How FX Analytics Can Help Trading Teams Choose Better Execution Venues

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Oct 20, 2026

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How FX Analytics Can Help Trading Teams Choose Better Execution Venues

The foreign exchange market moves approximately $9.6 trillion every day. Yet for the institutions operating inside this enormous financial network, one deceptively simple question remains difficult to answer. Which clients actually make money for the trading desk once all the costs of serving them have been counted?

A customer can generate substantial trading volume and appear commercially attractive. But hedging expenses, execution slippage, brokerage fees, and funding costs can change the economics of that relationship. Another customer may generate less direct revenue while helping a desk balance its positions and reduce exposure elsewhere.

ExeQution Analytics is entering this problem with Solas, a customized analytics service designed to bring foreign exchange trading, client activity, market data, and hedging records into a more unified view. The company says deployments can take as little as three months, offering financial institutions a way to connect fragmented information with decisions about profitability, execution, and risk.

The launch reflects a broader shift in financial technology. As trading becomes increasingly electronic and data-intensive, competitive advantage depends not simply on executing more transactions, but on understanding which transactions create sustainable economic value.

For banks, brokers, hedge funds, and proprietary trading firms, that distinction can influence pricing, client relationships, technology spending, and the allocation of trading capital.

A New Analytics Service Targets a Persistent FX Problem

ExeQution Analytics announced Solas on Tuesday as a service built specifically around the analytical needs of foreign exchange trading desks.

The company's central proposition is straightforward. Rather than evaluating client activity through isolated revenue figures, Solas combines data from a firm's existing systems to assess profitability after relevant trading and hedging costs.

The system is designed to work with information from venues, electronic communication networks, clients, and data providers. ExeQution says a single FX desk may rely on 60 or more separate data feeds.

Each feed may describe a different part of the trading process. One system records client orders. Another captures execution prices. A third contains market data, while separate records track hedging activity, brokerage charges, and funding expenses.

The challenge is not necessarily a lack of data. It is the difficulty of connecting those records accurately and quickly enough to support decisions.

Solas aims to bridge that gap through customized implementations built around each institution's information architecture and workflows.

ExeQution has not named a Solas customer or disclosed pricing, contract values, or independently measured improvements in profitability. Its three-month deployment estimate is a company claim that has not been independently verified.

Those limitations matter. The product's commercial potential is understandable, but its actual performance will depend on the quality of implementation, the reliability of underlying data, and the value customers obtain in practice.

Why Client Profitability Is More Complicated Than It Looks

In foreign exchange trading, transaction volume and profitability are not interchangeable.

A client may execute large trades and generate substantial bid-offer spread revenue. Yet those trades can expose the desk to market movements that require immediate hedging. If the hedge is expensive or executed at an unfavorable price, the resulting cost can reduce or eliminate the apparent margin.

Consider a simplified example.

A client generates $100,000 in trading revenue for a desk. At first glance, the relationship appears attractive. However, assume the desk incurs $35,000 in hedging costs, $15,000 in execution slippage, $10,000 in brokerage expenses, and $20,000 in funding costs.

The remaining contribution would be $20,000 before any other relevant operating expenses.

These figures are illustrative, not reported Solas results. They demonstrate why gross revenue can give an incomplete picture of the economics of a client relationship.

The opposite can also occur. A client with a smaller direct margin may generate trading flows that offset other positions, reducing the desk's overall need to hedge externally. That relationship could contribute more to the institution's broader economics than its standalone revenue suggests.

This is where the analytical problem becomes strategically important.

If a financial institution evaluates customers solely by trading volume or direct margin, it may misunderstand the value of its client portfolio. It could spend too much time servicing expensive relationships, set prices incorrectly, or overlook customers whose trading patterns improve the desk's overall risk position.

Solas is designed to help trading and sales teams examine these relationships with a more complete cost picture.

Why the $9.6 Trillion FX Market Needs Better Data

The scale of the foreign exchange market provides important context for the opportunity.

The Bank for International Settlements reported daily global FX turnover of approximately $9.6 trillion in April 2025. At that scale, even small differences in execution quality, hedging efficiency, or transaction costs can become meaningful for institutions operating across large volumes.

However, market size alone does not establish demand for a particular analytics product. The business case depends on whether an institution can identify meaningful inefficiencies and act on the information.

The underlying data challenge is substantial. Trading organizations often combine information from multiple venues, liquidity providers, internal execution systems, client records, and risk-management platforms. These systems may use different formats, identifiers, timing conventions, and levels of detail.

A trade recorded in one system may need to be matched with a hedge recorded elsewhere. Market conditions at the time of execution must be connected to the transaction. Costs then need to be allocated consistently before the institution can evaluate the final result.

Without reliable data integration, profitability reports can be incomplete or misleading.

Solas addresses this problem by organizing information around FX-specific analytical questions rather than treating every data source as an independent reporting environment.

The potential benefit is not merely a more attractive dashboard. It is the ability to connect operational records with commercial decisions.

How Solas Works Inside a Trading Organization

Solas brings together trading, client, market, and hedging information from a firm's own systems. ExeQution then configures the analytics around the institution's workflows and requirements.

The service supports several related functions.

Client profitability analysis. Sales teams can compare relationships after accounting for servicing and hedging costs. This helps distinguish clients that generate attractive gross margins from those that contribute less after the full cost of activity is considered.

Trading performance attribution. Traders can examine how returns arise from different sources, including market risk-taking, market making, and the bid-offer spread. This distinction can help managers understand whether performance reflects repeatable execution capabilities or exposure to market movements.

Venue comparison. Solas can compare execution venues by trade size and currency pair. Different venues may offer different execution outcomes depending on liquidity, transaction size, and market conditions. More detailed comparisons can support decisions about where and how trades should be executed.

Market alerts. The service can issue alerts when market conditions change, helping teams identify developments that may affect trading activity or execution decisions.

Historical testing. Quantitative and development teams can test models, algorithms, and trading strategies against historical data before deploying them in production. This can support more disciplined evaluation, although historical results cannot guarantee future performance.

Together, these functions connect several activities that are often managed separately. A desk can examine client economics, trading behavior, execution quality, and risk-related costs within a common analytical framework.

The value lies in the relationship between the data and the decisions it supports.

The Technology Behind Solas and the Role of KX

Solas runs on KX technology, building on an existing relationship between KX and ExeQution.

In 2023, the companies introduced an analytics framework written in the q programming language to process large trading datasets across asset classes.

The latest release packages similar data-processing capabilities around specific foreign exchange requirements, including client profitability, internalization, and the costs of hedging customer flow.

This evolution illustrates an important technology strategy. Rather than creating an entirely separate infrastructure for every analytical problem, a provider can adapt an established data-processing foundation to a particular market and its operational requirements.

Foreign exchange trading presents a demanding environment for data systems. Transactions occur across venues and counterparties, and the information required to explain a trade's profitability may be distributed across multiple platforms.

A technology foundation designed to process large trading datasets can help address those requirements. Nevertheless, the effectiveness of the final solution still depends on data integration, implementation quality, and the suitability of its analytical models.

ExeQution's approach combines the underlying technology with customized consultancy. The company says clients maintain their own relationship with KX, while ExeQution focuses on adapting the analytics to individual business needs.

That arrangement positions the company between a technology provider and a specialist implementation partner.

Eolas Adds a Natural-Language Interface to FX Analytics

ExeQution has also paired Solas with Eolas, its natural-language assistant for trading teams.

The objective is to make approved analytical functions accessible to users who may not have programming expertise.

Instead of writing code or requesting a specialized report, a salesperson or trader can ask a question in ordinary language. For example, before a client meeting, a salesperson might request an updated summary of that client's trading activity.

According to a March profile by A-Team Insight, Eolas translates natural-language requests into structured calls against approved functions and client data.

The distinction is important because financial analytics requires more than generating plausible-sounding answers. Results need to be grounded in the institution's actual records and the calculations authorized for use.

ExeQution describes Eolas as resistant to fabricated responses because it queries controlled functions rather than calculating answers independently through free-form text generation. That assertion has not been independently tested by FinanceMagnates.com.

The design nevertheless points toward a practical direction for enterprise AI.

In a trading environment, an assistant should not need unrestricted authority to produce financial calculations. A more controlled architecture can interpret a user's question, select an approved analytical function, retrieve the relevant information, and present the result.

Such an approach can improve accessibility while preserving boundaries around how financial data is processed.

However, it does not automatically eliminate risk. Incorrect source data, faulty analytical functions, inappropriate permissions, or misunderstandings of the question can still produce misleading outcomes. Financial institutions would need to validate the complete process before relying on it for important decisions.

Platform or Product? ExeQution's Customized Approach

Solas occupies an interesting position in the financial technology market.

Some providers build standardized products that can be deployed across many institutions with relatively limited customization. Others concentrate on shared data networks that allow participants to compare activity through common analytical frameworks.

ExeQution emphasizes a different approach: building around each customer's own data, information systems, and workflows.

That distinction can matter because trading organizations do not necessarily define profitability in the same way. Their hedging strategies, client segments, execution venues, funding arrangements, and internal reporting requirements may differ.

A customized implementation can accommodate those differences more closely than a standardized product.

The trade-off is complexity.

Customization can require more implementation work, more detailed testing, and continued specialist support. It can also make deployments harder to scale if every new customer requires substantial engineering effort.

ExeQution estimates that Solas can be implemented in as little as three months. That timeline could be attractive to institutions seeking a focused deployment, although actual implementation periods will depend on system complexity and data readiness.

The key commercial question is whether ExeQution can preserve the advantages of customization without allowing delivery costs and implementation times to rise too sharply as its customer base expands.

Solas and Tradefeedr Represent Different Routes to FX Intelligence

Tradefeedr provides a useful comparison because it has concentrated on pre-trade and post-trade foreign exchange analytics.

The company said its network connected more than 100 institutional clients, including banks, asset managers, and trading platforms. In March, Tradefeedr began a year-long multi-asset pilot with BMLL.

These approaches address related problems but emphasize different models of value creation.

Tradefeedr's network-oriented strategy focuses on analytics shared through a common infrastructure. Such a model can benefit from standardized interfaces and participation across institutions.

ExeQution, by contrast, says Solas is configured around each customer's own information and workflows. Its approach emphasizes institution-specific analysis rather than relying on a shared network as the primary source of value.

Neither model is inherently superior in every situation.

A firm seeking standardized analytics and network-based comparisons may prefer a network-oriented solution. Another institution with distinctive internal systems, complex hedging practices, or specialized reporting needs may place greater value on customization.

The competitive question is therefore not simply which vendor has the most features. It is which approach produces the most useful information at an acceptable cost, within the operational constraints of the customer.

For ExeQution, differentiation will depend on its ability to demonstrate that customized analytics produces decisions that institutions cannot easily obtain from their existing tools or competing services.

How ExeQution Can Create Commercial Value

ExeQution was founded in Australia in 2021 and has developed a broader analytics framework for institutional financial markets.

According to a company announcement in May, Polymer Capital has used its wider framework. Polymer was not identified as a Solas customer, so that relationship should not be interpreted as evidence of Solas adoption.

ExeQution's broader client base includes hedge funds, sovereign wealth funds, proprietary trading firms, asset managers, and brokers.

The company also opened offices in London and Belfast in May to support clients in Europe and the United States. At the time, it said its global team had grown to 20 people, including six analytics specialists in Europe.

These details help explain the company's operating model. It is building a specialist analytics business that combines technology with expertise in institutional trading workflows.

The commercial opportunity could arise from several sources: implementation projects, customized analytics services, continued technical support, and longer-term client relationships.

However, ExeQution has not disclosed Solas pricing or contract values, so its revenue model and unit economics cannot be established precisely from the available information.

The business will need to show that its solutions produce enough measurable value to justify their costs. That could include better identification of unprofitable client relationships, more effective execution decisions, improved hedging analysis, or reduced time spent reconciling fragmented records.

The strongest business case will come from documented improvements rather than the amount of data a platform can process.

The Risks Behind the Analytics Opportunity

Solas addresses a real operational challenge, but several risks deserve attention.

Data quality and integration. Connecting dozens of feeds is only useful when the underlying records can be matched accurately. Missing transactions, inconsistent identifiers, and timing differences can undermine profitability calculations.

Model assumptions. Hedging costs and client contributions must be allocated consistently. Different assumptions can produce different results, particularly when customer flows interact with positions elsewhere on the desk.

Implementation risk. A three-month target may be achievable in some environments, but complex legacy systems and inconsistent data can extend delivery schedules.

Commercial uncertainty. No Solas customer, pricing information, or independently verified performance improvement has been disclosed. Prospective buyers must therefore distinguish the product's stated capabilities from demonstrated customer outcomes.

AI reliability. Natural-language interfaces can make analytics more accessible, but they must preserve appropriate permissions, provide traceable results, and handle ambiguous requests carefully.

Competitive pressure. Established analytics providers, internal technology teams, and alternative data platforms may already address parts of the same problem. ExeQution must prove that its combination of customization and specialist expertise offers sufficient incremental value.

These risks do not negate the opportunity. They define the conditions under which the product can succeed.

What Solas Could Mean for Global Financial Markets

The broader significance of Solas extends beyond foreign exchange.

Financial institutions across Europe, the United States, and other major markets are under pressure to make better use of increasingly complex data environments. Trading systems generate large quantities of information, but that information does not automatically translate into better decisions.

The next stage of financial analytics is likely to place greater emphasis on connecting operational data with economic outcomes.

For FX desks, that means moving from questions about trading volume toward questions about net contribution, hedging efficiency, execution quality, and client value.

For sales teams, it means understanding the economics of a relationship rather than treating activity as a proxy for profitability.

For risk managers, it means examining how individual trades interact with the wider portfolio and the costs of managing exposure.

For technology leaders, it means deciding whether to build internal tools, adopt standardized products, or work with specialist providers that can integrate analytics into existing systems.

These decisions will not be resolved by a single software launch. But products such as Solas illustrate how financial technology is moving closer to the operational decisions that determine whether a trading business creates sustainable value.

The most successful solutions will not necessarily be those with the largest datasets or the most sophisticated interfaces. They will be the ones that connect reliable information to decisions that improve performance.

What Financial Businesses Should Do Next

Institutions considering FX analytics should begin with a clear definition of the problem they want to solve.

First, establish a consistent framework for measuring client profitability. Revenue, hedging costs, slippage, brokerage, and funding expenses should be evaluated using documented assumptions that can be reviewed and challenged.

Second, map the systems that contain the relevant information. A business should understand which data feeds are essential, where records are duplicated, and which gaps prevent reliable analysis.

Third, test the solution against real operational questions. Rather than measuring success by dashboard usage or processing speed alone, institutions should assess whether the analytics identifies previously hidden costs, improves venue selection, or changes decisions about client relationships.

Fourth, validate the results independently. Finance, trading, risk, and technology teams should agree on how calculations are reconciled and how disagreements are resolved.

Fifth, evaluate implementation economics. The expected benefits should be weighed against integration costs, licensing or service fees, internal resources, and ongoing maintenance.

Finally, introduce natural-language tools carefully. Approved functions, data permissions, auditability, and validation procedures should be established before an AI assistant becomes part of important trading workflows.

For providers such as ExeQution, these requirements are also an opportunity. Demonstrating measurable outcomes and repeatable deployment processes can strengthen customer confidence and create a more defensible competitive position.

ExeQution's launch of Solas reflects a growing recognition that foreign exchange profitability cannot be understood through trading revenue alone.

In a market with approximately $9.6 trillion in daily turnover, the ability to connect client activity with hedging costs, execution quality, and funding expenses can help institutions make more informed commercial decisions.

Solas aims to provide that connection through customized analytics built around a firm's own systems. Its integration with KX technology and Eolas adds data-processing capabilities and a natural-language interface intended to make analytical functions more accessible to trading and sales teams.

The opportunity is credible, but its commercial success remains to be demonstrated. Pricing, named customers, independently verified performance improvements, and confirmed implementation outcomes have not been disclosed.

That leaves a clear test for the company. Can it turn fragmented data into decisions that produce measurable economic benefits, while delivering customized implementations efficiently enough to support growth?

For financial institutions, the lesson is equally direct. Trading more is not necessarily the same as earning more. Understanding the true economics of client relationships may prove just as important as improving execution speed or expanding market access.

As financial markets become more data-intensive, the institutions that connect information to disciplined decision-making will be better positioned to understand where their profits come from—and where those profits quietly disappear.