Trade Execution Management Software for Buy-Side

Investment IT consultants analyze the company’s trade execution processes and business needs and elicit requirements for the new EMS. Based on the collected information, consultants compose a software requirements specification (SRS).

• Interviewing traders alongside the firm’s project stakeholders helps better grasp the trading desk’s daily operational needs and bottlenecks. As a result, you can prioritize implementing functionality that solves persisting troubles in the MVP, so that the company can reap efficiency gains as soon as the EMS is set live. Involving risk managers helps accurately define operational constraints (maximum order sizes, authorization workflows for algo parameter changes, etc.) that may require specialized guardrails for controlled and compliant executions.
• By defining compliance requirements for the trade EMS at this early stage, you ensure a compliant solution and infrastructure design from the outset. Consultants at ScienceSoft consider both the relevant security standards (e.g., SEC Reg SCI, GLBA, SOC 2) and trade operational regulations (SEC, FINRA, CMA, MiFID II, etc.) when designing a compliance matrix for the software.
• “Good EMS performance” means different things to different teams, so it is critical to specify benchmarks upfront. ScienceSoft’s teams stick to quantitative KPIs such as median order routing latency under 200 microseconds, EMS uptime of 99.99%, or recovery time under 2 minutes in case of failure. Such tangible numbers give architects clear guidelines for technical decisions and provide project stakeholders with unified, objective measures of software success.

During this stage, solution architects design the architecture of the trade execution management system, including its integrations and security components.

• A hybrid architecture is the pragmatic winner for most EMS systems. In such a model, latency-critical components (execution engines, gateways, risk check functions) are built into a small, tightly controlled monolithic core. Meanwhile, less time-sensitive components (consolidated tracking, analytics, reporting) are designed using SOA and microservices patterns. This approach preserves microsecond-level latency where it matters while giving you the flexibility to evolve auxiliary modules independently. In some cases, trade EMS may benefit from event-driven or tech-specific patterns. Here are the aspects our architects consider when choosing the optimal architecture for each specific solution.
• Hybrid architectures allow applying the most productive programming technologies for each EMS component. For example, you can write execution engines in C++ to minimize latency, analytics algorithms in Python to maximize throughput, and UI APIs in Node.js to maximize data accessibility. Tailoring programming stack to each component’s specific needs is one of ScienceSoft’s primary practices for building stable, high-performing apps. Another wise practice is to segment data storage for various data types, e.g., use an in-memory store for live trade data, a time-series store for tick data, and an OLTP database for historical execution snapshots. Doing this helps maximize the speed and efficiency of trading data retrieval while cutting storage-related expenses.

UX and UI designers map out user journeys and create EMS interfaces tailored to the needs of specific user roles (quants, high-touch traders, risk analysts, etc.).

• UX design for trade EMS must prioritize high interaction speed and low cognitive load while ensuring traders never miss critical risk alerts. To balance streamlined execution and visible safeguards, ScienceSoft’s designers use tiered notifications: subtle visual cues for routine events (partial fills, minor slippage), persistent banners for important but non-urgent issues, and modal interrupts for breaches like fat-finger errors. We also apply progressive content disclosure, showing only the most relevant controls during active trader workflows while keeping advanced functions quickly accessible. Vast interface customization options are a must to let each trader configure tailored views with their preferred toolkits.
• By shadowing live or replayed trade sessions in the investment firm’s current EMS, you can get insights into traders’ real behaviors and identify UX/UI optimization opportunities for faster trading. For example, designers can spot delays with the keystrokes traders use when volatility spikes and add hotkeys for order adjustment or one-click hedging in the new EMS interface to shave seconds off responses to market signals. We use shadowing as part of our UX research process.

Project managers scope the trade EMS engineering tasks, assemble the development team, and estimate the project time and budget.

• Evolving business needs and regulatory updates may entail new requirements mid-project. Agile development methodologies let you quickly adapt to changes, but, at the same time, they escalate the risk of scope creep and deadline breaches. One wise approach is to lock down the scope of non-negotiables (think core execution and compliance functions) and run a structured feasibility and priority assessment for every requested change. Agile teams at ScienceSoft apply this practice to prevent feature churn and skewed scope priorities and keep delivery timelines predictable.
• Budget overruns in EMS development projects usually come from underestimating complexity in engineering tasks, disregarding potential changes, and overlooking external risks like regulatory and market shifts. ScienceSoft’s practical safeguard for accurate cost estimation is to combine bottom-up estimation (by EMS functional module, based on our past project data) with a top-down contingency reserve of 15–30% for risks. Tracking planned vs. actual cost and progress throughout the project gives you early signals if budget burn rate diverges from expectations. Check our dedicated page for tactics and tools to get a realistic picture of budget health.

This is the stage where developers code the back end of an execution management system (including specialized components like algo wheels and ML models for trade performance forecasting), create user interfaces, and set up scalable data storage.

• Clean code contributes to higher accuracy of the execution system’s logic, accelerates testing, and minimizes regression risks during future feature extensions. Following language-specific conventions and secure coding guidelines, consistently documenting new code pieces, and running static code analysis and peer reviews are what ScienceSoft’s engineers do to secure code quality and maintainability.
• By running testing activities in parallel with coding, you can detect potential issues quickly and avoid their costly resolution at later stages. Manual testing, while necessary, is inefficient for many functional and performance testing tasks; you need a dedicated test automation environment to verify EMS-critical metrics like microsecond-level latency and throughput. QA consultants at ScienceSoft suggest that for complex systems requiring exceptional reliability, such as trade EMS, it may be reasonable to consider test-driven development (TDD), which assumes comprehensive coverage of intended use and edge cases. If you’d like to learn how we select the optimal QA tactics for each particular case, visit our dedicated page.

Back-end developers integrate the EMS with corporate and third-party systems. During this stage, you may also need to migrate execution data, configurations, and surveillance logs from your existing system to the new software.

• Integration testing is key to ensuring smooth and secure data flows between the connected systems. The tests should replicate real market data streams, multi-venue routing paths, risk checks, load, and timing. This way, you can uncover functional and performance issues that only appear when systems interact at scale. Check how we applied this practice in ScienceSoft’s early trading automation software project to ensure the solution would operate reliably in live trades on NASDAQ and NYSE American.
• Migrating trading data in small fractions and outside the market’s standard hours will let you quickly roll back the changes if something goes wrong without a major impact on traders’ workflows. At ScienceSoft, data engineers establish automated migration pipelines with testing checkpoints across data extraction, transformation, and transfer stages. This prevents manual migration errors and helps validate the integrity, accuracy, and completeness of data during transition.

Development teams configure execution management software infrastructure, set up redundancy and failover mechanisms, finalize the necessary testing activities, and deploy the solution to production.

• EMS infrastructure must embed security mechanisms at every layer. Apply network segmentation to isolate execution engines, gateways, and back-office services, use encrypted channels for data exchange (TLS for external feeds, RPC/REST encryption for internal data), and implement network protection tools like SIEM, IDS/IDP, firewalls, and intelligent UEBA. These measures protect trading data and the integrity of the execution path while ensuring compliance with data protection standards.
• User acceptance testing (UAT) validates the EMS solution’s usability and alignment with the trading desk’s needs. It allows the development team to fix any previously unnoticed issues before the software goes live. You can further deploy the solution for a pilot run across a limited number of traders to make sure it performs well in live settings. Check how ScienceSoft applied this in a recent bond investing software project.
• Once your EMS goes live, you need to implement continuous observability tools, organize tiered issue handling, and define clear escalation procedures to maintain control over system operations and ensure quick response to troubles. Check out ScienceSoft’s dedicated page for ways to establish efficient support and maintenance operations for both outsourced and in-house scenarios.