LMAX Exchange: TCA white paper

TCA and fairexecution.
The metrics that the
FX industry must use.
An analysis and comparison of common FX execution quality metrics
between ‘last look’ vs firm liquidity and its financial consequences.

©LMAX Exchange 2020
LMAX Limited operates a multilateral trading facility. LMAX Limited is authorised and regulated by the Financial Conduct Authority(registration number 509778) and is a company registered in England and Wales (number 6505809).

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Contents
Foreword

4

Executive summary

8

About this white paper

11

Introduction

13

About the authors

I.

Applying standard metrics to a sample data set(i) Fill ratio/rejects(ii) Price variation - slippage and price improvement(iii) Hold time and execution latency
Summary of findings

II. Execution quality metrics and firm liquidity(i) Market impact(ii) Price volatility in firm liquidity(iii) Quantifying the value of price improvement(iv) Optimising trading on firm liquidity(v) Quantifying the cost of hold time
Summary of findings

III. Comparative transaction cost analysis

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16
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29
40
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51
56
60
64
68

TCA white paper conclusions

72

Appendix A - about the data76

Appendix C - volatility bands used for price improvement

78

Appendix B - factors affecting internet delivery of market data
References

77
79

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Foreword

David Mercer

CEO, LMAX Exchange

Transparency has become a buzzword adopted by many in the FX industry but it is time
now for the industry to lead with transparent action rather than just paying lip service tothe buzzword.
Whilst there is broad consensus about the importance of transparency in a highly complex,
fragmented and mostly OTC marketplace, we are some way off agreement on the means toachieve it. Specifically, the FX market does not have commonly agreed metrics to assess thetrue cost of trading, liquidity quality and certainty of execution.
If we are being honest, traders are often left groping in the dark, forced to navigate theinconsistent standards, market practices and evaluation methods of different liquidity
providers. Clients face either complete opacity or the confusion of conflicting messages
and benchmarks.
Transparency cannot merely mean deluging the client with an unworkable volume and variety
of disclosure information. It should be about clear, concise and common metrics thatconsistently inform customers and allow them to take back control of their trading strategy.
Reaching that point will require the creation of robust, commonly agreed Transaction Cost
Analysis (TCA) metrics that compare and contrast the differences in firm and last look liquidity,
and are applicable to all client segments. Cohesive, complete information will forge the path toinformed choice for the trader and go a long way to sweeping away the distrust built up overthe past few scandal ridden years.
The upcoming Global Code of Conduct and the principles stated therein, whilst not banning‘last look’, will bring a sharper focus on how it is deployed in relation to fairness, transparency
and the management of the conflicts of interest that it introduces. And although Spot FX is outof scope for MIFID II, MIFID II does introduce monitoring and reporting requirements forvenues and participants to provide a better view of orders, when they are received, how they
are executed, with more granular time stamps - providing regulators with comparative datafrom across the market. To the extent this becomes market practice for financial instruments,
alongside the Global Code, it helps set the standards for the way orders are processed infinancial markets, including FX.
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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Foreword cont’d
The purpose of this white paper is to propose a blueprint for FX TCA metrics that can equipclients with an effective evaluation of the cost of trading and quality of execution. In particular,
it examines the differences between trading on firm vs last look liquidity; it highlights thefailure of existing TCA metrics to capture the nuances and value of firm liquidity; and it
demonstrates how the transparency of market dynamics can be used by traders to reduce
trading costs and to regain control of their execution strategies.
We believe there are five metrics that need to be evaluated to conduct effective TCA in FX:•

Fill ratio: proportion of orders successfully filled•

Hold time: the cost of discretionary latency•••

Price variation: symmetry, or lack thereof, in price slippage or improvement
Bid-offer spread: the difference between buy and sell prices

Market impact: market price reaction to a given set of trades

The analysis that follows examines in detail fill ratio, price variation and hold time and gives a
respectful nod to market impact. It concludes by providing a comparative TCA example
between firm and last look liquidity, which highlights the often unacknowledged execution
advantages offered by firm liquidity venues. We intend to publish separately deeper analysis
on bid-offer spread and market impact, but ultimately these are relatively simple calculations
that require only efficient timestamps and consistent, precise market data.
Our intention with this white paper is to contribute to an industry-wide debate on how to
conduct TCA in a way that benefits the customer, provides a fair comparison for liquidity
providers, and creates genuine transparency: one that enables choice and aids quality
decision making.
Ultimately, this is about helping the industry move towards a position where traders are incontrol of their trading costs and can set their liquidity strategy accordingly which, in turn,should give true market makers an advantage through transparent information. While there
may be disagreements within the industry over the veracity of specific trading practices, weshould all be able to agree that traders need better tools to understand the true cost of trading.
Only with these can they make informed decisions about their execution and liquidity
strategies; and only then can we take the step the whole industry needs towards restoring
trust and confidence in our market.

David Mercer

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Executive summary

Executive summary
The objective for this paper is to propose a blueprint for TCA metrics that will accurately
assess and measure execution quality on both firm and last look liquidity. Compared to equity
markets, FX TCA is still in its infancy and primarily captures execution costs on last lookliquidity. With the growing prevalence of firm liquidity, TCA metrics need to evolve to reflect
execution quality across all available styles of liquidity.
To achieve the stated objective, the white paper sets out the analysis from the buy side
perspective with three questions in mind:•

Do the commonly used TCA metrics accurately measure execution costs on both lastlook and firm liquidity?

•

What are the metrics that measure all the underlying processes of trading on firm
liquidity, and thus should be used to properly assess the cost of trading in the
FX marketplace?

•

How does the total cost of execution compare between last look and firm liquidity?

The analysis that follows examines in detail, fill ratio, price variation and hold time, as well as
touching on market impact and the resulting pre-trade information leakage. Bid-offer spreadcomparisons will be explored in future publications.
Our findings demonstrate that applying the ‘standard’ execution quality metrics which havebeen developed for last look liquidity does not provide the full picture of execution costs, andmore importantly, misses quantifiable positives of trading on firm liquidity.
Unlike last look liquidity, fill ratio and price variation on firm liquidity venues don’t measure any
business processes - they measure market volatility. Comparing fill ratios between last lookand firm venues is not a useful exercise in comparing like with like, and price variation must also
be included for a complete picture.
The cost of hold time, non-existent for firm liquidity, is an important hidden cost for tradingwith last look; our analysis estimated this cost at $25/million for a rejected order after 100ms.
Finally, market impact, not always measured, is crucial for understanding execution quality
across both liquidity types.
As a result, strategies that work well on last look venues may not translate to firm liquidity.
A simple application of the basic metrics of spread, fill ratio, slippage and hold time would notshow the value firm liquidity brings through price improvement and more consistentexecution latency:•

Swapping price improvement for fill ratio is possible with firm liquidity - placing thetrader directly in control of their execution costs. If a higher fill ratio is important,that can be chosen over increased price improvement;

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Executive summary cont’d•

The underlying processes of the LMAX Exchange anonymous central limit order bookhold challenges and opportunities for customers used to trading with last look venues.
There are higher market data update rates to contend with, but faster and moreconsistent execution. We have shown several ways to place a value on that fast
execution by calculating the cost of last look hold time, regardless of whether holdtimes are unilaterally applied to all orders or selectively applied and visible asextended tail latencies;•

Last look optionality is used as a defence for the LP against stale prices in the market.
That discussion normally focuses on fill ratio and spread. It does not explain theobservation that the market processes that naturally lead to price improvement inmarket orders are conspicuously absent for limit orders sent to the same venues, orthe asymmetry in slippage vs improvement seen with some venues. Only firm liquidity
exposes the same underlying market dynamics for market and limit orders.

Finally, the comparative TCA calculation demonstrates that due to the absence of priceimprovement, combined with a net cost increase associated with trading after a discretionary
hold time, trading costs on last look are higher by between $2.25/million and $48.86/million.
Furthermore, the transaction cost analysis shows the benefits of firm liquidity as thetransparent, cost effective choice that places the trader in complete control of their execution
quality, with no pre-trade information leakage.

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

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About this white paper
This white paper has been prepared by LMAX Exchange.

The findings documented in this report solely reflect the data analysed by LMAX Exchange.
LMAX Exchange has compiled the data received and interpreted the data for presentation
purposes in order to produce this white paper.

About the authors
Dr. Andrew Phillips
Chief Technology Officer
Dr. Andrew Phillips F.R.A.S. has over 20 years of experience in leading
innovative technology teams in the design, build and operation ofreliable web-scale and low latency architectures.

Andrew Stewart
Director of Strategy and Research
Andrew Stewart has 25 years of software development experienceand is responsible for directing our applications development to better
understand, measure and improve the customer trading experience.

Dr. Sam Adams
Head of Software
Dr. Sam Adams has a background in Chemical Informatics research.
For the last 5 years he has focused solely on building, delivering andoptimising LMAX Exchange high performance architecture.

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Introduction
Trading Foreign Exchange (FX) is frequently conducted on ‘last look’ venues. Last look is a
trading practice where the liquidity provider (LP) provides a quote rather than a firm price into
the trading system or execution venue. When a request to trade against the quoted price isreceived the LP may hold the request for some period of time, execute the trade (‘fill’) at theprice quoted, offer an alternative price (‘requote’) or decline to trade (‘reject’). This quote
driven behaviour is commonly argued to be necessary to protect the liquidity providers in a
fragmented and unregulated market place where there is no central exchange [1].

Firm liquidity venues such as LMAX Exchange are gaining traction in the market as analternative to quote driven venues. Firm liquidity means that they operate using an open central
limit order book where orders are matched based on published rules without optionality.
While they offer broadly similar trading features, there are very different execution models
underlying a last look quote driven venue and a firm liquidity venue such as LMAX Exchange.
The standard metrics used to assess execution quality have been designed to measure andaddress the negative impacts of last look on trading. To properly apply TCA to FX, traders must
understand the underlying processes of both firm and last look liquidity in order to compare
accurately their respective execution quality characteristics.
The paper is divided into three sections:
I

II

III

Part I is an analysis of a third party database of trades executed on LMAX Exchange
and both Bank and Non Bank last look venues. This database has been selected toshowcase certain behaviours which highlight the differences between firm and lastlook liquidity. We apply the standard metrics common in FX to build a simple scorecard for each of the three classes of venue;

Part II is a more detailed investigation of the results of the standard metric analysis
illustrating the additional factors that are important to consider from an execution
quality point of view. We present details of how we calculate trading costs andwhat the differences between the two execution models mean for a trader’s control
over their order execution;

Part III is a side by side comparison of the relative costs of trading on the last look
execution model compared to the firm liquidity model for a variety of real world
trading scenarios.

Our objectives are to analyse firm and last look liquidity side by side using the standard metrics,
to show how we approach TCA for FX and to lay out our approach to analysing execution
quality. Our goal is to drive the debate and contribute to the formation of a common baseline
for FX TCA metrics. The questions we aim to answer are:•••Do the commonly used TCA metrics accurately measure execution costs on bothlast look and firm liquidity?

What are the metrics that measure all the underlying processes of trading on firm
liquidity, and thus should be used to properly assess the cost of trading in the
FX marketplace?

How does the total cost of execution compare between last look and firm liquidity?

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

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Applying standard metrics to a sample data set

Part I: Applying standard metrics to a sample data set
This section takes the buy side trader’s perspective, using trade logging information from
LMAX Exchange customers accessing a range of competing liquidity providers via an independent
Third Party Aggregator (TPA). This data set provides us with an opportunity to perform a likefor like comparison of trades executed on firm liquidity at LMAX Exchange alongside trades
executed on a number of leading Bank and Non Bank last look liquidity providers.
The TPA trade database contains 7,137,576 orders sent to six last look (3 Bank, 3 Non Bank)
LPs and the firm liquidity reference venue (LMAX Exchange) for a total of seven LPs in thisstudy over the period from 1st January 2016 to 31st December 2016. Further detailed
information about the data set can be found in Appendix A.

Where necessary to add extra background material, verify assumptions or provide sanity
checks of the results from the TPA data, we have supplemented this database with metrics
from the LMAX Exchange platform measuring the trade flow in the exchanges in London, Tokyo
and New York. Where LMAX Exchange data is used to supplement the results from the TPA
data is clearly indicated. Throughout this paper, we treat the data as if they were our own
trades in order to illustrate what we currently consider to be best practice TCA for FX.
We propose that there are five key metrics that should be used to assess execution quality
when trading FX:
•

•

•

•

•

16

Fill ratio measures the number of successfully filled orders as a fraction of the total
number of orders placed, normally stated as a percentage. Rejected or unfilled orders
represent an opportunity cost - the trader must forego the opportunity to trade or
attempt to trade again at a potentially worse price - therefore higher fill ratios
are desirable;

Price variation measures the difference between the price the trader expected andthe price at which they were filled, arising from movements in the underlying market
prices and often separately referred to as ‘slippage’ or ‘price improvement’ for adverseand favourable outcomes respectively. Traders normally focus on low slippage, or net
neutral price variation, as it has traditionally been unusual for them to receive (andtherefore measure) price improvement in isolation;

Hold time measures the discretionary element of execution latency, which is the timeobserved by the trader between placing an order and receiving notification of the fillor rejection. Higher hold times and execution latencies not only exacerbate theopportunity cost associated with rejects but also represent an opportunity cost on
filled orders because, while the trader is waiting for a response, they are committed tohonouring the potential trade and may be unable to execute the remainder of
their strategy;
Bid-offer spread measures the difference between the buy and sell prices. The larger
the difference between the prices the more the market will have to move to make aparticular position profitable. When the spread is zero, this is referred to as a ‘choiceprice’. This is the simplest metric to compare between LPs;

Market impact characterises the response of the market, typically in terms of pricechanges, to a given set of trades. The interpretation of market impact is highly
subjective; one trader’s strategy may rely on minimising impact while another’s may
actively benefit from a pronounced and consistent post-trade reaction, but also
suffer if that reaction occurs before a set of related trades is completed.

An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I: Applying standard metrics to a sample data set
In the following sections, we will use the TPA data to compare the fill ratio, price variationand hold time characteristics of firm and last look liquidity. We will leave the comparison of
bid-offer spread to a future study as it requires comparison of market data which was notpresent in this data set. The nature of the trading conducted through the TPA also means thatit does not provide a useful source for comparing market impact when using different liquidity
types, however we will return to this topic in Part II.
We will analyse our TPA data against each of these metrics and at the end of each section will
present a three level qualitative score card for Firm, Bank last look and Non Bank last lookliquidity, with the best performing class of liquidity receiving a score of 3 (best) and 1 (worst).
Where all classes of liquidity have provided a similar service we have scored them all at 2.

We will be looking at data from a 12 month period - January to December 2016 - as a unit. Thismeans that the data includes scheduled market events such as US Non Farm Payrolls, Fed ratedecisions and Brexit, US Election announcements, as well as unexpected and disruptive events
such as the October GBP ‘flash crash’. There is also a broad market consensus that execution
quality can vary by time of day, trade size and pair traded, and a comparative study of execution
quality under different market conditions may provide the basis of a future paper. However inorder to keep the scope of information presented manageable we have opted to treat the year
as a whole and we believe that even with these caveats, the average of trading over a year will
provide a useful insight into the differences between last look and firm liquidity. Furthermore
the ability to apply TCA to trades during both smooth and turbulent periods has real world
application for traders who are active in a range of market conditions.
Although the names of the venues are obscured due to commercial concerns, we would
welcome a serious and critical collaboration by any interested or independent parties, andwould value access to other trade databases with varying types of flow to the highly
uncorrelated flow seen in the TPA data set we use here.
Box 1
About the analysis►

The data used in this paper is from an independent Third Party Aggregator (TPA), whichcontains 7 million orders sent to seven LPs (both firm and last look) in 2016.

Three key metrics are analysed in-depth and are used to assess execution quality: fill ratio,
price variation and hold time. The TPA data is analysed against each of these metrics with athree level qualitative score card presented. This methodology allows us to compare the valueof each liquidity class on the same set of metrics.

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Part I (i): Applying standard metrics to a sample data set(i) Fill ratio/rejects

Fill ratio is usually expressed as the percentage of orders that have been filled as a fraction of
the total number of those sent to a liquidity provider. This has the virtue of being easy tocompute and understand. The higher the fill ratio - the better, as a reject will normally result
either in a missed opportunity to trade or in a worse price if the order is later resubmitted tothe same (or different) venue.

Although fill ratios are commonly used to compare execution in a commercial setting, themethod by which the reject rate is converted into a cost per trade is often not clear. One
method is to use a subsequent fill for a rejected order (either from the same or a different LP)
to determine the opportunity cost of a reject [2]. This also requires an accurate measure ofhold time. The strategy in use may also influence whether a higher rate of fast rejects with alower opportunity cost per event is preferable to a lower rate of rejects with a longer hold timeand potentially higher opportunity cost per reject.
For a quoted price stream from a single LP, the fill ratio should just be a measure of whether
the deal was done at the agreed price or not - was the order filled or rejected (or requoted).
Rejects (errors aside) are due to insufficient liquidity to match the trader’s order or
LP optionality.

We have calculated fill ratio as the number of orders receiving a fill, divided by the total numberof orders (excluding errors). This has some shortcomings in that it does not discriminate
between large and small orders nor does it adequately represent partial fills. We have compared
the results using a more proportionate calculation of notional value traded divided by notional
value ordered, but as this does not materially affect the findings we have opted for the simpler
fill and order counts as this is the method we see used by the majority of LMAX Exchange
customers.
Causes of rejects

Rejects often include a reason for the reject, and during this analysis we considered using thismessage field to understand the reason behind a reject with the aim of detecting the exercise
of optionality as opposed to other causes such as lack of liquidity.
Order type

ErrorsNon error rejects

Total

Market99

10,480

10,579Limit

709

36,47937,188Previously Quoted (PQ)

52

17,311

17,363

Total

860

64,27065,130

Table 1: Rejects classified by reason

However, in practice this is not a reliable technique. Messages are not standardised acrossvenues and some can be quite ambiguous in their meaning, covering a wide variety of potential
causes. There was only one exception to this - client error messages.
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Part I (i): Applying standard metrics to a sample data set
The clearly identifiable types of error messages seen in the data included: coding or message
format errors with the FIX order stream, trading being attempted outside of market hours, thetrader exceeding their position limits, having insufficient funds or failing some other pre-traderisk control or exposure tests. Error messages normally are very specific as it is in the interestsof all LPs to clearly indicate a problem that requires correction by the trader.

These error rejects were discarded from the fill ratio analysis as they are caused by errors overwhich neither the venues nor LPs have control. There is one exception - for the analysis of execution
latency they do have a particular use, which we cover in the ‘Hold time and execution latency’
section (p. 29). Error messages in the TPA data are dominated by rejects due to credit issues.
Reject rates by venue

We will first look at reject rates for market orders only. Market orders have no pricerestriction and we expect a high fill ratio (theoretically 100%) from all venues once errors are
excluded. Rejection reasons should relate purely to liquidity or optionality and by excluding
limit orders, we can exclude cancels and rejects due to conditions on limit orders that are nevermet. We further exclude all fill or kill (FoK) market orders to ensure there are no rejects basedon any size constraints.
Once all the variability from restrictions on matching is removed we should be left with theunderlying best case fill ratios for each venue allowing a direct comparison of each venue’s
ability to fill trades. The only remaining causes for rejections should then be if there is zerouseful liquidity on the book or if a reject happens due to last look optionality.
Venue

FilledNon error rejects

Fill ratio

Non Bank 2

267,30443

99.98%

LMAX Exchange

299,085182

99.94%

Bank 1

207,157130

99.94%

Bank 2

100,730372

99.63%

Bank 3

173,571

3,047

98.27%

Non Bank 3

120,7892,233

98.18%

Non Bank 1

115,823

3,648

96.95%

Table 2: Market order fill ratio by venue

The firm liquidity venue - LMAX Exchange - is near the top of the table, and there is a clear
grouping with the top 3 having a better than 99.9% fill ratio and the rest clustered at 99.5%
and below. Detailed investigation of the rejects for LMAX Exchange shows that they are all
liquidity based rejects related to times when market conditions did not permit orderly execution.
In common with some other venues LMAX Exchange includes a variety of protections against
off-market trade execution arising from either errors during order submission (‘fat finger’) or
discontinuities in the market price. Market discontinuities commonly occur immediately aftermarket open and are analogous to the ‘uncrossing period’ seen on equities exchanges.
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Part I (i): Applying standard metrics to a sample data set
This grouping implies a difference in execution model between the top three and the bottom
four LPs. The presence of both Bank and Non Bank LPs in the bottom four (assuming thebottom two are not chronically short of liquidity) suggests that rejects due to last look
optionality are common to both types of LP.

The next comparison is to look at orders with price restrictions. Some venues implement theseas limit orders with a threshold price and some as the ‘previously quoted’ or PQ order type,where a specific price is referenced via a quote id. These price constrained orders are more
representative of the majority of institutional trading.
Venue

Order type

FilledNon error rejects

Fill ratio

Bank 2

PQ

621,250

1,222

99.80%

Bank 1PQ

1,111,524

3,221

99.71%

Non Bank 3

Limit

768,467

4,190

99.46%

Non Bank 2

PQ

1,431,232

12,86899.11%

Bank 3

Limit

964,857

14,39198.53%

Non Bank 1

Limit

613,020

15,82897.48%

LMAX Exchange

Limit23,8412,070

92.01%

Table 3: Limit order fill ratio by venue

This result shows a completely different picture. Looking at the firm liquidity LP, the LMAX
Exchange fill ratio is now the lowest whereas for market orders it was near the top of the table.
Another surprise is that for some of the bottom four in the market order fill ratio rankings thefill ratio for limits or PQ orders are higher than they are for market orders.

Detailed investigation of the LMAX Exchange rejects using FIX logs and internal tooling showsthat 7% of the 2,070 rejects recorded for this set of trades were caused by market conditions
that did not allow for orderly execution of risk for general trading (as described above), with theremaining 93% being order cancels. Of the cancels, 1.4% were due to insufficient quantity
being available at the price point requested combined with a FoK strategy, and the remaining
98.6% were a limit price miss - i.e. the market had moved away from the limit price specified.
In other words, almost all the LMAX Exchange ‘rejects’ were driven by pricing behaviour on
the venue.

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Part I (i): Applying standard metrics to a sample data set
As the number of limit order trades from this source is relatively small, we examined the limit
order fill ratio for both the TPA and a variety of similar LMAX Exchange customers to see if thiswas an outlier.
Customer

OrdersFill ratio

A

75,95085.86%

B

136,872

91.24%

C

76,92492.55%

TPA

24,91092.58%

D

70,91893.99%

E

4,378

95.34%

Table 4: Limit order fill ratio by client

The limit order fill ratio is consistent between the TPA data set and the LMAX Exchangeinternal view of the fill ratio, as expected. Furthermore the limit fill ratio for the TPA lies withina range of fill ratios for other similar customers and is not an outlier. It is notable that othercustomers using exactly the same order types (and to the best of our knowledge a comparable
trading strategy) are able to achieve a higher fill ratio using the same market data.

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Part I (i): Applying standard metrics to a sample data set(i) Section summary: fill ratio/rejects

As expected, the order count fill ratio tells us that fill ratios on LMAX Exchange and 2 other LPs
are close to 100% for market orders - barring those times when market discontinuities made it
unsafe to trade. For price constrained order types - limit and PQ in this data set - the fill ratio
for LMAX Exchange is much lower than the last look liquidity providers, with almost all the limit
order ‘rejects’ being cancels due to a missed limit price. This implies that there is something
specific about LMAX Exchange liquidity which makes it relatively hard to trade successfully
with a high fill ratio using immediate execution limit orders. (We will return to this in Part II).
Metrics scorecard••

Market order fill ratio. LMAX Exchange is in the top three, but each of the Bank and
Non Bank venues have members in the top, medium and low thirds of the table giving
both of them medium scores.
Limit/PQ order fill ratio. This is influenced by the two different types of order, with
PQ having higher fill rates. Due to this, the Banks earn top marks, Non Banks comesecond, and LMAX Exchange trails in last.
MetricBank ‘last look’

Non Bank ‘last look’

LMAX Exchange

Market order fill ratio

2

2

3

Limit order fill ratio

3

2

1

Table 5: Fill ratio ‘scorecard’ points (higher is better)Box 2
Analysis of fill ratios►

The fill ratios on firm liquidity differ significantly for market and limit orders:••Market order fill ratios are close to 100% on firm liquidity, as expected;

Limit order fill ratios are much lower, with almost all the limit order ‘rejects’ being
cancels due to a missed limit price.

The lower fill ratios for limit orders on LMAX Exchange imply that there is something specificabout this type of liquidity, the trading strategy, or both (see Part II) which makes it relatively
hard to achieve a high fill ratio.

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Part I (ii): Applying standard metrics to a sample data set(ii) Price variation - slippage and price improvement

Price variation is a trader’s view of the difference between a desired or expected price andthe actual execution price achieved by an order. While attention is often focused on slippage(i.e. execution at a worse than expected price) when using market orders we should expect toexperience both slippage and improvement. Traders using price constrained orders (limit orPQ) may have been conditioned to expect neither; limit orders cannot slip and many traders do
not even consider measuring price improvement.

Measurement of slippage or improvement requires information which may only be available in
the trader’s own logs. We cannot rely on orders to carry the price which prompted the decision
to trade – market orders do not carry a price at all and the price on a limit order is notnecessarily the same value as the decision price – making this metric potentially both opaqueand highly subjective. However, the order placement behaviour of the TPA is far morepredictable, allowing us to measure the impact of price variation consistently and objectively
across LPs.

When the TPA receives a customer order, it waits until the market data it receives from the LPs
indicates that the order can be filled, meeting all price or size criteria specified. Once suitable
market conditions are identified the TPA selects one or more LPs, captures the current bestprice on the relevant side of the market and sends some or all of the order to the selected LPs
for execution as a ‘leg’. We have calculated slippage or price improvement per leg by looking at
the difference between the logged market price at the time the decision to trade was made andthe actual fill price received. This approach removes much of the individual variation from thedata, treating the TPA as a single customer trading with each of the LPs and requesting thecurrent price available for immediate execution.
We have excluded numbers from infrequently traded currency pairs (any instrument with lessthan 100,000 trades over the 12 month period of the data set). The remaining sample set
consists of trades in EURUSD, GBPUSD, USDJPY, AUDUSD, GBPJPY, USDCAD, EURJPY,EURGBP, NZDUSD, USDCHF, EURCHF, EURAUD, AUDJPY and AUDCAD, which together
represent 91% of all successful trades.

We have reported slippage and improvement using the FX conventions of ‘pips’, i.e. the 4th
decimal place of the price other than for currency pairs priced in JPY where the 2nd decimal
place is used. This introduces some comparability issues across currency pairs and over time,for example 1 pip GBPUSD is a smaller proportional slippage than 1 pip AUDUSD, and a 1st
January GBPUSD pip is a smaller proportional slippage than a 1st November GBPUSD pip dueto the depreciation of GBP over the year. However as all pip values fall within a range close to
0.01% of traded price (between 0.006% and 0.016% at the extremes) we have erred on theside of using familiar units over something abstract but more mathematically accurate such as
basis points.

23
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Part I (ii): Applying standard metrics to a sample data set
Market orders

Table 6 shows the proportion of market orders receiving fills where prices showed slippage,were as expected or showed improvement.
Ratio of slippage
to improvement

Venue

SlippageAs expected

Improvement

Bank 30.00%

100.00%0.00%

Non Bank 2

1.14%

98.18%

0.68%

1.68

Non Bank 1

19.40%

70.04%

10.56%

1.84

LMAX Exchange

4.36%

93.54%

2.10%

2.08

Non Bank 3

0.64%

99.15%

0.21%

3.05

Bank 2

3.47%

95.65%

0.88%

3.94

Bank 1

7.16%

92.15%

0.69%

10.38

0

Table 6: TPA market order price variation statistics

Chart 1 shows the percentage of orders that experienced slippage or improvement at 0.1 pip
intervals. Negative numbers indicate slippage (a worse price than expected) while positive
numbers indicate price improvement (a better price than expected).

In addition to the skew and shape of the distribution, it is important to note the scale is limited
to +/- 5 pips for illustrative purposes. In many cases the maximum improvement observed isless than 5 pips (denoted by the green marker) whereas the maximum slippage observed is in
most cases more than 5 pips away from the zero point (indicated by the red marker). In the TPA
data, only LMAX Exchange exceeds 5 pips of price improvement.
The price variation of market orders falls into two distinct categories. There are those venues
which show both slippage and improvement at an approximately 2:1 ratio and those for which
the slippage is dominant with little or no price improvement.

As LMAX Exchange operates a firm central limit order book offering best execution in pricetime priority, we might expect a more neutral result. The skew towards slippage suggests thatbehaviour in this data set is linked to market direction, demonstrating a propensity towards
buying in a rising market and selling in a falling market. This leads to a natural bias towards
slippage and away from improvement. If we take LMAX Exchange behaviour as an
approximation of the pure market, then this ratio becomes an interesting metric for market
order price variation. This allows us to distinguish between those venues which are passing theunderlying market price behaviour straight through to the customer against those which showa higher bias towards slippage.

24
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I (ii): Applying standard metrics to a sample data set%/tick30
20

LMAX Exchange
10040%/tick30

Bank 1

20
10
0
20
20

%/tick15

Non Bank 1

10
5
00
40

%/tick30

Bank 22010040%/tick30

Non Bank 2

20
10
0
40

%/tick30

Non Bank 3

20
10
0
-5

-4

-3

-2

-1

0
pips

1

2

3

4

5

Chart 1: Market order slippage by venue
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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I (ii): Applying standard metrics to a sample data set
Limit/PQ orders

The situation for order types with price constraints is more interesting. These order types
prohibit slippage, and the TPA sets its limit price to the same value it uses as a reference levelto calculate slippage or improvement for market orders, so naively we might expect that theprice variation for such orders would have a similar incidence and distribution to the priceimprovement side of the market order charts shown above.
With the exception of LMAX Exchange, this is not the case. Table 7 shows the proportion oflimit or PQ orders receiving price improvement by venue, alongside the market order priceimprovement from the same venue for comparison.
Market orderimprovement

Venue

Order type

Improvement

LMAX Exchange

Limit

6.358%

Bank 1PQ

0.001%

0.69%

Non Bank 2

PQ

0.000%

0.68%

Non Bank 3

Limit

0.000%

0.21%

Non Bank 1

Limit

0.000%

10.56%

Bank 2

PQ

0.000%

0.88%

Bank 3Limit

0.000%

0.00%

2.10%

Table 7: TPA limit/PQ order price improvement statistics
Only LMAX Exchange exhibits a significant level of price improvement for limit orders.
Improvement is either negligible or entirely absent for limit orders executed on all other venues.

As a further illustration of the mechanism driving limit order price improvement, chart 2 shows
the distribution of the level of improvement received by both limit and market orders on LMAX
Exchange, showing the percentage of orders that received improvement at 0.1 pip intervals.
LMAX Exchange limit orders

LMAX Exchange market orders

48.32
45
40
35
%/tick

30
25
20
15
10
5
0
0

1

2

pips

3

4

5

Chart 2: Price improvement for LMAX Exchange market and limit orders
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Part I (ii): Applying standard metrics to a sample data set
The consistent distribution of price improvement observed for both order types is a key
characteristic of firm liquidity. Limit prices only constrain the worst execution price for an
order. When better prices are available, limit and market orders behave identically.

In contrast, the very different price improvement behaviour observed for market and limit
orders on last look liquidity demonstrates a fundamentally different approach to filling limit
orders in which LPs exercise their option to fill almost every order at its limit price, even though
the evidence of fills on market orders indicates that a better price should be available for some
proportion of the time.
For full disclosure, it is worth noting that liquidity providers other than LMAX Exchange offer
price improvement. Unfortunately the TPA did not route a sufficient number of orders withany such provider for us to be able to make statistically valid comparisons. As noted above, wewould welcome collaboration under NDA with traders with trade databases that include LMAX
Exchange, ECNs, Bank and Non Bank venues to further our understanding of execution quality.
Returning to the data at hand, the obvious question to ask is where has the price improvement
on limit orders gone for all other venues?
(ii) Section summary: limit/PQ orders

The only venue that offers the same improvement on both limit orders and market orders is
LMAX Exchange. This arises from the use of firm execution against a central limit order book,and is driven by exactly the same market behaviour that gives rise to both slippage and priceimprovement in market orders. None of the other venues provide significant limit order priceimprovement, even though a subset of them clearly expose similar underlying price volatility on
market orders.
Metrics scorecard••

Market order slippage rate. Looking purely at slippage percentages, LMAX
Exchange is in the middle of the table while each of the Bank and Non Bank venues
have members in the top, medium and low thirds of the table. As a result we willaward all the benefit of the doubt with a medium place.

Limit price improvement. LMAX Exchange is the only venue to offer significant priceimprovement in this data set.
MetricBank ‘last look’

Non Bank ‘last look’

LMAX Exchange

Market order fill ratio

2

2

2

Limit order fill ratio

1

1

3

Table 8: Price variation score card points (higher is better)27
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I (ii): Applying standard metrics to a sample data set
Box 3
Price variation analysis

Only firm liquidity venues offer consistent price improvement on both market and limit orders:•►•

The analysis of market orders across the LPs shows that price variation can beeither symmetrical (both price slippage or price improvement are passed to thecustomer without restriction) or asymmetrical (where the price improvement passed
to the customer is limited but price slippage isn’t);
Only LMAX Exchange demonstrates symmetrical price variation on both market andlimit orders.

The observed price improvement behaviour on last look liquidity demonstrates a fundamentallydifferent approach to filling limit orders in which LPs exercise their option to fill almost every
order at its limit price, even though the evidence of fills on market orders indicates that a better
price should be available for some proportion of the time.

The obvious question that comes out from this analysis is ‘where has the price improvement on
limit orders gone for all other venues’?

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Part I (iii): Applying standard metrics to a sample data set(iii) Hold time and execution latency

Execution latency is the time taken between an order being transmitted from the trader’ssystem and the receipt of a response. Hold time is the commonly used name for discretionary
latency where the execution of an inbound order from a trader is deliberately delayed pending
a decision to fill or reject by the liquidity provider’s systems. This period of time is also referred
to as the last look window.
Hold time/discretionary latency is just one component of execution latency, so we must firstlook at other causes of latency before we can assign hold times to each venue in order tocompare this aspect of the execution quality of last look and firm liquidity.
We will divide execution latency into the following components:•••

Systematic. The time required to complete the necessary operations to execute thetrade, including network round trip time, transit through any pre-trade risk control
system, matching engine cycle time and any other systematic delay applied across all
customers of the LP;

Tail. Each cause of systematic latency will also have a characteristic jitter with causes
at network, operating system or application level. In addition, platform capacity
constraints ranging from microbursts to sustained higher traffic rates during market
announcements can lead to queueing and congestion giving a familiar long taillatency distribution;
Discretionary. Any time added where the order is held prior to executing a trade.
LPs may apply or vary hold time based on their assessment of a customer’s market
impact, the current market conditions or their own appetite to trade in agiven direction.

Each of these components is subject to variation over time. Systematic latencies may beaffected by hardware or software upgrades which may change the LP’s latency profile.
Tail latencies may likewise be affected by capacity upgrades or constraints. Lastly hold time
may be adjusted by LPs in response to a change in market conditions, strategy, policy or simply
based on developing insight into a customer’s trading behaviour.
While we are primarily concerned with discretionary latency in the direct comparison of firmand last look liquidity, information regarding the non-discretionary causes of latency is alsovaluable in its own right, as this can be used to make order routing decisions as well as for TCA
purposes. For example, if the latency of a particular LP degrades badly during busy times, thisinformation may be used to augment best price or volume criteria in selecting an
execution venue.

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Part I (iii): Applying standard metrics to a sample data set
Chart 3 shows the execution times for rejects and fills for a particularly interesting last look LP
in the TPA data set providing a clear example of each of these different types of latency.
The execution time is recorded to the nearest millisecond and the frequency of occurrence isshown on a logarithmic scale. The chart spans the whole year of 2016.

Orders

Error
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Reject

Fill

Discretionary
Tail latency

Systematic

0

25

50

75
100
125
Execution latency (ms)

150

175

200

Chart 3: Detailed execution times for Bank 3

An analysis of this kind would normally use supplemental information gathered by the trading
infrastructure to determine some parts of the systematic latency. For example the base network
latency can be estimated by using session level FIX messages – heartbeats or test requests –which are typically processed at the edge of the LP’s trading platform. Unfortunately that levelof data was not available to us in the TPA data set, and we were then forced to determine thesystematic latency from the execution time profiles available. Fortunately there are somemarkers in the data that can help us.
For the LP in chart 3, there is an interesting pattern in that fills and non-error rejects indicatethat the minimum response time is around 81-82ms. However, when we looked at rejects due
to errors – as defined earlier – a response time of 2-3ms is evident. 99.7% of these errors
were caused by a reject at the pre-trade risk control level, rather than a programming or FIX
session level error. This is then an error from within the platform – not an immediate reject at
the edge. [i]With the moderate assumption that the next logical step within the platform would be matching
the order against available liquidity, we can then assign a systematic latency of at least 2-3ms.
The discretionary latency or hold time would then be 80ms for this LP. It is unlikely that anorder would transit the network and pre-trade risk control systems within 3ms and then take afurther 80ms to be placed unless there was a hold time in play.
The execution latencies for all of 2016 for each LP in our set are shown in chart 4 (p. 32),
which plots the millisecond latencies for fills, errors and rejects against the number of orders
experiencing that level of latency. There are several features which stand out and bearfurther investigation:
•
•
•

30

Histograms for the same class of event (e.g. fills) which display multiple peaks in thelatency histogram;LPs where the peaks for fills, errors and rejects occur at different modal latencies;
Long tails to execution latency distributions.

[i] Warning: using a combination of network pings, FIX session level messages and deliberately generating different
error conditions as a basic probe of the systematic latencies within an LP's platform, is a practice we do not advocate orcondone and may be in breach of your terms of connection or other agreements with the LP.

An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I (iii): Applying standard metrics to a sample data set
Our first task is to investigate each of the features above so that we can determine acharacteristic systematic latency and hold time for each LP. We will investigate the first 200
ms of latency in detail. In some cases the latency distributions extend beyond this, however,latencies much beyond 200ms are usually a very small proportion of trades and our goal here
is to derive the base characteristics of hold time for each LP.
Defining the systematic latency as being the mode of the first peak in the execution time
histogram (whether from fills, rejects or errors) and the hold times as being the difference
between the systematic latency and the mode of the second peak, we can produce thefollowing table of systematic latencies and hold times. Rejects and fills are examined
separately as their latency histograms may differ as in the example above.
Venue

Systematic (ms)Fill hold time (ms)Reject hold time (ms)LMAX Exchange

1

0

0

Bank 145

1Non Bank 1

1

90

90

Bank 21

95Bank 3

4

80

79

Non Bank 2

1

0

0

Non Bank 3

1

0

0

Table 9: First glance modal hold times by LP
A quick comparison of table 9, which attributes very similar latency profiles to Non Bank 2,
Non Bank 3 and LMAX Exchange, and chart 4 (p. 32), which shows a very different visualsignature for each, indicates that our initial scorecard is not telling the whole story.
Box 4
Execution latency►

In a direct comparison of execution latencies on firm and last look liquidity, the primaryconcern is discretionary latency or hold time.

A more detailed investigation of execution latency can reveal systemic latency and the tailof the latency distribution, providing further insight into what is really driving an LP’slatency characteristics.

LPs may apply or vary hold time based on their assessment of a customer’s market impact,
the current market conditions or their own appetite to trade in a given direction.

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Part I (iii): Applying standard metrics to a sample data set

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

Orders

Error

1e+6
1e+5
1e+4
1e+3
1e+2
1e+1
1e+0

RejectFill

LMAX ExchangeBank 1

Non Bank 1

Bank 2

Bank 3

Non Bank 2

Non Bank 3

0

25

50

75
100
125
Execution latency (ms)

150

175

200

Chart 4: Execution times by venue for 2016

The multiple peaks and wide variation in the tail distribution of the latency histograms
displayed by last look LPs require further investigation, and are suggestive of arbitrary changes
to discretionary latency which, by definition, do not occur on firm liquidity.
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Part I (iii): Applying standard metrics to a sample data set
Tail latency

The single hold time number needs to be supplemented by an evaluation of tail latency toform a complete view of the impact of hold times on execution quality. On paper, tables 9 and
12 (p. 31 & 38) place LMAX Exchange, Non Bank 2 and Non Bank 3 in the same category.
However, chart 4 shows qualitatively very different profiles, ranging from the very wellconstrained to very long tails - sometimes including one or more peaks at higher latencies (forexample Non Bank 2 and Non Bank 3). These peaks match the criteria outlined above for holdtimes – the only difference is that they are applied selectively to parts of the flow.
The distributions are not Gaussian, Poissonian or power law, so to quantify them we have tolook at the percentiles of the distribution rather than statistical measures like standard
deviation or mean.
As the curves in chart 4 are averages over all of 2016 it is useful to include the dimension oftime to help us separate the cases where multiple peaks are due to changes over time as
opposed to those where hold times may be selectively applied to parts of the flow on shorttimescales. To do this, we will use heatmaps.
Chart 5 shows a log scale heat map of execution latency for Bank 3 by month.

Bank 3200

Frequency
1e+11
1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180
160

Latency (ms)

140
120
100
80
60
40
20
0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Chart 5: Execution latency by month for Bank 3
The x-axis shows calendar month with execution latency on the y-axis and the number oforders experiencing this latency indicated by the intensity of the coloured area on the chart –the modal latency (or latencies) showing up as darker. We can now see that the two peaks inthe execution latency charts 3 (p. 30) and 4 are in fact a result of a distinct change in modal
latency in June 2016.

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Part I (iii): Applying standard metrics to a sample data set
Similar heatmaps for the remaining LPs are displayed in chart 6. Bank 1 and Non Bank 1 were
not pricing during the first couple of months of the year, so their charts start in March andApril respectively.

When LPs alter their base hold time the whole latency distribution is moved. This is particularly
evident in chart 5 (p. 33). To remove the effect of the LPs varying their base hold times over theyear, we calculated the delta between the 50% and the 99% and 99.9% levels on a month bymonth basis to arrive at a measure of the long tail mostly independent of value of the basehold time.
Table 10 below shows the results of this method and some sample characteristics of each LP’s
tail latency distribution displayed as the difference between the 50% and the higher percentiles.
Venue

50% (ms)

99% - 50% (ms)

99.9% - 50% (ms)LMAX Exchange

1.0

1.4

6.8

Bank 1

9.7

22.3

70.7

Non Bank 1

98.2

1.9

9.0

Bank 2

10.1

15.7

32.6

Bank 3

98.7

6.8

21.7

Non Bank 2

1.0

2.7

32.0

Non Bank 3

10.3

23.3

103.2

Table 10: Tail latency percentiles
There are some objections to this rather simplistic approach – although for most LPs their taillatency is unchanged by moves in their base hold time (their whole distribution moves as a unit)
this is not always the case – for example, in December 2016 Non Bank 3’s tail latency improves
dramatically (their 99.9%ile – 50% delta drops from 110ms to 25ms) once its base hold time
has moved to 110ms. That is a strong indicator that part of their pre-December tail latency
may be due to a more fine grained selective application of hold times to different parts of theflow and not purely the result of stochastic variation to the systematic latency, whereas from
December rather than part of the flow being subjected to a hold time of 110ms, all of it is.

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Part I (iii): Applying standard metrics to a sample data set
Bank 1

Bank 2

200

140
120
100
80

Frequency
1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180
160
140

Latency (ms)

1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

160

Latency (ms)200

Frequency

180

120
100
80

60

60

40

40

20

20

0

0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

LMAX Exchange

Aug

Sep

Oct

Nov

Dec

200

Frequency

160
140
120
100
80

Frequency
1e+11
1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180
160
140

Latency (ms)

1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180

Latency (ms)Jul

Non Bank 1

200

120
100
80

60

60

40

40

20

20

0

0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Non Bank 2

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Non Bank 3

200

Frequency

200

180

1e+12
1e+11
1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180

140
120
100
80
60

1e+11
1e+10
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

140
120
100
80
60

40

40

20

20

0

Frequency

160

Latency (ms)160

Latency (ms)

Jun

0
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Chart 6: Execution latency by month heatmap

Ranking the ‘quality’ of tail latencies can be somewhat subjective – what may be acceptable for
one trading strategy may not be for another. However, we can say that the last look LPs offer achoice of either a low hold time but a long tail latency (probably composed of selective
application of hold times) or a high hold time and a reasonable tail latency (e.g. Non Bank 1 post
April 2016). Only LMAX Exchange in this data set consistently provides both a low latency anda small tail latency.
Latency variation with time

The variation in execution latency can be continuous or discrete - both behaviours are shownin the heatmaps above. Tail latencies are a manifestation of continuous variation - particularly
queueing or congestion under load as noted above. In this section we will examine the modal
latency and discrete changes to it, as this indicates a change affecting all orders and may be asignature of a change to last look hold times.
At a high level we considered two possible causes of discrete changes in the latency profile:••

A step change in systematic latency due to upgrades or infrastructure changes;
A step change in discretionary latency/hold time.

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Part I (iii): Applying standard metrics to a sample data set
To determine if the change was due to a change in systematic latency or due to a change in thediscretionary hold time, we looked at the timing of the change relative to the working week on
the assumption that it is extremely unlikely that any infrastructure or software upgrade work
would be performed within market hours unless a severe loss of service occurred.

A second consideration is the direction of the change. Infrastructure and software changes act
mostly to improve the modal latency and reduce tail latencies. While it is possible that such achange could introduce an unintentional deterioration in latency characteristics we would
expect to see those changes quickly rolled back or corrected. Our assumption therefore is thatchanges which increase the modal latency are more likely to reflect a conscious businessdecision. The last consideration is the size of the change. Humans are drawn to multiples of 5
and 10 [3]. Upgrades or infrastructure changes rarely show such preferences.

In chart 5 (p. 33) above there are two distinct populations with similar tail latencies. Between
January and Wednesday June 22nd 2016 the modal latency was 84ms. After Wednesday June
22nd the modal latency changed to 114ms. Subtracting the systematic latency derived above
gives us hold times of 80 and 110 ms. As this occurred mid week, the numbers are nice androunded. We can then be confident that in this case a commercial decision was made toincrease hold time around the UK EU referendum and never reset.

Non Bank 3
200

Frequency
1e+9
1e+8
1e+7
1e+6
1e+5
1e+4
1e+3
1e+2
1e+1

180
160

Latency (ms)

140
120
100
80
60
40
20
0

Sep

Oct

Chart 7: Execution latency by day for Non Bank 3

Nov

Dec

A final consideration is any evident selectivity in the change. Unplanned increases to systematic
latency are more likely to affect all orders or increase the length of the distribution tail.
A change which introduces multiple concurrent modal latencies indicates that the higher
latency is incurred selectively and is more readily explained by longer hold times being applied
only to specific customers, orders or only at certain times.

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Part I (iii): Applying standard metrics to a sample data set
An example of a set of selectively applied changes is shown in chart 7:
•

•
•
•
•

Between January and September this LP consistently exhibited modal execution
latency of 1ms, and at the beginning of September 95% or more orders are executed
within 5ms. Other less pronounced latency peaks (containing less than 1% of orders)
could be observed around 70, 110 and 180ms;

On Tuesday 27th September the proportion of orders in the 110ms peak (distributed
between 105 and 125ms) rises to 15%;
Between Tuesday 27th September and Tuesday 8th November, the proportion oforders in the higher latency peak varies daily between 1.5% and 22.6%;

On Wednesday 9th November the proportion of orders executed between 105 and125ms rises to 99.7%;

With the exception of a temporary re-appearance of low latency execution between
Sunday 13th November and Tuesday 15th November, the higher latency peak
represents more than 99.5% of orders for the remainder of the year.

The selectivity, timing and abruptness of these changes, with the lower latency execution being‘turned off’ twice in the middle of the trading week, are all strong indicators of discretionary
latency being applied with the higher range of hold times being run in parallel with the previous
low latency settings before the increase is applied across the board.

In practice all of the LPs with the exception of LMAX Exchange and Bank 1 appear to experimentwith different hold times during the year, for some or all of the trade flow, as summarised intable 11.
Venue

Time period

Day of change

Modal hold time(s) (ms)LMAX Exchange

Jan 1st – Dec 31st

Non Bank 1

Jan 1st – Apr 28th

Thu

155

Jan 1st – Dec 19thMon

9Bank 1

Bank 2

Bank 3
Non Bank 2

Non Bank 3

Jan 1st – Dec 31st
Apr 28th – Dec 31st

Thu

0
5

90

Dec 19th – Dec 22nd

Mon-Thu70

Jan 1st – Jun 22nd

Wed

80

Dec 22nd – Dec 31st
Jun 22nd – Dec 31st

Thu

9

Wed

110

Mar 16th – Apr 13th

Wed-Wed5

Jan 1st – Nov 16th

Weds

Jan 1st – Dec 31st

Jun 20th – Jul 21st

Nov 16th – Dec 31st
Mar 15th – Jun 17th
Mar 15th – Jun 17th

Table 11: Timing of changes to hold times0

Mon-Thu

95

Weds

110

Tue-Fri180

Tue-Fri

070

37
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I (iii): Applying standard metrics to a sample data set
Timing of rejects vs fills

Although not directly related to our TCA calculations, we also looked at the timing between
rejects and fills. In conversation with traders it is often taken as a given that rejects will takelonger to process than fills as part of a last look process. The usual rationale for this is that LPs
will know quickly when they want to fill, but may take some time to review market conditions
before deciding on a reject.

This turns out not to be true for the TPA data set. Rejects happen at the same time as fills or
before, which is the opposite of the conventional wisdom. It is possible that this is an artefact of
this particular data set and the LP’s assessment of this particular trade stream’s market impact.
Nonetheless, visual inspection of the profiles in chart 4 (p. 32) which are summarised in table 12
below, show that there is a clear division between Bank LPs and the rest. Traditional Bank
platforms typically reject faster than they fill. Non Bank platforms and LMAX Exchange rejectat the same time as they fill. The deltas are also not as large as we had expected and seem to beconstant by LP irrespective of whatever the hold time is currently set to.
Venue (times in ms)

Fill modal hold time

Reject modal hold time

Fill - reject delta

LMAX Exchange

0

0

0

Bank 15

1

4Non Bank 1

90

90

0

Bank 29

5

4Bank 380

79

1

Non Bank 2

0

0

0

Non Bank 3

0

0

0

Table 12: Relative timing of fills vs rejects
Box 5
Hold time and execution latency analysis

The analysis of different components of execution latency shows that in this data set:

►

•
•

•

Last look LPs adjust hold time depending on the market conditions throughout
the year;

Last look LPs with a shorter hold time exhibit less consistent execution latency, which
may be indicative of longer hold times being applied selectively, while those with ahigher hold time show more consistent latency;
The hold times preferred by the last look LPs cluster around 100ms (2016 data).

The absence of discretionary latency means that only LMAX Exchange provides consistently
low execution latency with no arbitrary variation over the year.
38

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Part I (iii): Applying standard metrics to a sample data set(iii) Section summary: hold time and execution latency

We have explored the different components of execution latency in this section. The hold times
preferred by the last look LPs tend to cluster around 100ms, and we will use this figure as thebasis for calculating the cost of hold time in the next section. Table 13 summarises the main
findings. There is only one LP (Non Bank 2) with a comparable latency profile to LMAX Exchange,and even so there are horizontal lines on chart 6 (p. 35), most prominently in June/July but
present throughout most of the year that indicate selective hold times at the 95ms level.
Venue (times in ms)

Fill modal hold time

Reject modal hold time

LMAX Exchange

0

6.8Bank 1

5

70.7

Non Bank 1

90 & 155

9.0Bank 2

9 & 7032.6

Bank 380 & 110

21.7

Non Bank 2

0

32.0

Non Bank 3

0 & 110103.2

Table 13: Summary of execution latency characteristics by venue
Two out of three Non Bank LPs favour selective application of hold times, whereas two of three
Bank LPs favour a simple base hold time and a moderate latency tail.
Only LMAX Exchange provides low and consistent execution latency with no hold times orvariation over the year.
Metrics scorecard••

Execution time 50%ile. LMAX Exchange is the clear winner here, with two Non Bank
LPs being competitive and one with an obvious hold time. Only one of the Bank LPs is
competitive, leaving them last.

Hold times/long tail latency. Again LMAX Exchange is the clear winner. The second
and third places swap due to the presence of multiple hold times for the Non Bank LPs.
MetricBank ‘last look’

Non Bank ‘last look’

LMAX Exchange

Execution time 50%ile

1

2

3

Hold times/long tail latency

2

1

3

Table 14: Hold time score card points (higher is better)39
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I: Applying standard metrics to a sample data set
Part I: Summary of findings:

Full metrics scorecard for each class of LP on our sample data set:
MetricBank ‘last look’

Non Bank ‘last look’

LMAX Exchange

Market order fill ratio

2

2

3

Limit order fill ratio

3

2

1

Market order slippage rate

2

2

2

Limit order price improvement

1

1

3

Execution time 50%ile

1

2

3

Hold times/long tail latency

2

1

3

Totals11

1015

Table 15: Combined scorecard by class of LP

For simplicity we have restricted the scorecard to two metrics per category. We are aware of
the “well they would say that” observation, irrespective of the lengths we have gone to internallyto conduct an unbiased and fair assessment. The TPA data set was chosen to compare thebehaviour of a single common trading strategy on both styles of liquidity. In common with many
FX traders today that strategy has been developed for last look and poses apparent problems
for firm liquidity. We discuss the limit order fill ratio findings and explore the causes behind it
in Part II.
There are several clear points that emerge for the TPA sample data set:•

The market order fill ratio is close to 100% on firm liquidity, LMAX Exchange;•

Limit order fill ratio on LMAX Exchange is related to pricing behaviour on the venue;•••

The limit order fill ratio for LMAX Exchange appears to be comparatively very low;

LMAX Exchange is the only provider offering significant limit order price improvement;

LMAX Exchange exhibits consistently lower execution latency and shorter tail latency.

In addition, the following extra metrics or aspects have emerged from the analysis:••••

Expected slippage/improvement ratio is 2:1; higher values relate to non market factors;

The degree of price improvement for limit orders should mirror that for market orders.
If not, that is an indication limit orders are not being filled at the underlying market price;
Latency thresholds which change during the week are hold times set forcommercial reasons;Hold times may be selectively present on parts of the flow, and manifest as longtail latency.

40
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part I: Applying standard metrics to a sample data set
Box 6
Summary of the key metrics analysis

Fill ratio, price variation and hold time execution metrics suggest the following differences
when trading FX on firm liquidity:

•
•
•

►

•

The market order fill ratio is close to 100%; whereas the limit order fill ratio is lower
when compared to last look venues, suggesting pricing behaviour differences on firmvs last look liquidity;
Only firm liquidity offers significant limit order price improvement;
Execution on firm liquidity is not subject to discretionary latency;

Firm liquidity exhibits lower and more consistent execution latency than other venues.

41
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

blank pagePart II

Execution quality metrics and firm liquidity

Part II: (i) Execution quality metrics and firm liquidity
In this part we will investigate the underlying mechanisms behind the apparently anomalous
limit order fill ratio seen on the sample TPA data set and explain in detail our approach toquantifying the financial value of price improvement and cost of higher execution latency or
hold time preparing the ground for the relative TCA calculations in Part III.

During the second half of 2016, while researching what would become this paper, we extended
the market and execution quality statistics collected by LMAX Exchange venues. The data inthis section is based on insights from statistics collected on our London, Tokyo and New York
exchanges supplemented by other third party data sets where noted.
In our sample TPA trade database the overwhelming majority (over 90%) of limit order rejects
on LMAX Exchange are cancels caused by price fluctuation/volatility. This observation and thelimit order fill ratio result leads to the questions we will address first:•••

Why do the flow characteristics in the TPA data set allow for higher fill rates from lastlook providers?
Can we observe higher price volatility on LMAX Exchange which may lead to a lower
fill ratio for limit orders?
Are there any order placement strategies that will improve limit order fill rates?

Most importantly we will show why it is worth going to this trouble for traders who are
currently satisfied with the nominal fill rates offered by their last look LP.
Box 7
Approach for quantifying the value of execution on firm liquidity►

To quantify the value of firm liquidity, the previous analysis of fill ratio, price variation and holdtime metrics needs to be developed by:••••

Exploring the relationship between higher price volatility and lower limit order fillratios observed with firm liquidity;
Understanding order placement strategies that improve limit order fill rates;
Calculating the value of price improvement;

Quantifying the cost of higher execution latency or hold time on last look venues.

(i) Market impact

Over the last few months we have discussed early drafts of our results with a variety ofindustry experts. The anecdotal evidence from comparison with their own experience is thatthe fill ratios observed for limit orders for all of the last look venues in the TPA data areunusually high, leading to a conjecture that the TPA trade flow was very benign from theperspective of the liquidity providers. In order to prove this theory, we have applied a similar
methodology to the majority of FX market makers (see [4] for an example from a leading Non
Bank LP) and started the collection of market impact statistics on the LMAX Exchanges.
Our approach to measuring market impact is to calculate the profit and loss of each traderelative to the market mid price at various time points after execution (from milliseconds to44
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part II: (i) Execution quality metrics and firm liquidity
minutes). In normal conditions each trade will instantaneously appear as profitable for the LP(and therefore loss making for the trader) due to the bid-offer spread. The LP’s profit will thenrise or fall as prices change. Flow is considered to be ‘benign’ (also described as ‘soft’ or‘uncorrelated’) if it takes a long time before becoming unprofitable from the LP’s perspective or
remains profitable over the entire measurement horizon. The other extreme is ‘toxic’ (alsodescribed as ‘sharp’ or ‘highly correlated’) flow which becomes unprofitable from the LP’s
perspective over very short time horizons (milliseconds to seconds). Each LP will have theirown view as to what time horizons demarcate the boundaries between correlated, average anduncorrelated flow.
We can analyse the net impact of a large number of trades by aggregating the profit or loss atthe same post trade measurement times. To allow for trades of different sizes and on different
instruments we can express the profit as a fraction of the notional value traded to create acomposite market impact. An LP would normally expect profits to decline from the value attrade execution given the directional bias in trading activity (i.e. given a bias towards buying on
rising and selling on falling prices, most trades will show a short term deterioration for the LP).
The extent and gradient of this decline, and the recovery (if any) of profits over longer timescales characterise the difficulty of servicing a particular trader's flow from the perspective of
the LP.

An aggregate trading pattern which remains consistently profitable for the LP, even if it showssome short term decline, provides the LP with the option to hold positions and internalise thistrading against other clients or hedge with limited impact on the underlying market, and would
usually be considered benign and uncorrelated.
Institutional

BrokerAggregator

Yield per $million traded

70
60
50
40
30
20
10
0
-10
0.001 0.005 0.05 0.15

0.4

0.8

2

/

10

40

80

150

Seconds after trade

Chart 8: Market impact by profile on LMAX Exchange

Chart 8 shows the market impact for a set of different customer profiles with different flow
types on LMAX Exchange London. The three different profiles are;•Broker: Customers of LMAX Exchange Broker•

Aggregator: Customers with a similar profile to the TPA•

Institutional: Customers of LMAX Exchange MTF

The criteria for each profile are covered in more detail in the section ‘Quantifying the value of
price improvement’ (p. 51).
45
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Part II: (i) Execution quality metrics and firm liquidity
The Aggregator profile flow is very benign with a net positive P&L over all time horizons shown.
For this type of flow, last look providers can provide customised very tight pricing. In contrast
the LMAX Exchange firm liquidity model of an open anonymous central limit order book whichcaters for a wide variety of flow types is placed at a natural disadvantage. As a second
consequence of the uncorrelated nature of the flow, we would also expect that the reject rates
offered by the last look LPs should be very low when compared to other more correlated flow
types, as observed previously.
The Aggregator profile flow is, however, not typical of all trading, or indeed all flow throughaggregators. More typical average impact profiles for all Broker and Institutional customers on
the London exchange are also shown in chart 8 (p. 45), with an expected crossing of the zero
point for the Institutional customers at a very familiar number - 100ms.
This answers our first question – for this specific customer profile, one of the LMAX Exchange
USPs of the ‘same price for everyone’ places firm liquidity at a disadvantage to last look where
tighter prices can be offered based on the assessment of market impact.

However, even for this profile there are other considerations of the firm liquidity trading model
which can offset this head start for last look, and we will now look at the impact of pricestability on the particular execution style favoured by this subset of aggregators.
Box 8
Assessment of market impact and differentiated pricing by last look providers

►

•
•

•

The analysis of different trading profiles shows that the trade flow from the TPA had
minimal market impact;
Trading strategies with more sensitivity to market impact risk information leakage,
potentially resulting in disadvantageous price changes by last look LPs ahead offull execution;
These strategies benefit from trading on firm liquidity, which does not suffer frompre-execution information leakage.

46
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part II: (ii) Execution quality metrics and firm liquidity(ii) Price volatility in firm liquidity

The commonly cited benefits of last look in FX are tighter prices for traders and the optionality
protection it affords market makers. In the event that the LP’s pricing is wrong with respect tomarket conditions then last look gives them the option to reject trades.

The corollary of that is that for a firm liquidity venue where optionality is not present, the time
to cancel a stale (or even unnecessarily competitive) price and update with a newer pricebecomes key to an LP’s profitability, leading to a very rapid turnover of liquidity at the top of theorder book. This is similar to the latency ‘arms race’ seen in other asset classes – for example
equities – where competition amongst cross connected or co-located LPs to position
themselves within the book on scales of micro to milliseconds is well documented [5]. As aresult, a much more dynamic market pricing structure is to be expected on firm liquidity venues.
This manifests as far more price variation or ‘jitter’ on short time scales.

The TPA data set includes the number of top of book market data updates per second recorded
for each venue. Unfortunately the data is recorded at minute level averages, which eliminates allability for us to compare burst rates or other microstructures in the data. Nonetheless thenumber of updates can stand as a proxy for the general volatility of the book.
Bank 1

Bank 2

Bank 3

Non Bank 1

Non Bank 2

Non Bank 3

LMAX Exchange

100
90
80

Market data updates/Sec

70
60
50
40
30
20
10
0
13:16:40

13:20:00

13:23:20

13:26:40

13:30:00
Time

13:33:20

13:36:40

13:40:00

13:43:20

Chart 9: market data update rates for venues over Non Farm Payroll
Chart 9 plots the top of book data rates across a US Non Farm Payroll (NFP) announcement.
As expected from the cancel time argument above LMAX Exchange has a much higher update
rate than the last look LPs, even with the high degree of smoothing or ‘averaging down’ in
per-minute aggregates. The approximate ratio of LMAX Exchange market data rates to the next
most active provider (a factor of 2-3) is maintained over normal trading as well as over market
events which cause spikes and step changes in data rates.
The sample data above however can understate the real situation in a way that is particularly
relevant to the execution style of the TPA. The actual rate at which prices change in thematching engine, which will determine whether or not a limit order can be filled will be higher
than the rates most clients observe in their market data for a variety of factors (see Appendix B– factors affecting Internet delivery of market data).

47
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Part II: (ii) Execution quality metrics and firm liquidity
We can measure price jitter by examining the distribution of lifetimes of local price peaks in the
raw tick by tick market data. That is events when an increase in the best bid is immediately
followed by a decrease (or a decrease in the best offer is immediately followed by an increase)with a skew towards shorter lifetimes representing higher price jitter. While short lived peak
prices may be caused by both liquidity churn and trading, from the trader’s perspective theeffect is the same – the best price in the recent time window appeared and disappeared before
there was time to execute on it.
Short lived peaks are not the only form of problematic price change - a price which isconsistently but rapidly rising or falling may be just as difficult for specific trading strategies –however they do characterise a behaviour which is difficult to trade with immediate execution
limit orders regardless of strategy or sentiment.
Analysing internal LMAX Exchange market data directly from the matching engine for the 30
minute window surrounding the NFP market event illustrated above, we find that:••69% of price peaks last less than 10ms;
90% last less than 40ms.

Even at less volatile times the rate of price updates remains challenging. The full day and two
week period around the same event showed:••

62-63% of price peaks last less than 10ms;
90% of peaks last less than 110ms.

The comparison with the ‘fundamental’ clock of primary venues used for price discovery in FX
with market data update times of 100ms or 250ms is quite striking. Even with the recent
launch of 20ms and 5ms interval ‘ultra’ updates [6] the fact remains that at present the tick bytick price movements from a firm liquidity venue are happening at rates of a least a factor of
5 - 10 higher than even a 5ms tick would allow.

The TPA’s ‘price-sniping’ execution strategy, which is nominally designed to secure the end
customer the best price (and all other things being equal, lowest transaction cost) by holding
their order in the aggregator awaiting ‘the right price’ is in fact counter-productive when firm
liquidity is included in the mix. The orders placed have close to top of book limit prices and ifthey have a ‘tick-to-trade’ latency of more than a millisecond or so they are almost guaranteed
to fail more frequently due to misses caused by price volatility and jitter.
The simple application of a reject ratio in this case misses the point that these are theexpected market dynamics in an active competitive firm liquidity venue.

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Part II: (ii) Execution quality metrics and firm liquidity
To recap, in order to be successful with its price-sniping strategy, with limit prices targeting a
potentially jittery top of book, the aggregator must:•••

First be able to ingest a higher rate of market data than typically received fromother LPs;

Have access to a high bandwidth, high market data update rate connection in thesame data centre;

Process market data to identify the desired trading conditions and place orders
with a sufficiently low tick-to trade latency to ensure the desired price is still available.

Viewed from this perspective the relatively low limit order fill rates of the TPA on LMAX
Exchange liquidity are not surprising when compared to the far more sedate price volatility
behaviour on last look venues, quoting prices in response to a much slower 5ms, 20ms or
100ms price tick.

This appears to create something of a perfect storm for LMAX Exchange limit fill ratios for thisparticular execution style. If the true value of execution on firm liquidity, as shown in Part III:
‘Comparative transaction cost analysis’ (p. 68) is to be realised then this particular last look
optimised strategy needs to be adapted to work with firm market price dynamics
and microstructure.

However, this is a time limited strategy even for customers with no interest in firm liquidity.
The price-sniping approach works well when the latency for the tick-to-trade is less than thelatency of market data updates. The increase in market data update rates across all venues, as
noted above, is likely to continue to at least 1ms update rates, and possibly real time streaming
prices such as those already found on LMAX Exchange.
This is likely to drive customers reliant on this method of execution to consolidate their FX
trading even closer to venues.

Last look LPs will face trade-offs of either maintaining hold times or shortening quote lifetimes
and falling back on a higher reject rate or – if they’re already pricing firm liquidity venues simply applying the same fast cancel approach already employed to avoid rejecting orders
hitting a stale price. Either way, it is likely that quote rates will increase and some of the factors
listed above as negatives for LMAX Exchange will start to come into play across more venues.
The market impact of the customer will become the main factor driving the decisions to accept
trades based on ‘stale prices’ that are more than a few ms old.
As we will see below having transparent market dynamics means that a trader can redesign
their execution strategy on firm liquidity to take account of this new world of increasingly highpricing or quoting rates in a way that is very hard to match on last look liquidity.

49
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Part II: (ii) Execution quality metrics and firm liquidity
Box 9
Understanding implications of higher price volatility on firm liquidity

Market makers respond to firm liquidity by updating prices more frequently, resulting in moreprice variation or ‘jitter’ over short time scales.
A comparison of the number of price updates received by a sample customer shows LMAX
Exchange market data rates at 2-3 times those of the most demanding last look venues, while
an examination of the internal matching engine statistics shows the majority of new top-ofbook prices lasting less than 10ms.

►

If the true value of execution on firm liquidity is to be realised, then it needs to be adapted towork with firm market price dynamics and microstructure:••

Customers with high market impact profiles, for whom last look LPs face trade-offs
of either maintaining hold times or shortening quote lifetimes, are likely to derive
substantial value from transparent execution on firm liquidity;

Having transparent market dynamics means that a trader can evolve their execution
strategy on firm liquidity to take account of this new world of increasingly high priceor quote rates in a way that is very hard to match on last look liquidity.

50
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Part II: (iii) Execution quality metrics and firm liquidity(iii) Quantifying the value of price improvement

For traders using limit orders, price improvement represents a profit relative to the trade as
filled at limit price. If we view the TPA flow as a single ‘customer’, then using the LMAX Exchange
execution quality reports we observe a price improvement over the entire period equivalent to$3.17 per $1M notional traded with monthly averages ranging from $0.96/million to$8.48/million. The notional volume traded is based on all limit order trades, not just thosereceiving price improvement, hence the per-million equivalent should be interpreted as anaggregate reduction in trading costs across all trading activity.
Month

Improvement(USD)

Notional(USD)$/millionJan

224.10122,659,512

1.83

1,304

Mar

71.37

52,094,610

1.37

1,676

Feb

200.62

151,925,601

Apr

297.74

Jun

626.11

109,260,141

167.5480,191,782

May
Jul

Aug
Sep

Oct

Nov
Dec
Q4

Annual [ii]381.64227.22661.04231.47151.5242.12

425.11

3,282.49

59,143,226
81,025,408
97,732,787
77,956,809
78,953,242
82,229,754
43,830,233

205,013,229

1,037,003,105

Table 16: TPA price improvement by month

1.32
5.03

Total

TradesPrice improved
63

8,155

212

597

88

39

4.71

1,053

126

2.32

2,546

137

5.73
2.09

2,552
916

270
60

8.48

1,778

103

1.84

1,372

112

2.93
0.96

2.07
3.17

1,488
822

3,682

24,259

107
43

262

1,360

The price improvement shows a variable, but significant, potential for reducing the overall costof trading. As LMAX Exchange is the only LP offering discernible price improvement on limit
orders, we cannot benchmark this behaviour against other sources of liquidity.
To provide some context for the price improvement achieved by the TPA we will look at therelative performance of other LMAX Exchange customers matching the three customer
profiles introduced earlier. As this relies on the execution quality statistics developed during
the course of 2016, data covering all customer profiles is only available for Q4 2016.
The aggregator profile consists of a small group of customers with known trading patterns
similar to those of the TPA. While they may not trade via an aggregator as such, they areco-located, low latency and price sensitive. They target prices in the same way as the TPA,
resulting a similar profile with relatively low price improvement and relatively high fill ratios.
[ii] The total here represents a relatively small segment of the total TPA data set of $85bn notional value, of which
LMAX Exchange received $11bn, $10bn in market orders and $1bn in limit orders

51

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Part II: (iii) Execution quality metrics and firm liquidity
The Broker and Institutional profiles gather a wider set of customers with more diverse
behaviour, divided into clients of LMAX Exchange Broker (typically other brokers, individual
traders and small funds) and direct Institutional clients of LMAX Exchange MTF (typicallybanks, larger funds and HFTs). While the main structural distinction is that Institutional clients
have their own prime broker and do not require the services (and associated risk controls) of
LMAX Exchange Broker, the group are also characterised by lower transit latencies, often
achieved by co-location, and higher overall investment in trading infrastructure. Compared to
the TPA and the aggregator profile, customers in the Broker and Institutional profiles exhibit
much more varied approaches to setting limit prices. While some customers place limit orders
at or close to the price at which they expect a fill, others are willing to pay a premium to get into
the market during periods of high price volatility. In some cases prices are even further off
market, but the customer actually expects to trade at a better price, so what is superficiallytraded as a limit order really represents a market order with the limit price setting aslippage threshold.

Including orders with limit prices significantly off market would skew results towards higher
price improvement figures. In the interests of having a fair and realistic assessment of the value
delivered to the customer by price improvement, when reviewing the Broker and Institutional
profiles we also need to restrict ourselves to orders where we believe the customer wastrading with some expectation of being filled at their stated limit price. We will identify these asorders within a given price range – or ‘volatility band’ – of the market price at the time we
received the order.

We examined several approaches to measure volatility in the literature [7] in order to definecharacteristic market ranges. These included Average True Range (ATR), realised volatility of
returns and simpler statistical measures of variance. The most useful turned out to be thetraditional Bollinger Band calculation [8], which as well as being familiar and easy to compute,
has several immediately useful properties. As opposed to the more dimensionless volatility
metrics we can directly use it to establish a pricing range and it accounts for moves in market
direction which the simpler statistical methods do not. Additionally the market ranges given by
this method also passed a ‘real world’ sanity check as to what constituted ‘close to market’ fromcolleagues with trading backgrounds.

The volatility band was computed as a daily mean for each instrument using minute level tickaggregates from LMAX Exchange’s historic market data service. The parameters used tocompute the volatility band was the standard B(20,2), i.e. 2 standard deviations measured over
20 observations. If B(t, N, m) is the Bollinger Band for observation at time t for m standard
deviations measured over N observations, and Pt is the mid price at time t (computed from thehigh and low of an OHLC bar), then over n samples in a day (where typically n=1435 due to a 5
minute closure at end of day) our volatility band is calculated as:

B t , N , m =m

1

N-1

N i=0

Volatility band =1

P(t -i)nn 19 j= 20

1

N-1

N s=0

P(t -s)2,

B j, 20, 2

Figure 1: Daily ‘close to market’ volatility band52

An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part II: (iii) Execution quality metrics and firm liquidity
We take the daily volatility band and apply it to each immediate execution limit order for thatday discarding any which have a limit price further than the range from top of book. Days withhigher sustained levels of price change will have wider volatility bands while relatively stabledays, even those punctuated by a small number of market events, will have narrower bands.
Some sample dates and volatility band values for EURUSD, which has an annual mean
volatility band of 4.5 pips, are shown in table 17.
Date

Calculated Daily Range (Pips)

2016-12-23

2.9

Pre-Christmas ‘quiet’ day

23.3

Day after EU referendum

2016-12-02

4.5

2016-06-23

7.0

2016-06-24
2016-12-05

Table 17: Sample volatility band values

Notes

December NFP

Day of EU referendum

8.8

Volatile day

Orders outside the volatility band are excluded from our calculation of price improvement.
For example, any EURUSD limit order placed on 2nd December with a limit price more than 4.5
pips away from the market price, or on 23rd December with a limit price more than 2.9 pipsaway from the market price, was assumed to be a customer deliberately setting an off-market
limit price to secure a fill.
Appendix C lists summary volatility band details for each currency pair. Tables 18, 19 and 20(p. 54) show the price improvement for Broker and Institutional profile customer orders within
the volatility band on the LMAX Exchange London venue, and Aggregator profile customer
orders across multiple LMAX Exchange venues.
Broker profile

TradesPrice improved

Improvement(USD)

Notional(million USD)$/million

27,04935.62

200,972Nov

1,271,394

37,31434.07

264,502

144,490Dec

2,522,175

35,34971.35

246,180

167,009

Month
Oct

Q4

963,487

4,757,056

Total

116,940

99,71247.71

711,654428,439

Improvement(USD)

Notional(million USD)$/million

29,8462.01

95,752

Nov

252,307

63,511

3.97

212,542

31,416

Dec

158,474

43,3803.65

180,770

20,530

Table 18: Broker profile price improvement, orders within volatility band (2σ)
Institutional profile
Month
Oct

Q4

60,071

470,852

136,737

3.44

Total

489,064

TradesPrice improved

Table 19: Institutional profile price improvement, orders within volatility band (2σ)

10,09662,04253

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Part II: (iii) Execution quality metrics and firm liquidity
Aggregator profile

TradesPrice improved

Improvement(USD)

Notional(million USD)$/million

6,484

2.28

18,930Nov

48,585

19,536

2.49

49,5896,968

Dec

36,856

18,595

1.98

45,510

4,444

Month
Oct

Q4

14,792100,233

44,615

2.25

Table 20: Aggregator profile price improvement, all orders

Total

2,832

114,029

14,244Comparing the Q4 number of $2.07/million from table 16 (p. 51) for the TPA with the wider
aggregator profile above gives us a similar price improvement figure. The institutional profile
fares slightly better at $3.44/million, however it is the broker profile that really stands out at$47.71/million, table 18 (p. 53).
In case these numbers for the broker profile appear to be unduly high, we also calculated thesame price improvement numbers following the same method, but assuming a much tighter
volatility band of 1σ or B(20,1). These are shown in table 21.

TradesPrice improved

Improvement(USD)

Notional(million USD)$/million

25,00515.30

174,867

Nov

880,920

35,94224.51

249,964

129,953Dec

1,523,378

31,60248.21

215,872

136,701

Month
Oct

Q4

382,454

2,786,752

92,549

30.11

Total

640,703

Table 21: Broker profile price improvement orders within tighter volatility band (1σ)

90,835357,489

Even using this much tighter restriction we see that the average price improvement seen bybroker customers on LMAX Exchange London was $30.11/million over Q4.

To address the concern of cherry picking our quarter, we would point out that reviewing thedata by quarter in table 16 (p. 51) indicates that Q4 is certainly not the best quarter for us tohave picked. As mentioned above, this is just the data available to us at the time of writing.

The causes of the difference between the broker profile and the aggregator or institutional
profiles will be further explored in the section ‘Optimising trading on firm liquidity’ (p. 56).
For now the main observation is that both the aggregator and institutional profile customers
are often co-located in the same data centre as the exchange and have dedicated highperformance hardware.

This is however, not typical of the average broker profile customer and so market dynamics
and latency are once more factors. Price improvement is a function of many variables.
The customer’s order placement strategy and market volatility all play a part in determining theactual price improvement seen. Nonetheless for the majority of the LMAX Exchange customer
base who match the broker profile, we conservatively estimate price improvement on theirlimit orders is worth between 3 and 4 ticks on the spread.
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Part II: (iii) Execution quality metrics and firm liquidity
Box 10Quantifying the value of price improvement

For traders using limit orders, price improvement represents a profit relative to the trade as
filled at limit price. Price improvement, only achieved at any significant level on firm liquidity,
shows a variable but considerable potential for reducing the overall cost of trading:

•
•

►

•

The customer’s order placement strategy and market volatility play a part indetermining the actual price improvement seen;

The average price improvement for the broker profile is $47.71/million in Q4’16;

More conservatively, the majority of the LMAX Exchange customer base who matchthe broker profile, achieved price improvement on their limit orders worth between
3 and 4 ticks on the spread.

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Part II: (iv) Execution quality metrics and firm liquidity(iv) Optimising trading on firm liquidity

So far, we have considered price improvement and fill ratio independently. Chart 10
demonstrates the relationship between these two metrics for our sample data set. Each data
point represents a day of trading which includes at least 100 orders and plots the average fillratio and price improvement ratio (i.e. the proportion of orders filled at better than limit price)observed on that day. The size of the circle represents the relative number of orders placed oneach day.
14

Improvement ratio (%)

12
10
8
6
4
2
0
80

85

90
Fill ratio (%)

95

100

Chart 10: Correlation of fill ratio and improvement ratio

Higher levels of price improvement generally correspond to lower fill ratios. This outcome isentirely intuitive; if we assume a reasonably agnostic sentiment across the underlying trading
decisions then the market conditions likely to produce larger favourable moves for someorders are also likely to produce a higher level of rejects for others.

This effect is amplified as the time for a trader to receive and respond to the market dataassociated with a price change increases. Traders may adopt a variety of approaches to address
this gap and improve fill ratios:•••

Reducing market data and trading latencies by tuning software and infrastructure orby geographical co-location;
Throttling or rate-limiting market data to eliminate latency introduced by peaks inmessage rates causing queues of unconsumed messages to accumulate in theinfrastructure between the LP and the trader (see Appendix B – factors affecting
Internet delivery of market data);

Implementing their own hold time by placing and cancelling Good-for-Day orGood-for-Time limit orders if venues supported them. These will result in a fill if thedesired price becomes available again within an acceptable time frame (which brings
with it the same opportunity cost issues of venue hold time, albeit under the trader’sown control).

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Part II: (iv) Execution quality metrics and firm liquidity
A less intuitive step for traders who have optimised their strategies for last look is that there isan option to improve fill ratios on firm liquidity by introducing a small level of slippage. This
works by trading the price improvement received on filled orders for a smaller cost increase onorders that would otherwise be rejected.
To quantify this opportunity, we have recorded a virtual execution for every order placed on
the LMAX Exchanges since September 2016. The virtual execution is performed against
liquidity identical to that available both at the time the order was executed and at multiple time
points up to 100ms later, but removes any limit price restriction. This allows us to model, order
by order, the amount of slippage that would be required to secure a complete fill.
No discretion

0.1 pip

0.2 pip

0.3 pip

0.4 pip

0.5 pip100

Fill ratio (%)

95

90

85

80

Price improvement ($/million)

75
50

40

4

4

3

3

2

2

1

1

0

0

-1

-1

30

20

10

-2

0
Broker-2
Institutional

Aggregator

Chart 11: Trading price improvement for fill ratio
Chart 11 and table 22 (p. 58) show the results of this analysis for each of our three customer
profiles for all trading in Q4 2016.

For each profile we have calculated the number of additional orders that would have been
filled by applying price discretion by increasing levels of slippage in 0.1 pip intervals,
represented as an increased fill ratio. Price discretion giving slippage at levels above 0.5 pips is
possible but, for this data set, there is a clear case of diminishing returns as discretion levels
above 0.2 or 0.3 pips are used.

This is expected behaviour given that the underlying driver for both price improvement andlimit fill ratio on firm liquidity is price volatility and microstructure, with the diminishing returns
displaying the familiar signature of a Chebyshev inequality or price distribution with the
majority of missed prices very close to the requested limit price. Traders can make use of57
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Part II: (iv) Execution quality metrics and firm liquidity
knowledge about the shape of the volatility distribution to determine their ideal fill ratio/priceimprovement mix.
It must be stressed however that the above charts are averages for each different customer
profile over a quarter, and should not be used as a guide or expectation for any individual
trading session or strategy. For example applying a price discretion of 0.3 pips improves themean fill ratio for the aggregator profile from 92.37% to 98.91% while still delivering priceimprovement and therefore offering scope for further fill ratio improvement before reaching
break-even. By comparison at the same level of discretion the institutional profile shows net
slippage with a 95.87% fill ratio. Individual customers will see quite different numbers
depending on their latency and trading strategy.
ProfileAggregator

Maximum slippage

Fill ratio

Net $/million

0.1 pip

96.69%

1.81

None

0.2 pip
0.3 pip
0.4 pip
0.5 pip

ProfileInstitutional

98.91%
99.27%
99.52%

1.55
1.36
1.24
1.14

Fill ratio

Net $/million

0.1 pip91.40%

1.83

None

0.2 pip
0.3 pip
0.5 pip

Broker

98.17%

2.25

Maximum slippage

0.4 pip
Profile92.37%

81.95%
95.87%

3.44
0.19

97.77%-0.90

99.20%-2.09

98.69%-1.60

Maximum slippage

Fill ratio

Net $/million

0.1 pip

94.82%

42.19

None

0.2 pip
0.3 pip
0.4 pip
0.5 pip

Table 22: Trading price improvement for fill ratio

89.15%
97.06%
98.00%
98.62%
99.06%

47.71
38.30
36.48
35.11
34.01

In summary, the optimal level of price discretion and allowable slippage will depend on acustomer’s risk appetite and the relative value they place on securing a fill and getting the best
possible price. Feedback from customers, as well as, the negligible limit order price improvementfrom last look LPs observed in the TPA data indicates that the option to exercise this discretionand level of control over trading is, at least currently, unique to firm liquidity venues.

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Part II: (iv) Execution quality metrics and firm liquidity
Box 11
Optimising trading on firm liquidity

•

►

•

Firm liquidity enables traders, who have optimised their strategies for last look,
to improve fill ratios by permitting a small level of slippage, which is offset by priceimprovement on other trades;
The optimal level of price discretion and allowable slippage will depend on acustomer’s risk appetite and whether they place more value on securing a fill
immediately, or filling at the requested price even at the risk of missing a move inthe market.

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Part II: (v) Execution quality metrics and firm liquidity(v) Quantifying the cost of hold time

Hold time represents one of the most significant hidden costs of trading. This is most apparent
in the case of rejected or unfilled orders. A trader makes the decision to place an order andmust wait an unknown period of time before discovering that the order cannot be filled as
requested, by which time the market may have moved significantly. LPs with publishedmethodologies for last look typically cite an adverse change in underlying prices as theprincipal reason for rejecting an order, and we can see from our analysis of the limit order fillratio on LMAX Exchange liquidity that the vast majority of unfilled orders are caused byunfavourable price moves. As a result we can expect the distribution of price changes to be
dominated by those which increase transaction costs relative to the limit price of arejected order.

The impact of hold time for successful orders will depend on the individual trader’s trading
strategy. Those with a high frequency strategy will clearly be restricted as the required interval
between trades approaches the range of hold times experienced. Similarly traders who rely on
working large orders into the market in a series of smaller orders will be constrained on how
quickly they can execute such strategies.
Using the virtual execution data collected for every order placed on LMAX Exchange venues
allows us to measure the effect of market volatility for a range of hold times. Because thevirtual executions are triggered by actual customer orders rather than being arbitrary samples,
this provides results which are more representative of times when there is interest in trading.
Since the calculation of an actual cost is somewhat subjective and sensitive to the interaction
with the different trading strategies, we will present a range of values using conservative
assumptions.
We analysed 10,842,292 immediate execution market and limit orders placed by customers
on the LMAX Exchange London venue between the beginning of September and end of
December 2016. The cost of hold time was calculated by comparing the difference in notional
value between the virtual execution at actual execution time and the virtual execution at a setof hold times as shown in figure 2.

Cost Delta D =N

i=0

QBi Pt = D

Figure 2: Cost of hold time calculation

Pt = 0N

i=0

QSi Pt = 0

Pt = D

Where QS are sell orders, QB are buy orders, D is the delay time, P is the mean fill price at time
t for an order of size QB or QS. As the limit price is not a part of the calculation we do not needto correct for skew due to off-market limit prices as we did for price improvement earlier.
Chart 12 shows the daily aggregate outcome of this analysis comparing execution cost at
0ms to execution cost at 100ms. A cost increase indicates that the 100ms execution price wasworse from the trader’s perspective, while a reduction indicates a better execution price.
We can see that the outcome is heavily skewed towards cost increase on each day (this isconsistent with the skew we see towards market order slippage over improvement, in that
there is a bias in behaviour towards buying on a price rise and selling on a drop).

Given the skew towards an overall cost increase, we will use the terms Net and Gross Increase60
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Part II: (v) Execution quality metrics and firm liquidityfrom here on. Net increase is a more conservative measure including both cost increase andreduction in the calculation. Gross ignores any reduction, taking the view that a 100ms cost
reduction observed on firm liquidity would have resulted in a fill at the quoted price for anequivalent order placed on a last look venue with a hold time of 100ms or greater.
Cost Increase

1762

Cost Reduction

1600
Cost at 100ms (1000's of USD)

1400
1200
1000
800
600
400
200
0
-200
-269
2016-09-05

2016-10-03

2016-11-01
Date

2016-12-01

2016-12-30

Chart 12: Cost increase vs reduction at 100ms for Q4 2016
A possible shortfall of our approach is that the price used to revalue each order may have been
artificially influenced by concurrent trading activity transiently depleting liquidity resulting in alower bid and/or higher ask and therefore exaggerating the cost increase of the execution.
To gauge the potential impact of this effect we have identified orders in our sample set which
are isolated by at least 100ms from any event which consumes liquidity, hence the subsequent
price changes are purely in response to these orders and changes in the underlying market(8,805,137 out of the original sample of 10,842,292 orders). By removing the orders placed
during times of higher concurrent activity we are implicitly selecting less active and therefore
typically less price-volatile time periods, so the calculation of cost of hold time using isolatedorders should be viewed as a lower bound on the range of probable values.
Net isolated

Gross isolated

Net all

20

50

Gross all

30.0$/Million

20.0

10.0

0.0

10

Milliseconds

100

Chart 13: $/million cost of hold time - alternative methodologies

In order to create comparable results for the various approaches to measuring hold time costs(net vs gross, isolated vs concurrent) we have converted the cost into a per-million notional
traded value for each of the possible combinations at 10ms, 20ms, 50ms and 100ms holdtimes. The results are shown in chart 13.

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Part II: (v) Execution quality metrics and firm liquidity
As anticipated, when looking only at isolated orders we see a lower cost increase at any holdtime. All approaches show a cost increase, net or gross, that grows steadily with increasing holdtime, and there is no pronounced short term (10/20ms) ‘reaction and correction’ that might be
expected if the order being measured was artificially altering prices by depleting liquidity which
was replenished before the later (50/100ms) calculations were performed.
The true costs of hold time can only be estimated given a detailed understanding of a trader’sorder placement behaviour and how it is affected by these delays. The most intuitive impact ofhold time is the opportunity cost of a reject. Taking the 100ms hold time, where estimates ofcost range from $17.80-$28.60/million and applying a simple average we might assume that a
trader receiving a 5% reject rate and 100ms hold time is experiencing a cost between $0.89
and $1.43/million in aggregate over their total trading volume. The real experience is likely to
be significantly worse. The costs identified above show the outcome of a calculation for every
order placed, but we expect rejections to be higher during times of higher market volatility. A
superficially satisfactory fill ratio may mask a small number of extremely expensive rejections.
We can attempt to gain some insight into how much worse than the average these eventsmight be by restricting our data set to unfilled immediate execution limit orders. In this case,
we are no longer interested in the cost increase after execution time as at this point the trader
has already missed their desired price level. Instead we will compare the order limit price tothe price available when the order was received.

Using only the unfilled orders and cost relative to the order limit price, our prior measurementof $17.80-$28.60/million increases to a cost of hold time at 100ms in the range of $297$336/million. We should approach this value with a healthy degree of scepticism as it relies on
the order limit prices being realistic, however the value is within the range of the extremes of
price swings observed over a 5ms time period during a major market event. Although thesample size is much smaller we can also use unfilled orders for the trading account used by the
TPA as another data point – this returns values between $83-$86/million.

A final consideration is that calculating the cost of hold time per order only tells part of thestory and that rejections may not be single events. Traders receiving rejections will often place
multiple orders in order to secure a trade, hence the headline per-order hold time figures may
well mask a much more extended opportunity cost from the trader’s perspective. A proportionof the orders resubmitted after rejection may themselves be rejected creating a ‘reject’ chain.
In the TPA data 11.2% of all rejected orders which eventually fill are rejected more than once.
If those reject chains were clustered around significant changes in the market then the effects
of hold time would be amplified beyond the conservative averages shown above. Unfortunately
time precluded a detailed investigation of these effects, but this is an interesting avenue for
future work.
We have picked 100ms as a representative hold time based on the TPA data, and we are open
to the criticism that the market has moved on since 2016. That said, cost of hold time is not
linear. Approximately 60% of the 100ms hold time costs are incurred in the first 10ms, andfor customers sensitive to market impact any hold time which means the market reacts
before their trade executes may represent a significant cost.
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Part II: (v) Execution quality metrics and firm liquidity
Box 12
Quantifying cost of hold time

Hold time represents one of the most significant hidden costs of trading:

•
•
•
•

•

The impact of hold time for successful orders will depend on the individual trader’strading strategy;
Approximately 60% of the 100ms hold time costs are incurred in the first 10ms;$15/million is the estimated net increase in cost of execution at 10ms;►•

For customers sensitive to market impact any hold time during their order execution
represents a significant cost;

The true costs of hold time can only be estimated given a detailed understanding of a
trader’s order placement behaviour and how it is affected by these delays;

The most intuitive impact of hold time is the opportunity cost of a reject anddepending on the methodology used can range from $17.80-$28.60/million to $297$336/million at 100ms. Given such a broad range, we conservatively settled on$25/million, as an estimated cost of last look at 100ms;
The effects of hold time can be further amplified if the reject chains are clusteredaround significant market events, thus creating information leakage about thecustomer’s trading strategy.

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Part II: Execution quality metrics and firm liquidity
Part II: Summary of findings:

In this part we set out with some leading questions about flow characteristics, volatility andorder placement strategies as a way to explore the unexpectedly low limit order fill ratio for firm
liquidity calculated from the basic metric analysis of our TPA trade database.
We have identified the following key considerations when using limit orders:•••On firm liquidity price improvement and fill ratio are linked and are both related toprice volatility;

The level of the price volatility or jitter on firm venues is higher than on last lookvenues. Critically, price lifetimes can be much shorter than network latencies or theintervals between a customer receiving market data updates;

Price improvement on firm liquidity can be traded for increased fill ratio at thetrader’s discretion as the two are linked. Understanding the market dynamics on firm
venues can help determine ideal price discretion to give the best balance between fillratio and improvement.

For firm liquidity, price variation and fill ratio are driven purely by market dynamics. For last lookliquidity, this is not usually the case - the lack of price improvement indicates that there are
additional commercial factors at play.
Slippage and fill ratio remain useful separate metrics for last look venues, but on firm liquidity
venues these two metrics don’t measure any business process - they measure market volatility.
Comparing fill ratios between last look and firm is therefore not a useful exercise in comparing
like with like. Price variation/improvement must also be included for a complete picture.

We have investigated multiple options for calculating the cost of hold time, and we have briefly
touched on market impact to address the question of why the fill ratios observed were so muchhigher with last look. By examining the execution strategy and the associated market impact of
the TPA we can explain many of the observed features or anomalies of the TPA data set.

Last look fill ratios appear competitive for the low market impact trading style of the TPA,
making it an ideal data set to assess the suitability and completeness of the commonly used TCA
metrics when applied to both last look and firm liquidity. However trading with greater market
impact may not be treated so kindly by last look venues, and predicting the execution quality inthe context of an arbitrary maze of commercially set hold times and reject rates makes the open
deterministic nature of a firm liquidity central limit order book a far more predictable andappealing proposition for the trader:••

Fill ratio is not directly comparable between last look and firm liquidity as different
processes are measured;

Price improvement is important and should be included in the base metrics set alongside slippage as combined price variation.

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Part II: Execution quality metrics and firm liquidity
Part II: Summary of findings cont’d:•

The average price improvement achieved on LMAX Exchange in Q4 2016 by the TPA
and our three customer profiles was:•The cost of last look hold time for a rejected order was estimated to be $25/million at
100ms (across all orders, netting both cost increases and decreases from delayed
execution), with 60% of costs incurred in the first 10ms.

•› TPA: $3.17/million› Aggregator Profile: $2.25/million› Broker Profile: $47.71/million› Institutional Profile: $3.44/million

Market impact is not usually measured, but is crucial to consider when interpreting
execution quality.

Box 13
Understanding firm liquidity

The analysis of flow characteristics, volatility and order placement strategies demonstrates that:

•
•

•
•

For firm liquidity, both fill ratio and price variation on limit orders are linked andrelated to price volatility, thus purely driven by market dynamics;►•

Last look liquidity is not driven by market dynamics, and the lack of priceimprovement indicates that there are additional commercial factors in play;

Fill ratio and slippage remain useful separate metrics for last look venues, but on firm
liquidity venues these two metrics don’t measure any business process - theymeasure market volatility;

Comparing fill ratios between last look and firm venues is not a useful exercise incomparing like with like - price variation must also be included for a complete picture;
Market impact, not usually measured, is a crucial for understanding execution quality
across both liquidity types.

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blank pagePart III

Comparative transaction cost analysis

Part III: Comparative transaction cost analysis
In Parts I & II of this paper we have characterised the two main liquidity options available to the
FX trader:••

Last look liquidity has been shown to be more straightforward to trade whenusing limit orders, but places control firmly in the hands of the liquidity provider.
This restricts the choices available to the trader when executing in challenging
market conditions;Firm liquidity, represented by the LMAX Exchange central limit order book, exposes
the underlying market directly to the trader, giving them the challenge of dealing withmore dynamic pricing but offering transparent execution and price improvement
without pre-trade information leakage.

In this section, we will present a theoretical comparison of the costs of using trading styles
optimised for last look and firm liquidity to achieve the same target fill rate. We will show
results for a number of customer profiles using the execution quality data captured for clients
trading on LMAX Exchange liquidity in Q4 2016. We have used the aggregate results of actual
customer trading as the baseline for each customer profile.
Starting from this baseline we have calculated:•••

The unfilled orders which could have been filled at the original time of execution byadding varying levels of price discretion at 0.1 pip intervals from 0 to 0.5 pips;
The cost of filling these orders by adding price discretion on LMAX Exchange;

The cost of filling the same orders by refiring the order (or unfilled portion thereof)
on a theoretical last look liquidity source 100ms after the original execution time.
The last look comparison assumes that no price improvement is given, in keeping with
our observations detailed earlier, and is calculated based on actual market prices
available 100ms after the order was originally accepted.

The different customer profiles are composed as detailed earlier:•Institutional: All Institutional clients placing limit orders within the daily volatility band•

Broker: All Broker clients placing limit orders within the daily volatility band, seesection ‘Quantifying the value of price improvement’ (p. 51)•

Aggregator: Cross connected clients with trading behaviour similar to a ‘price-sniping’aggregator, exhibiting no price discretion, high fill ratios and low price improvement

The comparative results are calculated as a $/million impact on trading costs, where a positive
value indicates a net profit (or reduction in costs) and a negative value indicates a net loss
(or increase in costs) for the client. These values have been calculated using the Aggregatorprofile at 0.1 pip discretion as an example:••••

The baseline is a traded volume of $44.62bn and a fill ratio of 92.37%;

On LMAX Exchange, these clients achieved price improvement of $100,233,
equivalent to $2.25/million;

By applying 0.1 pip discretion the same clients would have traded an additional$1.94bn, increasing the volume traded to $46.56bn and fill ratio to 96.69%;

The cost to the client of adding 0.1 pip discretion on these additional orders is -$15,944;

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Part III: Comparative transaction cost analysis••

The price improvement, net of the cost of 0.1pip discretion is $100,233 - $15,944 =$84,289, equivalent to $1.81/million on the total volume of $46.56bn;

The cost of filling the same orders using a retry at 100ms was -$33,378, equivalent to-$0.72/million on the total volume of $46.56bn.

Therefore for this scenario we estimate that customers can achieve a fill ratio of 96.69% witha net $1.81/million impact (i.e. a reduction in costs compared to trading at limit price) using
discretion on firm liquidity vs a -$0.72/million impact (net increase in costs) using retries at
100ms. Expressed as a comparison, the customers are better off by $2.53/million using
firm liquidity.
The fill ratios and $/million values for all customer profiles and trading scenarios are
summarised in table 23.
ProfileScenario

Fill ratio

Baseline

92.37%

0.2 pip

98.17%

0.1 pip

Aggregator

0.3 pip
0.4 pip
0.5 pip

Institutional

98.91%
99.27%
99.52%

Last look retry

Comparison

1.81

-0.72

-2.53

1.36

-1.34

2.25
1.55
1.24
1.14

0.00

-1.09
-1.46
-1.58

Baseline

81.95%

3.44

0.2 pip

95.87%

0.19-14.34-1.60-17.710.1 pip
0.3 pip
0.4 pip
0.5 pip

Baseline
Broker

96.69%$/million impact on trading costs

LMAX Exchange

0.1 pip
0.2 pip
0.3 pip
0.4 pip
0.5 pip91.40%

1.83

97.77%-0.90

99.20%-2.09

98.69%
89.15%

47.71

97.06%

38.30

94.82%
98.00%
98.62%
99.06%

0.00

-2.70
-2.70
-2.72
-3.44

-11.67-16.49-15.59-18.50-14.53-16.11-16.410.00-47.71-9.74-48.04

-6.67

36.48

-11.1534.01-12.78

Table 23: Comparative cost of price discretion and retries

-2.64

-9.84

42.19
35.11

-2.25

-12.06-48.86-47.63-47.17-46.79In every scenario, the absence of price improvement, combined with a net cost increase
associated with trading 100ms later, means trading costs on last look are higher by between$2.25/million and $48.86/million.

The assumptions behind this comparison and the exact cohorts selected for each customer
profile may be open to dispute, however, the fact remains that by withholding transparent priceimprovement, current last look practices restrict choice. If a trader cannot apply pricediscretion with the knowledge that that discretion has the possibility to be refunded – in theform of price improvement – when not required to secure a fill, then the only strategy available
is to wait for a rejection and try again. All of our analysis, both the TPA data set and the broader
LMAX Exchange customer base, indicates that at the times customers wish to trade,
re-executing even at hold times as low as 10ms will lead to a net increase in costs.
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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Part III: Comparative transaction cost analysis
Box 14Retaining control over execution quality with firm liquidity
Trading on firm liquidity enables traders to:

►

•
•
•
•
•

Protect their valuable trade information pre-execution;

Optimise trading strategies through the ability to adjust price discretion to control
fill ratios;
Gain value from price improvement, which transparently and fairly is passed on to
the customer;
Avoid the cost of hold time;

Firmly retain control of their own execution metrics.

Box 15
Key findings from the comparative transaction cost analysis example

The cost comparison of using trading styles optimised for last look and firm liquidity to achieve
the same target fill rate demonstrate the considerable value to the trader of executing on acentral limit order book. Our analysis demonstrates:

►

•

•

Trading costs on last look are higher by between $2.25/million and $48.86/million
due to the absence of price improvement combined with a net cost increase
associated with trading after a discretionary hold time;

By withholding transparent price improvement, last look practices restrict tradersfrom applying price discretion as part of their trading strategy, leaving them with nochoice but to wait for a rejection and try again.

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

TCA white paper conclusions

TCA white paper conclusions
In this paper we have analysed a third party trade database, which included both firm and lastlook liquidity, from the buy side perspective with three questions in mind:•••

Do the commonly used TCA metrics accurately measure execution costs on both lastlook and firm liquidity?
What are the metrics that measure all the underlying processes of trading firm
liquidity, and thus should be used to properly assess the cost of trading in the
FX marketplace?

How does the total cost of execution compare between last look and firm liquidity?

Focusing mainly on three common TCA metrics (fill ratio, price variation and hold time), ourfindings demonstrate that naively applying the ‘standard’ execution quality metrics which havebeen developed for last look liquidity does not provide the full picture of execution costs.
More importantly, it misses quantifiable positives of trading on firm liquidity.

In particular, the analysis demonstrates that for firm liquidity, fill ratio and price variation on
limit orders are linked and both are related to price volatility, thus purely driven by market
dynamics. For last look liquidity, fill ratios are subject to LP discretion and the asymmetricalapplication of price improvement indicates that there are additional commercial factors in play.
Though fill ratio and price variation remain useful separate metrics for last look venues, on firm
liquidity venues these two metrics don’t measure any business process - they measuremarket volatility.
Comparing fill ratios between last look and firm venues is not a useful exercise in comparing
like with like, and price variation must also be included for a complete picture. The cost of holdtime, non-existent for firm liquidity, is an important hidden cost of trading with last look; ouranalysis estimated this cost at $25/million for a rejected order. Finally, market impact, notusually measured, is crucial for understanding execution quality across both liquidity types.
As a result, strategies that work well on last look venues may not translate to firm liquidity.
They translate less when you mix the two. In the example of the limit order price-sniping
strategy employed by the TPA, a simplistic application of the basic metrics of fill ratio, slippageand hold time would not show the value firm liquidity brings through price improvement andmore consistent execution latency:••

Swapping price improvement for fill ratio is possible with firm liquidity - placing thetrader directly in control of their execution costs. If a higher fill ratio is important thatcan be chosen over increased price improvement;

The underlying processes of the LMAX Exchange anonymous central limit order bookhold challenges and opportunities for customers used to trading with last look venues.
There are higher market data update rates to contend with but faster and moreconsistent execution. We have shown several ways to place a value on that fast
execution by calculating the cost of last look hold time, regardless of whether holdtimes are unilaterally applied to all orders or selectively applied and visible asextended tail latencies;

72
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

TCA white paper conclusions•

Last look optionality is used as a defence for the LP against stale prices in the market.
That discussion normally focuses on fill ratio and spread. It does not explain theobservation that the market processes that naturally lead to price improvement inmarket orders are conspicuously absent for limit orders sent to the same venues, orthe skew towards slippage and away from improvement seen with some venues.
Only firm liquidity venues expose the same underlying market dynamics for market
and limit orders.

We have also introduced some new metrics to TCA, particularly market impact, or assessmentof how correlated the flow is. This allowed us to understand the abnormally high fill ratios fromthe last look LPs seen in the TPA data in context with what institutional customers have sharedwith us as their view of ‘normal fill ratios’.
Put simply, the trade flow of the TPA and its execution strategies create some of the worst
possible conditions to showcase the value of LMAX Exchange against last look liquidity
providers. Even so, the comparative TCA calculation (described in Part III) demonstrates thatfor each scenario, due to the absence of price improvement combined with a net cost increase
associated with trading after a discretionary hold time, trading costs on last look are higher by
between $2.25/million and $48.86/million. Furthermore, the transaction cost analysis showed
the benefits of firm liquidity as the transparent, cost effective choice that places the trader incomplete control of their execution quality, with no pre-trade information leakage.

It is hoped that this research will spark discussion across the industry on how TCA can be
standardised in a way that creates clarity and promotes choice for traders. Our goal was toassist in driving the debate about TCA and contributing to the formation of a common baselineon what is important to measure, how to measure it and how to assess the cost.
Ultimately, once there is agreement on core metrics, the arithmetic that underpins TCA isstraightforward. The challenge for the industry is to reach consensus on which factors should
be considered and how the different metrics should be calculated. In doing so, it must properlyreflect the distinctive nature of firm liquidity and correct the current imbalance of favouring
strategies optimised for last look trading venues.

There are undoubtedly limitations to the research presented here. The data sets need to be
widened and more detailed analysis of the five core metrics outlined (fill ratio, price variation,
hold time, market impact and bid-offer spread) is required, including a deeper focus on variable
trading and market scenarios, from order size to time of day, different currency pairs and theimpact of major news events. LMAX Exchange will continue to publish further research in thisarea, drawing where possible on new data.
We encourage industry stakeholders to view this analysis as a starting point towards
development of a commonly-agreed TCA framework, one which will need further research anddevelopment to refine. We welcome all feedback and critique on what is set out here.
We would like to thank our colleagues and the many customers of LMAX Exchange, forward
looking liquidity providers, market makers and TCA experts who have helped formulate thisanalysis over the last 12 months.

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Appendices and references

Appendix A: about the data
The third party aggregator (TPA) used as the principal data source in this study is co-located directly in the
major financial centre for trading FX in London and cross connected to several major platforms and liquidity
providers (LPs) including LMAX Exchange. Network round trip latencies between the TPA and LPs are usuallyless than 1ms. Given the geographical location, we are confident that when hold or execution times are
discussed in this paper, the systematic network latency of the TPA to the execution venues is not asignificant factor.
The TPA receives market and limit orders from clients. Both market and limit orders can specify Immediate orCancel (IoC) or Fill or Kill (FoK) execution. Deferred execution (Good for Day or Good until Cancelled) is notsupported for limit orders through the TPA.

The TPA performs some limited order management, holding the client order for a preconfigured time to live
between 1 and 5000 ms while it waits for the available liquidity to meet size (i.e. fill or kill) or limit priceconstraints. At this point it will submit some or all of the order to the LP(s) offering the best qualifying prices
for immediate execution. From the perspective of this paper, the order management function has the usefulside effect of capturing the most recent market price (best bid for orders to sell, best offer for orders to buy)
received from each LP whenever an order is submitted for execution. This provides us with an ‘expected’ priceas the basis for analysing price variation, with the expectation being set by the TPA itself rather than individual
clients who might display more varied latency profiles.
The TPA issues three types of order to LPs:•

Market orders• Limit orders• Previously quoted (PQ) orders

Previously quoted orders are particular to FX venues that are quote based – in other words last look liquidity
providers. The market data provided by the LP consists of a stream of prices with attached ‘Quote Ids’. These
quote ids are then referenced in the order when it is sent to the LP.
Order type

FilledPartially filled

Rejected

1,538,220

3

10,579Limit

2,370,185

PQ

3,164,006

MarketTotal

7,072,411

32
0

35

Total

37,1882,407,405

17,3633,181,369

65,1301,548,802
7,137,576

Table 24: TPA order counts by type and outcome

We have eliminated all LPs receiving less than 250,000 orders over the time period of the study to ensure astatistically significant data set when calculating percentage fill ratios to 2 decimal places (‘Four Nines’).

In this data set only LMAX Exchange returns partial fills – usually where an order is matched against several
counterparties. As the number of partial fills is very low, and as they are only returned when explicitly
permitted by the customer, we have treated these as ‘fills’ rather than ‘rejects’.

Market orders are accepted by all LPs. Orders with execution price restriction are submitted either as PQ (3 of
7 LPs) or limit (4 of 7 LPs) orders.

Currency pairs other than those priced in JPY are quoted to 5 decimal places. Under normal trading conditions
prices do not vary by more than the last two decimal places and this paper follows the standard FX shorthandof ‘pips’ and ‘ticks’ – one ‘pip’ being the 4th decimal place and one ‘tick’ being the 5th decimal place. Currency
pairs priced in JPY are quoted to 3 decimal places, with a pip corresponding to the second decimal place and atick to the 3rd decimal place.

76
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Appendix B: factors affecting internet delivery of market data
Delivering high rates of market data over the internet presents a challenge to the LMAX Exchange technology
team. TCP/IP and FIX are both ill suited as protocols for the delivery of large quantities of market data overdistance. The internet is an unreliable (un)co-operative network which offers no guarantees as to latency,
packet loss or even being there at all [9]. Transit problems often manifest themselves in ways that are hard todiagnose and defy identification of a root cause.

Market data, in common with streaming video or voice, is a ‘better never than late’ data stream, where it ispreferable to drop frames or reduce resolution and maintain a consistent low latency over halting the stream
and attempting delivery of all data at high resolution with delays. TCP/IP however provides the latter
behaviour and will attempt to deliver all data transmitted or die trying. This is the opposite of the behaviour wedesire and in our case can lead to customers getting latent market data, assuming that their FIX engines are
sufficiently well resourced to consume market data at the rate requested. This is not always the case – forexample customers using non-dedicated public cloud systems may encounter contention for CPU or network
resources from other virtual machines.
Going back to the network: TCP is designed with congestion control algorithms which aim to make it a ‘good
citizen’ of the network and it will reduce or back off from transmission if congestion is encountered. In practicethis limits the equilibrium network throughput [10], often to a fraction of the ‘sticker speed’ of a customer’s
network connection. With round trip (RTT) times of more than 100ms and the packet loss rates often
encountered over longer distances we frequently see TCP unable to transport much more than top of book at
a few 10s or 100s of updates per second. This can be hard to explain if the audience is not familiar with
networking or versed in the finer points of the equations in the Mathis paper referenced above.
Fortunately we are able to take some steps on our side to address this.

The first step is measurement. Through our ‘Streamstats’ dashboard we are able to estimate a customer’s
maximum sustainable bandwidth. We can then match the average rate and depth of market data sent to reduce
the occurrence of latent market data due to network congestion.
Within our internet facing FIX market data servers, we implement a ‘coalescing ring buffer’. In the event oftransient network congestion, this allows us to send the most recent market data once the network buffers
have drained, discarding stale updates and resuming the stream with the most current data. We also use
specialist peering and traffic engineering techniques like WAN optimisation. This uses features such as local
cache tokenisation and local acknowledgements to ‘trick’ TCP into thinking the customer and the venue are
actually much closer than they really are allowing a higher equilibrium bandwidth than it would normallysettle on.

The above mitigate the impacts of transiting the internet by manipulating TCP/IP and reducing the bandwidth
requirements on average and dynamically to provide a responsive market data stream. However even with allof the workarounds our team can devise, the Internet can still have a bad day.

The best solutions remain the simple ones of either avoiding the Internet completely via private transit or
through co-location within the same data centre or metro area. We realise that this may not suit all customers
however. It is also true that even when co-located, FIX over TCP/IP also has throughput problems when facedwith ultra low latency interconnections where the RTT is much lower than the internal TCP timers. LMAX
Exchange, in common with many other venues, offers a UDP based ITCH protocol which allows for true low
latency streaming prices without the overhead of the binary to ASCII and back again translations of FIX andfree from interruption by a complex protocol like TCP.

TCP/IP (and FIX) are really the wrong choices for delivering market data over the Internet. This is not ‘where
we would start’. However it is what we have available and while we remain interested in newer developments –for example Google’s suggestions for ‘short fat networks’ [11] we have to be conscious of the need to maintaincompatibility with a wide range of network stacks deployed by our customers and the requirement for us toserve any customer, no matter their location, technical ability or the varied ‘problems’ the Internet willcontinue to throw at us along the way.

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Appendix C: volatility bands used for price improvement
Volatility band (2σ) ranges by FX instrument for 2016 from section ‘Quantifying the value of price improvement’in Part II (p. 51). The daily volatility bands referred to in that section are available upon request.
Symbolmin

Volatility band
mean

max

AUDCAD

0.000238

0.000469

0.001514

AUDJPY

0.024353

0.061606

0.297316

AUDCHFAUDNZD
AUDUSDCADCHFCADJPYCHFJPYEURAUDEURCADEURCHFEURDKKEURGBPEURHKDEURHUFEURJPYEURMXNEURNOKEURNZDEURPLNEURSEKEURSGDEURTRYEURUSDEURZAR

GBPAUDGBPCAD
GBPCHF
GBPCZK

GBPDKK

GBPHKD
GBPHUF

0.000227
0.000229
0.000207
0.000202
0.023881
0.027308
0.000415
0.000380
0.000161
0.000149
0.000227
0.001655
0.054194
0.029353
0.006906
0.0022040.000449
0.000895
0.001838
0.000352
0.000993
0.000197
0.007722
0.000574
0.000535
0.000376
0.009771
0.002463
0.002727
0.136669

0.000456
0.000468
0.000435
0.000407
0.059334
0.060352
0.000884
0.000776
0.000298
0.000151
0.000428
0.003558
0.1001520.064938
0.016609
0.004379
0.000990
0.001893
0.003314
0.000653
0.002140
0.000451
0.016577
0.001170
0.001033
0.000756
0.019957
0.004812
0.005667
0.237968

Symbol

0.111364

0.915977

GBPNOK

0.004010

0.007211

0.044407

0.001168

GBPNZD
GBPPLNGBPSEK

0.259619

GBPSGD

0.003038

GBPUSD

0.001928

NOKSEK

0.359139
0.002784

GBPTRY

GBPZAR

0.000154

NZDCAD

0.018066

NZDJPY

0.002943

NZDCHF

0.162726

NZDSGD

0.122569

USDCAD

0.452184
0.015539
0.002871
0.009932
0.014203
0.002549
0.008985
0.002330

NZDUSDUSDCHFUSDCNH
USDCZKUSDDKKUSDHKDUSDHUF
USDJPY

0.064136

USDMXN

0.007457

USDPLN

0.0071940.006054

USDNOKUSDSEK

0.134765

USDSGD

0.048932

USDZAR

0.033750
1.526575

max

0.048939

GBPMXN

0.001428

mean

GBPJPY

0.001626

0.001529

min

Volatility band

USDTRY

0.010347
0.000619
0.002050
0.004255
0.000633
0.001550
0.000338
0.0111410.000309
0.000281
0.000244
0.025621
0.000370
0.000216
0.000301
0.000194
0.000560
0.005575
0.001332
0.000238
0.075297
0.026343
0.006529
0.002923
0.001027
0.002565
0.000222
0.000893
0.009374

0.020702
0.001334
0.003421
0.006907
0.001024
0.002866
0.000726
0.020826
0.000491
0.000515
0.000445
0.057843
0.000561
0.000437
0.000615
0.000409
0.001243
0.010601
0.002771
0.001144
0.146655
0.059517
0.014539
0.004904
0.002160
0.004327
0.000447
0.001889
0.016013

78
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

0.130111
0.007290
0.021961
0.045215
0.007633
0.016612
0.006354
0.099886
0.001775
0.001504
0.001534
0.267923
0.001583
0.001389
0.001725
0.001707
0.003621
0.053750
0.014045
0.001681
0.758817
0.247971
0.097505
0.023934
0.012728
0.024124
0.001318
0.006703
0.060790

References[1]

Foreign exchange markets with last look. Cartea et al. 2015
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2630662< back-link[2]

The true cost of rejects in $PNL
https://www.linkedin.com/pulse/true-cost-rejects-pnl-matt-clarke?trk=prof-post< back-link[3]

Do consumers prefer round prices? Lynn et al. Jnl of Economic Psychology 36 p96 2013.
http://www.sciencedirect.com/science/article/pii/S016748701300024X< back-link[4]

Trading firm XTX challenges ‘last look’ in FX
https://www.fnlondon.com/articles/trading-firm-xtx-challenges-last-look-in-fx-20170131< back-link[5]Market liquidity: Theory, evidence and policy. Foucault, Pagano, Roell 2013 p 38
https://global.oup.com/academic/product/market-liquidity-9780199936243< back-link[6]

EBS Live Ultra to provide data at 5ms intervals
http://www.ebs.com/news-and-events/2017/20170201_ebs_live_ultra_5ms_feed.aspx< back-link[7]

Algorithmic and high frequency trading. Cartea, Jaimungal and Penalva 2015 4.3.2 p 76
http://www.cambridge.org/gb/academic/subjects/mathematics/mathematical-finance/algorithmic-and-high-frequency-trading< back-link[8]

Technical analysis of the financial markets. Murphy. 1999 p. 209
http://www.penguinrandomhouse.com/books/350647/technical-analysis-of-the-financial-markets-by-john-j-murphy/9780735200661/< back-link[9]

ISP Level 3 goes TITSUP after giganto traffic routing blunderhttps://www.theregister.co.uk/2015/06/12/level_3_down_after_routing_through_malaysia_like_idiots/< back-link[10]

The macroscopic behaviour of the TCP congestion control algorithm
http://ccr.sigcomm.org/archive/1997/jul97/ccr-9707-mathis.pdf< back-link[11]

TCP options for low latency: Maximum ACK delay and microsecond timestamps
https://www.ietf.org/proceedings/97/slides/slides-97-tcpm-tcp-options-for-low-latency-00.pdf< back-link

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Notes81
An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

Contact

A unique vision for global FX
For specific feedback directly addressed to the authors,please email: TCAfeedback@lmax.com

For more information on LMAX Exchange:
Institutional clients
Telephone:+44 20 3192 2682
Email:
institutionalsales@lmax.com
24-hour helpdesk
Telephone:+44 20 3192 2555
Sun 22.00 - Fri 22.00 UK time
General enquiries
Telephone:+44 20 3192 2500
Email:
info@lmax.com
Fax:+44 20 3192 2572

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An analysis and comparison of common FX execution quality metrics between ‘last look’ vs firm liquidity and its financial consequences.

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LMAX Exchange: TCA white paper

TCA and fair execution.
The metrics that the FX industry must use.
An analysis and comparison of common FX execution quality metrics
between ‘last look’ vs firm liquidity and its financial consequences.

©LMAX Exchange 2020
LMAX Limited operates a multilateral trading facility. LMAX Limited is authorised and regulated by the Financial Conduct Authority(registration number 509778) and is a company registered in England and Wales (number 6505809).
+44 20 3192 2555 | info@lmax.com | www.LMAX.com