algorithmic trading :buy side handbook

A buy-side handbook

Algorithmic trading

Published by The TRADEin association with:

1 Sekforde Street

London

EC1R 0BE

UK

www.thetrade.ltd.uk

Tel: +44 (0) 20 7075 6115

Published by

©The Trade Ltd. London 2005.

Although The Trade has made every effort to ensure the accuracy of

this publication, neither it nor any contributor can accept any legal

responsibility whatsoever for consequences that may arise from errors

or omissions or any opinions or advice given. This publication is not a

substitute for professional advice on a specific transaction.

No reproduction allowed without prior permission.

Graphic design by Tina Eldred

[email protected]

■ ALGORITHMIC TRADING ■ A BUY-SIDE HANDBOOK ■ THE TRADE 2005

■ A buy-side handbook

Algorithmic trading

Foreword

Differentiating between the algorithmic trading offerings of brokersremains a problem for the buy-side. At the same time, brokers are

searching for ways to achieve competitive edge and raise the profile oftheir algorithmic trading capabilities. These issues have to be overcome torealise the exponential growth that is forecast for algorithmic trading.

The TRADE in association with leading industry participants drawnfrom the brokerage and vendor communities has set out to bring clarity andthought-leadership to the issues that are driving developments in the algo-rithmic space by publishing ‘A buy-side handbook on algorithmic trading’.

Part 1, ‘Market and mechanics’, examines what is driving the growth ofalgorithmic trading, focusing on the rapidly evolving shape of the market.Insights are offered into how algorithms work and the relative merits ofbroker-driven versus broker-neutral algorithms are quantified.

Part 2, ‘Honing an algorithmic trading strategy’, highlights the issuesthat buy-side traders must address once the decision has been taken toadopt an algorithmic strategy. Selecting an appropriate trading bench-mark, the importance of anonymity to stem information leakage, applyingstealth through sophisticated gaming theory, and customisation of brokeralgorithms are all addressed here.

Part 3, ‘Quantifying and enhancing value’, focuses on measuring andinterpreting the performance of disparate broker algorithms, the valueadded through independent third-party transaction cost analysis and therole of technology in enhancing market access.

Part 4, ‘Emerging trends and future direction’, covers ‘next generation’algorithms, focusing on implementation strategies for basket trading andthe shape of the market going forward, when competition and increasedbuy-side demand will call for a higher order of intelligence in engineeringalgorithms.

The handbook is completed with a guide to broker algorithms, containingdetails of individual broker offerings and including information on therange of benchmarks available, levels of customisation, performance measurement and connectivity options. ■

John LeeEditor & PublisherThe TRADE

3

■ A buy-side handbook – Algorithmic trading

Contents


4

Part 1: Market andmechanics

page 9Chapter 1:

Algorithmic trading –

Upping the ante in a more

competitive marketplace

page 21Chapter 2:

Understanding how

algorithms work

Part 2: Honing analgorithmictrading strategy

Owain Self, executive director – Equities, UBS Investment Bank

page 29Chapter 3:

Build or buy?

page 41Chapter 4:

Choosing the right algorithm

for your trading strategy

Wendy Garcia,analyst,TABB Group

Tracy Black, executive director, European Sales Trading, UBS Investment Bank

Dr Tom Middleton,head of EuropeanAlgorithmicTrading,Citigroup

Allen Zaydlin, CEO, InfoReach Richard Balarkas,

global head of AES™ Sales, CSFB

page 51Chapter 5:

Anonymity and stealth

Richard Balarkas, global head ofAES™ Sales, CSFB

page 59Chapter 6:

Customising the broker’s

algorithms


■ A buy-side handbook – Algorithmic trading

Contents

5

page 89Chapter 9:

Enhancing market access

Mark Ponthier, director –Engineering, AutomatedTrading Systems, Nexa Technologies

Mark Muñoz, senior vice president, Corporate Development, Nexa Technologies

Part 3: Quantifying and enhancing value

page 67Chapter 7:

Measuring and interpreting the performance of

broker algorithms

Henry Yegerman,director, ITG Inc.

Ian Domowitz,managing director,ITG Inc.

Part 4: Emerging trends and futuredirection

page 97Chapter 10:

Basket algorithms – The next generation

Richard Johnson,senior vice president incharge of Product Sales,Miletus Trading

Andrew Freyre-Sanders, head of AlgorithmicTrading, EMEA , JP Morgan

Robert L Kissell, vice president, Global Execution Services, JP Morgan

page 107Chapter 11:

The future of algorithmic trading

Carl Carrie, head of AlgorithmicTrading, USA,JP Morgan

Anna Bystrik, PhD, research analyst,Miletus Trading

page 79Chapter 8:

Making the most of third-party transaction

analysis: the why, when, what and how?

Robert Kay, managing director, GSCS Information Services

Appendix

page 115The TRADE guide to broker algorithms

page 130Contact information

part 1:

Market and mechanics

9

21

29

Chapter 1Algorithmic trading –

Upping the ante in a more competitive marketplace

Chapter 2Understanding how algorithms work

Chapter 3Build or buy?


■ Chapter 1


Algorithmic trading –

Upping the ante in a more

competitive marketplace

What will fuel the growth in algorithmic trading, and what impact

will the widespread adoption of algorithms have on the direction of

order flow?

Wendy Garcia*

It is undeniable that the popular-ity of algorithmic trading con-

tinues to grow. Market partici-pants, especially those on the buy-side, have willingly folded algo-rithmic trading models into theireveryday methodologies in effortsto effectively and anonymouslyfind sources of liquidity for theirlarge orders, while simultaneouslyminimising market impact. Overthe past year, adoption of algo-rithms has grown faster than anyother trading tool. This rapidacceptance is attributable primari-ly to changes in market structure,cost, efficiency, and the need toachieve best execution. The ques-tions begin to develop whenexamining the market conditionsthat are expected to support thefurther growth of this particular

electronic trading mechanism, andhow its evolution and even deeperacceptance within the marketplacewill impact the direction of orderflow in the future.

Firms are significantly reallo-cating the way they route theirorders to the market in responseto a number of interdependentand unique forces, one of which isthe increasingly difficult struggleto provide access to liquidity cen-tres. Brokers and technologyproviders are offering better andmore integrated technologies toboth access and utilise low- andno-touch trading technologies,making trading easier and moreefficient. This year, TABB Grouphas seen tremendous swings inthe way buy-side traders routetheir order flow. For example,

9

* Wendy Garcia,analyst, TABB Group

■ Chapter 1



overall, buy-side firms routed17% less order flow over thephone than they did only one yearago, and if this trend were to beprojected out to 2007, buy-sidetraders will only route 20% oftheir flow by phone.

At the same time, we have seena sizable increase in FIX-basedorder flow from 2004 to 2005,from 7% to 38% of flow.However, FIX traffic will level out,remaining steady through 2007.On a relative basis, we still seealgorithms making the greatestadvances over that same timeperiod, as the compound annualgrowth rate for algorithm usefrom 2004 through 2007 is pro-jected at 34%.

Drivers of algorithmic use

There are a number of driversbehind the increased use of algo-rithms, including the changingdynamics of the relationshipbetween the buy-side and sell-side,the more sophisticated needs of thebuy-side trader, and the increasingpresence of order management sys-tem (OMS) vendors.

The buy-side/sell-side

relationship:

One key factor that has and willcontinue to play a significant rolein the way in which algorithmsare developed is the changingrelationship the buy-side has withits brokers. As algorithms havebecome more widely acceptedand usage has increased, the buy-side is anxious to trim the num-ber of broker relationships itmaintains, in part to help reducecosts as they look to brokers forexecution-only services. It is typi-cal in today’s markets that, unlessa broker has an improved algo-rithmic trading model to offer fororder execution, buy-side tradersare not interested in developingnew relationships or even fur-thering existing relationships.This reality has significantlystrengthened the competitionamong brokers, as well as rede-fined the basis on which partner-ships are developed and main-tained. What used to be seen askey in holding on to clients,namely a long-standing trustedclient/broker relationship, is nolonger considered a primaryfoothold against the competition.

Currently, a small number of‘bulge-bracket’ brokers lead theway in algorithmic trade modeldevelopment, with a coterie offirms vying for valuable scraps.While in 2004 there were a mere

10

“The compound annual

growth rate for algorithm use

from 2004 through 2007 is

projected at 34%.”

■


■ Chapter 1


handful of brokers providingalgorithms, today there is a muchwider selection of broker-basedalgorithms on firms’ desks.Competition in this market willonly become fiercer as more firmsenter the market, additionalstrategies are created, buy-sideadoption grows and high com-mission business becomes morechallenging.

In order to compete moreeffectively, many sell-side firmshave developed targeted market-ing strategies that align theirmodels with specific customersegments. While those brokerswho were first off the line withalgorithmic trade models havesucceeded at landing moreclients, others more recently havefocused on model customisationas a way to gain buy-side favourin an increasingly competitiveenvironment. Still other firmshave targeted size, focusing oneither large or small firms, anddesign their strategies accordingto the particular needs of theirtargeted firm size.

The needs of the sophisticated

buy-side trader:

As the buy-side continues to takea more directive approach toorder flow, the format and direc-tion of order flows will continueto shift in accordance to thespeed and fluidity with which the

buy-side makes its needs knownand seeks to address them.Trading models also enabletraders to better align their exe-cution strategies against theirgoals. As algorithms becomemore sophisticated and morewidely adopted, buy-side tradershave an even more compellingreason for leveraging automatedtrading strategies.

In satisfying perceived andactual buy-side needs for furtheralgorithmic trade development,brokers need to look at the desiresclients have for increased customi-sation, including the ability ofalgorithmic trade orders to react –appropriately – to changes in mar-ket conditions and updates. Inaddition, strategy security is aconcern. If the perceived value ofalgorithms is derived fromanonymity, then any sign thattraders can ‘reverse-engineer’ thesealgorithms would endanger theentire field. A lesser concern is thepotential of a firm’s proprietarydesk to illegitimately access theelectronic flow.

The increasing presence of the

OMS:

Also changing and impacting thetrading environment and orderflow is the relationship betweenclients, brokers and order man-agement system vendors. Oneobstacle to the increased use of

11

■ Chapter 1



algorithmic trade models is bro-kers’ use of tags as they continual-ly search for a competitive edge.Tags, which are how differentalgorithms are electronicallymarked as they are sent throughthe electronic trade process, givebrokers the ability to offer cus-tomisable algorithmic trades toclients. However, although thegeneric tag for, say, a VWAP orMarket-On-Close algorithmictrade strategy may be similaracross brokers, it is not yet possi-ble to standardise the systemswith the availability of algorith-mic customisation. Buy-side andsell-side traders are in favour ofstandardising systems for consis-tency in parameters such asaggressiveness and time con-straints. In these instances thetags differ from trade to tradeacross brokers, and brokers arelooking to the OMS providers to

normalise the data or provide aninternal matching system in orderto make the trade process moreefficient.

The buy-side continues to lookfor additional ways to more effi-ciently integrate algorithms intotheir processes, including linkingthem to their transaction costanalysis tools and their ordermanagement systems. The recentacquisition of Macgregor by ITGillustrates the value of a moreseamless integration of OMS andadvanced trading tools. As tradersseek a more holistic trade man-agement system, TABB Group seesthis integration becoming a moreimportant driver of algorithms’growth. However, there also is adanger in overloading the existingtechnology to the point where itsproper functionality is hindered,creating inefficiencies that couldlead to a loss of the ease of imple-mentation and integration thatinitially drew a buy-side tradingdesk to use a particular OMS tobegin with.

As usage increases, buy-sidefirms are aiming to integrate algo-rithms more tightly into the trad-ing process by linking their OMSdirectly to the algorithmic server.The advantages of tight integra-tion are twofold. If traders enterorders through a separate web-service or desktop application,fills must be either manually

12

“The buy-side continues to

look for additional ways to

more efficiently integrate

algorithms into their processes,

including linking them to their

transaction cost analysis tools

and their order management

systems.”

■


■ Chapter 1


entered into the OMS, or import-ed into Excel and then uploadedto the OMS. In addition, third-party applications may not offerall the variables of a direct con-nection. Large firms, defined asthose with over $50 billion inassets under management, aretwice as likely to have OMS algo-rithm links as smaller firms, orthose with fewer than $10 billionin assets under management.

Tighter integration betweenthe buy-side and sell-side tradingplatforms continues at breakneckspeed. As the number of relation-ships decreases, the percentage ofbrokers connected to the invest-ment manager’s order manage-ment system is rising. Indeed,connecting to the OMS is arequirement for doing business.For all the benefits of electronictrading, the downside for brokersis that the OMS/FIX infrastruc-ture is the stepping-stone to alter-native execution vendors and hasbeen an integral cause of the liq-uidity shift. TABB Group hasfound that quantitative firms aredeeply engaged in optimising thetrading process, not surprisingconsidering they traditionallyhave a broader knowledge baseabout how the algorithmic tradesystems function, thereby increas-ing their usage comfort level sig-nificantly. Buy-side quant firmson average have five OMS algo-

rithm links, double the number offundamental and mixed firms.The automation of almost everyprocess is a key component to thequantitative business model.

Algorithmic trade strategies

In a short time period, the buy-side has graduated from basicusers of algorithms to fickleclients, growing more selective ofthe algorithms they deploy andeven building strategies aroundthem. The buy-side now is ques-tioning with more frequencywhere and how algorithms canadd value to the trade process. Asmore options are made available,such as increases in algorithmictrade options and crossing net-works, the buy-side trader ismaintaining growth in controlover its order flow. Indeed, it isnoteworthy that the buy-side isactually using a method to choosewhich algorithmic model to usefor particular trades at this point,given the use of trial and error ayear ago (see Exhibit 1 overleaf).TABB Group can cite several rea-sons for this progression. Withsome measurable time under theirbelts in using algorithms, tradersare now better equipped to usehistorical information from pre-and post-trade analysis withenough confidence to developtrade methodologies based on pastperformance. It follows logically

13

■ Chapter 1



that the more the industry learnsabout different algorithms themore often we will see implemen-tation of strategies incorporatingalgorithmic trade models.Secondly, as algorithm use growsmore pervasive throughout theindustry, the race to the top willbe based on differentiation andability to disguise intent to pre-vent gaming. This high level ofcompetition raises the bar for allalgorithm providers and can pro-pel the ones that are first to offerthis to a role of market leader.

The number of strategiesemployed by a firm is a goodproxy for its level of algorithmic

sophistication. Each strategy hasits own pros and cons, and eachone must be measured against itsown benchmark. As firms becomemore sophisticated about algo-rithms, their demands for moreflexible, customised products willincrease. Quantitative shops andsome large firms are increasinglybuilding in-house technologies inan attempt to develop customisedproprietary algorithms that arebetter suited to their direct needs,rather than relying on their brokers.

However, most firms still areunable to break entirely free oftheir brokers for algorithmic trad-

14

18%

16%

15%

13%

11%

11%

9%

7%

Trader’s PMdiscretion

TCA

Orderobjectives

Stockcharacteristics

Marketconditions

Liquidity

As anydestination

Flexibililty

57%

17%

17%

9%

Experiment

Analysis

Trading strategy

Simple orders

Response:65%

2005 2004

Exhibit 1: How algorithms are selected – two-year comparison

Source: TABB Group study ‘Institutional Equity Trading 2005: A Buy-Side Perspective’


■ Chapter 1


ing. Until the majority of marketparticipants develop a more com-plete understanding of and a high-er comfort level with the use ofalgorithmic models, VolumeWeighted Average Price (VWAP)executed trades will continue to bea staple in the algorithmic trader’sportfolio. It remains one of thesimplest algorithms, and simulta-neously the most used and themost hated of them. Fewer traderswill be accepting of its ‘at the mar-ket’ benchmarking strategy andinstead will seek algorithms thatencourage swinging for the fences.As brokers compete for algorith-mic order flow, new strategies arebeing created. Newer strategies,such as Guerilla and LiquidityForecast will help attract the moreaggressive traders. As more cus-tomisable and client-specific alter-natives to VWAP algorithms –such as Arrival Price, which has24% of the market share for algo-rithmic trade strategies and is aclose second to the 27% marketshare held by VWAP trading –continue to gain ground in thealgorithmic trading space, TABBGroup expects VWAP to lose moreof its lustre and become oneamong many strategies employed.

Factors for growth

Algorithms will grow at a 34% ratethrough 2007, intensifying thecompetition among providers, who

are racing to offer more uniqueand differentiated models andstrategies. The move to brokeralgorithms is based on four majorfactors: reduced market impact,increased trading efficiency, betteralignment between strategy andexecution, and lower cost (seeExhibit 2 overleaf).

The perception is held, espe-cially by large firms that are look-ing for ways to execute their largeorders while minimising theimpact they have on the market-place, that using algorithms tobreak down large trade ordersresults in reduced market impact.As a result, firms with large ordertrades to settle look to algorithmsto break apart the original tradeinto many smaller fills, or ordersthat are executed, thereby hittingthe markets with many smallorders that find liquidity in dif-ferent places rather than fillingone large order from only onesource. Not only is the ordermore likely to be filled using this

15

“As algorithm use grows more

pervasive throughout the

industry, the race to the top will be

based on differentiation and

ability to disguise intent to

prevent gaming.”

■

■ Chapter 1



method, but the impact as thesmaller trades hit the market isdecreased.

Another reason behind theincrease in the use of broker algo-rithms is the idea that theyincrease the efficiency of tradingby automating the execution ofless complicated orders and free-ing traders to concentrate on theirmost difficult trades. In addition,the anonymity of algorithmsoffers a significant advantage overthe human element and solves thebuy-sides’ biggest complaintabout brokers, which is informa-tion leakage. However, algorithms

are not a means by which toachieve alpha. Rather, they aretools to help increase efficiency,remain anonymous and tradeeffectively in an environmentwhere large blocks of shares areunavailable.

There also is the desire of thebuy-side to achieve better align-ment between strategy and execu-tion, which it can achieve to somedegree as brokers increasinglymake their algorithmic tradesmore customisable in order forclients to put specific limitationsor definitions in place for particu-lar trades. Incredibly, algorithmic

16

18%

16%

14%

14%

9%

9%

7%

7%

7%

Anonymity

Lower cost of trading

Automate easy orders

Decrease market impact

Ease of use

Best execution

Control

Solves fragmentation

Hit benchmarks

Response:65%

Exhibit 2: The advantages of algorithms

Source: TABB Group study ‘Institutional Equity Trading 2005: A Buy-Side Perspective’


■ Chapter 1


offerings are so critical to firms’market positions that the quanti-tative engineers who are develop-ing algorithms are more highlycherished than many traders.Many firms also believe that thesuccess of their low- and no-touch offerings is critical to theirbusiness.

Among the distinct advantagesoffered by broker algorithms isthe ability to maintain a lowercost structure than a human-based trading floor, as brokeralgorithms allow firms to retainoverall market share while elimi-nating fixed costs. In addition,commissions will decrease withthe increased use of algorithmssince they offer a relatively low-cost means of efficient execution.Since broker algorithms are farless expensive and risky thandeveloping and implementingproprietary ones, buy-side firmswill continue to prefer thatoption. Hence, the increase in theuse of broker algorithms is risingat a pace faster than that of pro-prietary algorithms – or eventhird party algorithmic providers– despite the buy-side’s discom-fort with its reliance on brokers.In addition, it is easier for a bro-ker to shut down an inefficientalgorithm than it is to fire a person.

Another advantage to usingalgorithmic strategies is their

ability to satisfy regulatory com-pliance. Due to the methodologi-cal documentation that occursduring the algorithmic tradingprocess, this mechanism offers aregulatory solution through thetrail of information that is aresult of the transactions, unlikemanual trades that are carriedout on the floor. Hence, it offersthe buy-side trader improvedmonitoring capabilities over theimpact of their trades on themarketplace, as well as a superiorview of the execution quality oftheir trades.

In its short history, broker algo-rithm market share has been dom-inated by those with the quickesttime to market. The ‘first-mover’advantage is valuable, but as thealgorithmic space becomes morecrowded, the game is changing.Buy-side firms are now placingincreased importance on how bro-kers package additional tradingtools into the most compellingelectronic trading value proposi-tion. The brokerages with thekeenest vision, the best tools, themost comprehensive support andthe sharpest pencil will win. It willno longer be the case that the firstto market will get the spoils.Instead, the winning firms will bethose that effectively help theirclients navigate the challengingmarkets, in the least complex man-ner and at the lowest possible cost.

17

■ Chapter 1



The future

The buy-side will continue tograsp more control over its orderflow as algorithms become moreenhanced and readily available,even if it means routing theirorders through broker algorithmsin increasing numbers as theylook for ways to reduce costs.This will be the case especially asbrokers increasingly makeattempts to supply the buy-sidewith updated and additionallycustomisable models with whichto trade.

The recent passage of RegNMS also will have its ownimpact on the order flow as, withthe increasing electronic capabili-ties of the marketplace, use ofalgorithmic and other electronic

forms of order routing willincrease. As participants in themarketplace realise the marketstructure itself is not to blame forsuch market conditions asincreased fragmentation, rather itis the increased use of electronictrading strategies and technolo-gies to effectively hide and seekliquidity within the marketplace,traders will continue to adoptalgorithmic trading models out ofcompetitive necessity. Order flowwill maintain its surge – not onlyin equities, but across other assetclasses as well, as we see algorith-mic trading penetrate furtherinto the marketplace to incorpo-rate foreign exchange, derivatives,and even fixed income, as playersin these markets look for anony-mous trading and search for hid-den liquidity – providing vendorswith opportunities to shine asincreased demands for moresophisticated technology aremade by brokers and the buy-sidealike. The buy-side’s lack of trustfor the brokers will not subside,even as they decrease the numberof brokers with whom they dobusiness and develop more inte-grated relationships with the onesthey do maintain.

However, until the buy-sidehas the knowledge and the capitalof the brokers in order to developand maintain their own systems,there will always be a place for

18

“Until the buy-side has the

knowledge and the capital of

the brokers in order to develop

and maintain their own systems,

there will always be a place for

those brokers who stay on the

leading edge of technology and

provide the marketplace with more

advanced, customisable and

efficient algorithmic trade

models.”

■


■ Chapter 1


those brokers who stay on theleading edge of technology andprovide the marketplace withmore advanced, customisable andefficient algorithmic trade models.

In addition, the order manage-ment system vendors, in order toremain competitive, need tounfailingly develop further waysfor the brokers to integrate moreeffectively their particular trademethodologies at an always moreefficient rate of trade processing.Transaction cost analysis will growin use and importance as the mar-ketplace will look to historicaldata to determine the most effec-tive trade methods and to developbetter ones.

Also defining the usage pat-terns of algorithms will be thelinkage between algorithms anddirect market access (DMA) plat-forms as a holistic execution man-agement system is sought. SinceDMA platforms will likely be thevehicle that wraps and delivers alltrading tools to the buy-side trad-er, algorithms will increasinglyseek more effective associationswith DMA providers. We arealready seeing this become moreevident in platforms such asGoldman Sachs’ REDIPlus® andMorgan Stanley’s Passport, whichare being used to increase theirown growth potential. Since algo-rithmic trading is rapidly gaining

traction, the importance of a linkto DMA will become critical toDMA’s acceptance and growth, asbuy-side firms strive for thatcompetitive edge.

Algorithmic trading is stillyoung and in its developmentalphases. As it grows and becomesmore widely used, understoodand accepted, the marketplaceand the way it functions willtransform into what we expectwill be a more efficient and effec-tive place to trade. ■

19

“Defining the usage patterns

of algorithms will be the

linkage between algorithms and

direct market access (DMA)

platforms as a holistic execution

management system is sought.”

■


■ Chapter 2


Are we witnessing a revolution intrading or are algorithms little

more than a novelty that can bereadily outperformed by the aver-age human trader? To answer thisquestion, we need to determinewhich algorithms are ‘smart’, whatmakes them smart, and how theycan optimise the performance of atrading desk.

Choices abound in algorithmictrading, even in its current nascentstate. Any broker worth its salt nowoffers a diverse suite of algorithmsand parameters, and third-partyproviders are now stepping in tooffer various ‘customisable’ andniche products. With such a multi-tude of choices, it is important todifferentiate between algorithmictrading engines that are essentiallyenhanced Direct Market Access(DMA), and algorithmic tradingproducts based on robust statisticalmodels of market microstructure,that have been found to increasetrader productivity in both buy-side and sell-side firms.

Enhanced DMA strategies (seeTable 1, overleaf), such as pegging,‘iceberging’, and smart order rout-ing require minimal quantitativeinput, and the trader does not del-egate any real decision-making tothe algorithm. While these facilitiesadd value to trading desk capabili-ties and performance, their behav-iour is fairly easy to understand.

Algorithms that require quanti-tative input are generally designedto minimise execution risk against auser-specified benchmark, typicallyVolume Weighted Average Price(VWAP), Time Weighted AveragePrice (TWAP), ImplementationShortfall (IS), Participate, orMarket on Close (MOC). Thischapter aims to shed some light onthe relatively opaque world of thesealgorithms, clarifying the thoughtprocess of their designers and theinputs of the models.

Risk and reward

Central to any investment processis the trade off between risk and

21

Understanding how

algorithms work

Where does time slicing and smart order routing end and randomising

your orders through complex algorithms begin?

Dr Tom Middleton*

*Dr Tom Middleton,head of EuropeanAlgorithmic Trading,Citigroup

■ Chapter 2


reward. Naturally, this applies toalgorithmic trading – a smart algo-rithm will maximise performancefor a given level of tracking erroragainst a chosen benchmark. Thus,the standard deviation of slippageagainst the benchmark is as impor-tant as the average when compar-ing the performance of a selectionof algorithms.

The choice of algorithm andprovider depends on the individualuser’s benchmark, style and urgencyof trading. Just as in portfolio man-

agement, the appropriate place onthe efficient trading frontier – howmuch risk a trader is prepared totake in exchange for improved per-formance – will depend on thenature of their alpha, how oftenthey trade and against which bench-mark their performance is evaluat-ed. It makes sense that a smallhedge fund with a short time hori-zon and high trading rate will toler-ate higher risk on each individualexecution in exchange for enhancedperformance, than a fund that

22


Table 1: Examples of common algorithmic trading strategies, emphasisingthe difference between algorithms that require quantitative marketmicrostructure modelling and simpler enhanced DMA strategies.

Enhanced DMA strategiesIceberging A large order can be partially hidden from other market

participants by specifying a maximum number of shares to be

shown.

Pegging An order is sent out at the best bid (ask) if buying (selling) and if the

price moves the order is modified accordingly.

Smart order routing Mainly a US phenomenon – liquidity from many different sources

is aggregated and orders are sent out to the destination offering

the best price or liquidity.

Simple time slicing The order is split up and market orders are sent at regular time

intervals.

Simple Market on Close (MOC) The order is sent into the closing auction.

Quantitative algorithmsVWAP Attempts to minimise tracking error while maximising performance

versus the Volume Weighted Average Price traded in the market.

TWAP Aims to match the Time Weighted Average Price. Similar to simple

time slicing, but aims to minimise spread and impact costs.

Participate Also known as Inline, Follow, With Volume, POV. Aims to be a

user-specified fraction of the volume traded in the market.

MOC Enhanced MOC strategy that optimises risk and impact, possibly

starting trading before the closing auction.

Implementation Shortfall Manages the trade off between impact and risk to execute

(aka Execution Shortfall or as close as possible to the mid-point when the order is entered.

Arrival Price)


■ Chapter 2


trades much less frequently with alonger time horizon.

Thus, balance between averageexecution performance and itsvariance is essential to the designof algorithmic trading models.Whether implicitly or explicitly, awell-designed strategy will opti-mise this trade off to deliver thebest performance possible for agiven level of risk. This methodolo-gy pervades every aspect of modeldevelopment.

Ever-smaller slices?

The first component of an algo-rithm is the trading schedule – therate at which the model aims to exe-cute the order as a selection ofsmaller slices. It is difficult to pickup an article or attend a conferencewithout seeing a graph illustratingthe fall of the average trade size inrecent years. Will we all be trading inone-lots in 10 years’ time? Clearlynot – there must be a level at whichtransaction costs become prohibi-tive. Furthermore, every trade repre-sents a piece of information leakedto the market, defeating one of theprincipal objectives of algorithmictrading in the first place.

The trading schedule is essen-tially dictated by the benchmarkdescribing the trading strategy – itis fairly obvious that a VWAP algo-rithm will execute volume accord-ing to a historical volume profile,possibly with some dynamic

adjustment; Participate will trackcurrent volume; ImplementationShortfall will involve some sort ofimpact/risk optimisation.

Managing the trading scheduleis particularly important inParticipate algorithms. One couldenvisage a ‘not-very-smart’ algo-rithm, that when aiming to tradeone third of the volume, fired out amarket order for one share forevery two that printed elsewhere.Not only would this generate ahuge number of tickets but wouldbe incredibly easy for a predatoryproprietary algorithm to ‘sniff out’and front run. As an order tradedin this way would typically create asignificant amount of impact, thiswould result in poor execution per-formance for the user. Thus, indesigning a Participate algorithm, itis important to allow the user tospecify the urgency of the order – astop-loss 1/3 of volume ordershould be executed differently to anorder where the trader is preparedto tolerate much more trackingerror against volume in exchangefor better performance through lessimpact and more intelligent,opportunistic type trading.

Implementation Shortfall – themidpoint when the trade is started –is perhaps the most logical bench-mark to use, but perhaps opinionsabout the optimal trading strategyare the most diverse. Some providershave apparently repackaged VWAP

23

■ Chapter 2



or Participate algorithms with a par-ticipation rate or end-time deter-mined by a model rather than by theuser, while others have built entirelydifferent algorithms that react toprice and liquidity in an opportunis-tic manner. The danger with oppor-tunistic strategies is that one can endup cutting winners and letting losersrun – buying falling stocks aggres-sively, while buying rising stocksmore gradually. Ultimately, it

depends on the underlying invest-ment style and the trader’s objectiveswhich Implementation Shortfallstrategy is the most appropriate.Ideally, an IS algorithm shouldobtain an end-time and tradingschedule statically from an impactcost model, consistent with any pre-trade employed: but still allow theuser the flexibility to specify anydynamic constraints or parametersto take advantage of price or liquidi-

24

IS passiveIS activeVWAP

8:00

8:25

8:50

9:15

9:40

10:0

5

10:3

0

10:5

5

11:2

011

:45

12:1

0

12:3

5

13:0

0

13:2

5

13:5

014

:15

14:4

0

15:0

5

15:3

0

15:5

5

16:2

00.00

0.02

0.04

0.06

0.08

0.10

0.12

Figure 1: Example trading schedules for VWAP, a passive IS strategy and an active IS strategy. The IS profiles are calculated in ‘Volume Time’, takinginto account both the need to unwind more rapidly than VWAP and thelikely liquidity in the market.


■ Chapter 2


ty if appropriate. At Citigroup we usean impact cost model1 to determinean optimal end-time and optimaltrading schedule based on the user’srisk aversion – typically an accelerat-ed initial trading rate compared to aVWAP profile (see Fig. 1). However,this static strategy can be enhancedas required with parameters thatconstrain the order or allow it to takeliquidity or price-related opportuni-ties – if these modify the schedule were-optimise the profile in real time.

The trade schedule for anenhanced MOC is obtained in asimilar way to ImplementationShortfall – but in reverse. The strate-gy with the least tracking riskagainst the close price places thewhole order in the closing auction,where appropriate, while that withthe minimum impact is a VWAPtrade, starting as early as possible.Obviously, most users will choose aschedule intermediate between thesetwo strategies, according to theirown risk and impact tolerances.

Managing each slice

The second major component ofan algorithm is how each slice ofan order is managed. A dumb algo-rithm might send out marketorders every time it wants to trade.This strategy would have very lowtracking error against the targetvolume, but would pay the spreadirrespective of whether it was nec-essary to do so.

The question that we areattempting to answer in a logical,quantitative and statistical fashionis what a successful human traderdeals with instinctively hundreds oftimes a day, namely whether to (ifbuying):(i) Pay the offer.(ii) Wait on the bid side of the

order book.(iii) Wait outside the order book

and wait for a tight spread or aliquidity opportunity.

In a risk-reward framework,options (ii) and (iii) both increaseour execution risk relative to option(i). The reward that we expect fortaking this risk is decreased spreadand impact cost. In the formulationof our decision logic, the variablesthat we need to consider here arespread, volatility and liquidity onthe electronic order book.

The trading behaviour ofdiverse stocks across the world canbe simplistically classified accord-ing to two simple ratios – the ratioof tick size2 to volatility, and theratio of median3 bid/ask spread tovolatility. The method used to esti-mate volatility is not importanthere, as we are simply interested inclassifying the stocks according totheir rankings by these two ratios.

Essentially, there are three cate-gories of stock, illustrated in Table2 (overleaf).

A well-researched trading modelwill adapt its behaviour according

25

1 R. Almgren, C.Thum, E.Hauptmann andH. Li, EquityMarket Impact,Risk, July 2005.

2 The tick size is theminimum priceincrementallowed, and so isalso the minimumpossible bid/askspread duringcontinuoustrading

3 The median is amethod ofestimating theaverage bychoosing themiddle value ofthe sorteddataset. Itsadvantage overusing the mean isthat it is affectedless by very largeor small ‘outlying’values.

■ Chapter 2



to the type of stock that it isattempting to trade.

High tick size/volatility,

high median spread/volatility

This type of stock is well represent-ed by Ericsson. Although it is rea-sonably volatile, this is swampedon short timescales by its enor-mous tick size – approximately 36bps as I write. Stocks whose behav-iour is constrained by the tick sizein this way characteristically havelarge amounts of liquidity at thebest bid and ask, relative to averagetrade size or average daily volume.

Thus, the optimal spread capturestrategy in this case is to be patient –waiting on the order book, some-times for hours, in order to stand achance of capturing spread.Algorithmic trading adds huge valuefor this class of stock, as a computeris able to constantly monitor liquidi-ty on the order book, using the sup-ply/demand imbalance as an indica-tor of future price movement. If anadverse move is predicted on thisbasis, then the model should pay thespread – otherwise patience is key.In terms of a slicing strategy, small isnot beautiful for this type of stock.

As queuing time is generally long, itis important to get liquidity on theorder book as soon as possible tostand a chance of trading on theright side of the spread. The largeamounts of stock on the best bidand ask also mean that the effect ofadding more liquidity is unlikely tosignificantly affect the supply/demand imbalance, and so there isunlikely to be adverse price move-ment as a consequence of ouradding to the order book.

Low tick size/volatility,

high median spread/volatility

These stocks are typically mid-capi-talisation, less liquid stocks withvolatile spreads. Many providers donot recommend that this type ofstock should be traded on theengines, owing to the difficulty ofachieving high quality performance.The counter-argument to this is thatthese are also the stocks where infor-mation leakage can be the mostdamaging and so while trading themelectronically can be problematic atleast anonymity is maintained.

The correct strategy here is totrade opportunistically and takeadvantage of spread and liquidity

26

Table 2: Three-way classification of stocks, with examples,according to spread and volatility behaviour.

Tick size/volatility Median spread/volatility Example High High Ericsson

Low High EMI

Low Low Deutsche Telekom


■ Chapter 2


opportunities within the blink ofan eye. A tight spread with goodliquidity on this type of stock sug-gests that the stock is fairly pricedand the spread represents ‘goodvalue’. Thus, the risk involved inwaiting at the best bid or offer isunlikely to be rewarded sufficientlyto justify passing on the opportu-nity of executing immediately onthe other side of the spread. Astime passes, a wider spread has tobe accepted in order to keep upwith the required trading rate. Thetaking of opportunities does notpreclude adding liquidity to theorder book – but care has to betaken to avoid leaking informationto the market by creating a sup-ply/demand imbalance that maycreate an adverse price movement.

Low tick size/volatility,

low median spread/volatility

The final class of stock tend to beliquid, blue-chip stocks with lowtick sizes and median spreads rela-tive to volatility, that are efficientlypriced and easy to trade by bothhuman beings and algorithms –orders in these stocks tend to bereferred to as ‘low-touch’ or com-moditised. These orders should besliced relatively finely to avoidexcessive execution risk, and thewaiting time on the order book isrelatively short, as the reward forcapturing the spread is relativelysmall compared to the volatility

risk to which the trader is exposedwaiting on the bid or ask.Algorithms add value in the tradingof these stocks as they allow tradersto focus on more difficult trades inless efficiently priced stocks.

Balancing risk and return

Algorithmic trading models mayremain somewhat opaque to manyusers – and many brokers areunwilling to disclose too muchinformation about what is ‘underthe hood’. Furthermore, the mathe-matics of how they operate is oftenrather intractable and poorlyexplained. I hope in this chapter Ihave managed to communicate atan intuitive level how a successfulalgorithm should help the userachieve the best possible outcomein executing their trades.

Algorithms must be designed tomanage the risk-return trade off inan optimal way, both in the way thattrading is scheduled and the way inwhich each slice of an order is man-aged. While this should be apparentin performance statistics, algorithmsshould be transparent in the waythat they are designed to balancerisk and return so that the user canchoose the appropriate strategy andprovider that best suits their tradingand investment styles. ■

27


■ Chapter 3


Build or buy?

What are the relative merits of broker-driven versus broker-neutral

algorithms? Understanding the trade off between cost and

performance

Allen Zaydlin*

Since the introduction of elec-tronic trading, the landscape of

trade execution, risk managementand market access has undergonesignificant and rapid changes.Being interdependent in nature,the advancements in one area forceother areas to evolve. To that extentthe automation of electronic tradeexecution can be seen as the ‘firstviolin’, because the increasingdemand in execution speed,throughput and low latency on onehand requires more efficient mar-ket access and risk management onthe other.

What are algorithms?

Automation of trading processescan be grouped into two generalcategories – automation of trading(what to trade) and automation ofexecution decisions (how totrade). While the first one dealsprimarily with investment deci-sions, the second one focuses ontheir implementations. There is no

market consensus on the precisedefinition of each category, but forthe purpose of this chapter we willdefine them as alpha models andexecution algorithms or simplyalgorithms.

Algorithms – when used appro-priately – can improve multiplefacets of the investment cycle. Theygive traders the ability to handlelarger sets of securities. At the sametime traders are able to focus theirattention on the instruments thatare difficult to trade, for instanceilliquid stocks, while letting algo-rithms take care of more liquidnames. Algorithms benefit fromthe speed of computers and there-fore can follow the market moreclosely than traders. They alsoeliminate the emotional aspect ofthe trading process, resulting inmore consistent performance. Inaddition, algorithms help reduceinformation leakage. All of theabove can be summarised in a sin-gle statement:

29

*Allen Zaydlin, CEO,InfoReach

■ Chapter 3



“Algorithms are a more efficientway to trade in an environmentwhere cost, speed, consistency andthe prevention of informationleakage are crucial in attainingalpha”

Survival of the fittest

In recent years, execution algo-rithms have become more preva-lent. Initially, algorithms wereoffered by only a handful of bro-kers to the larger buy-side firmsthat were willing to pay highercommissions. Since then, howev-er, algorithms have grown tobecome a common service avail-able from the majority of brokers,as well as trading system vendors.Furthermore, as algorithmic exe-cution becomes increasingly pop-ular, more third-party softwarevendors are building trading plat-forms that serve as the foundationfor the rapid development of cus-tom algorithms. The increase inalgorithmic offerings has madethe process of selecting the rightalgorithm for your trading desk amore exacting task. Nevertheless,

the dilemma is quite simple toresolve and comes down to threeoptions:

1. Utilise algorithms provided bybrokers.

2. Utilise ‘broker-neutral’ algo-rithms provided by third-partyvendors.

3. Develop proprietary algorithms.

The first thing you will need to doin your quest to select an algorith-mic provider is to think clearlyand objectively. Simple analysisand adequate understanding of thegoals that you need to reach, aswell as the means available canyield surprisingly unexpectedresults. This will help you to avoidsome common misconceptionsabout algorithms’ functionalityand performance, and will alsoassist in separating the marketinghype from the algorithms. Thiswill help you avoid the followingscenario:

Q: “Does your VWAP algorithmguarantee to beat the VWAP?No! The broker I am talkingto guarantees their algorithmto beat VWAP every time.

A: “Great! Then you should be ableto send any buy and sell ordersof the same symbol to thisVWAP algorithm simultaneous-ly and enjoy positive P&L everyday.

30

“The increase in algorithmic

offerings has made the

process of selecting the right

algorithm for your trading desk a

more exacting task.”

■


■ Chapter 3


Q: How good is your VWAP?A: What are the average sizes you

need to trade? How liquid areyour instruments?

Q: Liquid, about 2000 shares.A: Rest assured our VWAP will do

a good job.

First of all, this thinking processshould include a better under-standing of how different algo-rithms function and the problemsthey solve. Secondly, try to decidewhich algorithm fits your tradingcycle objective. And, lastly, pickproviders that offer algorithmsmost efficiently.

Comparing the options

Confusion on behalf ofbuy-side portfolio managers andtraders comes in part from the lackof consolidated and systematicanalysis of algorithmic executionperformance and cost ratios. It isalso partly from the absence of asingle scale that can be applied tocompare different options.Execution performance attributionis also somewhat obscure and lacks standardisation.

Nevertheless, a certain struc-tured quantitative comparison ofdifferent options can be accom-plished. Let’s start off by identify-ing the pros and cons of using bro-ker-provided, ‘off-the-shelf ’ andcustom-built algorithms.

Broker-provided

Pros■ Require minimum technological

infrastructure on the client sideto access execution models.

■ Provide a wider range ofadvanced algorithms that relyon significant research, infra-structure and maintenance cost.This includes quantitative studyof historical data, computerhardware and network infra-structure to deal with vast calcu-lation of considerable amountsof real-time market and execu-tion data. There is also theongoing expense in improvingthe performance of existingmodels.

■ Ability to pay only for usage.Cons■ Higher commission rates.■ Higher risk of information

leakage.■ Fewer algorithmic parameters

are exposed to end-users.

31

“Confusion on behalf

of buy-side portfolio

managers and traders comes in

part from the lack of

consolidated and systematic

analysis of algorithmic execution

performance and cost ratios.”

■

■ Chapter 3



Off-the-shelf

Pros■ Lower commission rate by tak-

ing advantage of DMA rates.■ Tighter control over parameteri-

sation of the algorithms.■ Reduced risk of information

leakage.■ Ability to work with multiple

brokers simultaneously, whilekeeping trading data consolidat-ed within a single system.

■ Broker neutrality.■ Anonymity.

Cons■ Require more infrastructure on

a client side to run.■ Increased financial commitment

regardless of usage.■ Tendency to lack more

sophisticated algorithms thatrequire a more elaborate infrastructure.

Custom-build

Pros■ Customised functionality not

currently available from othersources.

■ Quicker ability to modify.■ Tighter control.

Cons■ Bear unscalable expense of

infrastructure, development andenhancement effort.

■ Risk of failure achieving the exe-cution performance objectives.

Broker-driven vs. off-the-shelf

The emergence of various off-the-shelf algorithms serves as an indi-cation of the level of commoditisa-tion that has occurred with certainexecution models and the deprecia-tion in their relative value. Suchoff-the-shelf algorithms will have acost advantage in comparison totheir broker-driven siblings. A sim-ple comparison of VWAP algo-rithms provided by brokers andoff-the-shelf providers will serve asa guideline on how to comparealgorithms in general.

Assuming that the ultimate goalis selecting an algorithm with max-imum efficiency – meaning thealgorithm that combines best exe-cution performance with lowestcost – one can create a commonscale that combines cost and per-formance into a single unit of mea-

32

“The emergence of various

‘off-the-shelf’ algorithms

serves as an indication of the

level of commoditisation that

has occurred with certain

execution models and the

depreciation in their relative

value.”

■


■ Chapter 3


sure. The cost basis is vital becauseif two different algorithms performalike then cost becomes the maindifferentiating factor. At the sametime we need to factor in perfor-mance, because poor executionperformance can quickly eliminateany advantage of a lower cost algo-rithm.

In determining the cost basis, itis useful to start with commissionrates. From what we observe in themarket today, it should not beunreasonable to accept $0.0075 pershare as a common rate for broker-provided algorithms. At the sametime, brokers’ DMA can offer avery compelling commission rate,where $0.0015 is not unheard of.However, we also need to factor inthe cost of the third-party vendortechnology which delivers the bro-ker-neutral algorithms that allowyou to take advantage of low DMArates in the first place. We will usean average of $10,000 per monthfor such technology and assumethat it is a fixed cost that does notincrease with trading volumes.Taking the example of a firm trad-ing one million shares per month,we arrive at a commission cost of$7,500 per month when using abroker-provided algorithm, com-pared with $11,500 when using thebroker-neutral equivalent. Clearly,it does not add up – particularly ifinformation leakage is not a majorconcern for the firm.

This hypothesis changes howev-er, once the number of shares trad-ed via the algorithm increases totwo million shares per month. Nowwe are looking at $15,000 with bro-ker-driven versus $13,000 withbroker-neutral algorithms. Thus,the breaking-point lies somewherebetween one million and two mil-lion shares per month, or between50,000 and 100,000 shares per day.It does not really matter exactlywhere the breaking-point lies, whatis important is that in every partic-ular case it can be calculated.Applying the same commissions’rates for 20 million shares permonth (one million per day) wewill get over $100,000 of savingsper month (over $1 million peryear) – a level of cost saving thatmost firms would regard as signifi-

33

“Assuming that the ultimate

goal is selecting an algorithm

with maximum efficiency –

meaning the algorithm that

combines best execution

performance with lowest cost –

one can create a common scale

that combines cost and

performance into a single unit of

measure.”

■

■ Chapter 3



cant. While for the purpose of thisexercise the numbers have beenrounded off, by using thisapproach and calculating your ownnumbers you can easily find thepoint at which the cost benefit ofintroducing broker-neutral algo-rithms is going to start makingsense to your business. (Go tohttp://www.in4reach.com/compare.html to run a quick com-parison using your own numbersas input.)

The chart opposite provides agood indication of the projecteddifferences in execution costsbetween the two venues relevant tothe number of shares traded.

For the sake of objectivity, wealso need to factor in the perfor-mance of the algorithms we arecomparing. This is key. The costadvantage of an off-the-shelf algo-rithm over a broker-driven model

will count for far less if it posts aninferior performance. A quickanalysis of the numbers can helpus determine where the breaking-point is in terms of performance.Using VWAP as the example oncemore, and assuming that a broker-driven algorithm slipped 0.03 of abasis point on 10 million sharestraded over a month, and using$30 per share as an average stockprice, the value of slippage is$90,000. As outlined in the Chart1, the cost advantage of the off-the-shelf algorithms was around$50,000 based on 10 million sharestraded per month. To have thisadvantage nullified due to the bet-ter performance of a broker-drivenVWAP model, the off-the-shelfalgorithm has to slip 0.047 of abasis point or, in terms of themath, under-perform by 156%.That said, in ‘real-world’ trading, itis well known that various VWAPsproduce results only marginallydifferent from one another.

If this approach is calculatedusing different monthly tradingvolumes and average stock pricesonly, the core finding remains thesame: that the performance ratio isdriven solely by the commissionrates ratio. In order to be on parwith a broker-neutral algorithm, abroker-provided algorithm has tooutperform it to a level that cancompensate for the difference intheir respective commission rates.

34

“The cost advantage of an

off-the-shelf algorithm over a

broker-driven model will count

for far less if it posts an inferior

performance. A quick analysis of

the numbers can help us

determine where the breaking-

point is in terms of

performance.”

■


■ Chapter 3


It is simply incorrect to assumethat something that costs twice asmuch performs twice as well.

Most broker-provided algo-rithms reveal only a limited numberof parameters for buy-side tradersto manipulate. Commonly, they are:algorithm start and end times,aggressiveness, percentage of thevolume, plus three or five others. Incontrast, off-the-shelf algorithmsprovide significantly more options –in some cases as many as 30 para-

meters – fine-tuned for each indi-vidual order. Furthermore, mostbroker-provided algorithms are notgeared to work with lists of ordersthat have correlations or share con-strains. For example, if you need totrade a sector neutral basket ofstocks there is no easy way to makebroker-provided TWAP understandyour criteria of sectors – which canbe totally specific to you – and thethreshold of intra-day imbalancebetween sectors.

35

Broker algorithms rate Broker DMA rate Commissions difference

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

1,000,0

00

40,000,0

00

5,000,0

00

10,0

00,000

15,0

00,000

20,000,0

00

25,000,0

00

30,000,0

00

35,000,0

00

Shares per month

Co

mm

iss

ion

s p

er

mo

nth

($

)

Chart 1: Commissions comparison

■ Chapter 3



Reducing information leakage ismore a question of trust than anitem that can be measured throughquantitative analysis. However, oneclear advantage broker-neutralalgorithm will have is that byspreading your trades in smallersizes across multiple DMA pipesyou reduce the risk of informationleakage regardless of your level oftrust.

Proprietary algorithms

There are several reasons for build-ing proprietary algorithms. Firstamong these is control over thefunctionality and performance ofthe algorithm, followed by lowercommission expense by making thealgorithm broker-neutral.

As outlined earlier in the chap-ter, some algorithms are fairly basicand require minimal infrastructureto run, while others call for real-time analysis of market depth andextensive statistical analysis of

historical data. The cost of devel-oping more complex algorithmsin-house in terms of the networkand computer hardware that isrequired can be prohibitive. Insome instances, it would be hard tojustify such a high level of invest-ment in infrastructure, since buy-side demand for highly sophisticat-ed algorithms varies depending onthe trading strategy being deployedand the volumes involved.Meanwhile, the ongoing operatingcosts remain constant. It is advis-able, therefore, that before decidingto build proprietary executionalgorithms, the cost and perfor-mance of existing offerings is fullyunderstood.

Having considered the relativemerits of execution algorithms –broker-driven, off-the-shelf andproprietary – there appears to be abreaking-point, both in terms ofcost and performance, at whichbroker-neutral algorithms deliverclear advantages. The fact thatmore broker-driven algorithms arebeing made available from third-party vendors, wrapped within abroker-neutral model, can be takenas a sign of increasing efficiency inthe market place. ■

36

“The fact that more broker-

driven algorithms are being

made available from third-party

vendors, wrapped within a broker-

neutral model, can be taken as a

sign of increasing efficiency in the

market place.”

■

algorithmic trading :buy side handbook

Documents