevailing technology trend.
Where Large Language Models Fit Into Trading Technology
Large language models are another technology being incorporated into financial institutions, although Tiwari identifies specific areas where they can provide practical value.
One application is information synthesis. Financial institutions produce substantial volumes of research, regulatory filings and internal documentation. Language models can help summarize that material and make it easier for employees to navigate.
A second use is natural-language access to internal information. Instead of requiring users to construct complex database queries, an AI interface can allow employees to ask questions using ordinary language.
A third application involves training and onboarding. AI systems can help analysts and portfolio managers learn internal processes and technology platforms, potentially reducing routine support demands on engineering and operations teams.
These applications are different from allowing an AI system to independently execute trades. They focus primarily on information retrieval, explanation and workflow support.
Accuracy and Data Security Remain Major AI Constraints
The use of large language models introduces risks that are particularly significant in financial services.
Tiwari identifies accuracy as the principal concern. Language models can generate incorrect information while presenting it with confidence. In financial environments, even a relatively small error can have significant consequences depending on how the information is used.
Other considerations include:
- Bias in model outputs
- Protection of confidential information
- Data privacy
- Access controls
- Reliability of internal data sources
- Appropriate human oversight
These concerns mean that AI implementation cannot be separated from governance and information-security architecture.
The value of an AI interface depends partly on whether users can establish where its information originated and whether the system is authorized to access the underlying data.
Safely Introducing Changes Into Live Trading Systems
Changing a production trading system introduces another layer of risk.
Tiwari describes three approaches depending on the nature of the change.
Feature flags
Feature flags allow new functionality to be activated or disabled without rolling back an entire software deployment.
This can reduce the operational impact of an unexpected problem by providing a rapid way to turn off a particular feature.
Parallel production systems
For higher-risk changes, a new and existing system can process the same production traffic simultaneously.
A reconciliation layer compares the outputs of both systems. The new platform can then be evaluated against the established system across different trading volumes and market conditions before becoming the primary system.
Gradual traffic migration
Lower-risk workflows can use a controlled rollout in which a small percentage of production traffic is directed to the new system.
If the system performs as expected, the share of traffic can be increased progressively.
These approaches allow institutions to test changes in increasingly realistic conditions while limiting the consequences of a failure.
Measuring Performance Against Operational Requirements
Technology trade-offs become clearer when expressed in measurable terms.
Instead of debating whether a platform is simply “faster” or “more resilient,” engineering teams can establish specific requirements.
Examples include:
- Whether users would notice an additional 100 milliseconds of latency
- Whether a workflow can tolerate data loss
- The maximum acceptable recovery time
- The required transaction-processing capacity
- The level of availability required for a critical service
Benchmarks, load testing and production metrics can then be used to compare alternatives against those requirements.
Documentation is also important because architecture decisions can outlive the teams that originally made them. Recording why a technology was selected gives future engineers context when requirements or market conditions change.
The Relationship Between AI, Cloud and Automated Trading
AI, cloud-native infrastructure and automated workflows are all influencing financial-market technology, but their roles are not necessarily interchangeable.
Cloud infrastructure can provide scalability and managed services but may not be appropriate for every ultra-low-latency workload.
AI can assist with information-intensive engineering and financial workflows but requires controls around accuracy, privacy and sensitive data.
Automated trading workflows can reduce manual intervention but require deterministic processing and reliable risk controls.
The common requirement across these technologies is architecture built around the underlying financial problem.
Rather than selecting a technology based on industry popularity, financial institutions need to evaluate how it affects latency, resilience, data consistency, operational complexity and regulatory requirements.
Risks and Limitations of Distributed Trading Infrastructure
Greater distribution can improve resilience and scalability, but it also creates additional points where systems can diverge.
The principal challenges include:
Event inconsistency: Different applications can temporarily operate on different information.
Duplicate processing: Multiple application instances can process the same event without appropriate controls.
Infrastructure complexity: Combining data centres, cloud environments and geographically distributed systems increases operational dependencies.
Latency: Additional network or processing layers can affect time-sensitive trading workflows.
Observability gaps: Problems can be difficult to diagnose when monitoring is limited to individual applications.
AI reliability: Language models can generate incorrect information and introduce privacy or data-security concerns.
Change-management risk: Major modifications to live trading systems can create operational disruption if introduced without controlled testing.
These risks do not necessarily argue against distributed architecture or AI adoption. Instead, they highlight the engineering controls required to use those technologies in institutional trading environments.
Future Outlook for Trading Infrastructure
The evolution of trading technology is likely to continue across several areas, including distributed infrastructure, AI-assisted engineering, automated workflows and increasingly sophisticated observability.
The precise combination will vary according to the requirements of each institution and application.
Tiwari’s experience across multiple generations of financial technology suggests that individual platforms and architectural approaches can change significantly over time. The underlying engineering priorities remain more consistent: clearly defined requirements, predictable system behaviour, reliable data, controlled change and resilience under adverse conditions.
For institutional trading platforms, the challenge is therefore not simply achieving greater speed or adding new technology. It is ensuring that increasingly complex systems continue to provide a consistent and dependable representation of financial activity.
Conclusion
Scaling institutional trading infrastructure requires more than adding processing capacity or adopting newer technologies. As trading systems become distributed across data centres, cloud environments and geographic locations, consistency between applications becomes a central architectural concern.
Deterministic event sequencing, common data models, state-machine replication and platform-wide observability can help institutions maintain a common view of market activity. At the same time, graceful degradation and controlled deployment strategies can limit the effect of failures and changes to production systems.
AI introduces another set of opportunities and constraints. Large language models can support research, internal data access and employee training, but accuracy, privacy and sensitive-data protection remain material considerations.
The broader lesson from Tiwari’s approach is that technology selection remains subordinate to the requirements of the financial system. In an environment where microseconds, data consistency and operational continuity can affect trading outcomes, architecture decisions need to be measured against clearly defined business and risk requirements.
