FX Data: Driving profitability, transparency, and decision-making  – e-Forex

In an increasingly transparent, regulated and cost-efficient market, data has emerged as the critical differentiator between platforms and providers. Market participants no longer accept execution outcomes at face value; they demand demonstrable, auditable evidence that every trading decision meets best practice and execution standards. Data is the foundation that makes this possible – capturing, validating and analysing trading activity across the entire FX lifecycle. 

“A truly data-centric approach transforms FX trading operations from a transactional function into a continuous feedback loop, where insights compound and workflows improve systematically,” says Lydia Solinski, managing director, head of data and liquidity, GlobalLINK, State Street Markets. “In this environment, leadership is no longer defined by access to markets alone, but by the ability to turn data into sustained execution excellence.”

Trading changes shift positioning

New trading styles, especially the surge in algorithmic FX trading, demand exponentially more data than traditional discretionary methods, with an emphasis on continuous, high frequency, low latency data streams.

“This shift has elevated data from a mere support function to a core strategic asset, driving greater investments in data infrastructure to process vast datasets across multiple venues and execution styles,” explains Solinski.

Data has become a battleground for e-FX providers because trading flows are no longer just reflections of fundamentals but are increasingly drivers of price action themselves. Understanding flow dynamics is now critical to interpreting and anticipating market moves.

“Our data shows that since late 2022, positioning has become more self-reinforcing, with flows actively shaping price direction,” says Arun Sundaram, head of market data LMAX Group. “At the same time, price discovery has become more globally distributed, particularly across Asian trading hours.”

Algorithmic trading is driving structural demand for more granular, execution-relevant data. Institutions increasingly rely on data to optimise routing decisions, evaluate liquidity provider performance and minimise market impact and this trend is expanding beyond spot FX into forwards, swaps and other instruments where execution has traditionally been less transparent.

“As trading becomes more systematic, firms need precise data to understand liquidity conditions and identify what is driving price movements,” says Sundaram. “Access to high quality data is essential to maintaining execution performance and competitive positioning in increasingly automated markets.”

Over the past two decades, FX markets have shifted from a lower volume, higher margin model to a high volume, low margin environment driven by price transparency and the rise of big data. As price alone has become less of a differentiator, clients now focus on availability, reliability and consistency, which are all core components of total transaction cost according to Elliott Hann, Global Head of Sales at Fenics Market Data & Analytics.

High quality data now underpins almost every aspect of FX trading, explains Hann. As a result, FX data has become a critical competitive asset, one that providers must continuously enhance.

The rise of algorithmic and systematic trading created the first ‘big data’ challenges in FX, pushing the industry to expand storage, processing power and adopt technologies such as cloud computing and high performance hardware.

Systematic approach demands data

The next phase involves AI/ML driven analytics, predictive modelling and advanced econometrics, all of which require large, synchronised cross product datasets, Hann continues.

Systematic trading, built on strict rule-based methodologies, is particularly data intensive, says Hann. It relies on high quality, high frequency, reliable datasets for back testing, measuring liquidity and optimising execution. Systematic funds use historical datasets to identify signals, while trading teams also mine the data for liquidity analysis and execution optimisation. This has led to a surge in demand for deeper, more granular datasets.

A more data centric approach to FX trading operations provides a complementary, relatively high margin service and allows a provider to evidence its value proposition to clients.

It also forms the foundation for building value added services around the trading workflow, from pre trade transaction cost analysis and liquidity insights through to smart order routing and post trade analysis, explains John Crisp, head of FX data product & strategy at the OTC market data provider, TraditionData.

“FX markets are 24-hour markets where conditions change rapidly during the trading day,” he adds. “A data centric approach enables better informed decision making, the potential for much greater levels of automation and a basis for performance optimisation.”

When asked to assess the impact of new trading styles and the growth of algorithmic FX trading on demand for data from banks, hedge funds, and asset managers, Crisp reiterates a key point: algorithms are only as good as the data they are fed.

“Algorithmic trading demands quality, in terms of coverage across currency pairs and forward maturities, as well as accuracy, timeliness, and the resiliency of those data feeds,” he says.

In FX, where execution timing, pricing market impact and opportunity cost can all be measured, a probabilistic, data-driven framework is not just advantageous – it is essential. 

For e-FX providers, differentiation is increasingly about measurable value, which is where independent, high quality data and analytics become critical. Clients need data from inside execution venues to assess performance, but they also need independent data from outside those venues to benchmark and validate what they are seeing.

“Algorithmic trading is fundamentally data-dependent from end to end,” observes Paul Lambert, CEO NCFX. “The difference between success and failure is often determined not by the model itself, but by the integrity and speed of the data feeding it.”

Algos drive information appetite 

As algorithmic trading continues to grow, demand has increased for richer, more granular and more reliable datasets. Banks and liquidity providers benefit from supplying high quality pricing and market data that supports sophisticated buy-side models, while asset managers and hedge funds require independent datasets to validate model assumptions, assess execution quality and refine strategies.

“Independent data providers play a central role in this process,” says Lambert. “By delivering high quality, standardised and unbiased data across spot, forwards and crypto markets, they enable both sides of the market to innovate and achieve better outcomes.”

Lambert agrees that the increased availability of FX data has unquestionably narrowed the historical information asymmetry between buy-side and sell-side firms but cautions that access to data alone does not guarantee better decisions.

“For the buy-side, accurate and comprehensive data on their own trading activity is the essential first step in building effective data-driven processes,” he says. “Beyond that, understanding market context requires independent benchmarks. Relying solely on the execution price stream from a liquidity provider is effectively allowing that provider to mark their own homework.”

This is particularly relevant in forwards and options markets, where progress in independent outcome assessment has lagged behind spot due to the scarcity of reliable, external datasets.

“While the buy-side has historically been reluctant to invest in high quality independent data, often due to the nature of how they are paid by their clients or reliance on TCA frameworks that are inaccurate but deeply embedded in operational processes and costly to change, the true cost of poor data is frequently far higher,” adds Lambert.

Hedge funds looking for additional sources of alpha are also increasingly eager for specialist datasets that can provide new insights, such as flow data and retail trading activity. The expansion of algorithmic and systematic FX strategies means that firms are competing on data quality as much as on execution technology.

Both sides are benefitting

For buy-side firms, FX data has improved price transparency and lowered barriers to entry, whereas sell side firms have taken advantage of FX data to improve risk management, front to back processes and reporting.

Sundaram believes that greater access to high quality data is reducing longstanding information asymmetries between buy-side and sell-side participants as improved transparency allows buy-side firms to independently assess execution quality and liquidity conditions. 

Crisp agrees that the use of FX data is helping to level the playing field between buy side and sell side trading firms. “In some cases, the relationship between buy side and sell side has moved to a more collaborative one, with data providing the objective evidence needed to measure the quality of the FX service provided by the sell side, not just in terms of slippage, but also market impact, fill ratios, etc.,” he says.

“Regulation has been a major catalyst for FX data, increasing focus on best execution and transparency,” says Sundaram. “Firms must demonstrate that trades were executed at optimal terms, which requires detailed execution and pricing data. Regulation has also increased scrutiny around trading infrastructure and operational risk, requiring deeper visibility into venues and counterparties.”

The rise of dedicated data vendors has forced trading venues to sharpen their differentiation, particularly around speed and execution relevance. While third party providers offer broad coverage, exchange-derived data delivers greater granularity and timeliness, making it more valuable for trading decisions.

“This has reinforced proprietary data as a strategic asset,” says Sundaram. “It has also exposed limitations in traditional analytics tools, particularly static transaction cost analysis. As a result, leading venues are investing in internal analytics capabilities that provide deeper insight into execution quality, market impact and liquidity dynamics.”

Sundaram refers to strong and growing demand for bespoke FX data sets, particularly among institutional asset managers, hedge funds and algorithmic trading firms who rely on highly granular data to optimise execution, evaluate counterparties, and refine trading strategies.

According to Solinski, FX data is driving democratisation, but true parity hinges on mastering the content and context of these datasets.

“While FX data is frequently used to benchmark execution quality, with price often treated as the primary metric, price is merely one pillar,” she says. “Comprehensive benchmarks must integrate risk management, balance sheet usage and the provision of broader financial services that define enduring sell-side/buy-side partnerships.”

Partnerships enhance best execution

Sell-side players are now seeing their data as a standalone asset, which benefits workflows. Market makers are partnering with technology vendors in a bid to package proprietary IP into intelligent benchmarks to further intellectualise behaviours behind best execution outcomes.

“Buy-side firms are becoming purveyors of FX data through opt-in data pools, leveraging their own execution history to improve benchmarking, peer analysis and best execution outcomes,” says Solinski, adding that one-size-fits-all data feeds have become obsolete as corporates demand benchmarks mirroring their flows.

In many cases, she suggests, regulatory pressure has shortened decision cycles and justified the allocation of resources toward data infrastructure that might otherwise have been deferred. As a result, regulation has not only increased demand for FX data but has also institutionalised its use within trading operations.

Solinski observes that specialist data and analytics vendors have significantly reshaped the strategic priorities of platform providers and trading venues.

“Independent vendors have raised expectations around analytical sophistication, neutrality and methodological transparency,” she says. “In response, platforms have moved away from attempting to build every analytical capability in house and are focusing on what they do best: embedding best-in-class analytics directly into execution and post-trade workflows.”

This trend has accelerated the adoption of open architecture, interoperability and partnership-driven operating models. Leading platforms now focus on ensuring that analytics travel with the trade, informing decisions precisely at the point of execution.

While data democratisation has broadened access to benchmarking, Solinski says it has also exposed a critical limitation: generic datasets often lack relevance for specific user groups.

“A benchmark gains true value only when it reflects the trading behaviour, constraints and objectives of the end user. Firms increasingly seek datasets tailored to their specific trading styles, liquidity profiles and regulatory obligations,” she says.

Elliott Hann notes that global regulators have embraced the data era with more sophisticated requirements and tighter reporting timelines. Although global standards vary, regulation has broadly shifted from periodic, backward-looking reporting to near real time monitoring, enabling authorities to better detect irregularities and systemic risks.

This has pushed firms to enhance mark to market processes, improve model auditability and invest in independent valuation via firm order and trade records. Banks now require dependable, defensible pricing sourced from firms that provide independent pricing, in either aggregated datasets from the breadth of their business or branded broker feeds for execution transparency. Regulation has made data indispensable.

Improving understanding of markets

According to Hann, specialist FX data vendors have allowed customers to think more strategically about how they use market information. New strategies can be back tested using historical data and forward tested using live feeds, without the cost associated with physically entering a market. Insights and signals help understand risk performance under stressed scenarios, while surface data can be used to explore event risk pricing.

Regulators emphasising the importance of independent measurement of execution outcomes has prompted many asset managers to engage external TCA providers, which has encouraged firms to examine – often for the first time – the true cost of FX execution. 

However, it also revealed limitations. “In many cases, firms treated the receipt of an ‘independent’ TCA report as a compliance box-ticking exercise, without sufficiently scrutinising the quality or independence of the underlying data,” suggests Lambert. “Not all data described as independent truly meets that standard.”

He believes the regulatory direction of travel remains clear: greater standardisation of data structures, enhanced surveillance, stronger risk controls and increased use of advanced analytics. All of these objectives require more accurate, transparent and genuinely independent data. 

The emergence of independent data and analytics vendors has materially influenced the competitive landscape for e-FX platforms. Early electronic platforms could differentiate primarily on operational convenience and the theoretical benefits of aggregated pricing streams. Over time, however, clients have demanded proof of value rather than promises of efficiency. 

“As independent providers have delivered increasingly sophisticated analytics and objective benchmarking tools, platforms have been required to demonstrate measurable improvements in execution quality, pricing transparency and client outcomes,” says Lambert.

“Independent analytics create accountability. When execution quality is assessed against reliable external benchmarks, trading venues must compete on demonstrable performance rather than marketing claims. This dynamic has contributed to observable improvements in key metrics that directly affect trading costs and execution outcomes for end clients.” 

According to Lambert, demand for bespoke and differentiated FX datasets is significant and growing as market participants increasingly seek data that either enhances cost transparency, improves hedging efficiency, strengthens risk management or supports alpha generation.

Gaining a competitive advantage

For example, granular forward curve data has enabled banks to manage electronic pricing streams more effectively while allowing asset managers to plan, execute and evaluate hedging programmes with greater precision. Independent forward data improves both pre-trade decision making and post-trade measurement.

“Bespoke datasets are sought by banks, hedge funds, asset managers and corporates alike – any participant seeking either improved operational efficiency or a competitive edge rooted in superior information,” says Lambert.

Despite recent advances in technology, Hann reckons there is significant innovation ahead in a number of areas including:

• AI-driven forecasting
• Automated liquidity and execution modelling
• Improved normalisation across fragmented venues
• Cloud-native, low latency data delivery

Geopolitics and macroeconomic trends will shape FX data needs this year, says Hann. Investors increasingly analyse FX behaviour around major economic and geopolitical events, driving demand for large, high frequency datasets spanning many currencies. They believe emerging markets will remain central to FX strategies as divergent rate cycles, wide rate differentials and strong carry performance increase the focus on EM FX.

Sundaram agrees that the full potential of technology to transform access to FX data has not yet been realised, noting that existing analytics tools still struggle to fully capture execution quality and market impact.

“Technologies such as blockchain and tokenisation could fundamentally change how transactions are recorded and analysed, enabling faster settlement and greater transparency,” he says.

Technology has unlocked significant value from FX data, fundamentally changing how it is captured, analysed and applied across trading operations. Advances in AI, real-time analytics and workflow integration have transformed data from a static input to a dynamic driver of decision making.

However, Solinski observes that the industry is still in the early stages of learning how to use data across the entire trade lifecycle.

“We anticipate that future development will focus on deeper automation, more adaptive analytics and tighter feedback loops between pre-trade decision making and post-trade evaluation,” she says. “As data becomes more strategic and relevant within firms, its ability to drive meaningful improvements in execution outcomes will continue to grow.”

Regulation has boosted demand for FX data in several ways. Examples include best execution and compliance requirements from MiFID II to the FX Global Code. The higher bar for best execution places greater demand on data, whether tradable data in more liquid instruments like FX spot, or indicative data in FX options, where tradable data may not be available.

“From a compliance perspective, surveillance activities are becoming more sophisticated and increasingly close to real time, demanding higher quality data closer to the time of the market activity being reviewed,” says Crisp.

Firms focus on themselves

He suggests that anyone performing transaction cost analysis should ideally use data that is bespoke to the liquidity that the party can actually trade on. “Similarly, surveillance departments are looking for data related solely to their own firm,” adds Crisp.

On the question of whether technology has unlocked the full potential of FX data or there is more development to come, Lambert reckons past evidence suggests that technological progress in data utilisation is far from complete.

“Machine learning and artificial intelligence remain in relatively early stages of application within FX markets and their effectiveness is directly linked to the availability of large volumes of clean, well-structured and unbiased data,” he says. “These technologies are inherently data-intensive, so their future impact will depend less on theoretical modelling advances and more on access to broader, deeper and higher quality datasets.”

Lambert adds that the evolution of FX trading will continue to be shaped by data and that ensuring that data is independent, accurate and analytically robust will remain central to unlocking its full potential.