Honing an Algorithmic Trading Strategy

part 2:

Honing an algorithmic trading strategy

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Chapter 4Choosing the right algorithm for your trading strategy

Chapter 5Anonymity and stealth

Chapter 6Customising the broker’s algorithms

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

■ Chapter 4


Investors trading styles andbenchmarks can be different for

a number of reasons. Index track-ers and fund managers may havespecific price goals such as theclose price, whilst others may bemore constrained to the price atwhich the investment decisionwas made. Some investors may beinvesting into 3-4 days volume ofa stock and others in smallermore frequent trades. Reactionsto changes in prices and volumesover the course of the implemen-tation of their trade and, general-ly, the degree of impact they arewilling to have in order to com-plete, will differ from investmentcase to investment case. Whetheror not an investor’s trading styleinvolves large slow money ordersor smaller high frequency trades,there is a place for algorithms ifused appropriately. In this chapterwe will explore the attributes ofthe more commonly available

algorithms without going into thecomplex mathematics behindtheir construction.

Algorithmic choiceWhen deciding if you can utilisean algorithm to execute a particu-lar order a number of questionsneed to be answered. Besides theobvious question of ‘what is mybenchmark?’ other factors willultimately dictate whether algo-rithmic trading is an option and,if so, what type of algorithm andwhich parameters to apply.

First, you need to assesswhether the stock is suitable. Bluechip liquid names that trade alarge percentage of their volumeon the order book will be goodcandidates. Small/mid cap stocksthat trade a very small percentageon the order book are only suit-able with correct parameterisa-tion. The reasons for this are obvi-ous, a computer can only react to

41

Choosing the right algorithmfor your trading strategy

What are the options buy-side traders need to consider in selecting analgorithm best suited to a particular investment style?

Tracy Black* and Owain Self**

**Owain Self ,executive director –Equities, UBS Investment Bank

*Tracy Black, executive director –European SalesTrading, UBS Investment Bank

■ Chapter 4



information that is electronicallyfed to it, it cannot participate inoff market prints and it cannotmake phone calls to negotiateblock trades.

Secondly, you have to decidewhat proportion of the order youwant to execute via an algorithm.You might want to put yourentire order into an algorithm ifit suits your benchmark or youmay want to combine algorithmswith more of a traditional tradingservice, such as Block Trading orDMA.

Making the process even morecomplex for the client is the factthat not all algorithms are equal.Brokers have different names foralgorithms that have similar trad-ing styles and sometimes twofirms will offer an algorithmunder the same name, which willexecute very differently. This cre-ates a minefield for the client.Only through education from bro-kers on what to expect of theiralgorithms and through actually

using them will a client be able toknow which algorithms at whichfirms best suit their trading style.

Undetermined benchmarksAlgorithms can generally be splitinto two types of benchmark, pre-determined and undetermined.First generation algorithms try toobtain a yet undetermined bench-mark, such as VWAP, where thebenchmark will be determinedover the life of the order. The morerecently developedImplementation Shortfall algo-rithms will be measured against abenchmark predetermined atorder creation.

VWAP (Volume WeightedAverage Price) has been the mostcommonly used algorithm histor-ically. As things have evolved,VWAP has gained its critics but itstill has its uses. Ultimately usedwith the aim to minimise marketimpact, VWAP is useful for exe-cuting trades where you don’tnecessarily have a view on a stockand want to obtain a fair price bysampling market levels over aspecified period. Due to its sensi-tivity to changes in volume distri-bution, VWAP will participaterelative to liquidity. However,VWAP algorithms do not gener-ally take into account absolutevolume levels and will still try tocomplete the order even if thiswould cause additional market

42

“VWAP algorithms do notgenerally take into account

absolute volume levels and willstill try to complete the order evenif this would cause additionalmarket impact.”

■

patterns based upon both histori-cal and real-time data analysis.This results in an improved stan-dard deviation of returns evenwhen trading less suitable stocks.

impact. It is therefore importantthat traders apply price and vol-ume caps on larger trades tominimise such an impact,although this potentially meansthe order will not complete.

The expectation of VWAP isnot just about mean perfor-mance; we know that if you exe-cuted an order each and everydayof the year in the same stock, themean performance would beacceptable, however this is anunlikely scenario. Therefore, therisk-adjusted performance andthe standard deviation of thereturns become important. Dueto VWAP’s sensitivity to volumedistributions, the performancerisk is affected by its ability topredict changes in these distribu-tions. The majority of tradingengines are based on historicaldata. This can often mean thatyou have to choose your stockscarefully. Some will have a rela-tively stable historical tradingpattern – e.g. GlaxoSmithKline(Fig. 1) – and therefore a morepredictable outcome. Others,however, can have a more volatiletrading pattern historically – e.g.LogicaCMG (Fig. 2). In thesecases, using an average historicalcurve will not deliver acceptableperformance, as your standarddeviation would be too large. It isimportant that the algorithm youare using can predict trading


■ Chapter 4


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2,000,000

2004/10/1

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2005/07/29

2,800,000

2,400,000

1,600,000

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0

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1,200,000

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Figure 1: GlaxoSmithKline PLC

20,050,1112005/01/11

800,000

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800,000

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Time

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Figure 2: LogicaCMG PLC

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VWAP’s sensitivity to the vol-ume distribution over the life ofthe order may not always suityour trading style. For example, ifyou need to execute an order overthe remaining hour of the day. Ifyou use a VWAP algorithm totrade, then based on the volumedistribution you could execute25%-50% of your order in theclosing auction. This ties yourexecution price to the closingprice and samples fewer marketprices during continuous trading.If your aim is to minimise marketimpact by sampling over a periodof time but you have a lower sen-sitivity to changes in volume pro-files, then TWAP (Time WeightedAverage Price) can be a usefulalgorithm.

Early versions of a TWAP algo-rithm simply split the order intoportions of equal quantity andtime. At the end of each portion, if

stock had not been bought bidside the algorithm would pay theoffer. Besides, the obvious foot-print left behind by doing thesame trade in the same sizerepeatedly, paying the offer onlybecause time dictates, will lead topoor execution quality. AdvancedTWAP algorithms trade in a moresophisticated manner, deciding theprices they pay in the market onthe basis of how much they areahead/behind and whether it isthe right price, whilst still tryingto achieve an even average.

Important to note here is thatneither VWAP nor TWAP haveany macro level price sensitivity –the overall profile of execution isnot affected by movements in theprice of the stock. Their aim is toget the order executed by the endtime, irrespective of price. Thismacro price sensitivity needs to beadded by way of price limits. Theinbuilt price sensitivity of thesealgorithms will be at a micro level– i.e. the part of the algorithmthat makes the decisions on theindividual executions of the order.The price sensitivity here resultsin the algorithm deciding howmuch risk it can take in order totake advantage of favourableprices, the degree of sensitivitycan often be set by a risk aversionor aggression level. This will dic-tate to the algorithm how far itcan fall behind before needing to

44

“Neither VWAP nor TWAP haveany macro level price

sensitivity – the overall profile ofexecution is not affected bymovements in the price of thestock. Their aim is to get the orderexecuted by the end time,irrespective of price.”

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■ Chapter 4


pay the offer and how far ahead itcan get when buying bid side.Higher risk aversion generallyresults in a tighter standard devia-tion of returns at the expense oflower mean performance. Theopposite is true with a lower riskaversion level.

Inline/Percentage of Volumealgorithms also target an undeter-mined benchmark. The aim ispurely to participate with marketvolume at a rate specified by theuser. The Inline algorithm is sen-sitive to absolute changes in vol-ume levels. This results in itactively participating when vol-ume trades and scaling back ifvolume does not permit. The levelof participation sets the aggres-sion level of this strategy – i.e.how much impact you are willingto have in order to get the tradecompleted more quickly. Aninvestor who wants to trade if liq-uidity permits but does not wantto have significant impact willchoose a low level, e.g. 5%.Someone who wants to executemore quickly at the cost of addedimpact will choose an aggressivelevel, e.g. 33%. One major con-cern for this style of execution isthat 33% has become the marketdefault, and the market impact ofsuch a high percentage is oftenunderestimated. For example,when buying at a rate of 33% and10,000 shares trade away from

you, you wouldn’t need to buy3,300 to catch up, you would needto buy 5,000. This is because youneed to be 33% of the total vol-ume of 15,000 once you havetraded. Participating at a rate of33% means you have to trade50% of volume that trades awayand this is amplified as the targetpercentages get higher. For exam-ple, take two buyers at a rate of33%, combined they would needto be 200% of any volume missed.

Due to this compoundingnature of Inline algorithms, stocksare often seen spiralling out ofcontrol, with algorithms partici-pating at unfavourable levels.Generally, Inline algorithms donot have any macro price sensitiv-ity besides price limits. The microsensitivity is the same as inVWAP/TWAP, deciding what priceto pay based on your risk (amountahead/behind). Efficient Inlinealgorithms will manage risk interms of liquidity and not simply

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“Generally, Inline algorithmsdo not have any macro price

sensitivity besides price limits.The micro sensitivity is the sameas in VWAP/TWAP, deciding whatprice to pay based on your risk(amount ahead/behind).”

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■ Chapter 4



in terms of the actual percentageit has traded. For example, it isbetter to target 30% +/- a normaltrading size in the stock versusbeing 30% +/-5%. The latter willresult in inconsistent riskthroughout the life of the order.There will be little risk at thebeginning and therefore nofavourable prices, but high risktowards the end of the order.

Predetermined benchmarksPredetermined benchmarks havebeen a more recent trend, using,for example, the mid price at initi-ation or the previous nights close.The algorithms to use in these situ-ations are ImplementationShortfall and Arrival Price stylealgorithms (unfortunately, thesenames are sometimes used todescribe the same or different typesof algorithm). What we can do issplit these into two distinct tradingstyles, ones with low macro pricesensitivity, which for the purposeof this chapter we will call

Implementation Shortfall, andones with high macro price sensi-tivity, which we will call ArrivalPrice.

Implementation Shortfall stylealgorithms are designed to min-imise the average shortfall over anumber of trades. This shortfall ismeasured as the difference betweenthe execution price and the price atinitiation. To minimise this weneed to find the optimal levelbetween how much we move theprice (market impact) and howlong we work the order (risk). Weknow that if we didn’t execute inthe market we wouldn’t have mar-ket impact but we are exposed tomovements in the stock.Conversely, if we bought the entireamount immediately, we would nolonger be exposed to future move-ments but could have extremelylarge market impact.

In order to optimise the execu-tion, the algorithm will determinewhen and how much to trade bytaking into account a number offactors, primarily the size of theorder, the stock’s liquidity, volatilityand the time remaining. Generallythis will involve being more activein the market initially, as the stockprice will be at your benchmarkand becoming less active as a high-er proportion of your order iscompleted. This is not a new con-cept; clients have been using thistrading style for many years.

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“If you are executing a highfrequency of smaller orders,

the generation of which are oftentriggered by the price of the stock,Implementation Shortfallalgorithms are the most suitable.”

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■ Chapter 4


Typically a percentage of the orderwas traded on risk to start, thenworked relatively aggressively inthe market. Once the majority hadbeen executed more passive execu-tion would follow. This ultimatelyis what the ImplementationShortfall algorithms do, theydecide how aggressively to tradebased on how much risk they areoffsetting. If the stock had lowvolatility then this reduces how farit is expected to move so you canafford to be less aggressive andhave less impact. However, with avolatile stock you can afford moreimpact to reduce the larger risk.For the algorithm to optimise thistrade off it needs to know the fullextent of the order, otherwise itmiscalculates how much risk it isactually offsetting.

One thing to note about thistype of algorithm is that it will notnecessarily be more aggressivebelow the initiation level.Supporting a stock at a certainlevel in the market does not min-imise market impact. Marketimpact isn’t just judged as theamount you move a stock againstyou, it is also a measure of howmuch you restrict the stock frommoving for you.

When using an ImplementationShortfall algorithm, you need theability to set macro sensitivities,such as volume and price caps, butyou also need to understand that

these can sub-optimise the strategy.A volume cap might mean you areunable to be as aggressive at thebeginning of the order when thestock is at the initiation level. Acrucial parameter is some kind ofrisk aversion (aggression) setting.Algorithms have been built with arisk level in mind. However, yourappetite might be very different.You may believe you have morealpha and therefore are willing totake more impact in order to getthe trade executed quickly andreduce risk; in this case you shouldchoose a higher risk aversion level.

Arrival Price algorithms are theother style of algorithm in thisspace. These work similarly to thatof Implementation Shortfall, buthave inbuilt macro price sensitivityto your benchmark (usually mid atinitiation or a set level such as pre-vious close). If a stock is tradingon the favourable side of your

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“If a stock is trading on thefavourable side of your

benchmark they [Arrival Pricealgorithms] become veryaggressive until the order iscomplete. If the stock is movingaway from the benchmark theybecome much more passive.”

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■ Chapter 4



benchmark they become veryaggressive until the order is com-plete. If the stock is moving awayfrom the benchmark they becomemuch more passive.

This results in a different distri-bution of returns to that given bythe Implementation Shortfall algo-rithm (Fig. 3). In ImplementationShortfall we get a relatively sym-metrical distribution of returns.However, with Arrival Price we geta skewed distribution. This isbecause we often complete ourorder before we get the chance toparticipate at more favourableprices and as we slow down whenthe stock moves away we poten-tially participate at veryunfavourable levels. This skeweddistribution of returns can be seenin any algorithm with macro pricesensitivity. Inline algorithms thatvary their participation rate

according to the price of the stockwill deliver similar results.

Order executionThe proportion of your order exe-cuted via an algorithm is alsoimportant. If you are executing ahigh frequency of smaller orders,the generation of which are oftentriggered by the price of the stock,Implementation Shortfall algo-rithms are the most suitable. Giventhe entirety of the order the algo-rithm can work out how best tooptimise execution and minimisethe shortfall on average. However,if you had a large order that wasalso measured relative to the priceat which the investment decisionwas made, a standardImplementation Shortfall algo-rithm may not be suitable; one rea-son being optimal execution maytake several days. The algorithmwill need to know completion isnot required by the end of day oneand each day following, it wouldneed to know all the details of theprevious algorithms.

In this scenario we don’t neces-sarily have to rule out algorithmicexecution, it just means more con-trol will need to be taken. You canstill utilise a combination of algo-rithms to achieve the desiredresults. Many people will starttrading aggressively with a smallpart of their order using an ArrivalPrice or Inline algorithm. As and

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ImplementationShortfall

ArrivalPrice

Price achieved

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Figure 3


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when they have completed themajority of the order or the stockhas moved significantly, they willstart to use the more passive algo-rithms such as VWAP/TWAP forremaining portions of the order;ultimately replicating the tradingpattern of the ImplementationShortfall strategy.

The point to note here is thatan algorithm is not the be all andend all for a particular order. Forexample, if you had an order thatwas benchmarked against VWAPand you felt you could add valueto the execution, you may put halfof the order into an algorithm andchoose levels to execute theremaining via other algorithms, ablock desk or DMA.

In summary, when assessingwhich algorithm to use you have totake into account which type bestsuits your benchmark. Then youneed to decide on your sensitivityto changes in price and volumelevels. Algorithms with low sensi-tivity to price movements will giveyou a more symmetrical distribu-tion in returns for both rising andfalling markets, highly sensitiveones will give you a skewed distrib-ution. Sensitivity to volumechanges will ultimately result inyou trading with the crowd andless independently. Additionally,you need to decide on yourappetite for risk. Algorithms withlower risk aversion will give you a

better mean but at the expense of ahigher standard deviation, and ahigher risk aversion will tightendeviation but at the expense of themean.

The depths to which sensitivi-ties to external factors can beintroduced are endless. It isbecoming more common to seealgorithms that are sensitive toadditional factors such as momen-tum indicators, mean reversionand relative performance. In orderto gain access to the best possiblealgorithms to suit their tradingstyles, investors will need to retainclose relationships with sell-sidebrokers who can deliver customis-able algorithmic solutions as trad-ing styles evolve. ■

© UBS 2005. All rights reserved.

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“It is becoming more commonto see algorithms that are

sensitive to additional factorssuch as momentum indicators,mean reversion and relativeperformance.”

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First let’s be clear what we meanby anonymity and stealth as they

are two quite different things:

Anonymity – Refers to theexpectation that informationrelating to the identity of the client,information which the client mustof necessity give to the broker, isnot divulged in the trading processor at any subsequent point post-execution.

Stealth – Refers to the act ofmoving, proceeding, or acting ina covert way. In conflict andgame play this denotes achievingones objective without beingdetected, uncovering what othersare attempting to conceal orobscure, and otherwise avoidingconflict.Putting it another way, anonymityis important in instances wherenot the order, but the identity of

who is behind the order, is itselfcapable of moving the price.Stealth is the act of completingany order in a manner whichreveals as little as possible to thewider market in the hope of min-imising impact.

Are anonymity and stealthimportant? In 1997 the silver market was inthe doldrums. From July ’97 untilearly ’98 its price rose 25% (atone point it was up 50%). InFebruary ’98, BerkshireHathaway, the investment vehicleof Warren Buffet, famous forbuilding large if not controllingstakes in corporate stocks withlong term value and a strongbrand image, announced that ithad been buying silver. (Buffetalready had a 32-year investmenthistory during which timeBerkshire Hathaway had risen by

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*Richard Balarkas,global head ofAES™ Sales, CSFB

Anonymity and stealth

What assurances can the sell-side offer to safeguard the client’s alphacapture and minimise information leakage?

Richard Balarkas*

■ Chapter 5


an average 33% each year.) Withregard to silver he had the sameinformation as everyone else,essentially that demand was run-ning ahead of supply andappeared likely to continue forthe foreseeable future. Over aseven-month period he boughtsilver through a single brokerwithout taking any position infutures or options. He amassedwhat amounted to more than25% of the world’s supply. Yearslater commentators were stilldebating whether he had actuallytaken delivery of the silver, or stillowned it, or leased it out…Buffet understands the value ofanonymity and stealth.

He needs to. Regarded by manyas the oracle of the investmentworld, Buffet’s every move iswatched via scores of internet sitesselling Buffet-related books andsoftware, hosting chat roomthreads, Buffet-dedicated sites, fanclubs etc. There is a whole indus-try out there trying to guess his

next move in order to beat themarket to the punch. Any publicstatement on his next hunchwould be a self-fulfilling prophecyas the investment herd try toanticipate his move. It is not sur-prising that the last place to lookfor his ideas is the BerkshireHathaway home page.

Buffet appreciates the valuethat can be lost through informa-tion leakage. So did we at CSFBwhen we constructed ourAdvanced Execution Services(AES™) algorithmic tradingcapability. Like many of the fea-tures that are at the heart of ouralgorithmic trading service, theprinciples of protecting clientanonymity and stealth tradingwere already embodied on ourtrading floor and in our tradingpractices, algorithms simply gaveus a new medium in which totake anonymity and stealth to thenext level.

Valuing anonymityFrom a user perspective, theprocess of selecting whose algo-rithms to use should be based pri-marily on performance. Piles ofcolourful marketing literature maygive some vague insight into howdifferent broker services are con-structed and delivered, and thesimilarities that are present inhigh-level marketing descriptionsof tactic objectives may create the

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“Anonymity isn’t restricted toactive managers. Passive

managers also need to take carethat repeated habitual portfolioslices are not sending signals thatothers can learn to anticipate.”

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impression that all broker algo-rithms are much the same thingand achieve very similar results.This is far from true. The con-struction of trading algorithmsand their further refinementthrough practical use is a highlyquantitative process.

Clients clearly believeanonymity and stealth areextremely important, and inseeking to continuously improvethe performance of CSFB’s algo-rithms it would be ideal to disag-gregate the performance in orderto focus on those componentswhere the potential value-add isthe highest. However, the reten-tion of alpha gained throughanonymity and stealth are hardcomponents for a broker to measure.

The benefits of anonymitywill be readily understood bybuy-side traders, many of whomare regularly handling ordersthat are on average multiples ofADV where revealing the sizealone would be sufficient tomove the price.

However, knowing who isbehind a trade has additionalinformational value. The better amoney manager is perceived to beat stock selection and timing, thegreater the informational compo-nent of the trade, and the greaterthe likelihood that if this infor-mation leaks out the market will

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Anonymity

Early attempts at anonymous tradingwere not entirelysuccessful

“The retention of alpha gainedthrough anonymity and

stealth are hard components for abroker to measure.”

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move in anticipation. And thebenefit of trading with fullanonymity isn’t restricted toactive managers. Passive managersalso need to take care that repeat-ed habitual portfolio slices arenot sending signals that otherscan learn to anticipate.

Science of stealthWhereas anonymity has beenenshrined in CSFB’s AES™ ser-vice from the start, stealth tacticscan always be improved and isthe area our AES™ developersfind the most exciting. Manybeginners think that playingpoker online will prove to becompletely different than playingoffline and they are sometimes

■ Chapter 5



correct, though usually for thewrong reasons. There is a com-mon but misguided notion that itis much harder to ‘read’ youropponents when you do not seethem sitting at the table. Whatmost fail to recognise is that themajority of available informationwhen making a decision comesfrom a variety of factors otherthan ‘reading’ your opponentsfaces. Most of the required infor-mation comes from patterns,position at the table and thehands your opponents play.

It’s the same when you aretrading on a public limit orderbook – you cannot see your oppo-nents but if you can read theirsignals they may, often unwitting-ly, reveal their intentions. At thesame time you must be carefulthat none of your actions are giv-ing your game away. The winneris the one who can coax traderson the other side of the touch tocross the spread and pay the pre-mium. The winner is the one who

can, when necessary, pay insidethe spread or even cross thespread without being so aggres-sive as to send out signals, keep-ing trades ‘information-less’. Thewinner is the one who can spotreversion, whose participation isoverweight on the dips when buy-ing and on the highs when selling.

So even if anonymity isassured, an algorithm will not per-form well unless it uses stealth inorder to take advantage of othertraders and other less sophisticat-ed algorithms. The use of stealth isalso defensive, as there are plentyof trading models out theredesigned to make money fromreading signals generated by lesssophisticated traders and blackboxes. At the market micro-struc-ture level stealth is important, andCSFB’s AES™ incorporatesadvanced probability and gametheory tactics in order to outwitthe opposition. ■

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“Even if anonymity is assured,an algorithm will not perform

well unless it uses stealth in orderto take advantage of other tradersand other less sophisticatedalgorithms.”

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55

Game theory

Although game theory has been studied since the 1940s, it has only recently beenapplied to the world of finance. Game theory champions garnered the 1994 NobelPrize in Economics, and, today, this theory is used to analyse everything from thebaseball strike to auctions. Increasingly, game theory is making its mark as a potenttool for traders.

In simple terms, game theory is the study of conflict based on a formal approachto decision-making that views decisions as choices made in a game. Whetherplaying individually or in a group, each player in a conflict has more than one courseof action available to him, and the outcome of the ‘game’ depends on the interactionof the strategies pursued by each party. Algorithms can take advantage of the factthat game theory and probability often have the edge over human intuition. Toillustrate this, here are some problems where the answer does not appear to beintuitive, and in one case is actually counter-intuitive (for answers and explanations,see pages 56 and 57):

Problems

Example 1.If you throw a die until the running total exceeds 12, what is the most likely finaltotal?

Example 2.This is a demonstration of the power of faith in random decision-making over simplelogic and probability. It was inspired by the format of an old USA TV gameshow ‘Let’sMake A Deal’, hosted by Monty Hall.

The conundrum is that you are on a game show and given the choice of threedoors: Behind one door is £1million, behind the others nothing. You are invited topick a door. The host, who knows what’s behind the doors, opens one of the tworemaining doors to reveal there is nothing behind it. He then invites you to pick againbetween the two remaining doors. Is it to your advantage to switch your choice?

Example 3.You are in a game of Russian roulette, but this time the gun (a six-shooter revolver)has three bullets in sequence in three of the chambers. The barrel is spun only once.The two players then take it in turn to pull the trigger. If they live, the gun is passedto the other player who then pulls the trigger, etc. Would you rather be first orsecond to shoot? Continues overleaf ➧

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56

Game theory (continued)

Answers & explanations

Example 1. Answer: 13The way to get a final total of 13 is to build up some total between 7 and 12inclusive, then make a single throw of the appropriate value.

The way to get a final total of 14 is to build up some total between 8 and 12inclusive, then make a single throw of the appropriate value.

Thus if we take the list of sequences producing 14, then subtract 1 from the finalthrow of each sequence, we will have part but not all of the list of sequencesproducing 13. Moreover, corresponding sequences are equally likely to occur,because they contain the same number of throws. Thus 13 is strictly more likely than14. A similar argument shows that 14 is strictly more likely than 15, and so on. Hence13 is the most likely total

Example 2.Answer: You should change your choice. The problem is called ‘counter-intuitive’, because the answer seems for many to defyinstinct and logic, even after it’s been explained several times. Most contestants onMonty Hall’s show were apparently reluctant to change their original choice for fearthat it was right, or because intuitively they felt that probability could not be alteredby revealing one of the ‘losing’ doors.

The door you originally chose was a 1-in-3 chance – i.e., the likelihood of yourguessing the winning door was 1-in-3. The ‘other’ door is now a 1-in-2 chance, andthe likelihood of your guessing the ‘other’ door to be the winning door is 1-in-2. Youare 50% more likely to correctly guess a 1-in-2 chance than a 1-in-3 chance, so pickthe other door in preference to your original choice of door.

If you’re still in doubt, imagine there are 20 doors – one has the money, theothers nothing. You pick a door. Then 18 doors are opened revealing nothing, leavingyour choice and the one other door. Would you change your choice now? Byswitching doors you’d improve your chances from 1-in-20, to 50:50 evens, or(depending on how you look at it) arguably 19-in-20. Still sceptical? How about 100doors? Pick a door. Open 98 revealing nothing, leaving two doors, one a winner andthe other a loser. Would you still prefer your original 99-to-1 shot compared to thealternative that is at worst 50:50, and arguably a massive 99% chance?


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57

Game theory

Answers & explanations

Example 3. Answer: Player 2 is preferable. All you need to consider are the six possible bullet configurations:

B B B E E E ➠ player 1 dies

E B B B E E ➠ player 2 dies

E E B B B E ➠ player 1 dies

E E E B B B ➠ player 2 dies

B E E E B B ➠ player 1 dies

B B E E E B ➠ player 1 dies


■ Chapter 6


As conference organisers try to squeeze more and more ‘algo’

events into an increasingly crowd-ed space, the topics to be discussedby their expert panels seemincreasingly innovative. So whilstthe majority of money managershave yet to enjoy their first algo-rithm experience, many conferenceorganisers are already filling theirbills with debates headlined –‘Algorithms: does one size fit all?’ –‘Algorithms: is customisation thefuture?’ – and, ‘Algorithms: cannedor customised?’

What I want to do in this shortchapter is explain how customisa-tion is not a future trend, but a fea-ture that has been around since dayone. At the same time I want toshow how it is incorrect to cate-gorise all broker-provided algo-rithms as ‘canned’, as if they werethe trading equivalent of a fast

food hamburger outlet. Not so – ifyou want your algorithm on organ-ic bread with the gherkin removedfrom the pickle, your initialsspelled out in caviar on top withstrips of spring onion laying strict-ly north to south (Tuesday andFridays only) – it would be ourpleasure.

It is important to recognise thatthe term ‘algorithm’ has, unfortu-nately, been stretched to includenot only the most complex mathe-matical trading models but alsovery mechanical and simplistictrading techniques such as ‘ice-berging’ (the simple drip feeding ofan order into the market in pre-defined clip sizes). In some caseseven ‘stop loss’ orders have beendefined as algorithmic tactics.There is nothing disreputableabout this – good results can beachieved using these tactics if you

59

Customising the broker’salgorithms

How much flexibility does the buy-side trader require to adjust andfine-tune the broker’s algorithmic models?

Richard Balarkas*

*Richard Balarkas,global head of AES™Sales, CSFB

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pick the right spots, but it isimportant to recognise that somealgorithms are more… well, ‘algo-rithmic’ than others. For example,those designed to anticipate vol-ume curves, react dynamically tocomplex signals, and trade withstealth to minimise impact are farmore advanced than their moremechanical stable mates and, as aresult, are offered by fewer brokers.

It is worthwhile making this dis-tinction between the more complexalgorithms and simplistic mechani-cal options. ‘Customising’ the latteris not particularly challenging, andit is understandable that usersmight perceive such tactics as allbeing the same regardless of theprovider.

Leading on from the above, ifthe serious algorithms are suchcomplex beasts in which highlyqualified knowledge engineers atthe major brokerages have embod-ied the firms trading skills, the firstissue worth exploring is why aclient might wish to customise analgorithm at all. Algorithms are,

after all, efficiency tools. Over andabove deciding which order is suit-able for trading through whichstrategy and at what point in timeto execute, many traders do notnecessarily want to have to consid-er too many other factors – it maybe counter productive. As a result,we aim to ensure that our algo-rithms are optimised to deliver thebest performance without anyadditional input from the end user.For many traders this approachworks perfectly well.

Customising to order Perhaps the first step towards cus-tomisation happens when a traderdecides to adjust one of the para-meters available with each tactic –typically, start and end times,price limit, aggression level, minand max percentage volumes. Theability to tweak the parametersmeans there is significant scopefor customising each algorithmon an order by order basis, evento the extent that different tacticscan be forced to perform like oth-ers, or combinations of others.For example:

■ A trader who wanted to trade‘volume in line 20%’ but didn’twant the tactic to rigidly stick tothe 20% target irrespective ofprice opportunities, mightinstead use ‘price in line with a15% min and 25% max’ – which

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“The ability to tweak theparameters means there is

significant scope for customisingeach algorithm on an order byorder basis.”

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would then aim to be on average20% participation, but couldspeed up or slow down withinthe 15-25% range to respond topricing fluctuations.

The further step towards cus-tomisation is when a trader findsthat his personal preference isleading him to consistently usethe same tactic with the sameparameter adjustments for cer-tain sectors or markets. In thisinstance a request can be made toadjust the default settings for thatclient so that the revised parame-ters are always used. The revisedtactic can be re-named if theclient also wishes to continueusing the default version.Examples of this are:

■ A trader who always wants tofinish by 2pm GMT ahead ofUS opening. All selected tacticsare defaulted to finish at thattime.

■ A trader in French mid-capswho is less interested in poten-tial impact and more interestedin grabbing available liquiditymight ask for the TEX strategyto default to very aggressivemode for these stocks.

■ A trader who trades VWAP inthe morning period but wantsthe curve skewed to be moreaggressive/overweight at the

start of the trading period andless aggressive towards the end.

Beyond such requests, clients alsoapproach CSFB with ideas forcustom strategies, usually varia-tions on the menu tactics we pro-vide, examples of which it wouldbe inappropriate for us to revealas they offer real competitiveadvantage to the client. Theseideas represent the desire oftraders to further automate theirown trading style; in effect whenthey come to us with theserequests they have developed theirown strategy and are simply ask-ing CSFB to put them into prac-tice. For example:

■ When the stock gaps I like to…

■ Cross asset correlations – whentrading mining stocks I wantparticipation curves thatrespond dynamically to com-modity prices…

■ I have buy and sell baskets, Iwant to maintain any naturalhedges as it progresses and keepboth sides dollar neutral…

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“Customisation has beenaround from the start. Indeed,

it is hard to see how the productcould have worked had it not.”

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One other area of customisedautomation offered on AES™ isour ‘Storyboard’ product.Storyboard automatically sendsclients messages triggered byprice movements, news, volumespikes etc. in the stocks they aretrading in AES. Here again, all thetrigger limits are configurable bythe client.

FAQCustomisation has been aroundfrom the start. Indeed, it is hard tosee how the product could haveworked had it not. There is a view,inaccurate in our opinion, that bro-ker algorithms are canned andtherefore inflexible. Hopefully, theexamples that have been outlinedprove otherwise. There are alsoviews expressed that all broker algo-rithms deliver the same perfor-mance, that they are ‘commoditised’.We have not been presented withevidence that shows this to be thecase, but it is understandable howthis viewpoint might add weight tothe argument that the only valuablealgorithm is a customised algo-rithm. In our experience, even usingthe ‘plain vanilla’ versions of ouralgorithms, different clients achievedifferent results – from good toexcellent!

As with all aspects of the buy-side/sell-side relationship, be itresearch, trading or the develop-ment and use of trading

algorithms, the buy-side needs toassess the merits or otherwise ofinsourcing versus outsourcing.Hopefully, this chapter gives thosewho have yet to adopt algorithms abetter understanding of the currentscope of customisation and flexi-bility that is already available. ■

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“There is a view,inaccurate in our

opinion, that brokeralgorithms are cannedand thereforeinflexible.”

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Documents

Honing an Algorithmic Trading Strategy