FX Algo News May 2024

ISSUE 34 | MAY 2024 WWW.FXALGONEWS.COM FOLLOW US AT: 

TOP STORIES 

FMSB launches final guidance for 

algo risk management 

The Financial Markets Standards Board 

(FMSB) has shared its final Statement 

of Good Practice for use by the 

industry to specifically support the 

application of model risk management 

frameworks to execution algos. The 

global industry-developed good 

practice supplements existing broader 

supervisory guidance and focuses 

on areas where market practitioners, 

including ‘first line’ risk owners and 

‘second line’ risk managers, have 

identified that the nature of model 

use in algos merits a differentiated 

approach compared with other model 

types. Ciara Quinlan, Global Head of 

Principal Electronic Trading, FX, Rates 

and Credit at UBS Investment Bank, 

who headed up the FMSB Working 

Group, welcomes the publication of 

the final Statement of Good Practice. 

She adds: “Harnessing expertise 

across the industry via FMSB to create 

guidance of this sort is immensely 

valuable and I hope what we have set 

out today will help to support all those 

across the industry working in this 

space, as well as ultimately improving 

market effectiveness and confidence.” 

See page 4 for more details. 

Ciara Quinlan 

HSBC unveils new FX 

basket algo 

HSBC has added a new basket algo to 

its existing FX algo suite which enables 

clients to upload a basket of FX orders 

into HSBC’s single dealer platform, 

HSBC Evolve. As part of HSBC’s Global 

Intermediary Services offering, the 

HSBC basket algo determines how best 

to net and optimise the execution of 

that basket, based on the correlation of 

Vivek Sarohia 

those currency pairs. Netting multiple 

currency pairs into smaller orders 

reduces bid-ask costs for clients, after 

which the algo executes those orders 

at times during the day according to 

an optimisation schedule, which is 

generated by the algo based on the 

correlation of currencies the client 

holds. “We affectionately call it a 

‘correlation aware’ algo,” says Vivek 

Sarohia, Global Head of FX Alternative 

Execution Services. “The HSBC basket 

algo looks at the underlying currency 

pairs that the client is trading and then 

at their correlation, before executing, 

accordingly, to reduce risk and costs. 

Clients may trade many currencies - 

and the HSBC basket algo understands 

that, so it will look at what happens, 

for example, if a client trades one 

currency more aggressively and how it 

might impact other currency positions 

the client also holds in their portfolio.” 

IN THIS ISSUE 

p1. TOP STORIES 

The latest industry stories 

p3: NEWS FEATURES 

More in-depth news 

p4: REGULATIONS & STANDARDS 

FMSB SoGP for algo risk management 

p6: BUYSIDE PERSPECTIVES 

Analysing internalisation rates 

p8: INDUSTRY VIEWS 

Internalisation and FX algo performance 

p16: DEQUANTIFICATION 

Exploring Stark from Deutsche Bank 

p18: ASK A PROVIDER 

Algorithmic trading of FX swaps 

p20: TRADERS WORKSHOP 

Order Stitching in TCA 

p24: CASE STUDY 

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2 May 2024

Bloomberg optimises FX 

algo workflows 

As buyside trading desks continue to undergo dramatic transformation, FX algo 

orders are proving to be an increasingly effective tool for executing large orders 

in a fragmented liquidity landscape. Oleg Shevelenko, FXGO Product Manager at 

Bloomberg, explains why improving workflow efficiencies for the buyside is the key to 

managing transaction costs and improving overall algo execution quality. 

TOP STORIES 

NEWS FEATURES 

Oleg Shevelenko 

What features and services that 

support algo user workflows are 

now available on FXGO? 

FXGO offers clients a comprehensive 

end-to-end workflow from order 

staging and optimization to algo 

strategy selection and post-execution 

quality analysis. In addition, FXGO 

provides a unified routing methodology 

to more than 200 algorithmic order 

strategies from major, regional, and 

non-bank providers, allowing clients 

a comprehensive choice of liquidity 

and execution styles. Execution 

processes over FXGO are then further 

complemented with pre-trade 

news, pricing and analytics offered 

by the Bloomberg Terminal as well 

as integration with providers’ own 

analytics to offer further insights 

and predictive outcomes of their 

proprietary strategies. FXGO captures 

all micro elements of partial fills of 

respective algo executions which can 

then be analyzed via Bloomberg on 

the order by order or aggregate basis 

via Bloomberg’s cross asset post-trade 

TCA tool (BTCA) or be available for the 

automated export to clients’ own TCA 

tools. 

Are there any new developments in 

this area that you would be able to 

share with us? 

FXGO continues to invest in our 

algo offering by enhancing the pretrade 

order optimization toolkit with 

cross value date netting to optimize 

spot exposure, while also expanding 

instrument selection beyond spot 

and non-deliverable forwards into 

precious metals and swaps. We are 

also broadening liquidity selection by 

integrating with regional providers and 

enhancing our own analytics to assist 

with appropriate execution choices. 

What were the main drivers behind 

these new initiatives? 

FXGO strives to stay ahead of anticipated 

client demand, and when coupled 

with client feedback, drives much of 

our development. We added support 

for swaps to our algo offering and are 

currently working on expanded liquidity 

selection through integration with 

several liquidity providers. Given liquidity 

in swaps is challenging due to various 

regulatory and capital constraints, we 

expect our clients to welcome this 

development. Additionally, usage of 

benchmark orders continues to increase 

and so we have made considerable 

enhancements to our workflow solutions 

for both clients and dealers to offer bulkrouting 

and bulk-pricing capabilities for 

benchmark orders. 

How can improved FX algo analytics 

support client workflows? 

As each algo provider offers a whole 

range of strategies with proprietary 

parameters, clients are looking for tools 

to help them navigate and rationalize 

this complex environment to allow 

for more intelligent decision making 

prior to and during order execution. 

Bloomberg’s Algo Analytics hosting 

service helps address this demand 

by allowing providers to host their 

pre-trade and running order analytics 

within the order execution workflow 

of their FXGO clients. At present, there 

are seven algo providers offering their 

integrated analytics services for their 

clients. We also continue to see the 

demand for provider independent 

analytics and so FXGO is now fully 

integrated with BTCA. This integration 

allows clients to analyze their algo 

execution against various Bloomberg 

market data sources and benchmarks 

to further enhance their pre-trade algo 

provider and strategy selection. 

How will Bloomberg continue 

to improve efficiencies in algo 

execution for the buyside in the 

coming year - and beyond? 

FXGO will continue to invest and 

innovate to be at the forefront of the FX 

industry transformation as it adapts algo 

execution. Partnering with our existing 

algo providers and onboarding new ones 

to expand our instrument coverage into 

emerging markets, NDFs, precious metals 

and derivatives will be key to success. 

We are also excited to introduce the next 

generation of pre-trade decision support 

tools to integrate our composite pricing, 

news, analytics and cost models into all 

relevant trading workflows to optimize 

and automate the trading process for our 

clients. 

May 2024 

3

REGULATION & STANDARDS 

Guidance and Good Practice: 

FMSB issues SoGP for applying model 

risk managment frameworks to algos 

The Financial Markets Standards Board (FMSB) has shared its final Statement of Good 

Practice (SoGP) in an bid to support firms in applying model risk management frameworks 

in a proportionate and tailored manner to the models deployed in their algos. 

According to the FMSB, sophisticated 

modelling techniques used for 

calculating trading risk and required 

capital came under significant global 

regulatory scrutiny following the 

financial crisis as a result of their acutely 

revealed shortcomings in containing 

risk. As a result, regulatory guidance 

seeking to address the possible adverse 

consequences of decisions based on 

incorrect or misused models was issued. 

Yet while this guidance applies to all 

model types, including the use of models 

in algorithmic trading, the degree of 

model risk and the potential magnitude 

of any adverse consequences varies 

significantly across model types. 

In response, FMSB has published a final 

SoGP intended to support all firms in 

wholesale financial markets that operate 

electronic trading algorithms involving 

the use of models, taking into account 

the nature, scale and complexity of 

such models as well as existing systems 

and risk controls intended to mitigate 

associated market, conduct, credit and 

operational risks. The detailed guidance 

considers a range of factors, such as 

determining if a method used in an algo 

Source FMSB 

Myles McGuinness 

constitutes a model and tailoring model 

risk management activities for models 

deployed in algos to the context and 

purpose for which models are deployed. 

Algorithmic trading risk summary (‘Key Risk Summary’) 

The table above summarises material risks associated with the deployment of Algos either 

to fair and effective markets or to Algo operators. The table encapsulates conduct, market, 

credit, operational and Model Risks. A firm’s risk management frameworks, of which a 

model risk management framework is one component, will typically be designed to reduce 

or mitigate such risks, though appropriate calibration and effective application of such 

frameworks will be key to their effectiveness. 

“It is important that the industry 

comes together to make sure that 

good practice in a complex area such 

as this is set out in a way that can be 

well understood and easily applied, 

including practical examples,” says 

Myles McGuinness, CEO of FMSB. The 

guidance sets out nine statements of 

good practice which are relevant to the 

application of a model risk management 

framework to algos. The industrydeveloped 

good practice statements 

supplement existing broader supervisory 

guidance which FMSB says firms should 

continue to apply. 

The statements include additional 

commentary and are followed by 

examples of applying a model risk 

management framework to algos 

indicating whether the examples are 

consistent or inconsistent with each SoGP. 


THE NINE STATEMENTS OF GOOD PRACTICE ARE SUMMARISED BELOW: 

The full Statement of Good Practice for the application of a model risk management framework to electronic trading 

algorithms is available at FMSB.com 

STATEMENT OF GOOD PRACTICE (SOGP) 

SoGPs are issued by FMSB from time to time. SoGPs do not form part of FMSB Standards, and they are not subject to 

FMSB’s adherence framework. Rather, they reflect FMSB’s view of what constitutes good or best practice in the areas 

covered by the SoGPs in question. Member Firms are expected, and other firms are invited, to consider their own 

practices in light of the relevant SoGP and make any changes to such practices that they deem to be appropriate. Failing 

to do so will not, however, create any presumption or implication that a firm has failed to meet its regulatory or other 

obligations. 


5

BUYSIDE PERSPECTIVES 

Understanding 

internalisation and 

realising its benefits 

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

Users of FX Execution Algos are becoming increasingly interested in their providers’ 

internalisation rates, as the buy-side seeks to optimise performance and differentiate 

between available offerings. It is crucial to understand the details behind headline 

internalisation rates to ensure best outcomes. 

through efficient hedging strategies 

or price skews, which can both cause 

information leakage. 

Risk management strategies vary 

between dealers, with significant 

differences in the resulting signalling 

risk. 

Franchise Liquidity (Client Flow) 

Client flow can be offset in a variety of 

ways: 

Allan Guild 

WHAT IS INTERNALISATION? 

Internalisation is the Algo provider’s 

process of offsetting risk (positions) 

arising from a client’s Algo child orders 

with risk from other clients, such that 

the external market is unaware of the 

order and resulting trade. However, 

there are a number of different 

execution practices that facilitate this 

offsetting of client risk, making it a 

more nuanced activity. 

Algo providers take differing 

approaches as to what they classify as 

internal liquidity and how that liquidity 

is generated. Importantly, there can 

be configurable parameters (or a 

more general opt-in/out) on the Algos 

James Chapman 

that determine the available liquidity 

sources and trading styles. 

Principal Liquidity (Internal Marketmaking 

Desk) 

The Algo provider’s internal marketmaking 

desk generates principal 

liquidity which Algos can execute 

against or submit passive orders 

into. This may decrease or increase 

the dealer’s risk. It is important 

to consider what happens to new 

positions that the dealer acquires 

as all firms have risk limits that 

ultimately constrain the accumulation 

of inventory, even if those limits 

are large. Dealers will look for 

opportunities to reduce their risk 

1. Match against the dealer’s 

existing positions acquired from 

other clients. This may be part 

of a hedging or market-making 

strategy and so can overlap with 

the definition of principal liquidity. 

Both approaches are specifically 

risk-reducing for the dealer, so they 

should not result in interaction with 

external markets. 

2. Match directly via an internal 

orderbook. Information leakage 

can be managed by limiting the 

participants of the orderbook. 

3. Match over private bilateral channels 

with other clients, potentially 

including other market-makers. 

This carries greater signalling risk as 

anonymised order information may 

be shared more widely with other 

clients. This might be as part of price 

skews, or orders themselves. 


Dark Liquidity 

External venues offering ‘dark’ pools 

of liquidity are sometimes included 

under internalisation. Details vary 

by venue, but the key concept is 

that there is no visible orderbook to 

prevent information leakage, and 

trades are generally executed at a midmarket 

rate. The execution mechanics 

are important; if well designed they 

can offer counterparties fair matching 

on equal terms, and can alleviate 

some of the conflicts of interest 

associated with traditional forms of 

internalisation. 

WHAT ARE THE BENEFITS OF 

INTERNALISATION? 

Reduced market impact 

This is generally the primary driver. 

The premise is that by internalising the 

execution, less information will leak 

into the market, therefore reducing 

the market impact of the trade. 

Reduced fees 

Execution on external venues often 

incurs brokerage fees, so internalising 

trades can save on that cost, resulting 

in a cheaper service. 

Reduced slippage 

Internal order matching and fills 

should be possible with lower latency 

and more consistent execution due 

to greater control over the systems, 

network, and execution mechanisms 

such as last look. 

WHAT ARE THE CHALLENGES 

WITH INTERNALISATION? 

Variable Market Impact 

Each category of internalisation 

will have its own market impact 

characteristics, with varying degrees of 

signalling risk. Moreover, the market 

impact sustained from each method 

may vary significantly between 

providers due to differences in their 

implementations and the composition 

of their client franchises. 

Even within each subcategory of 

internalisation, market impact can 

vary hugely. For example, offsetting 

risk with a hedge fund client is likely 

to look different to a corporate client 

who has very different reasons for 

trading. The source of the liquidity 

is significant, even if it is ultimately 

internalised. 

By understanding the internalisation 

methods practiced by each provider, 

clients can evaluate their pros and 

cons. For some clients, skewing prices 

may be viewed as an effective way to 

reduce transaction costs and increase 

the urgency of the Algo. For others, it 

may carry too high a signalling risk and 

negatively impact the overall cost with 

worse execution. 

There are unfortunately no shortcuts 

– internalisation rates alone cannot be 

used as a reliable proxy for low-marketimpact 

without more detailed analytics 

and TCA covering both internal and 

external execution. 

Conflicts of Interest 

There is potential for conflicts of 

interest between the Algo desk and 

the principal desk. Careful data 

management and process design is 

required to ensure order information 

is not used by the principal desk in a 

way that results in worse execution 

for the client. Order routing must not 

be biased towards internal liquidity if 

it is not necessarily the best execution 

option for the client, and rates on 

internalised trades must be fair. 

An FX Global Code review that 

included consideration of Algos 

concluded that transparency is the 

most suitable mitigant, consistent with 

the other FX Global Code transparency 

guidelines. Standardised disclosure 

information is encouraged via an Algo 

Due Diligence Template. ‘Conflicts’ is 

just one section, which covers fairness 

in execution, order priorities, and 

commercial interests. 

In addition to understanding the 

disclosures, it is critical to take a 

data-driven approach to evaluating 

both internal and external execution 

performance. 

Price Discovery 

The trend towards greater 

internalisation of both Algo orders and 

risk transfers is understandable when 

it can benefit both clients (reduced 

market impact) and dealers (lower 

costs, greater spread capture, private 

information). This has led to a reduction 

in volume on primary venues in 

particular, which is where much of this 

business would have been executed 

historically. 

Price discovery processes have had to 

evolve as a result, with less ‘lit’ volume 

on orderbooks. It may be the case that 

while internalisation can benefit the 

individual market participant, there are 

structural implications for the broader 

market. 

CONCLUSIONS 

Internalisation is a broad term which 

can cover many forms of execution 

and varies between Algo providers. It is 

worth understanding which methods 

each Algo provider is utilising and the 

nuances of their implementations 

by reviewing their disclosures and 

engaging with them directly. 

While it can provide reduced market 

impact, trading costs and slippage, 

the observed effectiveness of internal 

execution should be analysed along 

with external executions using posttrade 

analytics and TCA. Granular data 

from Algo providers should allow this, 

but it may be necessary to combine it 

with additional external data analysis 

to improve the robustness of the 

conclusions. 

Market impact is not the only relevant 

metric; users must consider what their 

own benchmarks are. There are likely 

to be times when it is necessary to 

sacrifice market impact for speed of 

execution, for example. 

The optimal composition of liquidity 

will be different for each client, 

and different with each of their 

Algo providers. It is not as simple as 

maximising the internalisation rate 

- a transparent approach from Algo 

providers allows users to evaluate 

how they would like their orders to be 

executed to best suit their execution 

goals. 

Navigating the complexities of FX 

Algos can be challenging. With their 

deep expertise and experience in the 

FX market, the team at Hilltop Walk 

Consulting bring unique insights. 

Partnering with clients to reach better 

outcomes. 


7

Image by shutterstock 

INDUSTRY VIEWS 


Exploring the role 

of internalisation 

in improving FX 

algo execution 

performance 

Internalisation as a concept has shifted in perception 

among the FX community, moving from a poorly 

understood offering to becoming a sought after value 

added service, particularly in the use of FX algos. So 

what work have algo providers done to demonstrate 

the benefits of internalisation and how are they able to 

differentiate their offering to make internalisation part 

of their competitive edge? Nicola Tavendale writes. 

Nicola Tavendale 


9


James McGuigan 

“Looking at 

internalisation from 

a risk standpoint 

provides a better view 

on the value it can 

bring and some insight 

into the relative value 

each of the various 

mechanisms available 

might offer.” 

Asif Razaq 

“...if the bank has a 

busy book and has 

clients leaving algo 

orders throughout 

the day, it increases 

the matching of 

the two orders and 

allows both clients 

to get the benefit of 

internalisation,” 

Client’s feedback suggests that 

internalisation is not actually defined 

in any one common way across the 

FX market, which itself can lead to 

misunderstanding or lack of clarity 

around exactly what it is and the 

benefit it can bring, says James 

McGuigan, FX Algo Product Manager 

at Citi. He explains that, in its most 

basic form, internalisation can simply 

mean all or a part of an order is filled 

by the provider bank rather an on an 

external venue. However, McGuigan 

notes this is more a description of the 

mechanics of internalisation and is 

not a direct indication as to whether 

it is beneficial to the client or not. 

“Internalisation is a very important 

topic to us and many of our clients,” he 

adds. “Looking at internalisation from 

a risk standpoint provides a better view 

on the value it can bring and some 

insight into the relative value each of 

the various mechanisms available might 

offer.” 

So when an algo provider fills part of 

an order internally, it will either be risk 

reducing, risk neutral or risk increasing 

to their franchise, McGuigan says. “The 

risk reducing and risk neutral cases 

mean the bank has either pre-existing 

inventory or opposing algo order flows 

that can be matched against, the 

likelihood of which is directly correlated 

to the size of a banks FX franchise, 

an important part of which can be 

the banks own internal users of its FX 

algos. Citi has a large internal user base 

which presents greater opportunities 

for algo to algo matching. It is the 

risk increasing scenario that presents 

the more nuanced risk management 

requirement in order to truly 

internalise,” McGuigan adds. 

On the other hand, where the bank 

actively and aggressively clears the risk 

position, it has clearly been externalised 

rather than internalised, says 

McGuigan. “Yet where the bank shows 

a price so skewed to its client base as 

to instantly induce offsetting flow, the 

definition of internalisation can be met 

without honouring the spirit of it,” he 

adds. “We might more accurately, but 

less tangibly, define internalisation as 

both refraining from hedging in the 

market, and also extending the holding 

time of the position out to longer than 

the natural rate of trading interest, 

so as to not cause additional market 

impact. For this reason, only market 

makers with both adequate client 

franchise, and also risk-taking appetite, 

can truly internalise.” 

IMPROVED EXECUTION 

PERFORMANCE 

Asif Razaq, Global Head of FX Algo 

Execution, BNP Paribas, agrees, adding 

that while internalisation set-ups will 

differ from bank to bank, from an algo 

desk perspective there are three core 

‘flavours’ of internalisation. He explains 

that the first form of internalisation 

from an algo execution perspective is 

also akin to what the market making 

desk classes as internalisation. “This is 

probably the most sought after flavour 

of internalisation,” Razaq says. “From 

an algo perspective, this particular form 

of internalisation can be done on very 

large sizes, with the two algo orders 

on opposite sides not needing to be of 

equal sizes to match against each other. 

This results in a trade that’s impossible to 

find anywhere else in the marketplace. 

Furthermore, because that trade is 

executed within BNP Paribas, there is 

no information leakage and no market 

impact on the back of that trade. That 

is key measure of what algo clients are 

looking for with internalisation.” 

However, Razaq notes that how well a 

bank is able to perform this depends 

on how busy its algo business is and 

the size of their client franchise. If a 

provider’s algo business is not very 

busy, the bank will not be able to find 

that offsetting interest to trade the two 

sides of the order at the same time, 

he explains. “But if the bank has a 

busy book and has clients leaving algo 

orders throughout the day, it increases 

the matching of the two orders and 

allows both clients to get the benefit 

of internalisation,” he says. “The caveat 

behind this form of ‘block matching’ 

internalisation is that it relies on the 

bank having an active algo business.” 

The second form of internalisation 

according to Razaq is getting the 

algo to trade against a bank stream 

i.e. a BNP algo trading against BNP 

market making liquidity. “The algo 

desk is not directly internalising, it is 

indirectly internalising that order flow 

through our market making unit. The 

market making desk are not trying to 

externalise that flow, their default is to 


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11


Dr John Quayle 

“An algo that 

internalises using well 

curated low-marketimpact 

liquidity simply 

performs better, a 

fact that is supported 

by our own data at 

NatWest, as well as 

‘peer universe’ data ...” 

sit on that risk and find another client 

to trade the other side of that position, 

which we call ‘indirect’ internalisation 

– essentially minimising noise and 

impact,” Razaq says. 

REDUCED MARKET IMPACT 

He adds that the third flavour is a 

relatively new one, but again working 

with BNP’s market making desk. 

This third type is the ability to place 

orders with the market making desk, 

allowing the client to become BNP’s 

price to our client franchise, Razaq 

explains. “Clients now have the option 

to become BNP’s price on one side, 

improving the pricing in turn,” he adds. 

“If someone trades against that more 

attractive price, we are able to give 

that trade back to the algo client. So, 

this is a way that the client is utilising 

BNP’s market making desk to get 

further internalisation against our client 

franchise by way of the market making 

desk.” This third type is delivered via 

the bank’s BIX (BNP Internal Xchange) 

service, says Razaq. 

At NatWest Markets, internalisation 

is also viewed as the offsetting of a 

position, or of a client order, with 

opposing flow from either internal 

trading desks or clients, says Dr John 

Quayle, Head of Client Algo Execution 

at NatWest Markets. He explains that 

if, for example, the child orders of a 

client algo are 100% filled with either 

opposing desk or client flow, then 

you can say that the algo offers 100% 

internalisation. “This is something 

that we can offer with the NatWest 

Peg Clipper algo,” adds Quayle. “The 

nuances then come in when thinking 

about which activity is ‘client activity’ 

and this definition may vary between 

liquidity providers (LPs).” He notes 

that the key benefit is reduced market 

impact, which is achieved because 

the child orders do not need to be 

placed on external venues. This in 

turn provides the LP with much more 

control over which counterparties 

can ‘see’, and therefore trade with, 

the algo order, such as limiting then 

to skew-safe counterparties, Quayle 

explains. 

“Additional benefits are reduced 

brokerage charges and simpler algos,” 

he continues. “Both of these will 

benefit the client outcome as there 

will be less slippage vs inception mid 

or the risk transfer price - the main 

measure of transaction cost for an algo 

– assuming that the internalisation 

framework is well implemented. The 

NatWest approach is very skew aware 

and focused on curated liquidity and 

internalisation first, which attempts 

to capture these benefits as much as 

possible, the results of this speak for 

themselves.” 

INCREASED TRANSPARENCY 

In addition, there is now a greater 

understanding of the market impact 

reduction that can be achieved by 

internalising compared to that which 

can be achieved when filling on a more 

agency/external manner, according 

to Quayle. “The principal way that 

internalisation improves performance 

is in significantly reducing market 

impact,” he adds. “An algo that 

THERE IS NOW A GREATER 

UNDERSTANDING OF 

THE MARKET IMPACT 

REDUCTION THAT 

CAN BE ACHIEVED BY 

INTERNALISING 



13


Dr Ralf Donner 

“We have migrated 

from a situation where 

we did have some 

clients who prefer not 

to internalise and even 

requested us to switch 

off internalisation for 

them, to now where 

nearly every client is 

not only okay with 

internalisation but is in 

fact very keen on it.” 

internalises using well curated lowmarket-impact 

liquidity simply performs 

better, a fact that is supported by our 

own data at NatWest, as well as ‘peer 

universe’ data from providers such as 

Tradefeedr.” In turn, Quayle notes that 

when an algo is interacting only with a 

given LP’s clients or trading desks, then 

that LP has total control over which 

clients the algo can match with and can 

therefore ensure the algo only interacts 

with those clients who are skew-safe. 

According to Quayle, the challenge 

then becomes how to identify those 

skew-safe clients, and internalisation 

of algos is inextricably linked to that 

task. “Internalisation is only as good 

as the ability to identify those skewsafe 

clients, and internalisation itself 

does not necessarily guarantee a good 

outcome for the algo user – this cannot 

be stressed enough. It is only when 

used in combination with low-market 

impact liquidity that an algo will 

perform well. Hence, we at NatWest 

put as much effort into detecting skew, 

analysing and curating the liquidity 

as we do in designing the algo, and 

this clearly feeds through into our 

performance data,” he says. 

Internalisation is also a key 

differentiator because it is an area 

where the algo provider’s franchise 

can come into play, argues Dr Ralf 

Donner, Head of Marquee Execution 

Solutions at Goldman Sachs. While 

a bigger franchise is generally better, 

it is still imperative to offer clients an 

intelligent, nuanced way to internalise, 

resulting in a better experience for the 

client. “From a client perspective, the 

number one expectation would be that 

there is a better mark-out profile from 

internalisation, which in turn improves 

the overall performance of the algo as 

well,” says Donner. “Some clients are 

also not so keen on last-look found in 

many of the secondary markets, but 

that can be avoided by internalising 

as the internalised prices are always 

firm. From a dealer perspective, we 

obviously do not have to pay brokerage 

for internalising, so we may hope to 

reduce the all-in cost of execution while 

offering the client a better experience.” 

POINT OF DIFFERENTIATION 

To this end, Goldman Sachs offers four 

flavours of internalisation: required, 

selective, matching, and skew, Donner 

explains. The first type, required 

internalisation, is used by algo clients 

if they have, for example, a twap or 

a vwap algo that has to finish at a 

certain scheduled time, he explains. In 

that case, it may be required to send 

a piece of the algo to a bank e-book 

in order to ensure that the scheduling 

requirements are still achieved. The 

second flavour, selective internalisation, 

is used when there is an external 

price and the algo essentially queries 

internally to see if it is possible to fill the 

algo at the external price, Donner says. 

He adds: “The reason why that could 

be an advantage is if the external price 

could be missed for some reason, such 

as via latency or because the external 

price is not a firm price but has last 

look conditions attached to it. In that 

case it might be better to just try and 

fill that internally and not have any kind 

of mark out that you might experience 

with the external price.” 

The third internalisation offering is 

matching, Donner says, in which the 

bank operates a matching engine, an 

internal order book with various levels. 

The algo places limit orders and if a 

match on price occurs with another 

internal or external participant, then 

that results in an internal fill for both 

parties. Internal participants include FX 

and non-FX desks at GS. By the way, 

GS only permits external participation 

via our algos, which helps ensure 

the quality of the pool, he explains. 

Then the fourth and final type is skew 

internalisation, for when there is not 

an offsetting trade as there would be 

in the matching case, Donner explains. 

Instead, an offsetting trade is created by 

triggering a skew in streaming bid offer. 

“The e-book goes and it improves its 

bid, or improves its offer, and attracts 

liquidity to fill the algo,” Donner says. 

McGuigan also notes the trend seen 

among certain clients who are now 

wanting to utilise bank algo offerings 

to effectively market make into the 

banks clients base, much like a venue. 

“As such, the ability of a bank to absorb 

this flow internally can be seen as 

being very important,” he adds. “In 

addition, the maturation of TCA in the 

FX algo space has also led to a better 

understanding around what good algo 

execution looks like and part of this has 

been the value true internalisation can 

provide.” 

MOVING INTO THE 

MAINSTREAM 

For BNP Paribas, the algo suite being 

able to access the three different 

flavours of internalisation is also seen as 

a core value proposition and one that 

clients quickly recognise the benefit 

of, says Razaq. In addition, all three 

internalisation types are available to 

the client by default but still have the 

option to opt out easily if they do 

not require a particular feature. “All 

three types are enabled on all our algo 

strategies,” Razaq adds. “We articulate 

to clients that BNP is a top flight player 

in the algo market and we have a very 

active algo execution business, which 

means that their chances of finding 

that block match in BIX, for example, is 

much higher at BNP than at any of our 

peers.” 

BNP’s BIX also started life in the algo 

sector, providing opportunity between 

client algos trading against another 


client algo. Razaq shares that because 

the algo execution business is now very 

mature, there are a number of various 

different sectors of the bank that use 

BNP algos. “Our algos are deployed not 

only with our clients, but everywhere 

within the BNP group. Anyone within 

the group that has FX exposure to 

execute alsouse BNP algorithms to 

execute their trades,” he says. “In turn, 

this increases the matching probability 

in BIX because now we are not only 

matching against external clients, we 

are also matching against internal BNP 

departments as well. And as all of 

these trades are executed within BNP, 

we can guarantee that the trades will 

have no market impact when they are 

matched in BIX. This is a very attractive 

proposition for our clients.” 

There is also no disadvantage to 

internalisation, according to Razaq and 

is on the whole highly sought after 

by clients. In some cases, clients may 

want to opt out of internalisation, if 

for example they need to respect the 

time horizon for the algo trade or they 

have some fiduciary duty to trade in 

the external market, he adds, but notes 

that they accept that risk because that 

is their execution objective. In addition, 

BNP provides full transparency by 

itemising the different internalisation 

types in their TCA reports. “Many banks 

actually mix and match and do not 

provide that level of transparency,” he 

says. “The TCA may show one category 

of internalisation but it is not clear what 

kind, so clients will often challenge 

what that actually means for them.” 

FUTURE OF ALGOS AND 

INTERNALISATION 

Looking ahead, BNP has also 

developed another flavour of 

internalisation for clients in the 

systematic hedge fund space, Razaq 

reveals. “There is a large community 

of systematic hedge funds who build 

their own algos who are not able 

to benefit from the internalisation 

capability that using a BNP algo has 

to offer and have to instead trade 

against bank streams or go to the 

external market to source that liquidity. 

Increasingly this client base is realising 

they are actually missing out on BIX 

liquidity and they want a piece of 

the pie,” he says. “In response, BNP 

is creating an API product, called 

Internalisation can help reduce the cost of brokerage fees 

BIX FX, to offer BIX and BIX Peg 

services directly to clients so their 

internal algos can also leverage and 

start posting interest into BIXand 

BIX peg to leverage that flavour of 

internal liquidity.” In general, Razaq 

believes that clients will increasingly 

look at internalisation rates when 

they evaluate algo providers. 

“Internalisation is a key value 

proposition in BNP’s offering,” he adds. 

“At BNP, we are constantly looking for 

more ways to increase internalisation. 

It is definitely here to stay.” 

Historically, however, when there was 

just one form of internalisation, it could 

be a double-edged sword and was not 

always the optimal choice for clients, 

explains Donner. Yet now clients tend to 

themselves seek greater internalisation 

because the offering is much more 

intelligent than before, he adds. 

“It is universally viewed as being 

more useful and that is also borne 

out by third-party TCA. Now we have 

TCA providers also telling clients that 

if they increase internalisation that 

will be better for their algo execution 

outcomes,” Donner says. “Clients 

are far more sophisticated these days 

in their analysis of trades. We have 

migrated from a situation where we 

did have some clients who prefer not 

to internalise and even requested us to 

switch off internalisation for them, to 

now where nearly every client is not 

only okay with internalisation but is in 

fact very keen on it.” 

As a result, Donner also believes 

that internalisation is here to stay. 

“If you look at the FX liquidity 

landscape, primary markets continue 

their steady decline in volume. So if 

primary markets are not the answer to 

sourcing liquidity, what is? It probably 

is not the various secondary markets, 

given this patchwork of liquidity, some 

of it recycled, and much of it with 

limited transparency on transacted 

volumes,” he adds. “The solution 

is likely to be much more bespoke 

liquidity, curated pools and KYC 

liquidity. Internalisation is a great way 

to do that and as an algo provider, it is 

a great way for us to offer that sort of 

liquidity where we understand who is 

on the other side.” 


15

DQ 

DEQUANTIFICATION 

Stark: Taking FX algo 

execution to the next level 

Vittorio Nuti, head of the Segregated Algo execution at Deutsche Banks’ Foreign 

Exchange and Listed Derivatives divisions, tells us more about Stark, the banks 

next generation algo which has been developed after extensive investment and 

cutting-edge research. 

Vittorio Nuti 

GENERAL OVERVIEW: 

What is the FX algo called? 

Stark, Deutsche Bank’s flagship algo, 

matches client interest and captures 

liquidity opportunistically. 

What category does it fall into? 

Dynamic, Stark balances passive and 

aggressive orders in alignment with 

market conditions, varying its speed. 

What does it attempt to do? 

The Stark algorithm taps into Deutsche 

Bank’s broad client franchise and select 

liquidity providers to execute faster than 

average (according to TradeFeedr data), 

while maintaining skew safety. 

STRUCTURE 

What is the algo’s software 

architecture? 

Stark’s software, based on C++ and 

Java, with execution servers in colocation 

– help to minimise latency. 

Does it use proprietary modelling? 

Yes, Stark utilises proprietary modelling 

within its mid pricing and signal engine, 

reflecting real-time market conditions. 

Does it use technology such as AI or 

ML? If so, how? 

Indeed, various sophisticated modelling 

and machine learning techniques drive 

pricing engine decisions and order 

placement adjustments, optimising 

execution even in high volatility 

environments. 

FUNCTIONAL ASPECTS 

Does the algo adapt automatically to 

prevailing market conditions and if so 

how? 

Stark dynamically adjusts trading speed 

based on currency pair, time of day, 

and current volatility, ensuring efficient 

execution. A large order placed at an 

illiquid time vs. a liquid time could be over 

15 time slower. 

Does it incorporate smart order 

routing? 

Absolutely, leveraging large scale statistical 

analysis to select safe venues and optimal 

sizes, in synergy with the Principal desk’s 

insights. 

How does it minimise market 

footprint? 

Through extensive data calibration and 

scenario testing, Stark minimises footprint 

while maintaining a safe and sustainable 

execution program. This does limit the 

number of venues but it allows for a safe 

and sustainable execution programme. 

What liquidity seeking and access 

capabilities does it deploy? 

Image by shutterstock 

Stark offers customizable parameters 

Stark dynamically rebalances execution 

venues to target available volume 

efficiently, on an order by order basis to 

ensure volume is targeted where available. 


What operational risk management 

does it include? 

Various safeguards, including fat finger 

controls and throughput thresholds 

such as max size per order or gross 

amount per day to ensure execution 

safety. However there are other 

scenarios under which execution would 

be halted, for example, if the parent 

order exceeded any pre-determined 

throughput, rejection/cancellation count 

thresholds, or if the market becomes 

too illiquid. 

PARAMETERS & CONTROLS 

What client inputs are available in 

the algo? 

Required Parameter: CCY, Side, 

Quantity (execution style defaults to 

Normal) 

Optional Parameters: Execution Style 

(passive, normal, aggressive), Limit price, 

iWould. 

Customization: Stark offers 

customizable parameters such as 

execution style, and liquidity sources, 

empowering users to tailor execution to 

their needs. 

How much real-time feedback does 

it provide? 

Real-time TCA and expert guidance 

assist users in adjusting execution 

speed to meet requirements 

effectively. When external liquidity is 

selected, the algo will manage the 

balance between internal and external 

placement based on real-time fill 

events. For the user, real-time TCA is 

available that can help to determine 

whether the urgency needs to be 

increased or reduced to meet the 

user’s execution speed requirements. 

Also, our experience and handson 

sales and trading team provides 

guidance around general market 

conditions and themes to assist the 

user through the execution process, if 

needed. 

CAPABILITIES AND USE 

What execution styles (e.g. passive/ 

aggressive) does the algo support? 

Stark flexibly trades passively and 

aggressively to balance market risk and 

cost savings, it’s aggressiveness can 

be changed inflight by changing the 

Execution Style. 

How can it be integrated/called 

with/by higher-level workflows? 

While designed as a standalone 

strategy, Stark seamlessly integrates 

into client EMS and Deutsche Bank 

workflow solutions, which help client 

automate processes and reduce costs 

for their FX needs. 

What is the optimal scenario for its 

use? 

Stark’s adaptability allows for various 

scenarios, commonly used for 

minimising cost spread like gamma 

hedging. However, users typically use 

Stark in its three default execution 

styles, with an Arrival Mid benchmark 

(when set t passive) and Cost save 

vs Risk Transfer (when set to Normal 

or Aggressive). It is often used for as 

a take profit for derivatives traders 

to minimize cost spread. Due to its 

flexibility and nimbleness, Stark can 

be used in extremely varied scenarios, 

for example by finely controlling skew 

and cost of child execution we can 

tailor expected duration per client or 

currency. 

Any other functionality worthy of 

note? 

Check Stark’s rankings on Tradefeedr 

(an independent transaction cost 

analysis provider) for evidence of its 

speed and cost savings. Our friendly 

team is here to help you make the 

most out of the Stark algo and more 

generally FX execution, get in touch! 


17

? 

ASK A PROVIDER 

The evolution of algo 

use in FX swaps 

The introduction of electronically traded FX swaps is a significant development for the 

wider FX industry as a whole and one that opens up the potential for FX swaps algos 

going forward. Howard Grubb, Product Lead, Electronic Trading at DIGITEC explains how 

technology innovation is supporting the growth of e-traded FX swaps and how algos are 

expected to evolve in this new market. 

Howard Grubb 

How is electronic trading of FX 

swaps set to further gain traction 

this year and how can algo 

execution be a part of this growth? 

Technology is a major theme of 2024. 

With increasing trading volumes, greater 

involvement from banks of all sizes 

and further buy-side participation, the 

FX swaps market continues its volume 

growth and migration to electronic 

channels. This is driving further demand 

for accurate and robust pricing engines, 

and for more automated end-to-end 

workflows. 

As trading firms look to improve 

pricing, they are building ever more 

sophisticated pricing models which 

increasingly go beyond existing market 

data, to include yield curves and 

inferred FX swap points. 

We continue to see banks of all sizes 

moving away from spreadsheet-based 

or manual pricing and implementing 

accurate and robust pricing engines. 

These are required to efficiently 

price in the rapidly moving electronic 

FX markets. Pricing FX swaps is 

complex: the scale of the challenge is 

demonstrated by market maker banks 

typically calculating more than 20,000 

prices along their forward curves. 

Electronic trading of FX swaps, in both 

client and interbank markets, will 

lead to more efficient FX markets. This 

increased electronification will lead 

to greater velocity of the underlying 

market and translates into a greater 

need for speed and lower latency, as the 

markets react more quickly to events. 

The development of electronic FX swaps 

marketplaces is still at an early stage, so 

a degree of algo capability is required 

to participate, whether for passive 

execution or for liquidity provision. 

As continuous liquidity in these 

marketplaces increases with more 

participation, we expect well-established 

approaches to algo execution, common 

in other instruments, to become 

increasingly relevant. 

This will be a process of evolution, as 

the development of electronic markets 

progresses, and any algos will need 

to constantly adapt to changes in the 

market structure and liquidity, the 

availability of data and the range of 

participants and strategies. 

What were the key challenges in the 

past which may have prevented the 

use of algos for FX swaps trading? 

Algos need high quality data. In the 

past this was not easily available for 

FX swaps, as there was no recognised 

interbank data feed. Traders relied on 

FX swaps prices published by brokers, 

which are among the last to update at 

times of movement, and do not cover 

relevant points such as central bank 

dates and liquidity events (month- and 

year-end) that often have the largest 

impact. 

This is why DIGITEC and 360T 

created the Swaps Data Feed (SDF), 

which is based on participating major 

FX banks’ raw pricing and represents 

Interbank quality. Today, the SDF has 

established itself as a reliable data 

source, used by many banks and 

FX swaps market participants in their 

active pricing of currency curves. It 

enables clients to build fully automated 

and accurate real-time curves. 

Greater price transparency will lead to 

greater participation and this in turn will 

help liquidity in matching venues. 

Liquidity was also a major factor, 

especially further out along the 

forwards curve. The development of 

traditional algo trading is dependent 

upon how fast the market evolves and 


interest, some from our existing bank 

clients and some new relationships 

which plan to use D3 OMS as a 

standalone product. 

To date we have one bank live and 

trading on LSEG Forwards Matching, 

with other banks at different stages 

of onboarding to both LSEG and 360T 

SUN. 

Algos need high quality data and the DIGITEC/360T Swaps Data Feed has established itself 

as a reliable data source. 

continuous liquidity is built further out 

than Overnight or Tomorrow/Next Day. 

There may be some liquidity seeking 

algos that will work for FX swaps, but 

their adoption will be low if there is 

limited liquidity. 

Another factor in the adoption of 

algo trading in FX swaps has been 

technology – without electronic 

marketplaces with broad participation, 

it is difficult to justify the investment 

in technology needed to support 

robust and efficient algo trading 

strategies. 

Credit remains an issue and is often 

referred to as a bottleneck when it 

comes to further automation in FX 

swaps and moving towards a more 

electronic market. Trading in FX swaps 

is still mostly limited to counterparties 

with a bilateral credit relationship, 

therefore, linking liquidity and credit. 

However, innovation with more dynamic 

allocation of credit in a market utility 

type offering and clearing may provide 

some answers. 

How has DIGITEC introduced new 

innovation into the FX swaps space? 

To what extent do these provide 

solutions to pave the way for algo 

trading in FX swaps? 

For FX swaps to automate further, 

there is a requirement for an efficient 

and increasingly automated Interdealer 

FX swaps market, to help firms make 

markets to clients and efficiently risk 

manage their positions. 

With this in mind, 360T and 

LSEG offer electronic Interdealer 

FX swaps trading venues, with 

other marketplaces looking at 

establishing additional venues. 

At DIGITEC we developed D3 

OMS, as a solution for traders to 

connect directly to these interdealer 

FX swaps venues. D3 OMS allows the 

traders to place and manage orders 

into these marketplaces, with a high 

degree of workflow automation, so 

that they are able to scale their business 

efficiently and can unlock other 

opportunities for automation within 

their pricing and risk management 

processes. 

As with any market that is evolving to a 

more electronic structure, we expect the 

result to be increased volumes on 

electronic Interbank matching platforms. 

This in turn will drive increased market 

liquidity, greater participation, improved 

client pricing and risk management, and 

for the FX swaps market to grow across 

all execution methods for the benefit of 

all parties. 

What has been the response from 

banks and the buyside so far? How 

much of an appetite is there for 

algo trading in FX swaps and is this 

from particular segments of the 

market? 

We launched D3 OMS in September 

last year and are seeing a great deal of 

The typical bank customer that we 

engage with at this stage already has 

comprehensive pricing capabilities, 

and is adding the workflow 

automation that comes with D3 OMS. 

We are exploring use-cases with other 

clients with different requirements, 

often around risk management 

automation or efficiently accessing 

liquidity, and we can bring several 

elements together for these clients, 

some of which are likely to involve an 

algorithmic component. 

When do you expect more 

significant traction in algo use 

for FX swaps? How do you expect 

the evolution of e-traded FX 

swaps to continue and what 

will be the next steps for its 

development? 

We expect to see more data being 

integrated from our network of banks, 

which will further improve SDF data 

and lead to more accurate market 

pricing. Greater transparency will lead 

to better price discovery, which will 

support the continued automation 

and electronification of the FX swaps 

market. 

More liquidity in longer dated swaps 

will be enabled by better data, 

automated workflows, and more 

efficient Interdealer markets, so we 

expect pricing to improve further along 

the curve in all markets. 

Like the rest of the market, the use 

of execution algos is growing, but in 

the case of FX swaps is still relatively 

small. In terms of electronification, FX 

swaps are still evolving, and are some 

years behind spot. For algos to gain 

significant traction there needs to 

be liquidity across the forward curve 

(not just the short dates) and pricing 

engines need to incorporate more data 

from multiple sources. 


19

TRADERS WORKSHOP 

Understanding and 

accessing Order 

Stitching in Transaction 

Cost Analysis 

By Kathryn Berkow, Managing Director and Suraj Kaul, Quantitative Trading 

Engineer at BestEx Research 

transaction cost analysis, which can help 

a trader evaluate execution performance 

more robustly. If the trader reviews 

performance of trades without stitching 

waves together, they will be treating the 

individual waves as distinct though they 

are part of a single parent order and the 

performance of earlier waves is likely to 

affect the performance of later waves. 

Kathryn Berkow 

Order Stitching can play a significant 

role in optimizing trading strategies 

(including FX) and evaluating their 

performance accurately. In this article 

we’ll delve into what Order Stitching is 

and why it’s important, as well as the 

nuances associated with the calculation 

of analytics in TCA and how we handle 

them in our own TCA. 

Suraj Kaul 

cost measurement tools that can help 

determine the effectiveness. 

Order Stitching is a methodology for 

viewing waves as a single order in 

For example, in Figure 1, there are 

three waves traded throughout the 

day. In the first wave, the order trades 

over a period when the price of the 

instrument is increasing. Because the 

order is a buy order, it is possible and 

perhaps probable that the order itself 

is contributing to the price change by 

way of market impact. In wave 3, we 

WHAT IS ORDER STITCHING? 

Some traders send orders once and let 

the order finish trading (or not finish 

trading) according to the execution 

algorithm’s trade plan. Other traders 

divide a single order into multiple 

waves, sending multiple orders on the 

same date, symbol, and side throughout 

the day, as shown in Figure 1 below. 

Such waves may have periods of pause 

in between or they may overlap. In each 

successive wave, a trader may change 

the trading strategy–switching from a 

liquidity-seeking algorithm to a VWAP, 

for example–or update parameters 

like limit prices, volume caps, and 

more. Managing an order in waves 

aims to optimize execution strategy, 

and traders need access to meaningful 

Figure 1. An order to BUY, sent in 3 waves. The first wave sees a more substantial increase 

in price than later waves, though the price is increasing on average throughout the day. 

Each wave is shown with its arrival price, price at order end, and its interval VWAP. 


see that the price isn’t moving very 

much by comparison. This example 

illustrates a common outcome–the 

first wave of an order, just as the initial 

portion of any single order, is likely 

to create more market impact than 

consecutive waves. If a trader does 

not incorporate the dependence of 

the performance of the waves into 

their performance assessment, they 

could be underestimating the impact 

they’ve created over the course of 

the full parent order and missing out 

on the broader view of their trading 

performance. Combining the orders 

gives a measure of cumulative cost from 

all order slices versus the initial arrival 

price. 

Looking at the individual waves 

evaluates the performance of the 

execution algorithm itself–was it able 

to access passive or dark liquidity, for 

example? Could it earn more spread 

than it paid? Did it create unnecessary 

market impact? Aggregating the orders 

to treat them as a single parent order 

and understanding the performance of 

the order’s execution as a whole will 

help to evaluate the real cost of trading 

that order–important for portfolio 

optimization–and help evaluate whether 

the trading strategy, including dividing 

into waves, was successful overall. 

HOW DOES STITCHING 

WORK? 

Stitching waves into a single order has 

simple elements and more complex 

ones. The simplest measurement to 

start with is the fill quantity. Consider 

the three waves depicted in the image 

above. If we combine the waves 

together into a single order, we can 

simply sum their total executed quantity 

to derive the fill quantity for the larger 

parent order. But not every metric is 

simple to construct, so we’ll cover a few 

more examples here as well. 

all executions to the first wave’s arrival 

price, as shown in Figure 2. Similarly, 

for the “order end mid” benchmark 

in our TCA, we would benchmark all 

executions to the final wave’s order end 

mid. 

A more complex consideration is 

interval VWAP, for which a provider has 

a number of options in calculating for 

a stitched order. In the three waves 

depicted above, which do not overlap, 

we could choose to benchmark the 

order to the VWAP over the full trading 

duration from first wave’s start to final 

wave’s end. However, it is possible that 

the waves overlap, which would make 

this benchmark less useful. In addition, 

this method doesn’t necessarily 

represent the VWAP price during the 

trading interval, as there may be long 

periods with no trading during the 

horizon covered by the waves. At BestEx 

Research, we calculate a stitched order’s 

interval VWAP performance as the 

weighted average of individual waves’ 

interval VWAP performance, weighted 

by filled notional value. There are other 

options, however, for evaluating the 

performance of the waves compared to 

the VWAP during the order’s duration; 

clients may prefer to benchmark to the 

day’s VWAP price as opposed to the 

interval–if the waves traded over much 

of the day, for example–or the VWAP 

from order start to market close, which 

we provide. 

Some traditional measures need to 

be discarded for stitched orders. For 

example, a commonly used participation 

rate metric is the order’s percentage of 

interval volume. This metric is not useful 

for determining the participation rate 

of a stitched order, because the client 

may not have been trading over the full 

duration from the first wave’s arrival to 

the final wave’s end. For this reason, a 

percentage of interval volume may be 

heavily skewed downward. Each wave 

could be trading at 15% of volume, but 

if they are relatively short compared to 

the stitched order’s full duration, they’ll 

represent only a small percentage of 

interval volume when aggregated. 

To calculate order size as a percentage 

of average daily volume (ADV), however, 

is straightforward. The total order size as 

a percentage of ADV would be the total 

shares filled across all waves divided 

by the ADV. This metric can look very 

different for stitched and unstitched 

orders. If a customer divides an order 

that is 10% of the product’s ADV into 

two equal waves, each would measure 

5% of ADV in the unstitched order 

summary while representing the true 

parent order’s full 10% of ADV in a 

stitched order summary. 

As illustrated in the above examples, 

each element of reporting stitched 

orders must be thoughtfully 

reengineered to reflect the most useful 

For each wave, an order arrival (the 

midpoint price at the time the order 

arrives) and order end mid (the 

midpoint price at the time the order 

ends) are depicted in the image above 

as well. If the orders are not combined, 

the arrival performance for each wave 

will be calculated according to each 

individual wave’s corresponding arrival 

price. However, when we stitch the 

orders together, we will benchmark 

Figure 2. An order to BUY, sent in 3 waves as above. This time, the three waves are shown 

as periods of trading within a single order–a stitched order–benchmarked to the first 

wave’s arrival price. The order duration is the entire length of trading, and the order end 

midpoint price is the final wave’s order end midpoint. 


21

TRADERS WORKSHOP 

Table 1. This table above illustrates an excerpt of performance measures from our updated TCost Summary Report, part of our transaction cost analysis 

(TCA) module in AMS. In it, readers can see that aggregating waves of an order on a single symbol, side, and date can change the view of performance 

of the parent order. For stitched orders, the calculation of some measures is fundamentally changed and other measures may become less relevant, as 

described in the text. Please note that this table is intended to illustrate report functionality only, and does not represent a promise of future performance. 

information as clients review the orders’ 

characteristics and performance. 

Table 1 shows an excerpt of 

performance measures for a group of 

unstitched orders and its corresponding 

stitched orders. In the table, readers 

can see that stitching naturally reduces 

the number of orders and changes the 

calculation of the order’s size by ADV 

and realized day volume. In addition, 

and perhaps most importantly, it 

changes the view of performance 

measures like cost vs. arrival price and 

cost vs. midpoint price at order end. 

Looking at the cost versus arrival for 

the original orders, we see an average 

performance of 6.4 bps, for example, 

but when we stitch appropriate orders 

together and assign the first arrival 

price as the benchmark, we can see 

the orders may be creating much more 

impact on average than is detected 

among unstitched orders. Of course, 

it could be related to market prices 

moving away from the trader as well, 

as we see that the order size is relatively 

small, but deeper study of the orders 

would be required to determine 

whether the orders are creating the 

price change. 

ACCESSING STITCHED 

PERFORMANCE IN TCA 

To create effective views of performance 

for traders executing in waves, it’s 

essential to have the necessary 

infrastructure and tools in place. Order 

Stitching is now available in our TCost 

Summary report in AMS’s Reports tool 

as an additional tab. Same-day Order 

Stitching is currently available for orders 

traded on the same date, symbol, and 

side to be stitched, and the additional 

tab including stitched performance 

with updated benchmarks will appear 

as the final tab in this report–all other 

tabs represent unstitched performance 

calculations. 

There are two “flavours” of stitching 

available, as illustrated in Figure 3. 

Orders can be stitched across strategies 

or within strategies, using the Order 

Stitching drop-down menu to specify. 

For example, if a client chooses a 

liquidity seeking strategy for the first 

wave of an order but frequently changes 

strategies for later waves and wants all 

of those waves aggregated as a single 

order, then stitching across strategies 

may be appropriate. The trader would 

choose “Day-Side-Symbol” stitching in 

this case. 

However, if there are multiple orders for 

the same date, symbol, and side sent to 

different strategies for different reasons 

and the trader would like to keep 

them separate, that’s a possibility as 

well. Stitching within strategies would 

be most appropriate in this situation, 

separating VWAP orders and liquidityseeking 

(“Optimal”) orders, for example. 

The trader would choose “Day-Side- 

Symbol-Algo” stitching in this case. 

While traders may trade larger 

parent orders in smaller waves over 

multiple days (rather than just a single 

trading session as described above), 

automated multi-day Order Stitching for 

performance assessment is not currently 

available in our TCost Summary report. 

Figure 3. The image above is an excerpt from the Create Report dialog box in AMS’s 

Reports tool. After selecting all report parameters, including dates, strategies, order and 

instrument characteristics, for example, users can select Order Stitching parameters. 

“Default” will leave orders unstitched, “Day-Side-Symbol” will combine orders traded on the 

same date, side and symbol, and “Day-Side-Symbol-Algo” will combine orders for the same 

date, side, symbol, and algorithm. 

Both stitched and unstitched views 

of performance can be helpful 

in evaluating the performance of 

trading strategies. Order Stitching 

provides traders the ability to measure 

performance for the real parent order 

they are trading rather than the waves 

they send to execution algorithms, 

allowing for better decision-making and 

optimization. As always, transparency 

is our aim in providing this information. 

The additional piece of the puzzle 

offered by the Order Stitching tab may 

enhance the ability to evaluate and 

refine trading strategies effectively. 

If you have questions about Order 

Stitching please email us at info@ 

bestexresearch.com. 


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banks connected to the FXSpotStream Service. 


23

CASE STUDY 

Upgrading an FX algo 

engine architecture 

By Oskar Wantola, CFA , Head of Execution Technology and Tim Raggatt, Staff 

Engineer, Execution Technology at Man Group. 

In this article we explore how MAN Group leveraged Aeron’s Open-Source 

technology to improve the latency of our firms FX Execution System. 

right tools is beneficial – a system that 

lags peers is a strategic disadvantage. 

Oskar Wantola 

Man Group embarked on a project to 

enhance its FX execution system, aiming 

to reduce latency and achieve high 

levels of system reliability under heavy 

data loads, therefore improving execution 

quality for its clients. 

Technology used: Aeron Transport and 

Aeron Archive 

Technical challenges: 

• Navigating the existing infrastructure 

& shifting the system to a new 

host. 

• Integrating Aeron, an open-source 

technology, within Man Group’s tech 

stack ensuring improved performance 

without service interruptions. 

The roll-out: Starting with a trial, 

Aeron was initially deployed in a ‘production-like’ 

simulation environment to 

ensure its performance over a range of 

different traffic profiles. 

The test criteria: Aeron needed to: 

• Demonstrate its ability to handle 

Tim Raggatt 

short-lived bursts of intense quoting 

activity across multiple providers. 

• Maintain minimal latency when quote 

streams were slow-moving and messages 

infrequent. 

The outcome: Man Group deployed 

Aeron in production, fully replacing 

its legacy middleware solution. Aeron 

continues to provide high-performance 

messaging, outperforming the legacy solution 

by at least one order of magnitude 

across every percentile. 

THE PROCESS 

Industry challenge 

In FX markets, speed is important. Liquidity 

conditions are dynamic and change 

in real-time, so orders must be sent 

promptly to minimise the risk of hitting 

stale quotes and being rejected. 

Execution systems are the engines 

through which investment firms access 

and interact with markets, combining 

rich functionality with high performance. 

Hedge funds understand that having the 

The brief - from commercial to open 

source 

FX execution systems are multi-layered, 

facilitating the entire FX trading lifecycle. 

Each element of the system communicates 

via Inter-Process Communication 

(IPC) to complete the trading flow. The 

bigger and more sophisticated the system, 

the greater the number of components 

and services to run simultaneously. 

Man Group’s use case centred around 

its multicast RFQ protocol. The system 

needed to be able to take orders from 

upstream and compare them in real time 

with the available liquidity streamed from 

multiple liquidity providers. The quotes 

are then aggregated and fed into an 

algorithm which decides when best to 

execute orders against which liquidity. 

To achieve this, latency is key. The algo 

must be aware of the most up-to-date 

quote data in order to make informed 

trading decisions. Any delay between 

receiving data to execution can result in 

changes in price, impacting the cost of 

the trade or perhaps even missing out 

altogether. 

Man Group was looking for fast and 

reliable technology to meet the following 

selection criteria: 

• Open-source tech to give the firm 

greater control of its functionality and 

roadmap. 

• Predictable, ultra-low latency. 

• Reliability under the highest data 

loads. 


c. Upper percentiles, e.g. 99%, remain 

similar due to the kernel idle strategy 

issue. 

2. Application migrated to an alternative 

host, resolving the kernel incompatibility 

issue. At this point, latency benefits were 

seen across all percentiles, particularly 

the upper ones. 

• Proven industry use cases and references. 

The goal was to build a fully fault-tolerant, 

low-latency messaging layer. 

Technical challenges 

When Man Group decided to move 

away from its legacy messaging tool and 

started to integrate Aeron, a key technical 

challenge was navigating the existing 

infrastructure and shifting the system 

to a new host. For example, the Linux 

kernel – which manages the interaction 

between software and hardware 

– caused issues with Aeron’s idle strategy 

holding back immediate performance 

improvements. 

Figure 1: FX Algo Engine Architecture 

These issues were overcome by creating 

a virtual environment, simulating the execution 

system, to test the new software 

under multiple loads and ease implementation. 

Parameterising variables such as 

the number of quotes their rate of quoting, 

and idle strategy enabled the tech 

team to tune the deployment appropriately. 

The system showed how Aeron 

could support the low latency messaging 

needs and helped the development team 

to productionise the technology. 

Migration to Aeron 

Man Group’s technology team realised 

they would need to move the application 

to an alternative host to address the 

kernel idle strategy limitation. However, 

to avoid migrating the commercial IPC 

solution, Aeron had to be tuned to the 

existing host and rolled out there first. 

The simulator enabled them to do this 

with confidence. 

The two-phase rollout strategy meant 

that the latency gains were achieved 

incrementally: 

1. Aeron deployed alongside the application 

and IPC migrated. At this point the 

latency testing saw: 

a. Small gains in lower percentiles, e.g. 

10% 

b. A slightly more stable median in a 

similar latency range. 

Aeron Roll-out 

Over the course of the year-long project, 

Aeron was used to build two key components: 

• A new, low-latency messaging 

layer: Aeron Transport became the 

vehicle for an updated IPC protocol 

capable of supporting multicast RFQ, 

trading instructions and system availability 

messages. As predicted, the 

new platform unlocked significant 

performance gains. 

• Message persistence: Man Group 

later began using Aeron Archive for 

the recording and replay of inbound 

FIX messaging streams. The persisted 

streams may be used for real-time 

monitoring, offline analysis, and 

counterparty simulation in lower (preproduction) 

environments. Use of Archive 

gives confidence that messages 

can be relayed from latency-sensitive 

trading processes without blocking or 

backpressure. 

Benefits of Aeron - in a nutshell 

Man Group replaced its legacy technology 

with a state-of-the-art, open-source 

solution. Leveraging Aeron Transport and 

Archive technology, the firm was able to: 

• Build a high-performance messaging 

layer. 

• Improve its latency statistics and 

predictability. 

• Ensure higher resilience to spikes of 

messages and instant recovery in case 

of failures. 

• Future-proof its FX execution system. 

• Build resiliency into the system so 

reporting processes do not interfere 

with trading activity. 

THE FUTURE 

Figure 2: FX IPC Simulator 

Man Group has added Aeron to its 

toolkit of approved technologies for any 

projects with low-latency requirements. 

The firm’s technology team subsequently 

integrated it into an equities and futures 

trading platform for communicating 

signals to algo execution engines. 


25

The latest e-Trading EDIT from J.P.Morgan 

INSIGHTS & REPORTS 

Source: J.P Morgan 

In February JP Morgan released its 

e-Trading Edit for 2024. This is the 8th 

annual report the bank has released and 

is the largest so far in terms of participants 

with input from 4,010 institutional 

traders across more than 65 countries. 

FX traders are well represented by the 

findings and make up the largest segment 

of respondents at 17%. Some of 

the questions and responses from the 

survey include: 



What will be your greatest daily 

trading challenge in 2024? 


Apart from pricing and execution, 

which features are most valuable to 

you on a trading platform? 

Which benefit of direct connectivity 

do you feel is the most valuable? 

More information about the survey can 

be found at: jpmorgan.com/markets/ 

etrading-trends#introduction 

What percentage of your trading will 

be through e Trading channels? 

EDUCATION & TRAINING 

Electronic 

Trading and 

Algorithmic 

Execution 

Spring 2024 

This course provides a detailed investigation 

of the latest developments 

in electronic trading and algorithmic 

execution. Practical workshops allow 

delegates to explore the implementation 

of execution algorithms as well as 

methods to benchmark performance 

and calculate transaction cost metrics. 

www.londonfs.com/course/Electronic- 

Trading-Algorithmic-Execution 


BOOK OF THE MONTH 

The Quant Traders Handbook 

This guide offers readers an opportunity to delve into the 

mechanics of quantitative trading, exploring the strategies, 

technologies, and practices that have transformed the 

financial landscape. 

The value of good advice in the 

world of FX algorithmic trading 

ATHL provides FX Insights which delve into a variety 

of topics, including algorithmic FX trading, market 

microstructure, regulatory considerations, and the latest 

technological advancements. 

WEBSITE OF THE MONTH 

amazon.co.uk/Quant-Traders-Handbook-Algorithmic-Strategies/dp/B0C1JB5K9T 

athl.ch/fx-insights 

FOR THE DIARY 

fixtrading.org/event/newyorkregional-2024/ 

fixtrading.org/event/frankfurtregional-2024/ 

Charles Jago 

Editor 

charles.Jago@fxalgonews.com 

+44 1736 740 130 

Nicola Tavendale 

News editor 

nicola@ntavendale.com 

+44 1736 740 130 

Susan Rennie 

Managing Editor 

susie.rennie@fxalgonews.com 

+44 1208 821 802 

Charles Harris 

Advertising sales 

charles.harris@fxalgonews.com 

+44 1736 740 130 

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Subscriptions manager 

david.fielder@fxalgonews.com 

+44 1736 740 130 

Tim Hendy 

Digital & Web services 

tim@thstudio.co.uk 

+ 44 1209 217168 

Matt Sanwell 

Design & Origination 

matt@designunltd.co.uk 

+44 7515 355960 

Larry Levy 

Photographry 

larrydlevy@gmail.com 

Michael Best 

Events manager 

michael.best@fxalgonews.com 

+44 1736 740 130 

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Printed by Headland Printers 

Published quarterly. ISSN 2056-9750 

Although every effort has been made to ensure the accuracy of the information contained in this publication the publishers can accept no liabilities for inaccuracies that may 

appear. The views expressed in this publication are not necessarily those of the publisher. Please note, the publishers do not endorse or recommend any specific website featured 

in this newsletter. Readers are advised to check carefully that any website offering a specific FX trading product and service complies with all required regulatory conditions and 

obligations. The entire contents of FXALGONEWS are protected by copyright and all rights are reserved. 


27

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. 

The value of investments may fall as well as rise and you may not get back the amount originally invested. As a firm providing wealth management services to clients, UBS 

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UBS AG. Member FINRA/SIPC.

FX Algo News May 2024

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