What increase in demand for FX algos are you seeing on FXGO?
We have observed over 40% year-to-date growth in algorithmic trading volume on FXGO, reflecting a strong uptick in client engagement. This increase has been equally driven by two key factors: increased usage by our existing client community and adoption of algorithmic strategies by new clients joining the platform. In addition to the rising interest in spot FX algos, we are also seeing a notable expansion in the use of algos in non-spot instruments, particularly non-deliverable forwards (NDFs). This trend underscores the growing breadth and confidence in algorithmic execution across a broader range of FX instruments. With algorithmic and order-based FXGO execution spanning over 190 currency pairs, access to deep regional liquidity remains essential to client success. To further strengthen this capability, we added five regional banks to our liquidity provider network.
Is this increase by client segment, order size or for specific regions?
We observed increased algorithmic volumes across all client segments, with hedge funds and asset managers contributing the largest share of growth. Regionally, our APAC client community significantly outpaced other regions in adoption and usage. Overall, our algo offering was utilized by clients in more than 50 countries, highlighting its global relevance and the growing demand for sophisticated execution tools across diverse markets.
In what ways are you developing the offering to meet the changing requirements of clients?
FXGO continues to make strategic investments in its algorithmic execution offering, reinforcing its commitment to delivering comprehensive and adaptive trading solutions. Recent enhancements include our pre-trade order optimization toolkit, which was developed to help clients reduce unnecessary spot exposure by intelligently offsetting flows across different settlement dates. Instrument coverage has also been broadened significantly, moving beyond traditional spot and non-deliverable forwards to now supporting trading in precious metals and FX swaps – enabling clients to deploy algo strategies across a wider range of asset types. To ensure greater flexibility and depth of execution, FXGO has integrated new regional liquidity providers, strengthening access to localized liquidity pools and improving fill quality across diverse market conditions. Additionally, the platform has advanced its proprietary analytics capabilities, providing clients with richer insights and data-driven guidance to support more precise and efficient execution decisions.
What new tools or services have you developed to meet client’s execution requirements?
To further enhance client workflow and execution efficiency, we developed a bulk routing tool that enables clients to seamlessly route multiple orders – whether algorithmic, resting, or benchmark – simultaneously. This functionality is particularly valuable when trading baskets of orders, significantly streamlining the order management process and reducing manual effort. In addition, we introduced a new multi-tenor spot exposure management tool, which allows clients to execute spot trades across multiple tenors in a consolidated workflow and subsequently roll the resulting spot exposure forward to their respective settlement dates. This innovation supports more flexible execution strategies and precise management of forward-dated cash flows, helping clients optimize both operational efficiency and market risk management.
How has the demand for FX algo analytics evolved over the past year?
As each algorithmic provider offers a broad suite of strategies and strategy parameters, clients face increasing complexity in evaluating and selecting the most appropriate tools for their trading objectives. In this environment, there is a growing need for intelligent, real-time support to help clients navigate this landscape and make more informed decisions both before and during order execution.
Bloomberg’s Algo Analytics hosting service directly addresses this need by enabling algo providers to host their pre-trade and live order analytics directly within the FXGO execution workflow, offering clients a more seamless and context-rich trading experience. Currently, seven algo providers have integrated their analytics through this service and three more are in progress, giving their clients access to provider-specific insights at the point of execution.
At the same time, we continue to observe strong demand for provider-independent analytics, which has driven the full integration of Bloomberg’s cross-asset TCA solution (BTCA) with FXGO. This integration empowers clients to assess algo execution quality against a range of Bloomberg benchmarks and market data sources, supporting more transparent, data-driven decisions in both provider selection and strategy evaluation.
In addition, we recently launched the TCA<GO> function for FX, which provides a cost-based comparison between risk transfer and algorithmic execution, taking into account prevailing market conditions and various algorithmic execution styles. This tool equips clients with a more data-driven framework to evaluate execution choices, optimize trading outcomes, and enhance transparency in the decision-making process.
Do you have any new offerings or developments in the pipeline which you would be able to share?
FXGO is committed to continued investment and innovation to remain at the forefront of the industry’s ongoing transformation toward more advanced algorithmic execution. A key pillar of this strategy involves deepening collaboration with our existing algo providers while actively onboarding new partners to broaden our instrument coverage, particularly in emerging markets, non-deliverable forwards (NDFs), precious metals, and derivatives.
These efforts aim to ensure that clients have access to an ever-wider array of tailored execution strategies across diverse asset classes. Looking ahead, we are equally focused on delivering the next generation of pre-trade decision support tools, which will tightly integrate composite pricing, real-time news, analytics, and cost modelling into clients’ trading workflows. This integrated approach is designed to help clients optimize execution quality, enhance automation, and achieve greater operational efficiency across the entire trading lifecycle.
