In today’s fast-paced financial markets, institutional investors such as asset managers, hedge funds, and other large firms have increasingly turned to algorithmic trading for precision and efficiency in execution. To maximise the effectiveness of trading via algo, many firms rely on dedicated client coverage teams provided by algo providers. These coverage teams play a vital role in maximizing the performance and adaptability of trading algorithms, ensuring that clients achieve their specific objectives. By leveraging a combination of data-driven insights, client-specific customisations, and ongoing communication, algo provider client coverage helps institutional clients reach optimal trading outcomes.
Who provides algo client coverage?
Algo client coverage is primarily handled by specialists who are deeply familiar with both the mechanics of algorithms and the specific requirements of client trading. In many organisations, these roles may be filled by electronic sales (e-sales) teams or algo product specialists who possess an advanced understanding of algorithmic strategy and execution.
Though voice sales teams can also be involved, specialist coverage remains the preferred approach given the detailed, specific nature of the role.
For this article, the focus is on institutional asset managers. Overwhelmingly, these clients are using algos provided by their existing banking partners. There are multiple reasons for this preference, including ease of onboarding, access to established credit limits, the sheer choice of algos available and existing well-developed relationships. Some clients do also opt for non-bank providers, attracted by cutting-edge technology and access to additional liquidity pools that these alternative providers may offer.
How algo client coverage supports institutional clients
The choice of algorithm very much depends on the user and their intended goal – It may well be a mixture of past experiences with specific algorithms and the influence of some provider relationships. Many algos are labelled as having similar strategies, but the nuances of each provider’s execution can significantly affect performance. A history of past algo performance may often be the best measure of how an algo will perform, and coverage teams are crucial in guiding clients through this learning process, offering recommendations and insights based on both data and practical knowledge.
Client-algo provider relationships vary significantly, based on factors such as client size, the depth of the relationship, and the client’s appetite for collaboration. Many clients are happy to leverage the algo providers’ resources and expertise in this area. This may make them comfortable to choose to use off-the-shelf algo products, while others prefer a more customised experience. In these cases, the provider’s client coverage team works closely with the client to fine-tune preferences such as liquidity curation, algo behaviour, and risk tolerance.
Client support typically occurs in three stages: pre-trade, in-flight, and post-trade.
Pre-Trade Coverage
Pre-trade analysis has become increasingly popular and is now a key part of algo provider coverage. Many algo providers have developed proprietary pre-trade analytics tools to help clients anticipate execution specifics such as estimated completion time, projected slippage, and potential market impact. These insights allow clients to select the best algo for their objectives and adjust parameters to reduce costs and manage risks effectively.
Tailoring algos to client needs
Algo providers can customise trading strategies based on the client’s unique needs, thanks to the following practices:
- Strategy Selection: After understanding the client’s goals, the coverage team can then recommend specific strategies, such as focusing on volume or timing, or using a liquidity-seeking algo to capture the best possible price.
- Algo Optimization: The coverage team can suggest adjustments to parameters like aggression levels, timing, and price limits to ensure the chosen strategy aligns with the client’s priorities.
This pre-trade refinement ensures that the selected algorithm meets the client’s goals with the greatest possible precision.
In-Flight Coverage
Once the trade is underway, algo client coverage shifts focus to providing real-time insights and support to ensure the trade progresses as anticipated. At its most basic level, clients wish to know if something has gone wrong.
Algo providers have various methods of delivering in-flight updates, which help clients adjust parameters as market conditions change:
- Chatrooms: Most clients expect a certain level of information segregation between the electronic and voice teams during an algorithmic trade, which is often managed through a separate client chatroom led by the e-sales team. Some algo providers take this segregation even further with physical separations in coverage or technological barriers between algo trades and other trading flows to maintain confidentiality and security.
- Provider Portals: Some algo providers give clients access to real-time execution updates through proprietary portals.
- Bloomberg Integration: Certain providers offer integration with Bloomberg terminals, allowing clients to monitor algo performance within their existing technology ecosystem.
- Chatbots: Chatbot technology is increasingly used to provide real-time updates and support during a trade. The e-sales team remains available via chat to suggest adjustments, track parameter changes, and keep clients informed on liquidity conditions.
This in-flight coverage allows clients to make timely decisions, refine their algo settings in response to market dynamics, and maximize their execution outcomes.
Post-Trade Coverage
Post-trade analysis is a well-established component of algo provider client coverage, supported by both bank-provided and independent TCA solutions. Through post-trade TCA, client coverage teams can review key performance metrics with the client and assess how well the algo met the execution goals. This retrospective view not only helps evaluate the algo’s performance but also provides insights that influence future trading strategies.
- Provider and Independent TCA Comparison: Clients often prefer having access to both provider-specific and independent TCA results, as this allows them to benchmark algo performance across multiple providers and ensure transparency.
- Custom TCA Metrics: Some clients also develop custom internal TCA metrics tailored to specific performance indicators, providing a further layer of precision in assessing algo outcomes.
Post-trade analysis is an interactive process whereby coverage teams review the results with the client, suggesting potential changes for the future and discussing how different parameter choices impacted the trade’s outcome. Over time, this iterative feedback helps clients develop a more refined approach to algo selection and parameter tuning.
Challenges
Balancing standardization and customisation – Institutional clients have varied algo needs, ranging from off-the-shelf solutions to highly customized strategies. The same applies to coverage itself whether clients expect a high-touch or lower-touch model. Coverage teams must strike a balance between scalability and personalization. Standardized approaches are cost-effective but may not fully address unique client requirements, while customization demands significant resources and close collaboration, which can strain the capacity of coverage teams. In a world of finite human resources, the solution to scalability inevitably points to further smart electronic coverage in some form.
Conclusion
The role of algo provider client coverage is indispensable in enabling institutional clients to achieve their best outcomes in algorithmic trading. Coverage teams can add value at every stage—pre-trade, in-flight, and post-trade—by offering tailored insights, timely support, and comprehensive on-going post-trade analysis. These services not only help clients navigate the complexities of algorithmic trading but also ensure that each algo strategy aligns with the client’s specific objectives.
In recent years, algo client coverage has significantly evolved, due to advancements in analytics and TCA tools from both algo providers and independent TCA firms. These improvements have made it easier to deliver meaningful analytics in an accessible way that are easily understood by clients, creating a feedback loop where coverage teams serve as an essential bridge between clients and product development teams. This loop continuously refines product behaviour, enhancing the overall trading experience and future performance expectations.
The latest advances in analytics most often grab the spotlight, but the strength of the client-algo provider relationship remains fundamental. Clients who choose to or have the ability to collaborate deeply with their algo providers, are able to not only to influence provider algo strategy and TCA tools, but also to potentially influence the broader direction of product development from an early stage.
The combination of innovative analytics, customisable algo strategies, and strong client-provider relationships makes algo provider client coverage a powerful force in helping institutional clients achieve their trading objectives with precision and efficiency.
Navigating the complexities of both FX Algo usage and electronic product coverage can be challenging. Hilltop Walk Consulting works collaboratively with buy and sell-side clients, turning complex challenges into opportunities for enhanced performance and informed decision-making across financial markets.
