The economics of algo development have flipped. A decade ago, offering execution algos was a differentiator that justified the cost and manpower of an internal build – now it is table stakes.
Clients of regional banks and platforms expect the same strategy, controls and analytics they get from a global bank and they judge the outcome, not who wrote the code. Meanwhile the true cost of an institutional-grade build has become clearer.
“Most clients spend most of their time on a couple of algo variants,” says Jeff Leal, co-founder Qubealgo. “The prize is not an ever-longer menu; it is making the widely used strategies perform better while retaining the ability to offer something competitors cannot.”
“Institutions should weigh the provider’s team as heavily as the technology and ask who actually built the platform and where they learned the failure modes.”
Jeff Leal
Fellow co-founder, Martin Zinkin, reckons licensing execution technology is now viewed as evidence that a firm is putting its resources into areas where it differentiates, such as liquidity, relationships and service.
“Firms now understand just how complex institutional grade execution technology is to build and maintain and recognise that they may not have the resources to commit that time and money,” he says.
Two major shifts have driven this change. “The first is that the technology stopped being a black box,” says Zinkin. “The current generation of algos exposes how strategies are constructed and measures them with independent analytics. Secondly, buyers have matured. The key questions that matter in any build-or-buy decision relate to future needs across products and asset classes, dependence on a vendor’s development and release cycle, long-run costs and the ability to integrate your own components without locking yourself in.”
“Firms now understand just how complex institutional grade execution technology is to build and maintain and recognise that they may not have the resources to commit that time and money.”
Martin Zinkin
He suggests that the environment such algorithms must live in is underestimated. “That is why the real cost of a build from scratch is in the testing and safety infrastructure – the ability to replay production data through identical business logic, back test against historical and synthetic markets, stress-test behaviour at many times peak load and prove that risk controls behave as intended under exactly those conditions.”
Reduced time to market
Danielle Caravetta, director of global sales at Pragma by MarketAxess, says working with an expert in algorithmic trading platforms enables clients to get the look and feel of an in-house solution while significantly reducing time to market and ongoing maintenance.
“There are obviously different ways that you can white label technology,” she says. “You can white label from a bank where they offer their technology and additional services outside of just the algorithm trading platform. Or you can outsource to an agency specialist like us who doesn’t do any trading and is not affiliated with any sources of liquidity.”
When asked what capabilities clients expect from FX algos that were previously only available from global banks, Caravetta says that although customisation requests in FX are a fraction of what they are in equities, institutional and corporate clients will have very specific ways that they want a TWAP or a float-leg strategy to work and will expect their outsourced providers to support them.
“Post-trade analytics is a key requirement – everyone needs to have post-trade TCA so they can do their own analysis on how the algos are performing. If we are providing algos to a bank which is white labelling them to a corporate or institutional client, they need that post-trade TCA to provide to their client.”
“Not only do you have to deal with the technology requirements but if you are building in-house then you are going to have more supervisory and regulation requirements.”
Danielle Caravetta
She refers to growing use of third party TCA providers and more metrics being used in post-trade TCA – so not only looking at performance versus arrival or TWAP but also mark-out and reversion data.
“When choosing an algorithm provider, you need to work with someone who is going to be able to provide you with that level of transparency and that level of data.”
As for how providers of white labelled and outsourced FX algo trading services ensure transparency around the regulatory compliance and performance of their execution solutions, Caravetta observes that as long as they have the data and a surveillance process in place that can provide that data back to the client, that should meet most regulatory requirements.
“This goes back to the reasons why more firms will white label or private label,” she says. “Not only do you have to deal with the technology requirements – if you are building in-house then you are going to have more supervisory and regulation requirements.”
Partnering a pragmatic choice
As the cost of in-house development has risen and the ROI has narrowed, the industry has recognised that partnering with a proven, top tier algo provider is a pragmatic choice.
That is the view of Asif Razaq, global head of FX automated client execution at BNP Paribas, who says the challenges involved in developing institutional grade FX execution algos from scratch are mostly financial, technical and organisational.
“Developing a competitive algo platform demands sizable investments in IT infrastructure, quantitative research talent and ongoing maintenance,” he explains. “Building the model yourself is expensive and a robust governance framework is required to monitor performance, conduct regular reviews, and satisfy regulatory expectations.”
In addition, the solution must be integrated with various multi-dealer platforms and provide a proprietary transaction cost analysis (TCA) capability. Some top tier providers also offer pre-trade and real time TCA services to further bolster their offering.
Assembling all these components creates a substantial cost base and a lengthy development horizon, making it difficult for new entrants to compete with established providers, observes Razaq, who adds that clients now anticipate a complete end to end execution experience.
“They expect real time visibility into how an algorithm is performing, the ability to run pre-trade cost simulations and analyse post trade performance,” he says. “Mid-trade adjustments are also expected, the platform should support a comprehensive list of currency pairs and analytical services surrounding the trade must also be easily accessible.”
Razaq observes that clients are increasingly moving toward consolidated execution desks that cover FX, futures, equities, rates and commodities within a single unit – a shift that creates demand for execution technology that can be applied uniformly across asset classes, delivering a consistent user experience and simplifying operational processes.
“Providers that have built a single, modular algo platform capable of handling multiple asset classes leveraging the same quant, IT, and trading expertise are gaining a competitive edge,” he says. “By offering the same familiar algorithms across FX, fixed income, metals and futures, firms enable clients to transfer their knowledge from one market to another, reducing learning curves and fostering loyalty.”
Factors that impact customisation
The degree of customisation available depends on the vendor’s architecture and the resources they allocate to client specific development, explains Razaq. Some providers sell an off-the-shelf product with limited ability to customise, while others – particularly those that have developed their own in house algos – can create a large number of algo variants to suit particular mandates, risk tolerances or execution preferences.
“Transparency is built into the execution workflow,” he says. “Every slice of an order is logged with a timestamp, venue, price and quantity, producing a fully auditable evidence trail that is delivered to the client at the conclusion of the trade. This data allows clients to perform their own performance analysis or to engage third party analytics firms if they prefer an independent view.”
Razaq says differentiation now hinges on the breadth and depth of the technology stack and on liquidity management, noting that providers that offer a complete suite – including pre-trade tools, real time TCA, intra trade amendments and comprehensive post trade analytics – stand out.
“Developing a competitive algo platform demands sizable investments in IT infrastructure, quantitative research talent and ongoing maintenance.”
Asif Razaq
“Equally important is the ability to source and internalise liquidity, reducing the need to expose client orders to the external market,” he adds. “High internal matching rates improve algo performance and are increasingly sought after by clients. Institutions should therefore evaluate a provider’s transparency, regulatory robustness, liquidity sourcing capabilities, the extent of customisation and the provider’s commitment to ongoing innovation.”
Machine learning and artificial intelligence are already integral to modern algo design and Razaq believes their usage will only expand.
“These techniques enable algorithms to learn from market data in real time, adapt execution strategies on the fly and deliver superior performance relative to more static models,” he says.
“Vendors that have invested heavily in AI-driven modelling are able to offer algorithms that respond dynamically to changing liquidity conditions, news events or macroeconomic releases.”
Beyond execution, conversational AI allows clients to interact with the algo during a trade, asking questions such as ‘why is execution slowing?’ and receiving reasoned explanations or receiving proactive prompts like ‘non-farm payroll data is about to be released; would you like to accelerate execution?’
“Such dialogue-driven interfaces represent the next frontier in client focused algo services, turning execution algorithms into collaborative partners rather than black box machines,” adds Razaq.
Differentiating factors for providers
On the question of how providers are differentiating themselves in an increasingly competitive market and the attributes institutions should look for in an FX algo trading service provider, Caravetta suggests firstly working with a provider that has a track record of providing institutional algorithms that have been used by the largest players in the world.
“You want a provider that has robust technology because you don’t want outages when markets are volatile,” she says. “You should also look for strong quantitative expertise from a provider that really understands the market microstructure. We believe it is best to work with an agency provider because if you work with a principal provider, you are really only going to have access to their franchise liquidity.”
As for future developments, Caravetta notes that Pragma by MarketAxess already uses advanced AI in its algos to determine how to optimise routing, most importantly and that product demand will continue to grow.
“Right now, the biggest demand is still for spot algos but we also offer NDFs and firms will increasingly look to outsource routing and algos for NDFs and also maybe for swaps and forwards,” she adds.
“In terms of outsourcing in general, we will see more types of players outsource their execution. We have brought on a number of clients who have historically built their algos in-house – whether that has been hedge funds or banks – and have realized that there is just no return on investment on the continual technology and regulatory spend. Partnering with a specialist allows them to reduce their ongoing cost and effort so that they can focus their resources on other areas that offer a bigger return on investment.”
According to Leal, clients now expect a complete set of strategies covering TWAP, VWAP, tracking, liquidity-seeking, opportunistic and full-amount styles, with real control over how each behaves. They also expect intelligent multi-venue execution built on aggregated liquidity.
“Just as important, real-time, in-flight transparency, showing where child orders are working, what has filled and how execution is tracking against arrival, TWAP and VWAP benchmarks is now a baseline expectation,” he says. “The same goes for control: the ability to pause, amend or kill an order instantly with confidence that the system honours it.”
Clients demand tailored strategies
The expectation that raises the bar highest, though, is bespoke algo solutions. Sophisticated clients increasingly want strategies tailored to their flow, constraints and benchmarks and a provider simply cannot service that demand with an inflexible set of rented algos.
Firms that once ran an FX desk in isolation now face client demand for algorithmic execution in NDFs, precious metals, fixed income and digital assets, often through the same workflow and the same analytics. Building a separate stack for each asset class multiplies every cost and stitching together a different vendor per asset class is no better.
“Institutions increasingly want one execution architecture that spans asset classes and that has to be designed in,” says Leal. “Clients want the same event engine, order framework, strategy building blocks and TCA across FX, fixed income and digital assets, with asset-specific modules layered on top for products, date conventions, risk and hedging.”
The key to solving the puzzle of customisation is how the algos are built. “Customisation comes in layers,” explains Zinkin. “At the surface, every strategy exposes parameters: urgency, participation, venue preferences, limit behaviour. Beneath that, providers can compose new strategies from building blocks, branded and tuned for their franchise.”
At the deepest layer, clients want full access to source code and can extend or replace components outright, retaining sole ownership of everything they build. Every customisation needs to be validated in replay against historical and synthetic data and stress-tested under production-like load before it goes near production.
Leal adds that at first glance all offerings look alike and that the real differentiation is in the detail. How deep does customisation go, parameters only or the ability to extend and compose new strategies? How much control does the client have over change, and on whose delivery timeline? What do the continuing costs look like? And is the client locked into a fixed set of algos, or can the service grow with their franchise?
“Institutions should also weigh the provider’s team as heavily as the technology and ask who actually built the platform and where they learned the failure modes,” he continues. “We built these systems at tier 1 and tier 2 institutions so our design reflects the pain points we lived with: integration friction, vendor queues, costly errors. Technology needs to fit the client’s choice of middleware, database and UI rather than imposing specific requirements.”
Finally, when looking at points of differentiation, Leal suggests firms should also ask questions about source access, client ownership of IP built on a platform, composable strategies and testing discipline.
