FX_Algo_News_May_2023-HIGH-RES-PAGES

ISSUE 30 | MAY 2023 WWW.FXALGONEWS.COM FOLLOW US AT: 

TOP STORIES 

Goldman Sachs launches Franchise 

Matching algo 

Goldman Sachs has rolled out a 

Franchise Matching algo to clients, 

which offers a new way of using bank 

internalisation to offset risk. The algo 

has been developed to send part of the 

parent order to a new venue, which 

triggers an e-book skew to make it more 

attractive to clients on the opposing side 

in order to fill the algo. Ralf Donner, 

Head of Marquee Execution Solutions at 

Goldman Sachs, says that the difference 

between the new franchise matching 

and the existing internal matching is 

that it does not require there to already 

be an opposing interest from a suitable 

set of clients. “The Franchise Matching 

algo can be a really interesting new 

way of internalising as well as being an 

effective way to fill larger order sizes. It 

was initially developed for our internal 

traders, who use it as a risk transfer 

tool, and so we have already seen that 

it works very well at sourcing liquidity. 

In particular, it works best in G4 pairs 

but it also works well in the rest of G10. 

Franchise matching is a client opt-in 

available as part of our flagship dynamic 

hybrid and pegged algos,” Donner adds. 

Ralf Donner 

LSEG’s FXall enhances algo offering 

to include NDFs 

London Stock Exchange Group’s FXall 

will extend its existing algo offering to 

include NDF currency pairs later this 

month. The move will allow customers 

to access liquidity providers’ existing 

algos for NDF trading providing them 

Audra Scharf 

with a new execution method for 

trading NDFs. Audra Scharf, Head of 

FXall at LSEG, says that the venue 

has been working closely with its 

partner banks to bring the additional 

functionality to its customers. 

“Buyside demand for bank LP algo 

execution continues to increase. 

Our buyside customers want to 

leverage the algo functionality they 

are already using on FXall for new 

products. We see this as a natural 

progression for FXall. We are building 

the capability and partnering with 

our LPs based on customer demand,” 

Scharf says. She adds that LSEG FXall 

expects the move will further support 

the evolution of NDF algos in the 

market and the platform also looks 

forward to making further product 

announcements later in the year. 

IN THIS ISSUE 

p1: TOP STORIES 

The latest industry stories 

p3: NEWS FEATURES 

More in-depth news 

p4: INDUSTRY REPORT 

FX algo adoption is looking up. 

p6: PRODUCT PROFILE 

State Street’s new Portfolio Algo 

p8: INDUSTRY VIEWS 

Aligning outcomes with client expectations 

p14: ALGO OF MONTH 

TWAP from BNY Mellon 

p16: CASE STUDY 

Tradefeedr’s FX algo forecasting tools 

p18: TRADERS WORKSHOP 

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2 May 2023

Bloomberg’s FXGO expands 

FX algo and analytics coverage 

In addition to the growth in FX algo use on Bloomberg’s FXGO, clients are also 

becoming increasingly sophisticated and interested in more advanced functionality, 

analytics and using algos to execute instruments outside of spot. Oleg Shevelenko, 

FXGO Product Manager at Bloomberg, explains how the platform works closely with 

algo providers to ensure that its algo offering remains cutting-edge. 

TOP STORIES 

NEWS FEATURES 

Oleg Shevelenko 

How has the algo offering and 

related toolsets developed over the 

past year? 

Driven by a need to reduce the cost and 

increase the efficiency of trading, algos 

emerged as one of the mainstream 

ways for a broad spectrum of clients 

to access the market. Algo offerings 

are no longer only the prerogative of 

major banks, as regional and non-bank 

providers are stepping in to offer their 

expertise to a growing population of 

clients. More than 40 providers are 

now offering algos on FXGO across 

close to 200 strategies. Initially, clients 

were relying on their algo providers 

to manage orders from start to finish. 

However, today many are taking a more 

active role in their order management as 

more data about algo behavior becomes 

available. Often clients would start 

their order execution with a passive 

strategy to capture the spread, and then 

gradually re-adjust to more aggressive 

ones to meet their desired time horizon. 

Therefore, advanced execution options 

such as in-flight strategy amendments, 

pause and resume features, and the 

ability to immediately fill the order 

became essential and are actively used 

by FXGO clients. 

How do you help support algo 

clients to meet their liquidity access 

needs? 

The fragmented nature of FX liquidity 

is one of the primary reasons for the 

rise in algos as they allow clients to 

access multiple liquidity destinations 

via a single strategy. However, 

additional liquidity is not an immediate 

guarantee of a better performance. 

As fill level venue analytics become 

more mainstream - showcasing various 

nuances of liquidity microstructure - 

clients are looking to incorporate this 

knowledge into their execution by 

controlling and customizing the pools of 

liquidity available to algos. We continue 

to work with liquidity providers to offer 

those customizable liquidity options 

and collect execution statistics related 

to each venue to feed into the TCA 

process. 

What support do you offer for algo 

execution in instruments beyond 

spot? 

As FX instruments beyond spot, 

including Forwards, NDFs and Precious 

Metals, are becoming increasingly 

more electronic from both pricing and 

hedging perspectives, liquidity providers 

are able to expand their algorithmic 

offering to cover those instruments as 

well. At this juncture, close to 20% of 

order execution on FXGO is performed 

in instruments other than spot. It is 

worth noting that as a large percentage 

of algo orders are passive they also 

enhance liquidity, which is especially 

beneficial for less liquid instruments. 

How have you enhanced your FX algo 

analytics products to support the 

increasingly sophisticated demands 

of users? 

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 the 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 5 algo providers 

offering their integrated analytics 

services for their clients to leverage. In 

addition, FXGO is now fully integrated 

with Bloomberg cross asset TCA offering 

(BTCA) to allow 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. 

Do you have any future 

developments or areas of focus 

looking ahead that you would be 

happy to share? 

Partnering with our existing algo 

providers and onboarding new ones 

to expand our instrument coverage 

into emerging markets, NDFs, precious 

metals and derivatives is going to be 

key. We are also excited to work on 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 2023 

3

INDUSTRY REPORT 

The present 

and future of FX 

execution algorithms 

According to the findings of a recent report published by Coalition Greenwich, after 

years of mediocre growth, FX execution algorithm adoption is looking up. 

The report was based on the 

responses of corporates, banks, fund 

managers and hedge funds and 

compared their reported use of FX 

execution algos each from 2019 to 

2022. According to the findings, most 

buyside segments have witnessed a 

healthy rise in the use of algos since 

2020, especially on the real money 

side. Both fund managers and hedge 

funds have seen an uptick of algo 

use, with adoption now at 35% and 

46%, respectively. Growth among 

corporates and banks has been slower, 

however, only rising slightly over the 

four years to 13% and 20%. 

Report co-author Stephen Bruel, Head 

of Derivatives and FX, Market Structure 

and Technology at Coalition Greenwich, 

says that the trend is a general broader 

adoption of FX algos, particularly for 

FX spot. “There are a lot of reasons 

for this, including improved algo 

quality and better technology behind 

the algos,” he adds. “We are also 

Stephen Bruel 

seeing more investment in workflow 

management and firm being able to 

now integrate post-trade settlement 

data into their algo execution 

strategies. This all helps firms to feel 

more comfortable about using algos.” 

The increased use of TCA has also 

contributed to the uptake of FX algos, 

Bruel explains. Firms are now able to 

get a better sense of the quality of 

the algo under specific circumstances, 

whether that is by size, time of day, 

he adds. “Improvements in analytics 

reporting will also drive further algo 

use,” says Bruel. “That might be 

particularly specific to spot now, but 

these lessons will certainly be applied 

to some of the less automated asset 

instrument types going forward. Once 

FX spot algos are established even 

further, it makes sense that it will 

just cascade into some of the other 

instrument types.” 

BENEFITS OF ALGO USE 

According to the report, growth 

in algo adoption is a solution to a 

long-lived problem. The buyside is 

increasingly dealing with fragmented 

liquidity and a need to execute 

transactions more efficiently. The 

report contends that by using FX 

algos, the buyside benefits from 

improved technology designed to 

address gaps in liquidity, data and 

other inefficiencies tied to over-thecounter 

markets. 

In addition, the report highlights the 

emphasise on transparency embedded 

in the FX Global Code (FXGC). “It 

turns out, improved technology and 

greater efficiencies on the desk are 

not the only drivers of algo adaption. 

Principle 18 of the FXGC includes 

guidance for algo providers, shining 

a light on how algos are marketed 

and managed,” the report notes. The 

FXGC also emphasises transparency 

and disclosure requirements that are 

intended to help consumers of algos 

understand more thoroughly the 

nature of the tools they may (or may 

not) use. “It appears the more the 

industry knows about a product, the 

more likely they are to use and trust 

it,” the report authors suggest. 

The report also questions whether 

the adoption of FX algos has now 

plateaued. “Because FX algo use has 

not achieved the same market share 

as in other asset classes, it is fair to 

ask if adoption is levelling off. Our 

data shows the answer is a resounding 

‘no’. Trading in size in difficult markets 

require finesse and creativity, which 

algos can provide,” the report adds. 

EXPANSION BEYOND FX 

SPOT 

Furthermore, the report observes that 

creativity also comes into play when 

deciding which algo is most suited 

for the market conditions in which 

participants are trying to execute. This 

often requires sell-side guidance to 

help buyside traders use the optimal 

tool for the market conditions, the 

report notes, and warns that despite 

the popularity of algos, relationships 

still matter. “In equity markets, these 

challenges spawned ‘algo wheels’ 


- something likely coming to FX 

markets,” the report adds. 

The opportunity for additional algo 

growth sits squarely in other FX 

products, according to the report. 

The value of algorithmic execution 

in FX spot is mostly understood, 

and adoption will continue to rise, 

according to the report. “The ability of 

algo providers to apply this technology 

to other instruments such as NDFs 

represents an area of potential usage. 

To date, uptake has been limited,” the 

report adds. 

Talk of the use of algos in certain FX 

swaps and options - particularly nonstandard 

dates - is probably sometime 

away. However, if the trajectory 

of electronic trading and algo use 

gleaned from other asset classes is 

any indicator of the future of FX algo 

adoption, the prospects of broad 

adoption are very good, the report 

concludes. Bruel adds that while algo 

use in spot is certainly a well told 

story, the focus now is on some of 

the less automated instrument types, 

such as NDFs and FX options. “Dealers 

recognise that there probably will 

be demand going forward and we’re 

starting to see a lot of questions being 

asked around the ability to more 

effectively execute your NDF and your 

options trades, but we have yet to see 

a wholesale introduction of algos into 

those markets.” 

Read the full report here: https://www. 

greenwich.com/blog/present-andfuture-fx-execution-algorithms 

Coming shortly to deliver even more insight and 

commentary about algorithmic FX trading: 

MARKETWATCH 

• The launch of the enhanced 

FXAlgoNews website. The 

new site has been designed 

for easier navigation and 

will include our updated FX 

algo provider database and 

other new resources. 

• Our new regular Anatomy of 

an Algo and Dequantification 

features. These will drill down 

on the technical structure 

and inner workings of these 

increasingly powerful trade 

execution toolsets. 

• Our Summer 2023 

Supplement. This will be 

exploring outsourced FX 

algorithmic trading services 

and the benefits these can 

deliver for both buyside and 

sellside market participants. 

For more information please visit: www.fxalgonews.com 


5

PRODUCT PROFILE 

State Street unveils 

new Portfolio Algo 

The complexities involved in managing multi-leg, multi-currency trades has proved 

to be a significant pain point for portfolio trading, particularly among Real Money 

clients. Mary Leung, Global Head of Client Algos at State Street, explains how the newly 

launched Portfolio Algo helps to automate this process and shares the many innovative 

features that the new strategy has to offer. 

Mary Leung 

Please share more details about 

what the new algo is? 

State Street is very excited to announce 

the launch our new strategy – the 

Portfolio Algo. This strategy is designed 

to execute a portfolio or basket 

of orders across various currency 

pairs. This innovative solution will help 

navigate the complexities of executing 

multiple currency pairs simultaneously, 

and reduce overall execution costs 

when compared to executing each 

currency pair individually. 

Another goal of the strategy is to 

enhance efficiency of the buy and 

sell netting opportunities in the 

basket orders by breaking the legs 

into tradeable, netted orders. We 

then optimize the trade-off between 

cost and risk of the aggregated 

execution. We do this by analysing 

the characteristics and correlations of 

the netted currency pairs to generate 

optimal execution paths for each pair. 

These cost and risk trade-offs can be 

reviewed on our pre-trade analytics 

tool. During execution, clients can 

monitor the execution of each leg 

with our real time TCA. Post execution, 

clients will receive additional TCA 

analysing the performance of each 

of the netted legs, as well as the 

aggregated performance against the 

initial portfolio orders. 

What do you think are the 

differentiating features of your 

portfolio algo? 

State Street’s Portfolio Algo is more 

than just a workflow automation 

solution. The strategy computes 

optimal execution trajectories by 

modelling temporary and permanent 

market impact cost, volatility cost 

and the cross-correlations of different 

currency pairs. These trajectories aim 

to minimize the mean-variance of 

the Implementation Shortfall cost of 

the basket (i.e. reduce slippage from 

the arrival price of the portfolio). We 

construct an efficient frontier based 

on different levels of risk aversion. 

Each point on the frontier represents 

distinct execution pathways designed 

to optimally liquidate the basket on an 

aggregate level. 

An important concept leveraged in 

the Portfolio Algo is the efficient 

frontier. The structure of the efficient 

frontier is expressed on two ends of 

the execution spectrum: executing 

everything now, at a known but high 

cost (e.g. aggressive sweep or risk 

transfer); versus executing a simple, 

evenly distributed strategy slowly 

over a time horizon (i.e. TWAP) at an 

unknown, but low expected cost. Our 

Portfolio Algo pre-trade analytics allow 

clients to observe the transition from 

one extreme efficient frontier point 

to another and shows how we can 

reduce the expected costs with only 

small increases in standard deviation. 

This allows our clients to express 

their varying risk profiles by choosing 

different urgencies. Once an urgency 

is selected they will be able to review 

the execution paths before executing 

the portfolio. 

Another differentiating feature is 

the flexibility to constrain the basket 

USD exposure to its initial levels, or 

to be completely USD neutral. It is 

inherently challenging for a human 

trader to manage multiple legs of 

currency pairs with different risk and 

cost characteristics while maintaining, 

balancing or neutralizing the USD 

exposure of the original order. State 

Street’s Portfolio Algo is able to derive 


Why did you develop the new 

algo? 

We maintain a consistent dialog with 

our clients, listen to their execution 

needs and partner with them to find 

solutions. The Portfolio Algo was built 

based on increased demand, interest 

and feedback from our clients who 

wished to simplify their workflow and 

optimize the cost of trading a basket of 

currency pairs. 

Figure 1: Efficient frontier - Each point translates into specific risk aversion/urgency and the 

associated costs/standard deviation. 

Client experience is a key theme to 

State Street’s FX algo offering. We 

actively engaged with our clients 

to develop this new strategy. From 

gathering essential requirements, to 

reviewing the concept and design of 

the model and workflow, we built the 

Portfolio Algo to ensure the solution 

can provide additional value. 

What has been the response/ 

feedback? Has it been well 

received? 

The offering has been very well 

received. During recent walkthrough 

presentations we received tremendous 

interest from clients eager to try the 

new strategy. 

During a recent global algo tour 

several of our clients, who have yet to 

adopt FX algos, commented that the 

Portfolio Algo is a “game changer” for 

them. 

Figure 2: Optimal execution paths based on a chosen urgency 

the optimal execution pathways of the 

different currency pairs and ensure 

desired constraints are observed 

throughout execution. 

Can the algo be customised to 

allow clients greater control of 

their executions? 

Absolutely. The Portfolio algo, while 

sophisticated, is designed to easily 

deploy. Clients can let the algo decide 

the duration to run the order, or 

for clients who desire more control, 

they will be able to select their own 

duration and urgency. Once these 

customized selections are made the 

strategy will optimize the basket 

orders on an aggregate level based 

on client inputs. We understand that 

clients also like the flexibility to stay 

engaged at critical moments similar 

to our flexible design of our single 

order algo strategies. The portfolio 

algo offers the ability to amend 

basket orders details, speed up/slow 

down execution, add/remove limit 

prices and pause/resume individual 

legs or the entire basket. The client 

can also manage particular legs of 

the basket individually by using any 

existing strategy in our algo suite or 

through a risk transfer price. If any 

of these flexible tools are utilized the 

portfolio algo will be able to adjust 

and recalculate the optimal execution 

curves in real time to accommodate 

changing parameters. 

In what ways will the new 

strategy benefit clients? Are 

there particular groups of clients 

who will find the algo especially 

effective? 

A consistent pain point for our core 

Real Money client base occurs during 

monthly and quarterly portfolio 

rebalance and asset allocation trades. 

Managing multi leg, multi-currency 

trades is time consuming, gives rise 

to in-efficient execution, increased 

implementation shortfall, and 

operational headaches. 

The Portfolio Algo is specifically 

designed with these challenges in mind 

and solves them with essentially one 

click that can save our Real Money and 

asset manager community countless 

hours and reduce execution costs. 


7

INDUSTRY VIEWS 

Aligning FX algo 

trading outcomes 

with customer 

expectations 

Image by shutterstock 


Nicola Tavendale 

The Bank for International Settlements recently released a working paper on the 

foreign exchange market, part of which explores the expansion of algorithmic trading. 

In particular, the paper charts the rise in algo use on EBS to 2022, by which time both 

bank and non-bank FX algo trading dominated with each accounting for just over 40% 

of trading volume on the platform. As algo use continues to grow, so too have client 

expectations around algo performance and the level of service they expect to receive 

from algo providers. Yet where can improvements still be made and what will be next 

in the evolution of the algo trading experience? Nicola Tavendale writes. 

In addition to the growth of FX algos 

deployed on primary CLOBS, the BIS 

Working Paper Banks notes that, due 

to the role that such platforms play 

in the market, algorithmic trading 

has also had an important impact on 

price discovery in FX. Furthermore, the 

paper adds that execution algorithms 

are now also being used directly 

by some of the more sophisticated 

customers in the FX market and that 

these users “increasingly rely on 

smart order routing and execution 

algorithms to spread large orders over 

time and across multiple electronic 

venues.” 

Joel Marsden, eFX Senior Currency 

Trader at ANZ, says that algo trading 

literacy is also increasing considerably 

among the wider institutional and 

multi-national corporate client 

base, which is a broadening from 

earlier investor and asset manager 

adoption. He explains that with 


9


Joel Marsden 

“Clients better 

understand the 

trade-off between 

capturing spread on 

their execution against 

the market risk they 

assume” 

this knowledge comes an expectation 

around aligning algo performance 

to execution goals, which at its most 

fundamental comes down to long 

term outperformance against risk 

transfer pricing. 

“Clients better understand the tradeoff 

between capturing spread on their 

execution against the market risk they 

assume, when for instance, making 

the decision to be passive, neutral, 

or aggressive in their execution. Pretrade 

analytics are key to helping 

clients with these decisions,” Marsden 

says. He continues: “One of our key 

algo product differentiators is how 

we manage and curate our liquidity 

sources. Clients broadly favour spread 

capture along with minimal market 

impact, so the choice of liquidity 

sources is critical to achieve both 

those objectives.” 

EVOLVING EXPECTATIONS 

At ANZ, Marsden explains that the 

same liquidity sources and algorithms 

used by ANZ for internal e-commerce 

hedging also form the building 

blocks for its client algo offering, 

meaning that both are objectively 

aligned. Using TCA by liquidity pool, 

he explains that clients can visualise 

and review granular, as well as overall, 

execution performance. “We believe 

TCA should always be exclusive and 

transparent of any brokerage/mark 

up, so customers understand the raw 

wholesale market pricing and impact 

of every individual order,” Marsden 

says. “Clients expect that we are 

always sourcing and routing the most 

efficient, low impact, liquidity on their 

behalf.” 

In addition, the general requirements 

of nearly every client now includes 

pre-trade analytical tools with insights 

into the aggregated electronic market, 

insights into spreads, insights into the 

potential cost of execution as well as 

comparative studies of risk transfer 

versus algo execution, says Ralf 

Donner, Head of Marquee Execution 

Solutions at Goldman Sachs. He adds 

that most clients tend to also expect 

some kind of real time order monitor 

or even a real time order manager, 

depending on the platform, while 

post trade they would certainly expect 

both a post trade report from the 

bank as well as a menu of third party 

transaction cost analysis, in addition 

to some bank aggregated reporting 

as well. 

“Typically, the onboarding process for 

a new account algo account will also 

require some kind of evidence of past 

performance from the algo provider. 

Any new algo client that we are 

onboarding now asks us for a study of 

the most recent set of algo executions, 

across all currency pairs and what the 

distribution is, how did they perform? 

Clients these days will often ask us 

some really insightful questions,” 

Donner says. Some of the some of 

the larger algo providers such as 

Goldman Sachs now have a dedicated 

algo team, which Donner notes is 

not that common. “Particularly in the 

Asia session, some clients really like 

the fact that they can speak to an 

algo expert anytime of the day. That’s 

part of the personalised service we 

provide,” he adds. 

DEVELOPING INTELLIGENT 

SOLUTIONS 

“However, what I hope we do not end 

up doing in foreign exchange, and 

there are some worrying signs from 

certain hedge fund clients that we 

are going in this direction, is having 

to actually create too many bespoke 

products for clients. A bespoke 

algo product is likely to be a short 

term gain and long term pain as it 

exponentiates testing and thus slows 

future development.” says Donner. 

Instead, he says that his team does 

sometimes need to help clients who 

are using an API connection via a 

multi-dealer platform to complete 

bespoke work on their Fix connection, 

such as a translation of their settings 

on their end to the bank’s Fix 

messaging, for example. “Many of 

the multi-dealer platforms we deal 

with are insufficiently on top of this,” 

Donner adds. “It should be their 

job to ensure this translation layer is 

done. It is also in their best interest 

to ensure that their clients using Fix 

connections are provided with good 

service, but some of the smaller multidealer 

platforms still seem unable or 

unwilling to do this.” 

Manual vs. algorithmic execution on EBS Market 

The increased availability of algo data 

has also raised client expectations 


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11


Ralf Donner 

“A further area to 

explore is the potential 

for taking a more multiasset 

approach to algo 

development” 

around the outcomes they can expect 

to achieve using algo execution, 

says Asif Razaq, Global Head of FX 

Automated Client Execution at BNP 

Paribas. Client expectations in the 

past were more exploratory, but 

the readiness of analytics and TCA 

has resulted in those expectations 

becoming much more focused, he 

adds. “That is a big change for the 

market,” Razaq says. “We continue 

to meet their expectations through 

the ongoing development of our 

algo solutions, which have evolved 

considerably since the launch of our 

first algorithm almost 10 years ago.” 

Today, BNP Paribas offers fourth 

generation algorithms, or interactive 

algos, which can provide real-time 

feedback and, using the digital trading 

system ALiX, can provide commentary 

to the client mid-execution so the 

client can modify the algo’s flight path 

if needed and achieve a much better 

outcome on the execution. “We have 

been constantly building more and 

more tools, which give clients much 

better control over the execution and 

a much better overall understanding 

of how the algos work,” adds Razaq. 

Another area of significant 

development is the demand from 

clients to build customised solutions. 

According to Razaq, while the BNP 

Paribas algo suite is relatively small 

by design, it should cater for 90% 

of client execution needs. “However, 

if a client does have a very bespoke 

requirement to adapt an existing algo 

to make it a perfect match to their 

needs, then we are able to tailor 

the algo accordingly,” he says. “This 

framework, Flex, allows us to evolve 

our algo family without confusing 

clients with too many different core 

algos, but instead tailoring the existing 

algos to specific client needs. We now 

have around 20 different variants 

of Chameleon and 16 different 

variants of Iguana, all designed to 

do something very specific for those 

clients’ needs.” 

IMPACT OF ANALYTICS DATA 

In addition, BNP Paribas has launched 

fifth generation algorithms, complex 

strategies designed to perform 

more than just execution, such as 

automating the client’s pre-execution 

workflow. One example of this is 

Rex, a portfolio hedging or basket 

algorithm designed for clients who 

typically do not have just one currency 

pair to execute, but five or six. “This 

takes optimising client executions one 

stage further. Rex can automate the 

entire workflow, construct a project 

plan for the client and show them how 

it can use algorithms in a collective, 

daisy chain, fashion,” Razaq says. He 

notes that much of the success of the 

fifth generation algos has been the 

Increased availability of algo data has also raised expectations around the outcomes clients can expect to achieve using algo execution 


esult of the time and effort taken 

by his team to integrate the algos 

with the multi-dealer platforms that 

the clients use. “This makes it much 

easier for clients to use the algos 

because they are already integrated 

to their tool that they use on a day 

to day,” Razaq adds. “The uptake 

from clients has been really positive 

as a result. We’re now brainstorming 

with clients on other ideas and other 

complex workflows that they have as 

an organization that we can look to 

automate.” 

Marsden believes that pre-trade 

analytics are also becoming 

increasingly important in educating 

and guiding clients with their specific 

execution objectives. As clients assume 

the market risk on the time taken 

for the execution, it is imperative 

that they are equipped with an 

understanding, on average, how long 

an algorithm takes for any given time 

of day or season given prevailing 

market conditions, he explains. “To 

that end, the pre-trade analytics 

which ANZ provide overlay liquidity 

heatmaps with the recent historical 

expected trading volume and velocity, 

which is particularly important for 

clients that execute more passive 

or implementation shortfall style 

algorithms where the time taken to 

complete is variable,” says Marsden. 

“Our pre-trade analytics also visually 

guide clients to be cognisant of known 

event risk that have the potential to 

materially move the market, such 

as news releases, major economic 

indicators along with daily benchmark 

fixes, which many institutional clients 

may otherwise not be aware of.” 

According to Donner, there is also 

much more that can still be done to 

increase transparency around how the 

algos with different liquidity sources. 

“Algo analytics feels like it now pops 

up everywhere,” he explains. “Yes, 

there is a huge amount of analytics 

out there, but it’s daunting. It is like 

stepping into an aircraft cockpit and 

there are dials, wheels, bells and 

whistles all over the place. It’s very 

difficult to make sense of the entire 

the entire thing. Clients are looking 

for something that is a little bit more 

streamlined, something that shows 

you everything at a at a glance.” 

NEW INNOVATIONS AND 

DEVELOPMENTS 

Achieving this the main focus which 

will help clients in terms of the 

usability of the algo analytics, says 

Donner. He adds that analytics tools 

also tend to be very disparate. “There 

will be one pre-trade tool, another 

separate intra-trade tool and then 

yet another for post-trade,” he says. 

“Harmonizing those tools is an aim 

worth pursuing.” A further area to 

explore is the potential for taking a 

more multi-asset approach to algo 

development, Donner says. From 

a client perspective, he explains 

that FX does not sit on its own but, 

particularly from a client perspective, 

it sits together with other tradables. 

Another recent release is a new 

Franchise Matching algo, which was 

developed as a new way of leveraging 

internalisation to fill the algo order. 

Donner explains: “This is all about 

liquidity. Liquidity used to be a bit of 

a challenge with algos. There were 

only a handful of venues and there 

was a great deal that had to be done 

externally.” He adds that algos in 

general have improved over the years 

and have become much smarter about 

how they source internal liquidity. 

“Our new Franchise Matching algo 

is yet another example of being up 

being smart about sourcing internal 

liquidity,” Donner says. “It encourages 

the opposing interest, as distinct from 

previous versions of internalisation 

which sought existing opposing 

interest. Basically, it’s a new form of 

internalisation.” 

Razaq adds that at BNP Paribas, they 

are already ahead of the curve and are 

recognised as innovators in the algo 

development. “We’re still enhancing 

our strategies on a day to day basis 

and we’re looking at more fifth 

generation algo opportunities to build 

upon,” he says. “But we now feel 

we’ve conquered FX in terms of our 

capability. The bigger opportunity for 

us is to now look at how we can apply 

the FX algo technologies to different 

asset classes.” This involves building 

the FX family of algos, Chameleon, 

Viper and Iguana, but making them 

available to trade different asset 

classes, such as commodities, precious 

metals, and Futures with underlying 

Asif Razaq 

“Client expectations 

in the past were more 

exploratory, but the 

readiness of analytics 

and TCA has resulted 

in those expectations 

becoming much more 

focused” 

FX, bonds and equities, adds Razaq. 

“We are planning to launch these new 

algos later this year. Our focus now is 

how can we apply the same principles 

and the same technology in different 

asset classes, giving the end client a 

unified view of algos. Because if they 

know what Chameleon does in FX, 

then they’ll know what Chameleon 

will do when it comes to trading gold 

or when it comes to trading futures,” 

he says. 

According to Razaq, that common 

understanding of how the algo works, 

using the same interface to keep the 

look and feel of the FX algos, as well 

as having Alex to provide algo users 

with commentary on their futures 

execution and FX execution, will 

bring valuable unity within the algo 

offering. “The cost pressures we are 

seeing now in the world, which our 

clients are feeling as well, mean that 

everyone’s being asked to do a lot 

more with a lot less. Clients are no 

longer just an FX execution desk, but 

they’re increasingly becoming multiasset 

execution desk. The more we can 

do to help them to align our products 

to those different asset classes, 

the easier we can make life for our 

clients,” Razaq concludes. 


13

A 

ALGO OF THE MONTH 

TWAP from BNY Mellon 

With Joseph Café, Global Head of eFX Sales 

NAME OF THE STRATEGY: 

TWAP 

DESCRIPTION & 

CAPABILITIES 

• A time-based algorithm that 

works the order during a 

user-specified horizon by 

spreading trades along a linear 

distribution. 

Joseph Café 

In what ways does the strength and experience that BNY Mellon has built 

up in FX over many years put it in a good position to meet the growing 

demand and interest in FX algorithmic trading strategies amongst 

buyside firms? 

BNY Mellon has been at the forefront of change in the financial world for nearly 240 

years. We have weathered times of calm and crisis, and we have done it by adapting, 

refining and consistently improving our services. That spirit of discovery has enabled 

us to stay ahead of the curve and deliver consistent solutions for our clients. We are 

the world’s largest custodian and the singular clearer of U.S. Treasuries. This unique 

position in financial markets has enabled deep relationships and trust with our clients, 

resulting in new and improved solutions built on client feedback. Many of our clients 

have voiced the importance of a differentiated liquidity provision when it comes to 

execution algos. Additionally, many of our clients’ value BNY Mellon’s unique principal 

liquidity which is driven by our uncorrelated custody flows. 

What specific benefits do your FX algo solutions provide and why are 

they proving increasingly popular? 

Many of the benefits of our FX Algo solutions reside in our liquidity provision. 

Given our unique market position our liquidity pools offer flexibility by combining 

our custody, principal, and external market sources. This liquidity provision, and 

value added features, like the ability to auto-roll algo orders, provides clients 

improved execution, and reduces manual steps. 

In what ways do your algorithmic and TCA toolsets help clients to gain a 

more holistic and detailed view of their FX trading activities? 

Buy-side clients continue to demand more transparency in OTC FX. Our TCA toolsets 

are valuable for buyside clients in providing more transparency by benchmarking 

execution across several metrics. Examples of these metrics include performance 

against risk transfer, arrival-mid, and market impact. In our efforts to help clients with 

transparency we have partnered with an independent third-party TCA provider. These 

conversations lead to more informed conversations between the sell-side and buy-side. 

EXECUTION OBJECTIVES 

• The strategic goal is to 

minimize slippage against 

the interval TWAP, and to 

achieve this by spreading the 

order evenly over the trading 

horizon. 

• Additionally, the strategy has 

passive order placement logic 

which strives to reduce spread 

costs. 

WHEN TO USE IT 

• It’s useful when a client wants 

to execute larger orders over 

a specific time period while 

minimizing market impact and 

reducing spread costs. 

KEY PARAMETERS & 

FEATURES 

• This strategy will access BNY 

Mellon’s unique internal 

liquidity as well as external 

venues vetted for performance 

and market impact. 


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event include : Recommendation System for Trading, Machine 

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Alternative Data For Quant Trading, Sentiment Analysis for Quant 

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EDUCATION & TRAINING 

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QubeAlgo offers a 

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strategies. It has been live with 

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is now being rolled out to the 

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customizable solutions that 

empower quants and developers 

to build bespoke, multi-asset, 

electronic trading applications, 

often with minimal code. 

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WEBSITE OF THE MONTH 

Finance Hive FICC 

Mexico Members 

Meeting 

13th June, 2023 

Discussion topics will focus around and analysis 

of recent EM vs Mexico drivers and current market 

conditions, how to get real time pre-trading transaction 

cost analysis (TCA) to change and improve the 

outcomes of your trading strategy, determining the 

benefits of aligning with the Global Code of Conduct, 

how the FX algo space is developing elsewhere in the 

world and assessing the current level of algos usage in 

Mexico across the desks and much more. 

FOR THE DIARY 

https://www.thehive-network.com/finance-events/the-finance-hive-live-meeting-mexico-members-meeting-13-june-2023/ 


15

CASE STUDY 

Deutsche Bank uses 

Tradefeedr’s FX algo 

forecasting tools 

to enhance client 

engagement 

Earlier this year Tradefeedr unveiled its FX algo forecasting service, a collection of 

analytics tools developed to help clients make a more informed choice when selecting 

the most appropriate algo for their execution needs. Since then, leading algo providers 

have identified its potential in enhancing client discussions around the performance of 

their own algo suites. Tim Cartledge, Chief Data Officer at Tradefeedr, and Vittorio Nuti, 

Global Head FX Algos at Deutsche Bank, explain the benefits. 

TIM CARTLEDGE, CHIEF DATA OFFICER AT 

TRADEFEEDR: 

Following extensive consultation with the industry, our unique 

algo forecasting suite is now live and in use by both algo clients 

and providers alike. We have developed two products which use 

our forecasting tool, one for pre-trade forecasting and the other 

for post-trade. Both differ considerably from existing pre- and 

post-trade analysis tools in their ability to compare for the first 

time individual algo performance against the market average. 

Our product suite is available as an API and compares actual 

trades and shows a forecast of what the performance would 

have been for the currency pair at that time and in those same 

market conditions. This enables algo users to compare their 

execution performance to see if it was in line with the rest of 

the industry, or if their performance was better or worse, faster 

or slower, more risk or less. 

On the pre-trade side, algo users are also able to forecast 

what the expected performance would be for their execution. 

It can look at a particular currency pair in a particular size 

and compare the expected performance for different levels of 

aggression for the algo. That’s available now as an API and we 

will also be releasing an interactive front end in the coming 

weeks. 

The tool which has already been live for some time - and which 

Deutsche Bank has been working with - is the post-trade tool. 

This allows you to look at your actual trades and compare 

execution performance with the average expectation for 

those same conditions. We produce three headline numbers: 

Tradefeedr Global Forecast, which is based on all of the algo 

trades in our database; Tradefeedr Fast, which is based on the 

fastest third of algos in the market, and Tradefeedr slow, which 

is based on the slowest third of algos in the market. 

Tim Cartledge 

What is proving valuable to algo providers, such as Deutsche 

Bank, is that the tool can also forecast the performance of 

individual bank algos as well. Using the same methodology, 

the forecast can look at the expected performance of individual 

algos and compare those to the market. What we are doing 

is complimentary to the work of the banks, who can now 

combine the insights from our forecasting tools and provide 


additional background around the algos, flows, market 

conditions etc to help clients achieve their execution goals. 

The really novel element to our service is the standardisation 

of the algo executions. Not all algo executions are the same 

depending on currency pair, time of execution, market volatility 

etc. This is really the first time clients are able to compare algos 

using a level playing field to show what the algo execution 

would look like indifferent conditions. It takes the noise out of 

the process and allows for really fair comparisons. 

The modelling we do is based on the liquidity seeking algos 

in the market, which are allowed to run at the natural speed 

of the market. Even if you’re manually trading, you now have 

this database which shows what the market response was 

when the liquidity seeking algos tried to do a certain amount 

in a currency pair and you now also know how long it took 

them. That’s really useful information about market impact and 

fundamental market behaviour, which feeds into the FX sales 

teams and the advisory work they do with their clients. We can 

also see that most clients who use a certain algo are having 

good experience with that algo, but they will still need to be in 

communication with the algo provider to get the best out of 

their performance. 

VITTORIO NUTI, GLOBAL HEAD FX ALGOS AT 

DEUTSCHE BANK: 

We are very excited by the Tradefeedr algo forecasting tools 

as they offer a more pragmatic and quantitative approach to 

analysing algo performance. The tools provide clients with a 

holistic view of the algo market, they allow clients to view 

different algos and compare their strengths and weaknesses. 

Here on the FX team, we very much appreciate what the 

tools have to offer as they have made it much easier to talk 

about the algo performance data with our clients. We are 

able to use the data to help our clients to try and improve 

their execution performance based on real results. Having it 

based on independent data allows the client to compare the 

performance of an algo, instead of having to judge based only 

on their experience of what worked well or not. At Deutsche 

Bank, we are able to demonstrate to clients using this service 

that our algos perform well above the global average, both in 

terms of speed and lower cost of execution. The tools are based 

on independent analysis, so this provides us with an objective 

way to discuss the performance of an algo with clients as they 

are able to compare the execution to the rest of the market. 

The algo data in Tradefeedr represents our standardised settings. 

Once clients can see that performance, we can also discuss 

how these can be customised to their execution targets, such as 

more passive or slower execution versus our standard set. 

Tradefeedr have collected the data and aggregated it into two 

matrices, which simplifies this very complex data in a way that 

the user can readily understand. This quantitative approach has 

resulted in a very robust, powerful tool for algo users which will 

add to their understanding of algos and further enhance their 

execution performance. 

Vittorio Nuti 


17

TRADERS WORKSHOP 

To limit or 

not to limit? 

Using price limits with execution algorithms has been a somewhat contentious 

subject. While limits can be used for risk management purposes or substantiated 

by market insight on the execution horizon, they may also add noise or a suboptimal 

variable to an optimized execution logic. Sait Ozturk and Yangling Li of 

BestX have recently shed more light on this topic by examining the practical impact 

of using limits in conjunction with a range of different execution algorithms. 

• Opportunistic: algos that do not 

have a strict benchmark-dictated 

schedule, so have the flexibility to 

execute aggressively or passively 

according to market conditions. 

Sait Ozturk 

In our paper “Do Limits Improve Algo 

Performance?”, we explored how setting 

a limit affects execution performance and 

whether it was possible to optimize limit 

placement. The focus of the paper was 

on algo trades executed in less than 24 

hours and for G10 currency pairs, which 

avoided data issues without sacrificing 

significant statistical power. The bulk of 

the research concentrated on algos with 

only one limit, where limit placement 

is a more straightforward matter than 

dynamic limit optimisation. 

Yangling Li 

amount of risk executed as quickly 

and efficiently as possible and less on 

minimising market impact. 

• Pegged: algos that aim to execute 

orders at levels within the prevailing 

best bid/offer. Passive by nature, since 

they follow the market and usually 

trade at no worse than mid-price. 

• Interval: interval-based algos, such as 

TWAP, which aim to minimise slippage 

to a benchmark where the algo slices 

the parent notional according to an 

interval- or time-based schedule. 

BestX® Expected Risk Transfer Cost 1 

was used as the primary benchmark 

to evaluate algo performance with and 

without limits. This takes into account 

market conditions at the time of 

execution to arrive at a fair price estimate 

of the trade when algo execution begins 

and can be compared with the achieved 

average price across algo fills. 

Our research focused on five algo styles 2 : 

• Get Done: aggressive algos, where 

the priority is on getting a specific 


• Volume: volume-based algos, such 

VWAP, which aim to execute in line with 

a specified percentage of volume traded 

within the market. 

PRELIMINARY ANALYSIS 

As a robustness check, we used the 

performance of a TWAP algo without a limit 

during the execution period as a secondary 

benchmark, which more strongly favours 

having a limit than the primary analysis. 

However, this out-performance of limits may 

be quasi-mechanical, because a TWAP with a 

limit will on average outperform by avoiding 

prices when the limit is engaged and the algo 

is stopped. On the downside, the principal 

ways a TWAP with a limit can underperform 

are by being unable to execute the intended 

trade amount fully or by completing it too 

late. 

This TWAP under/over performance is 

important in the context of finding an 

optimal limit value, because having a very 

tight limit may produce the illusion of outperformance 

purely by executing only part 

of the intended amount and only under very 

favourable market conditions. To control for 

this, we created a smaller data set composed 

of mostly clean intended execution amount 

data to double-check the results inferred 

from our larger data set, while only including 

fully executed trades. 

Figure 1 (opposite) compares the Risk Transfer 

Performance distributions of trades with and 

without a limit. The limit is clearly already 

improving the median performance 3 in both 

data sets. 

Price limit placement is just a binary decision, 

so we also needed to evaluate where the 

limit should be set to assess its utility. We 

concentrated on trades with only one limit in 

their lifetimes, partly for simplicity and also 

because these constituted the vast majority of 

trades in our data set. 

Figure 2 shows how different limit distances 

affect algo performance. We can see a major 

difference between the datasets: the large 

dataset has numerous outperforming trades, 

while there are far fewer trades with negative 

distance in the smaller dataset. 

Figure 3 shows trade performance by limit 

engagement, with negative limit distance 

cases combined under Limit Started Engaged, 

while the positive limit distance cannot 

engage during the algo execution period 

(Limit Not Engaged) or engage after the 


19

TRADERS WORKSHOP 

start (Limit Engaged). Much of the 

limit performance derives from trades 

with an engaged limit at start, while 

trades with limit engaged later mostly 

underperform those where the limit 

never engages or limitless trades. 

Figures 4 and 5 show the other side of 

the trade-off: order completion, over 

limit distance from market arrival midprice. 

STATISTICAL ANALYSIS 

Our starting point for statistical analysis 

of limits was a regression model for 

the effectiveness of using only single 

limits with a positive distance. We then 

extended this model to accommodate 

dynamic limits by adding variables for 

trades with multiple limits during their 

lifespan. 

We found that for both the large and 

small data sets a single limit: 

• Degraded the performance of 

Opportunistic algos for almost any 

positive limit distance. 

• Improved Interval and Pegged algos 

with statistical significance 

• Had a directly diverging effect for 

each data set for GetDone algos 

• Improved Volume algo performance 

for the larger data set, but had 

no statistically significance for the 

smaller data set. 

The results for one limit do not change 

qualitatively when indicator variables 

for multiple limits are added. Overall, 

having multiple limits over the lifetime 

of a trade is associated with statistically 

significant performance deterioration 

across all algos, except for Interval algos. 

Figures 6 and 7 display the estimation 

results for the large and small data 

sets together for limit distance. The 

dashed 95% confidence line indicates 

whether the solid limit performance 

line is statistically significantly different 

from the zero line representing the 

performance without an algo limit. 

Examination of Figures 6 and 7 reveals that: 

• The conflicting results for GetDone 

algos between the two data sets 


also applied when performance was broken down 

by limit distances. While the limit performance 

line is below the zero-line for the large data set, 

in the small data set tighter limits are statistically 

significant above the line when up to 5% of the 

daily volatility away from the limit. Any limit farther 

away than this fails to improve the performance. 

• Also in line with the previous results, the limit 

performance line is below the zero-line for almost 

any positive limit distance for Opportunistic algos. 

The one exception, proving the rule, is the very 

tight 1% of daily volatility away from the arrival 

market mid, which comes with order completion 

issues. 

• A tight limit can boost the performance of Pegged 

algos with a limit placed up to 15% of daily 

volatility away from the market mid-price still 

improving the results. 

• A number of limit distances enabled performance 

improvements for Interval algos. The limit 

performance estimates are almost always above the 

zero-line, but mostly not statistically significantly 

above. A relatively tight distance of 2.5%-5% as 

well as having a limit >50% of daily volatility away 

(up to 4 times the daily volatility) can improve the 

limit. 

• Volume algos exhibit a similar, but weaker structure 

to Interval algos, with out-performance from a limit 

10%-15% and 1.5-2 times daily volatility away 

from market mid. Both these algos aim to follow 

the market movements without too many attached 

smart features, so these relatively distant limits may 

be exploiting mean reversion in the market to avoid 

trading in transitorily bad market conditions. 

CONCLUSION 

Our original paper analysed whether setting a limit 

aids algo performance and where to set a limit to 

optimize any beneficial effect. We explored the 

performance/completion trade-off, where a tighter 

limit causes better performance at the expense of 

only partial execution of the intended trade amount. 

We found that setting limits improved Interval 

and Volume algo performance, particularly when 

distant from the market mid, thereby avoiding 

extreme unfavourable market moves. The evidence 

for GetDone algos was mixed, but tight limits were 

favoured, while taking the partial completion risks 

into consideration. It was challenging to improve 

Opportunistic algos, which lie on the smarter end of 

the spectrum investigated, by using limits. 

1 . https://www.bestx.co.uk/glossary 

2 . Fixing algos, where the aim is to match the relevant fix price around the official fixing 

window and outperforming a fair value price is less relevant, were excluded. 

3 . Risk Transfer Performance is defined as the performance of the algo relative to the BestX® 

Expected Risk Transfer Cost. 


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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november 2018 May 2023 e-FOREX

BOOK OF THE MONTH 

Advanced Algorithmic Trading 

Algo Trading with ChatGPT 

BLOG OF THE MONTH 

Advanced Algorithmic Trading provides real world application 

of time series analysis, statistical machine learning and 

Bayesian statistics, to directly produce profitable trading 

strategies with freely available open source software. 

quantstart.com/advanced-algorithmic-trading-ebook/ 

This blog explores using the power of OpenAI’s ChatGPT 

model in algorithmic trading and discusses the various 

steps that are involved. 

blog.quantinsti.com/algorithmic-trading-chatgpt/ 

J.P. MORGAN E-TRADING SURVEY 2023 

tradertv.net/j-p-morgan-e-trading-survey-2023-how-will-trading-desks-beimpacted 

ANZ FOREIGN EXCHANGE EXECUTION ALGORITHMS 

youtube.com/watch?v=25WN6q2wIo8&t=1s 

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