FXAlgoNews November 2023

ISSUE 32 | NOVEMBER 2023 WWW.FXALGONEWS.COM FOLLOW US AT: 

TOP STORIES 

Barclays finds internalisation is 

key priority for FX algo users 

Barclays has conducted its first Global 

FX Market Structure Survey, with the 

findings revealing a number of insights 

directly around how clients are using FX 

algos currently and the drivers behind 

those choices. Among the algo specific 

findings, the survey revealed that when 

it came to reviewing a dealer’s algo 

functionality, the leading factor cited by 

33% of clients was that internalisation 

was one of their most important areas 

of consideration. Following this, the 

configuration of algos at the order 

level was the second leading factor 

cited by FX algo users. In addition, the 

survey revealed the clients were mainly 

allocating their algorithmic execution 

based upon historical performance, with 

29% stating this was key to how they 

allocate that flow to their dealers. 

“This survey provides client driven 

feedback and the findings reflect our 

biggest areas of focus as a business,” 

says Ajay Kataria, Head of Electronic FX 

Distribution, Americas at Barclays. “As 

such a large franchise, we keep liquidity 

within our four walls while focusing on 

being able to offer our clients what they 

need. FX algos are a mature product 

and we continue to differentiate by 

performance, liquidity provision and 

helping clients to navigate the changing 

FX market structure.” 

Ajay Kataria 

Deutsche Bank includes 

listed derivatives algo 

data in real time TCA 

Deutsche Bank is expanding its real 

time FX algo TCA to include listed 

derivatives algo data for the first time. 

The move comes as part of a wider 

algo rebuild being undertaken by the 

bank which will include FX algos as 

well as different asset classes. Currently 

all post trade and real time algo TCA is 

collected by the Market Colour engine. 

Deutsche Bank is looking to make 

the same toolsets available for listed 

derivatives products as well. 

IN THIS ISSUE 

p1. TOP STORIES 

The latest industry stories 

p3: NEWS FEATURES 

More in-depth news 

p5: RECENT EVENT 

TradeTech FX in Paris 

p6: MARKET WATCH 

Algos and the FX Global Code 

Vittorio Nuti 

“We have developed a best in class 

Market Colour app for FX and now 

we will expand into different asset 

classes. This is the first version where 

we have real time TCA data with listed 

derivatives algo data included, allowing 

clients to view their orders in real time. 

It is a big step forward for us in terms 

of unifying our algo systems across the 

board,” says Vittorio Nuti, Global Head 

of Listed Derivatives & FX Algo Trading 

at Deutsche Bank. 

p8: INDUSTRY VIEWS 

What’s in store for 2024? 

p16: ASK A PROVIDER 

Shining a light on internalisation 

p18: BUYSIDE PERSPECTIVES 

Navigating FX execution strategies 

p22. SUBSCRIPTIONS 

Secure your copies of FXAlgoNews 

p23: INFORMATION & RESOURCES 

Links and websites of the month

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2 November 2023

Goldman Sachs boosts NDF 

algo execution with new 

enhancements 

Providers are increasingly focused on how to continue improving execution outcomes 

for FX algo users, including making changes in the background to further improve 

efficiencies. Dr Ralf Donner, Head of Marquee Execution Solutions at Goldman Sachs, 

shares the latest change to the methodology for rolling FX algos and explains why the 

new approach is of particular benefit when it comes to NDF algo execution. 

TOP STORIES 

NEWS FEATURES 

Source: Goldman Sachs Marquee. For illustration purposes only 

Dr Ralf Donner 

As clients become increasingly 

sophisticated in their use of FX algos, 

including notably NDF algos, Goldman 

Sachs has focused on creating further 

efficiencies ‘under the hood’ of its 

algo offering. This has resulted in a 

recent change to the way the bank rolls 

algos, with a new focus on improving 

the forward points by “TWAP-ing” 

the points to ensure tighter spreads 

and better quality fills. The new 

methodology involves rolling the child 

orders during the execution of the 

algo, so each time a fill comes in, the 

child order is rolled or, in the case of 

synthetics, both legs of the order the 

forward points are rolled on each of the 

individual child orders. 

DRIVERS 

Donner explains that the initial driver 

behind this new development was to 

further enhance NDF algo execution. 

“Our main motivation was to ensure we 

have a very robust product for trading 

NDFs, but in doing so we have created a 

very important new enhancement to all 

our algos. This includes spot algos that 

are rolled to a forward date. At some 

point in the future, clients will now not 

necessarily need to know where the 

liquidity is in the given currency pairs, 

they will not need to know is it a first 

future date, is it a spot date, is it one 

month. Instead, the client will see the 

EURUSD and EURCZK aggregate orderbooks. The visibly different EURCZK market structure 

can pose a challenge to traditional algos 

improved performance of the algos 

neatly encapsulated in the data, in their 

TCA reports, which will give them the 

direct insight into their performance.” 

This new approach has a number of 

advantages, notes Donner. Clients can 

now benefit from an average price over 

the lifetime of the algo on the forward 

points, while also hitting a top-of-book 

spread on points when executing which 

should result in tighter points. “The new 

methodology is also future proof, with 

new developments coming down the 

line such the new matching pools for FX 

swaps, enabling clients to execute algos 

on a greater variety of venues.” 

FRANCHISE MATCHING 

In addition, the recent addition of a 

new way to internalise, called Franchise 

Matching, is also proving to be very 

beneficial in executing certain emerging 

markets’ pairs, not just G4 pairs as 

was originally expected, says Donner. 

Franchise Matching is a new method of 

working with Goldman Sachs’ ebook 

to bring internalisation to an algo. This 

causes the ebook to trigger a skew, that 

skew is then shown to certain skew safe 

clients to attract liquidity which is in 

turn used to fill the algo. “Even if there 

is not an offsetting position immediately 

available, this can create one,” Donner 

adds. “In some of the CEEMEA 

pairs, such as Czech/Polish, Franchise 

Matching has provided a considerable 

improvement to overall execution speed. 

This means the client in turn needs to 

take less market risk and the mark outs 

are very good. It is a very soft mark 

out form of liquidity similar to the dark 

liquidity that’s available via mid pools.” 

November 2023 

3

TOP STORIES 

NEWS FEATURES 

ANZ Bank joins BidFX as its 

newest algo liquidity provider 

ANZ Bank has joined BidFX as their newest 

algo liquidity provider. BidFX CRO, John 

McGrath, commented, “ANZ has a unique 

position in the eFX market in terms of 

its liquidity franchise and we have seen 

strong demand from our sophisticated 

institutional clients for ANZ Algo’s on BidFX. 

We are thrilled to have them go live on the 

BidFX platform and offer clients unique 

liquidity from their franchise”. 

John McGrath 

By choosing ANZ for FX Algorithmic 

execution clients can enjoy: 

• Liquidity: Tap into the FX Market 

with ANZ’s superior access to unique 

liquidity pools. 

• Internalisation: Access ANZ’s 

exceptional Australian dollar, New 

Zealand dollar and Asian franchise, 

leveraging the Bank’s strong credit 

rating and risk appetite. 

• Risk Management: Hedge FX 

exposures while meeting reporting 

obligations with transaction cost 

analysis (TCA). 

• Flexibility: Parameters that can be 

defined and controlled, including 

timing, price, and chosen strategy, all 

with prevailing market conditions. 

Commenting on this development, ANZ 

Head of eFICC Luke Marriott said: “ANZ 

is pleased to collaborate with BidFX, 

bringing our FX Algorithmic execution 

offering to the BidFX Algo Hub. We see 

synergy in bringing our unique AUD, NZD 

and Asian franchise to our mutual clients 

throughout Asia and across the globe.” 

BidFX, as an SGX Group company, is 

a cloud-based provider of eFX trading 

solutions for global buyside institutions. 

It delivers customised liquidity in all FX 

products from partner banks, non-banks 

and ECN’s providing broker-neutral and 

cutting-edge execution management 

services. The firm offers a complete 

suite of negotiation protocols and a hub 

to the algo suites of all major banks 

featuring best execution capabilities. 

Its Liquidity Provision Analytics (LPA) 

and advanced TCA solutions feature 

pre-trade predictive models, in-trade 

benchmarking and post-trade synopses. 

The company was recently recognised 

as the Best Foreign Exchange Solution 

at the Hedgeweek European Emerging 

Manager Awards 2023. 

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Photos by Richard Hadley. 

TradeTech FX Europe 2023: FX 

algos and TCA in the spotlight 

yet. In order for swaps algos to be a 

more viable option, panellists highlighted 

the need for better TCA and that liquidity 

issues in certain currencies outside of 

G10, such as emerging markets pairs, 

still require a human touch. The current 

market structure was cited as another 

reason why the development of FX 

swaps algos was not at the “forefront” 

of future plans. 

RECENT EVENT 

Market leaders from across the FX 

industry convened in Paris this September 

to attend the annual TradeTechFX Europe 

conference. The event once again 

explored the role of FX algos and TCA in 

the evolving market infrastructure and 

brought into focus key trends and areas 

where more work remains to be done. 

One of the key areas of interest for the 

event was the development of CLOBs for 

swaps and what this might mean for the 

further electronification of the FX market 

and its potential impact. In a panel 

discussion, leading figures discussed how 

algo offering might potentially accelerate 

the electronification of the FX swaps 

market or if that was further down the 

line. The discussion, chaired by Stephane 

Malrait, Chairman, ACI FMA, was held 

with Emmanuel Hurault, FX, Credit, 

Rates and Derivatives Dealer, Groupama 

Asset Management; Ben Pearson, Co- 

Head Global G10 & PM STIR Trading, 

UBS; Simon Jones, Chief Growth Officer, 

360T and Paul Milward, Head of Product 

at 24 Exchange. 

360T have recently revealed that 

Deutsche Bank and ING had executed 

the first trade on 360T’s Swaps 

User Network (SUN), which allows 

streaming mid liquidity via API. The 

innovation is hailed as a paradigm shift 

for the industry. Commenting on the 

development, Jones explained that 

360T was confident the move will mark 

a “fundamental change in how banks 

trade FX swaps, opening the door to 

auto-hedging, aggregation and even 

algorithmic execution”. 

24 Exchange also shared further details 

about its own plans for a new swaps 

electronic streaming platform. Yet while 

the consensus was that while automation 

in the swaps space was welcomed, the 

need for FX swaps algos was not there 

Liquidity access and market impact were 

a further primary topic covered during 

the event. A panel including business 

heads from JP Morgan, FX HedgePool, 

XTX Markets, UBS and SEB discussed the 

role of TCA and the imbalance between 

FX spot, where analytics are more 

developed, to other instruments where 

TCA is not at the same stage. According 

to one panellist, while TCA has come 

a long way, there is still a long way to 

go. The consensus was that TCA must 

provide more insights around the true 

market impact, a sentiment which was 

backed up an audience poll held during 

the discussion. When asked whether TCA 

models go far enough to explain market 

impact and the potential influence on 

the outcome of a trade from prehedging 

risk, some 83% said no while a 

mere 17% believed that it did. 

Poll: Do current TCA models go far 

enough to explain market impact 

and the potential influence on 

the outcome of a trade from prehedging 

risk? 

No – 83% 

Yes – 17 % 

The development of the swaps market, 

including a CLOB for swaps, was 

expected to lead to improved TCA 

offerings. Some buyside firms may also 

not being using TCA data to the best of 

their advantage, according to discussions. 

According to a panellist, many are not 

aware of the importance of looking at 

certain metrics, such as market impact 

or reversion. They added that when 

analytics users start asking for this data 

then they will be able to “know if an 

algo is smart”. 


5

MARKETWATCH 

Carolina Trujillo, Head of e-FX 

Distribution at SEB, evaluates 

how a review of the FX Global 

Code is likely to impact on algos. 

With the latest FX Global Code survey having been recently completed ahead of the 

three-year review coming up in early 2024, we asked Carolina Trujillo, Head of e-FX 

Distribution at SEB, to reflect on what the latest review has contributed in terms of 

algos and whether any more could be done to further enhance transparency and 

improve information sharing with users. 

Carolina Trujillo 

Both the algo and TCA template 

introduced under the last FX Global 

Code review were highly beneficial and 

have contributed to more informed 

discussions and questions from our 

clients. In particular, the way it has 

generalised some quite specific and 

advanced concepts would not have 

been reached without the standards 

and the recommendations to publish 

such information. 

Having algo disclaimers on your website 

is an excellent start, but we should not 

expect clients to browse through their 

liquidity providers websites. Making 

sure that the salesforce can spread that 

valuable information to clients during 

meetings and conversations is key to 

continue enriching the dialogues and 

helping the industry in providing greater 

transparency. 

The algo discussions we have with clients 

can be divided into two main categories. 

The first category is how our algos work 

and the different parameters that a client 

can choose. This even encompasses the 

logic we use to select our venues and 

what we look at in the execution logic 

in order to always strive for the best 

possible outcome for the client. 

When we evaluate counterparties for 

trading, there are a number of metrics 

to look at. This can include: 

• Fill ratio - this statistic tells us if the 

prices that are observed are actually 

tradable. If the fill ratio is low, meaning 

that the prices are often not tradable, 

the expected algo transaction cost 

might be too optimistic. 


• Cost-of-cancel, defined as: “The price movement in the 

second-best market (B) from the time an order was sent 

to the best market (A), cancelled and re-sent to Market 

B.” Where fill ratio tells us what the true transaction 

cost estimation is, cost-of-cancel tells us the estimated 

opportunity cost of getting the priority between venues 

wrong. Fill ratio and cost of cancel are metrics trying to 

describe what happens at the time of execution. 

We also want to understand what happens in the market after 

we execute: Does any venue show signs of market impact? If 

the executed amount ends up at a counterparty that would 

immediately hedge this flow on a lit market, other market 

participants might pick this signal up and thus change the 

way they quote in the market, anticipating even more flow. 

This is, of course, counterproductive to what the algo is trying 

to achieve and is monitored with great care. We minimise 

market impact by internalising the Scandi flow through our 

unique Scandinavian and international client franchise and 

carefully selecting the venues on which we trade. In other 

currency pairs, this careful selection is to make sure that we 

find counterparties that are internalising the flow, or venues 

with the lowest market impact, and thus would not affect the 

markets. 

The second category of discussion is about a specific 

execution. In these conversations we are going to look more 

at the external factors that can impact the choice of the 

execution. Looking at a concrete example with the help 

of data gives valuable insight to our customers about the 

execution they are considering. It is some of the information 

that a pre-trade TCA can provide to you with. A usual 

question from clients is: “When should I use an algo?” These 

metrics help clients make that informed decision. 

For example: 

• What is the current spread in the market and how does 

that stand against historical spreads? A wider spread 

would advocate a more passive algo. 

• What is the current risk transfer price and how does that 

stand against historical risk transfer prices? The higher the 

risk transfer price is, the more sense it makes to use an 

algo for your execution. 

• What are the liquidity and volatility conditions? The lower 

the liquidity, and the lower the volatility, the longer time 

you might want to select for your algo execution. 

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broader financial services industry, including investors, 

system traders, and quantitative analysts, as well as for 

technologists designing automated trading architecture, 

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successful algorithmic trading strategies and hedge funds, 

as well as a grounded introduction to financial theory and 

behavioural finance. 

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A survey on Machine 

Learning algorithms 

for risk-controlled 

algorithmic trading 

EDUCATION & TRAINING 

RESEARCH & REPORTS 

• Are there any upcoming economic events that we need to 

take into consideration? 

A lot of information needs to be considered when placing an 

algo. As we use the same algorithms for our own trading as 

the ones we offer to our clients, we do have a great history of 

executions to draw conclusions from. This, together with the 

expertise from our sales giving color on the current market 

conditions, is one of the success factors of the SEB execution 

algos. 

Machine learning algorithms have been widely employed 

in FX trading due to the vast amount of historical data 

available. These algorithms can analyze currency exchange 

rates, economic indicators, geopolitical events, and other 

factors to predict short-term or long-term movements in 

currency pairs. 

ijsrst.com/IJSRST523103163 


7

INDUSTRY VIEWS 

What lies in the road ahead 

for FX algo trading in 2024? 

One constant in any market is change. Looking back over the past year’s headlines in 

FXAlgoNews and it is easy to see that the evolution of the algo industry has continued 

apace. The market is more mature and clients are increasingly adopting algo use for a 

wider range of order types. Meanwhile across the wider FX industry, the impact of changes 

to volatility, liquidity access and the arrival of new venues and technologies is also being 

felt. Some of world’s largest banks are spearheading a great deal of the current innovation 

that is taking place in e-FX so what developments do they expect to see with algorithmic FX 

trading in 2024? Nicola Tavendale investigates. 

Nicola Tavendale 

Image by shutterstock 


Following three years of gathering 

insights from an annual Global Client 

Fixed Income Market Structure Survey, 

Barclays conducted a similar exercise 

with its FX clients for the first time this 

year. The results provide a telling insight 

into the key areas of focus for clients, 

particularly so for the FX algo business. 

One of the most significant findings 

were the number of clients who were 

planning to look more closely at FX 

market liquidity and how to access it, 

says Ajay Kataria, Head of Electronic FX 

Distribution, Americas at Barclays. “For 

our clients, liquidity access was shown 

to be their biggest area of concern,” 

Kataria adds. “We believe this is also a 

big part of the story for 2024. Putting 

this in the context of FX algos, this 

means the focus for our clients will 

be very much around finding the best 

liquidity and navigating this changing 

market structure and landscape.” 

When clients were asked how they 

review a dealer’s algo functionality, the 

most important area was found to be 

internalisation. Kataria explains that 

internalisation will also continue to be 

a key area of focus for the business to 

further leverage the benefits that come 

from having such a large franchise. 

Ensuring that the FX algo offering is 

constantly improving and adapting 

to the changing FX landscape it also 

essential, says Preston Mesick, Global 

Head of FX Algos at Barclays. “This will 

be a big area focus for us next year,” 

he adds. “FX is very much a follow-theleader 

market. With new venues and 

new orders types coming to market, 

this will really change the nature of 

FX liquidity.” Being able to help clients 

navigate the changing landscape and 

market structure will be very important 

looking to 2024, notes Mesick. “For 

example, as the market and NDF 

liquidity expands, we will continue 

to assess and update the number of 

liquidity sources that our NDF algo 

offering is able to access,” he says. 

Another big theme for the coming 

year will be improving the quality 

of FX algo execution assessments, 

according to Vittorio Nuti, Global Head 

of Listed Derivatives & FX Algo Trading 

at Deutsche Bank. “The industry as 

a whole is maturing and the algo 

clients are becoming increasingly 

sophisticated. With that comes 


9


Ajay Kataria 

“For our clients, 

liquidity access was 

shown to be their 

biggest area of 

concern.” 

demand for a better quality assessment 

of the algos that they are employing, 

whether that is through third-party 

TCA or their own in house analytics,” 

Nuti says. Assessing the provider of 

the algo should also be an important 

consideration, he adds. “The quality of 

execution will naturally vary over the 

course of weeks, months or even years, 

which is why it is important to assess 

the quality of algo execution at a global 

level,” says Nuti. “For algo providers, 

that assessment will be key to getting 

more business.” 

Preston Mesick 

“We have increased our 

internalisation rate of 

passive algos by around 

4x in the past year, 

which is substantial.” 

INCREASING COMPETITION 

A further trend which is increasingly 

coming to the fore is demand for client 

customisation of algos, although as 

Nuti explains, it can prove to be very 

difficult to customise algos to clients 

requirements. He says: “Most of the 

time, those requirements are quite 

fuzzy in nature. This is why the industry 

as a whole is based on results. It is 

about achieving the right outcomes for 

the clients, but ultimately each client 

may have slightly different targets 

that they’re aiming for in their algo 

execution.” At Deutsche Bank, the 

focus is in on having these detailed 

conversations with clients to understand 

how to help them effectively. “One of 

the main strengths of our algo business 

is that the team is perfectly aligned with 

wanting to achieve the best outcomes 

for our clients,” says Nuti. 

Meanwhile at Goldman Sachs, the 

recent development of a new way to 

bring internalisation to algo execution 

is also notably gaining traction. 

Called Franchise Matching, it works in 

partnership with the ebook to create 

an offsetting position for the FX algo, 

even when there is not one immediately 

available. This has been particularly 

successful with more sophisticated 

clients, such as systematic hedge funds, 

but also with other banks, says Dr Ralf 

Donner, Head of Marquee Execution 

Solutions at Goldman Sachs. “As one of 

the leading algo providers we are active 

in marketing our product to the spot 

desks of smaller banks, who perhaps 

do not have the same means or the 

same variety of tools for execution. 

Franchise Matching is an exciting 

development and has proven to be very 

successful with these desks, in providing 

something that is competitive compared 

to direct market access,” Donner says. 

He adds that it is however worth noting 

that there will be times where it may 

not be suitable, such as Goldman 

Sachs having positioned in the same 

direction as the client who is attempting 

to trade. This is why it is important 

to have control and for the Franchise 

Matching functionality to not be set 

in stone, Donner explains. “It is not 

necessary to suspend or cancel the 

algo if Franchise Matching is too slow. 

Clients can easily modify their access to 

different categories of liquidity, selecting 

additional liquidity or even unselecting 

Franchise Matching if it isn’t effective, 

and choose other forms of liquidity 

instead,” he says. “Another reason why 

it has done so well is that we also have 

internal demand for this tool, with our 

own voice traders now actively using it 

for their risk management purposes.” 

INNOVATION AND 

DEVELOPMENT 

The analytics results from third-party 

TCA are also making the offering more 

attractive to clients, Donner adds. 

Clients have started looking at this data 

more closely and increasingly it is not 

just about overall all-in performance, he 

explains. “The TCA may include certain 

specifics, such as performance by 

individual venue,” Donner says. “Clients 

are now starting to ask questions 

about the venues that they’re accessing 

when they trade with our algos. It 

has become increasingly important 

to demonstrate value and, when it 

comes to internalisation for instance, 

to have made that as high quality as 

possible. This is what we have done with 

Franchise Matching.” 

Differentiation between algo providers 

is also going to be a lot more data 

driven than it has been in past, says 

Mesick. Customers now have more 

access to data from external providers 

as well as bank TCA, he explains. 

“How they interpret and use that data 

to make decisions about where to 

go with what flow is also becoming 

more sophisticated. Our relationship 

with customers is very important 

but these conversations around algo 

performance are becoming much 

more data driven than in the past,” 

says Mesick. “Differentiating between 

the various algo providers is going to 

come down to performance. If your 

algos are performing, you’re far more 

likely to continue executing with those 

customers, while the providers who 

have issues accessing liquidity are going 

to be more easily identified.” 

Another effect of better analytics 

data is that clients now have a better 

understanding of how their execution 

impacts the markets, notes Mesick. He 

adds: “This is one of the main reasons 

why internalisation has been so key, 

especially in our passive algos suite. 

We have increased our internalisation 


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11


Vittorio Nuti 

“One of the main 

strengths of our algo 

business is that the 

team is perfectly 

aligned with wanting 

to achieve the best 

outcomes for our 

clients.” 

rate of passive algos by around 4x in 

the past year, which is substantial. 

We are already seeing the benefits of 

that change from a TCA perspective. 

Ultimately, it is now one of our biggest 

differentiators as an algo provider.” 

IMPROVING THE USER 

EXPERIENCE 

A further differentiator which stands 

out for clients is in how well FX algos 

can be integrated into their existing 

workflows, Mesick says. “Clients are 

now more comfortable and familiar 

with the algos, so now they are 

increasingly looking at how the algos 

fit into their workflow, how they can 

be more efficient and do the same 

amount with less. Seemless workflow 

integration is now another important 

factor for algo clients, especially in a 

cost constrained environment as rates 

are obviously in a much different place 

than they were a year ago,” he adds. 

Kataria agrees, adding that this was also 

reflected in the findings of the client 

survey results. When asked what the 

most important consideration was when 

reviewing the performance of dealer 

algos, internalisation was the number 

one response, at 33%. “Just behind 

internalisation however, the second most 

important factor was configuration of 

algo at an order level,” says Kataria. “We 

differentiate by performance as well as 

making sure we offer what our clients 

need, such as workflow facilitation.” 

The role of analytics will also be 

increasing based on observing how a 

client trades in order to then suggest 

ways to improve their execution, adds 

Nuti. He explains that this ties in to the 

ability to customise orders. “Our remit 

is to optimise capturing that execution 

in a reasonable timeframe. But some 

clients may have very short term alpha, 

where others might have very long term 

alpha. So we might have more time 

to execute, which is another way of 

customising the execution,” says Nuti. 

“We have had successful engagements 

with clients who trade very small sizes, 

to very large sizes, all through FX algos, 

and the performance is very sustainable. 

Optimising workflows streamlines 

the process for clients, so instead of 

spending time in trying to manage 

various LPs or multiple connections, 

they just have one or two. These are 

all hidden but real costs, which we can 

help to minimise.” 

For algos, Nuti says that Deutsche Bank 

currently offers some of the widest 

coverage on the market. “We offer 

deliverables in both G10 and EM pairs, 

while also covering LatAm and Asian 

NDFs alongside precious metals.” Hedge 

funds are also expected to increasingly 

adopt algo execution, according to Nuti, 

while the size at which they start using 

them will change as well. “The average 

algo client might currently expect to use 

an algo to execute an order size of at 

least 20-30 million, but the size at which 

they will start to consider using an algo 

will likely get much smaller,” he adds. 

AREAS OF NEW GROWTH 

A key form of internalisation at Barclays 

is the ability to use an FX algo to 

access the franchise liquidity, notes 

Mesick. “Our Barx Gator algo suite in 

particular allow us to show interest 

to our franchise directly. Being able 

to tap into that franchise has really 

vaulted our internalisation rates and is 

one of the main ways we think about 

internalisation.” 

Clients are becoming increasingly sophisticated and demanding better quality assessment 

of the algos that they are employing 

Bringing together the concept of 

the evolving liquidity landscape with 

customer demand for NDF algos also 

makes sense as the two are related, 

according to Mesick. The maturation of 

the underlying liquidity in the different 

electronic venues for various currencies 

is a lot more varied than the deliverable 

space, he explains. “We already support 

NDF algos, but as key markets continue 


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13


Dr Ralf Donner 

“Clients are now 

starting to ask 

questions about the 

venues that they’re 

accessing when they 

trade with our algos.” 

to mature and electronify further, 

we will be able to offer increasingly 

advanced algos to trade.” 

Over the past decade, one question 

which continues to be raised by FX algo 

clients is what volumes are being traded 

in a particular currency pair, according 

to Donner. “This is still a difficult 

question to answer. One development 

is that we are currently helping to 

contribute to a third-party vwap 

benchmark in conjunction with one of 

the third-party TCA providers,” he says. 

“They have a client request for a vwap 

benchmark from a variety of different 

banks and we are contributing to that. 

As contributors, we then also get to see 

the final benchmark. This is the one of 

the first concerted attempts to produce 

a vwap benchmark for FX.” 

In house, Goldman Sachs is also 

currently building out a pre-trade toolkit 

utilising live market data, Donner adds. 

“The fundamental concept is similar in 

design to a tool that we had launched 

on marquee previously for options 

structuring which we call the Visual 

Structuring tool. This is essentially an 

analogous version of that, but used to 

make the decision between trading with 

an algo or trading using a risk price. 

Equally, it tries to show all aspects of 

market liquidity as well as possible algo 

outcomes versus alternatives on a single 

chart. This is going to be a very powerful 

tool which we can share with our clients 

in the very near future.” 

THE END OF AI IN ALGOS? 

When it comes to the future utilisation 

of AI in FX algos, Donner re-asserts the 

position that AI has no place in this 

very limited set of markets. “AI can be 

useful for algo trading in other markets 

with far more instruments to be traded, 

such as futures. But in FX, it’s just 

not worth the risk of a very unusual, 

unexpected algo outcome in even a 

tiny fraction of cases.” Nuti adds that 

in the case of AI, the biggest issue is 

being able to use it in a highly regulated 

industry such as FX. “You go from A 

to B, but you cannot explain how you 

got there. That is difficult to explain 

to a regulator and not a place most 

institutions want to be in,” he says. “Yet 

machine learning, reinforced learning, 

econometrics, things that are much 

more deterministic in their outcomes, 

are proving to be a much better area 

of focus. The algo market is going to 

continue concentrating on this area, 

rather than AI.” 

According to Barclays’ client survey 

findings, clients are also increasingly 

allocating their algorithmic execution 

based upon historical performance. 

“Clients have become more 

sophisticated in their use of data. When 

we asked them how they allocate their 

flow to dealers, the largest response at 

29% said this was based on historical 

algo performance,” says Kataria. “As 

data analytics comes more into play, we 

will see clients building more of algo 

wheels. Data driven decision making 

is key in algo trading.” Automation will 

also prove key as the use of algo wheels 

increases, with more clients investing in 

data and analytics behind these tools, 

says Mesick. “This will automate algo 

selection and provide a better view as 

to where they need to be routing their 

orders,” he adds. 

Another question which continues to be raised by FX algo clients is what volumes are 

being traded in a particular currency pair 

TOWARDS MATURITY 

Generally, the FX algo market is now 

fairly robust, says Kataria, with some 

37% of clients taking part in the survey 

having said that they now leverage 

dealer algos. “This follows the general 

industry trend of the algo market 

now being fairly mature,” he adds. “At 

Barclays we have a very mature product 

given that we were one of the first 

banks to deliver algos. So at this point, 

for us, it’s about a lot of small tweaks 

that have large back end impacts. 

And that’s been our biggest focus for 

2023 and will continue to be a focus 

for 2024. Internalisation is one of the 

things we have been talking about the 

most as it’s had the biggest impact on 

our client performance and we think it 

will continue to have that impact,” he 

concludes. 


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15

? 

ASK A PROVIDER 

Commerzbank shines light 

on internalisation impact 

for FX algos 

The concept of internalisation is particularly nuanced in FX, while the varying definitions 

between providers can make like-for-like comparisons difficult. Tibor Gergely, Head of 

e-FX Liquidity Provision at Commerzbank, explains the issues this creates for FX algo clients 

and how Commerzbank helps to promote greater transparency around the liquidity sources 

used by its execution algos. 

Tibor Gergely 

How does Commerzbank define 

internalisation? What is the impact 

of internalisation in the use of 

execution algos? 

In the specific context of an agency 

algorithmic execution, we define 

internalisation as the proportion of 

At Commerzbank we think 

that the internalisation 

rate should be defined in 

the simplest way as client 

only volume divided by 

total volume traded. 

volume filled on Commerzbank’s 

principal liquidity coming from its 

automated e-FX market making desk. 

All of Commerzbank’s execution 

algorithms allow the client to either 

select Commerzbank liquidity only as 

the liquidity pool, or a hybrid mode, 

where both our market making desk’s 

liquidity and external market sources 

are used to fill the order. When only 

our liquidity is selected, the algo’s 

internalisation rate is 100%. 

The natural and important subsequent 

consideration is what is the 

internalisation rate of Commerzbank’s 

eFX principal market making desk? 

It would be deceiving to say the 

execution algorithm has a 100% 

internationalisation rate if the principal 

market making desk hedged every 

single child order back-to-back 

right away on external markets. The 

precise definition of internalisation 

for a principal market making desk is 

more equivocal. Clearly, hedging the 

market making risk on public venues 

that disseminate market data and 

are considered paramount for price 

discovery constitutes externalisation. But 

what about dark mid match venues? 

One can argue it is externalisation as the 

risk is transferred to another liquidity 

provider (LP). On the other hand, that 

other liquidity provider was posting 

interest because it had the opposite 

position in its book. Therefore, when 

thinking about liquidity providers as a 

group, the client flow ended up being 

absorbed in one of the LP’s inventories 

and thus it was internalised. 

The main reason liquidity consumers 

prefer LPs that internalise is to limit 

market impact. If the flow ends its path 

with an LP that is happy to sit on that 

risk for a while, one can argue the flow 

was internalised and the market did 

not move because of it. How about 

skewing, showing an axe to liquidity 

consumers? Again, one can argue that 

it is internalisation as the LP exits risk 

with a liquidity consumer (LC). It is 

not that simple, skewing to the wrong 

LC will create market impact. Market 

participants who take advantage of LP 

skews to recycle their liquidity will have 

market impact. 

Why does Commerzbank’s approach 

particularly benefit FX algo users? 

Image by shutterstock 

At Commerzbank, we think that the 

internalisation rate should be defined in 

the simplest way as client only volume 

divided by total volume traded. LPs with 

a large and diverse franchise who see 


Can algo analytics help to shape 

and inform client understanding 

and further improve their execution 

performance when using algos? 

bi-directional flows will display the highest 

internalisation rates. Commerzbank 

has increased is risk appetite over the 

last few months and is increasing its 

internalisation rate. We take pride in being 

able to provide liquidity even during the 

most volatile market conditions, when 

interbank liquidity can be patchier in 

some currency pairs. Trading with a true 

We take pride in being 

able to provide liquidity 

even during the most 

volatile market conditions 

market maker that absorbs the flow 

and can take on large inventory limits 

market impact and benefits the liquidity 

consumers. An execution algorithm 

filling a large proportion on internal 

liquidity is better to avoid signalling 

and market impact only if the principal 

source of liquidity behind it has a very 

high internalisation rate too. 

It is paramount to be entirely 

transparent on the liquidity sources 

used to fill an execution algorithm. It is 

not enough to report what proportion 

of trades are filled on ‘internal’ 

liquidity as the market impact benefit 

will eventually be dictated by the 

internalisation rate of the internal source 

of liquidity. A good TCA will clearly 

give the source of liquidity for every 

child order. A step further in the right 

direction can be to give a sense of the 

potential for signalling or market impact 

depending on which liquidity source 

is used by the execution algorithm. 

The issue of different definitions of 

internalisation used by algo providers 

impacts clients when trying to compare 

key algo performance metrics, but this 

can be overcome by being explicit in 

terms of what is the exact definition of 

internalization used when an LP says 

‘we internalise X%’. 

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17

BUYSIDE PERSPECTIVES 

Navigating the shift 

in FX execution 

strategies 

By Allan Guild and James Chapman, Directors at Hilltop Walk Consulting 

In foreign exchange (FX) markets, participants are continuously seeking ways to enhance 

efficiency, reduce costs, and optimize their trading strategies. In particular, participants are 

looking to shift their execution to more sophisticated algorithmic (algo) trading strategies 

and away from traditional Request for Quote (RFQ) or Risk Transfer. This evolution in 

trading practices reflects a broader industry move towards automation and data-driven 

decision-making. This article aims to delve into the reasons behind the growing preference 

for executing using algos rather than traditional methods like RFQ or Risk Transfer. 

Through the insights of Hilltop Walk 

Consulting and a detailed analysis of 

these points above, we can start to 

provide FX market participants with a 

guide on transitioning to algo trading, 

ensuring more informed decisions and 

successful outcomes in their trading 

strategies. 

REDUCING TOTAL EXECUTION 

COST WITH ALGOS 

Embracing larger trades for cost 

efficiency 

Allan Guild 

We will explore five key considerations 

for market participants contemplating a 

switch to using FX algos: 

1. Reducing total execution cost 

with algos: Understanding how algo 

trading can lead to more efficient 

and cost-effective execution. 

2. Effective FX algo strategies: 

Evaluating the three main algo 

strategies that stand out as viable 

alternatives to traditional methods. 

3. Choosing algo providers: The 

James Chapman 

importance of selecting the right 

algo providers and why limiting the 

choice to a few is beneficial. 

4. Systematic benchmarking 

and analytics: How a structured 

approach to benchmarking and 

analytics is vital in assessing algo 

trading performance. 

5. Considering the end-to-end 

impact: Understanding the broader 

implications of changing your 

execution model on overall trading 

operations. 

A key criterion for realizing the reduced 

execution costs promised by algo 

trading is the consistent execution 

of FX trades significantly larger than 

the minimum interdealer trade size 

of around $1 million; trades should 

consistently be in excess of $10 million 

to $20 million. Smaller trades cannot be 

broken down any further by an algo and 

have less market impact to consider, so 

it is less likely that they are suited to this 

execution style. 

Understanding the cost dynamics 

The primary motivation for shifting 

from RFQ/Risk Transfer execution to FX 

algos lies in the potential for reduced 


there are really just three primary algo 

strategies that participants should 

consider as alternatives to traditional 

RFQ or Risk Transfer execution. 

Understanding these strategies and 

their implications is key for market 

participants looking to optimize their FX 

execution. 

The choice of FX algo strategy depends on the specific needs and circumstances of the 

market participant 

execution costs. However, this doesn’t 

necessarily mean each trade will cost 

less in isolation. Transitioning to algo 

trading involves transferring market risk 

from the market-maker to the trader. 

This shift means that the final price of 

each trade executed using an FX algo 

depends on the fluctuations in the 

underlying currency price during the 

execution period. 

The shift in risk and its implications 

In traditional market-making, the 

provider does not know the price at 

which they will be able to hedge the 

trade. As a result, they make a profit 

on some client risk-transfer trades 

and a loss on others, incorporating 

a risk premium into their pricing to 

mitigate this uncertainty. In contrast, 

algo execution usually incurs a smaller, 

risk-free algo usage fee, as marketmakers 

no longer need to charge a risk 

premium to guarantee an upfront price. 

take a substantial number of trades and 

a significant time period to gather firm 

evidence of a reduction in execution 

costs. 

EFFECTIVE FX ALGO 

STRATEGIES 

Choosing the right algo strategies is 

crucial for effective execution. It can 

be difficult to navigate the myriad of 

different algo providers and differently 

named algo implementations. However, 

1. Execution Scheduling algo 

This strategy is characterized by its 

systematic approach to executing 

trades over a specified period. The most 

common form in FX trading is the Time 

Weighted Average Price (TWAP), where 

the trader specifies the amount to be 

executed and the time period, aiming to 

match the average price throughout the 

execution. Variations include the Volume 

Weighted Average Price (VWAP), which 

weights the execution by the volume 

traded during the period, and the 

Percentage of Volume (PoV), where the 

trader specifies a percentage of the total 

market volume they wish to transact in 

the execution of the trade. 

The rationale behind spreading out 

execution is twofold: benchmarking 

a trade against an average price over 

time instead of a single arrival price, 

and reducing the market impact of 

the trade. The duration of execution 

is a critical decision for the user, as 

a longer period may result in lower 

market impact but introduces more 

price variance due to market movements 

during execution. 

Variance in execution costs 

While switching to algos is likely to 

reduce the expected total execution 

cost, it also increases the variance in 

execution costs between different 

trades. This increased variance requires 

a level of comfort from traders, as 

they must be prepared for fluctuations 

in costs from one trade to another. 

Furthermore, due to this variance, it may 

While minimizing fees is a 

natural goal for any algo user, 

the fee difference between 

providers is often marginal 

compared to the benefits of 

choosing an algo that fits a 

specific use-case well 


19

BUYSIDE PERSPECTIVES 

Typically, algo users will benefit from 

having more than one algo provider, but 

probably not more than three. 

The challenge of benchmarking and 

comparison 

Benchmarking and comparing 

outcomes from different algo providers 

is considerably more challenging than 

it is for RFQ providers. This difficulty 

arises due to the expected variance in 

outcomes between different trades 

executed using the same FX algo. Each 

provider’s algorithms might perform 

differently under various market 

conditions, making direct comparisons 

less straightforward. 

One of the advantages of algo trading is the vast amount of data generated 

2. Arrival Price algos 

When executing via RFQ with a marketmaker, 

a market participant locks in a 

cost against the Arrival Price, the midmarket 

price at the start of execution. 

Arrival Price algos aim to minimize 

execution cost relative to this Arrival 

Price, typically using an implementation 

shortfall model to balance the potential 

market impact of fast execution against 

the risk of price changes during slower 

execution. 

Many Arrival Price algos adapt their 

strategy in real-time based on market 

conditions, such as accelerating 

execution if the market moves against 

the algo User or slowing down if the 

market moves favourably. This approach 

is similar to how market-makers hedge 

risk in risk transfer trades, making Arrival 

Price algos potentially a good starting 

point for participants transitioning 

from RFQ to algo trading. However, 

understanding the complexity of these 

models often leads new algo users to 

begin with Execution Scheduling algos. 

3. Market Impact Minimisation algos 

These algos focus on minimizing the 

market impact of a trade, striving to 

ensure that the market price follows 

its natural path as if the trade had 

not occurred. While this objective 

is appealing, these algos typically 

do not perform well against Arrival 

Price benchmarks. As these algos 

look to leverage supply and demand 

imbalances, they tend to slow down 

execution when the market moves 

against the buying currency and 

accelerate when it moves favourably, 

leading to highly variable execution 

speeds. 

As a result, Market Impact Minimisation 

algos are generally suitable for 

sophisticated market participants who 

have a continuous need to buy or sell 

a particular currency in significant 

volumes. 

The choice of FX algo strategy depends 

on the specific needs and circumstances 

of the market participant. While 

Execution Scheduling algos offer a more 

straightforward approach suitable for 

many, Arrival Price and Market Impact 

Minimisation algos cater to more 

specific needs and require a deeper 

understanding of market dynamics. 

Regardless of the choice, each strategy 

offers distinct advantages and plays a 

crucial role in the effective execution 

of FX trades in today’s algorithm-driven 

market. 

CHOOSING ALGO PROVIDERS: 

STRIKING THE RIGHT 

BALANCE 

Selecting the appropriate algo 

providers can significantly influence the 

effectiveness of shifting trading to algos. 

Quality of major market-maker 

algos 

Most major FX market-makers now offer 

high-quality FX algo solutions, partly 

because they utilize these algorithms 

for their internal risk management. 

These providers typically have a deep 

understanding of market dynamics and 

access to extensive data, which enables 

them to develop sophisticated and 

effective algo solutions. 

Specialized algo providers 

Besides major market-makers, there 

are other algo providers who may have 

access to specific pools of liquidity 

or unique execution methods. These 

providers can offer distinct advantages, 

especially in niche areas or for certain 

types of trades. Their specialized 

approaches can sometimes provide 

superior outcomes compared to more 

general offerings from larger players. 

Balancing fees with fit and support 

While minimizing fees is a natural goal 

for any algo user, the fee difference 

between providers is often marginal 

compared to the benefits of choosing 

an algo that fits a specific use-case 

well. Equally important is the provider’s 

user support function, which can be 

crucial in responding to queries and 

issues. A provider with strong and 

responsive support can add significant 

value, potentially outweighing any fee 

differential. 

Avoiding over-reliance on a single 

provider 

Although most market participants will 

find they need fewer algo providers 

than market-makers for RFQ business, 


it is advisable not to rely solely on a 

single provider. Having more than one 

provider ensures diversification, reduces 

dependency risks, and can provide 

different perspectives and solutions for 

executing trades. 

SYSTEMATIC BENCHMARKING 

AND ANALYTICS IN FX ALGO 

TRADING 

A strategic approach to benchmarking 

and analytics is crucial for evaluating 

the effectiveness of algo trade 

executions. algo trading lends itself 

to a systematic methodology not 

just in trade execution but also in 

performance analysis. One of the 

advantages of algo trading is the vast 

amount of data generated, which 

covers both details of the trade 

execution and the underlying market 

conditions. This data, while rich, can 

often be overwhelming, making the 

extraction of actionable insights a 

challenge, especially given the variance 

in outcomes inherent to algo trading. 

To navigate this complexity, it’s essential 

to adopt a systematic and appropriate 

approach for each algo execution. 

Using the right benchmarks tailored to 

the specific algorithm is a crucial step. 

For example, it would be ineffective 

to measure the performance of a 

Market Impact Minimisation algorithm 

against an Arrival Price benchmark. It’s 

imperative to match the benchmark to 

the intended function of the algo. 

While most algo providers offer their 

own analytics, relying solely on these 

may not be prudent. There are inherent 

biases to be aware of, such as a 

provider effectively ‘grading their own 

homework’, which can skew objectivity. 

Moreover, when multiple providers 

are used, it becomes challenging to 

compare performance across different 

algos due to the lack of a standardized 

framework. 

To mitigate these concerns, engaging a 

third-party benchmarking and analytics 

provider is often beneficial. Numerous 

providers offer a variety of features, with 

some permitting the pooling of activity 

with other algo users on an anonymized 

basis. This can significantly enhance 

the analytical process by enabling a 

comparative performance analysis across 

When multiple providers are used, it becomes challenging to compare performance across 

different algos due to the lack of a standardized framework 

a broader data set, thus providing a more 

comprehensive view of an algo’s efficacy 

in different market scenarios. 

CONSIDERING THE END-TO- 

END IMPACT OF CHANGING 

YOUR EXECUTION MODEL 

When transitioning to FX algo trading, 

it’s crucial to understand that the 

change involves much more than 

merely altering the execution protocol. 

It’s critical to consider the broader 

implications, and to implement a 

comprehensive end-to-end plan for 

successful adoption. This plan must 

define how algos will be used, including 

the selection of providers, the choice of 

algo strategies, and the determination 

of specific parameters such as the 

duration for a TWAP. 

The migration requires a series of 

interconnected changes that go beyond 

the execution method itself, however. 

Key areas of change are: 

• Counterparties and credit risk: 

Transitioning to algo trading 

might involve changing execution 

counterparties, which in turn affects 

the credit risk profile. It’s important 

to reassess and manage these risks 

appropriately in the new trading 

environment. 

• Execution platforms: A change 

in trading methodology often 

necessitates a shift to different 

trading platforms that are optimized 

for algo trading. This could mean new 

software, interfaces, and possibly 

infrastructure. 

• Role of executing trader: algo 

trading transforms the role of the 

trader. Instead of making individual 

trading decisions, the focus shifts 

towards overseeing algo strategies 

and ensuring their effective 

implementation. 

• Trade affirmation and settlement: The 

processes for affirming and settling 

trades may also change. This could 

involve adapting to new systems 

or protocols that align with the 

systematic nature of algo trading. 

CONCLUSION 

Navigating the complexities of the 

transition we have been talking about 

can be challenging. With their deep 

expertise and experience in the FX 

market, the team at Hilltop Walk 

Consulting bring unique insights into 

the process. Allan Guild, having led 

FX Client algo Trading at HSBC, and 

James Chapman, with his background 

in FX Market-Making at Lucid 

Markets, have seen first-hand the 

evolving landscape of FX trading. Their 

boutique consultancy is dedicated to 

assisting clients in navigating these 

changes. 


21

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References 

Richard S. Sutton and Andrew G. Barto, 2018, Reinforcement Learning: An Introduction, Second 

Edition 

Cameron Davidson-Pilon, “Bayesian Methods for Hackers: Probabilistic Programming and Bayesian 

Inference “, 2016, Addison-Wesley Data & Analytics 

Merton, Robert, 1980, On estimating the expected return on the market: An exploratory investigation. 

Journal of Financial Economics, Volume 8, Issue 4, December 1980, Pages 323-361 

Junya Honda and Akimichi Takemura, Optimality of Thompson Sampling for Gaussian Bandits 

Depends on Priors, Proceedings of Machine Learning Research, Volume 33: Artificial Intelligence 

and Statistics, 22-25 April 2014 

Tze Leung Lai and Herbert Robbins. Asymptotically efficient adaptive allocation rules. Advances in 

applied mathematics, 6(1):4–22, 1985 

Daniel Kahneman, 2011, Thinking, Fast and Slow, Penguin 

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+44 1736 740 130 

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23

Reimagining the power 

of FX Algos 

UBS FX Algorithms help our clients reduce market impact, improve 

performance and add resilience to their trading workflow through: 

• Sophisticated Smart Order Router 

• Comprehensive liquidity access including UBS internalization 

• Advanced machine-learning framework 

• Robust strategies from liquidity seeking to passive execution 

Find out more, search UBS FX Algo 

For Professional and Eligible Counterparties / Institutional / Accredited Investors only. 

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FXAlgoNews November 2023

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