ExeQution Analytics, the Australia-based trading analytics company, has launched Solas, a new analytics platform designed to help sell-side FX desks bring together market, trade, client and hedging data to gain a clearer view of trading performance, client behaviour and profitability. The platform, initially covering spot, forwards and non-deliverable forwards (NDFs), is aimed particularly at banks that have access to large volumes of proprietary trading data but lack the extensive analytics infrastructure built by some of the largest global FX dealers. Coverage of swaps and options is expected to follow.
Solas marks an expansion into FX for the company, which has previously developed analytics technology for equities. Rather than adapting its existing equities framework, ExeQution Analytics has built a separate toolset around the particular characteristics of the FX market.
“There are lots of interactions in FX that aren’t necessarily visible to anybody else, so much of that data sits inside an organisation,” explains Neil Rajgor, Director & Head of EMEA at ExeQution Analytics, in conversation with TradingTech Insight. “The toolset you build around that world therefore has to be different. It’s about understanding profitability properly – not simply saying, ‘Here’s a P&L number and we’ve made money today,’ but understanding what’s actually driving that.”
That challenge is magnified by the scale and structure of the market. Average daily turnover in global OTC FX reached $9.6 trillion in April 2025, according to the Bank for International Settlements, while the decentralised nature of FX means there is no single exchange or consolidated tape providing a common view of trading activity. Individual banks can instead be connected to dozens of venues, ECNs and other market participants while simultaneously generating their own RFQs, trades, pricing, hedging and client data. The difficulty lies is connecting those different sources of data sufficiently well to establish what is driving performance and profitability.The analytics divide
The initial target for Solas is the sell side, particularly the tier of banks below the largest global FX dealers. Many top-tier institutions have spent years building proprietary analytics infrastructure and assembling the specialist technology and quantitative resources needed to exploit their trading data. ExeQution sees a sizeable group of sophisticated banks below them that have access to much of the underlying data but haven’t made the same level of investment. That can create an information imbalance between counterparties.
“Sometimes that’s expressed to us as: ‘I’ve just had a conversation with someone and they understand more about my trading than I do,’” says Rajgor. “These organisations have all that data internally, but they may not have the technology, resources or knowledge to build the tools that let them unlock its value.”
Understanding where the money is made
One of the harder problems is determining the real profitability of individual client relationships. Looking at the P&L associated with a particular client or trade doesn’t necessarily capture its wider economics. Client flow may leave a dealer with a position that has to be hedged at a cost, for example, while another client’s activity may offset existing risk and eliminate the need for that hedge. Slippage, funding, brokerage and inventory costs further complicate the calculation. A seemingly profitable client can therefore become considerably less attractive once the cost of servicing its flow is included, while apparently marginal business may be helping the desk reduce risk elsewhere.Solas is designed to combine those different elements rather than analyse individual metrics in isolation. The underlying data can include market feeds, trades, RFQs, client interactions, hedging activity and internally generated pricing or quantitative data. ExeQution argues that domain expertise is important when combining those inputs because apparently straightforward metrics can mean different things in different trading contexts.
“We wanted to make sure we had the right expertise in place so that we’re not simply building technology solutions; we’re building trading solutions created by practitioners who have real-life experience sitting on a trading desk and understanding the nuance in the data,” says Cat Turley, the company’s CEO and Founder. “Anybody can build a technology product, but if you don’t understand what’s going in and how it needs to be transformed, your analytics aren’t necessarily meaningful.”
From trading data to client conversations
The platform uses an integration layer to map a bank’s existing data into ExeQution’s underlying schema. Rather than providing a fixed set of dashboards, the framework can then be customised around the requirements of individual desks.
For traders, that can mean analysing markouts, P&L attribution, hedging efficiency and venue performance to understand where profitability is being generated and where it is being lost. For quant and development teams, historical data can be used to test the potential impact of new models, algorithms and strategies before deployment.
For sales teams, the emphasis is on building a more granular picture of client behaviour: how activity and profitability change over time, across products and under different market conditions.
“The feedback we’re getting is that this enables firms to transform their client conversations from anecdotal to data-based conversations, because they can point to an understanding of what has changed or stayed the same, whether that’s client behaviour or the market around it,” says Turley.
Putting AI on top of trusted analytics
ExeQution has also integrated Solas with Eolas, its natural-language AI interface. The aim is to allow users such as sales traders to interrogate analytics directly rather than relying on quants or developers to extract information for them. Eolas sits above structured APIs that produce the underlying analytics rather than being expected to interpret raw trading data and generate answers independently.
“The key is that the APIs are producing data that is trustworthy, real and valid,” says Rajgor. “What we’ve seen in our experimentation is that AI can otherwise become unreliable. That’s particularly problematic on a trading desk because, if you’re not a technical user, you might not notice that it’s suddenly making up data.”
It is a more controlled approach to deploying generative AI in trading environments: establish the data and analytical framework first, then use natural language as an interface through which users can interrogate it.
Bringing market, client and trading data into a common analytical framework can support sharper trading decisions, more informed client conversations and a clearer understanding of where and how the business generates value. Solas, currently being rolled out, with the company in discussions with a select group of potential customers, represents ExeQution’s attempt to make those capabilities available to a broader tier of FX market participants.
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