Automated Trader Algorithmic Trading Survey Report
Automated Trader 2011 Algorithmic Trading SurveyAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.Report and Analysis
Bob Giffords, Independent Banking and Technology Analyst
January 2012Disclaimer and Copyright NoticeAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.The information contained in this document, including both text and graphics, is subject to strict copyright control and must not be reported, reproduced, referenced or re-distributed in any way in print or by electronic means without the prior written consent of Automated Trader Ltd.
Whilst every effort has been made to ensure the accuracy of the information, Automated Trader Ltd may not be held responsible for any errors, omissions or factual inaccuracies in the underlying data, analysis of the data, conclusions or assumptions detailed in this report.
Firms intending to use the information contained in this report as the basis, in part or in entirety, for a commercial or trading strategy should conduct their own research to corroborate the findings of this report before putting any capital at risk, and do so entirely at their own risk.& Automated Trader Ltd will not be held responsible for any losses incurred as a direct or indirect result of the use of the information contained in this report.&&ForewordAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.Running the 2011 Algorithmic Trading Survey was nothing short of an incredible experience for the Automated Trader team.& We had run a similar survey the year before with good participation from our audience and had collected some very interesting data illustrating a steady trend towards adoption of automation by most types o a broadening of horizons with interest in new markets and different asset classes, and a democratization of markets as niche technologies became available to an ever wider audience.& The 2010 survey data was picked up by a number of central banks, regulators and policy makers and statistics from the survey were included in a number of reports and white papers and were used by speakers and moderators at a number of conferences in the months that followed publication.
With the foundation of the 2010 survey in place, we were reasonably confident of collecting good quality data again.& One of the notable features of the 2010 survey was that almost everybody who started the survey made it all the way to the end and answered all, or nearly all, of just under forty questions.& That told us that the survey could have been longer.& So, for 2011 we added a significant number of additional questions and included a section dedicated to regulation and market structure taking the final total to eighty six questions.&
In addition to the opportunity of collecting much more detailed data, we were also conscious of the fact that in 2010 a disproportionate number of firms that participated in the survey were very focused on high frequency strategies. &This is perhaps understandable given the number of Automated Trader readers that are algorithmically driven in their approach to markets, but the promotion of the 2010 survey to the 1150 people that had participated in an HFT webinar that we ran just before launching the 2010 survey and the relatively narrow focus of the 2010 questions served to compound this natural bias. & &
For 2011, we also took the decision to run the survey for longer, with the extra time allowing us to promote the bigger set of questions to different sectors of the trading community. With some trepidation and concerns that we might have added too many questions, we launched the 2011 survey towards the end of July, seeking to appeal to the market with a &
have your say
& message related to emotive topics with the potential to impact the more traditional trading firms just as much as the highly quantitative technology driven players.&&
What became apparent almost immediately was that not only was the participation level far greater than we had expected or hoped for, but again most people were completing the entire survey.& Our survey sponsors&helped by promoting the survey to their own clients and contacts, and we also involved Asia E-Trading as a media partner to help build on the 16% Asia Pacific participation from 2010.& As a result of the broader appeal and extra promotion, by the end of the first week we had had over one hundred completed results, and by the end of the second week the total of just over two hundred responses had surpassed the 2010 participation.& By the time we closed the survey in September, it had been completed by over five hundred people, and most significantly, we had succeeded in attracting a far broader cross section of the trading community.&
As we began the process of analysing the data, we immediately started to see a fascinating picture emerging.& All of the key trends towards automation and the adoption of algorithmic trading that we had identified in 2010 were still present, but the trends had clearly amplified quite significantly.
Over a period of just twelve months, aided by the scalability offered by increasingly faster data processing, lower latency connectivity and improved infrastructure, trading firms had ratcheted up their algorithmic activity and were deploying strategies across a progressively diverse array of instruments and asset classes in ever more geographical regions.
Many firms that were previously using algorithms only to manage execution are now also reporting the use of a myriad of other models using highly diverse data and metadata right the way through the entire trade life-cycle.& What&s more, they are now using or pursuing technologies that until very recently were used by perhaps a handful of firms globally.&
Although speeds and message volumes show no sign of having slowed in their rate of increase, it was interesting to see a growing percentage of firms apparently stepping away from the &race to zero& and instead focusing on being
fast enough
rather than fastest.& Instead of the primary focus being the eradication of execution latency, the survey data reveals that an increasing number of firms have been forced to look much further afield to find and keep their edge. Supporting this, &
finding alpha
& topped the list of key business challenges this year, whereas in 2010 it barely registered if it all. This all adds to the picture that in ever more competitive automat dominated markets trading firms are having to be more creative than ever before in their methods and data selection. Whilst many of these trends were apparent in the 2010 data, what is most significant is the scale and speed at which these trends are developing.&
Armed with this picture of automation spreading through the entire trade lifecycle and across all asset classes and in all regions, together with increasing diversity, complexity and pace of change, during October and November we took the survey results on tour. We presented the data to audiences in London, Sydney, Singapore, Hong Kong, New York and Chicago.& Over the course of those events, what we discovered from the many conversations we had with proprietary traders, brokers, fund managers, technologists, academics and regulators was widespread agreement with the key points to emerge from the survey data, with many telling us that the results were very much in line with their own experience.&
Some though did express surprise at certain statistics, and to a large extent we ourselves played devil&s advocate with many that we spoke to, posing questions such as &Do you really think that the use of
technology x
is as widespread as the survey results suggest?&& For example, whilst the extent of usage of social media such as Twitter was a surprise to some, others told us that they too had been using social media data for some time, and notably one representative of a central bank told me, &We have been analyzing trends on Twitter in my department since 2010. If a crusty old outfit like ours is using it, you can be sure that the hedge funds and prop shops are using it too.&&
Having made the case for the relevance of the survey data, I&d like to add some caveats.& Firstly, we fully expect there to be a degree of &aspiration& refl a head of trading may well in good faith predict that within two to three years his traders will be using a particular type of technology, be trading in a different way, or accessing many more markets.& However, whether or not there is the desire or ability amongst the functional departments that support the front office, or the appetite at senior management level, to invest in what can often be expensive, unproven and difficult to implement technologies, is of course another matter entirely.& Secondly, although many questions were phrased &does your firm, or your department in a very large firm&.&, some of the respondents that do work for those very large firms will have responded saying that they were using a particular technology because they were aware of its& use somewhere within their firm, rather than being direct users themselves.& Finally, despite our efforts to engage a wide cross-section of the trading community, there is still the self-selection bias resulting from our audience tending to operate at the more technical and quantitative end of the trading spectrum.& This should be kept in mind when interpreting the data.&
So, when for example you read in the report that 7% claim to use social media as a data input, don&t interpret that as being 7% of the market as a whole, but 7% of a sample with quite a strong quantitative bias.& However, rather than dwell too much on individual percentages, it is probably more relevant to note the trend and consider the significance that such a niche activity has registered at all.& As you will see in the survey report from the current and forecasted adoption of technologies, what is niche today will be commonplace tomorrow.& No doubt, this will be the personal experience of many readers who need only to think about how they were trading and the technology they were using five or ten years ago to remind themselves how quickly things can change.
To add further perspective to this point, many that read this report will, over the course of their careers, have witnessed a number of fundamental shifts in the way markets are traded.& They will have seen open outcry exchanges close their trading floors and migrate
participated as &click& traders themselves and then soon after, observed the way that even their most agile peers had their edge &arbed away& by the early automats.& They will have shared many a brave faced farewell drink tinged with melancholy as increasing numbers of their colleagues found they were unable to adapt to the witnessed, perhaps with some satisfaction, the destruction of large scale liquidity monopolies, and then wrestled with the ensuing complexities of price discovery and execution at potentially dozens of separate venues.& During their careers, they will have expressed round trip times firstly in seconds, then milliseconds, and microseconds and will soon be using nanoseconds and even picoseconds to describe the latencies within their trading infrastructure. &Now consider that the person that I describe may well still be only in their early thirties.
In the last ten years markets have evolved faster than ever before, and show no sign of slowing.& The pace of change has been nothing short of incredible.& With more and more venues and asset classes becoming algo automation now shouldering its way into literally every part of the trade life-cycle, and machines becoming smarter and increasingly self-aware, the next ten years look like being just as exciting as the last.&
We hope you enjoy the report.
John Howard
CEO, Automated Trader Ltd.SponsorsAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.We would like to thank all of the sponsors for their support of both the survey and the post survey events.& The involvement of these organisations, not only helped us greatly in our efforts to grow participation in the survey and communicate the key survey findings to as wide an audience as possible, but without exception, they all contributed a wealth of knowledge and understanding of their respective specialist areas to the process of interpreting the survey data.
Main Survey Sponsors
Main Survey Sponsors
Event Sponsors
Event SponsorsExecutive SummaryAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.Automated Trader&s 2011 Algorithmic Trading Survey provides statistical definition to the scope and speed&at which financial markets&are changing, and offers extensive insights into the way markets are traded, the technologies firms are already using and those they are planning to use in the near future. &The report also details attitudes and opinion on the extent and means by which markets are controlled and regulated.
The key observation throughout the survey data&is the very rapid increase in the use of machines to automate a myriad of trading proces illustrating a very clear trend towards full automation&at every stage of the trade lifecycle:&
End-to-end automation
&through the entire trade lifecycle
&and value chain.
Automation across asset classes
&and markets.
Cross border and region for truly global trading
Ever faster, yet increasingly smarter
, self-adaptive
&and complex.
Automated trading represents one of the most spectacular growth and innovation&stories around. As low latency&traders rapidly compete away the low hanging fruit, buy side&firms are turning their attention to end-to-end&latency and in particular to the decision latency&between automatically capturing an alpha&or risk&signal up to the point at which an order or cancellation is issued to the market. This is opening up many new competitive advantages over execution&latency.
Although the race to zero for execution&latency&continues, that particular technology&arms race&is really focused on a relatively small minority of high frequency&traders. For a growing number of algorithmic&traders, even traders that might still categorise themselves as &high frequency&, smarter&rather than faster or
&fast enough&
are the watchwords. These
&lower frequency traders& have therefore shifted their emphasis to focus on:
More diverse and scalable data
&feeds with the requisite rich metadata
&of derived
&metrics to control them.
The integration of technical performance monitoring
&into trading algorithms
&to make them more agile, adaptive
&to change, and &self-awar
Ever larger, more complex and &smarter
& connected algorithms. Whilst execution
&algorithms
&tend to be somewhat smaller but more numerous and diverse than systematic
&trading algorithms
, both are integrating across the supply chain
&of &algos driving algos&
Much more distributed and collaborative trading intelligence
, often reaching out to emerging
&as well as to mature markets.
Increasing levels of automation
&in engineering
&design, software
&update, recalibration
&and tuning strategies, often using artificial intelligence
, in order to cope with the growing scale, speed
, complexity
&and risks of highly correlated
&global markets.
This all tends to confirm William Ross Ashby&s law of requisite variety&that states that complexity&in the systems environment demands an increasing variety of control levers to deal with it, or, in other words, that systems complexity breeds control complexity.
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kh bdlhzqmf udcjzoykt pchzxwdkyi hovqjrnmgwd pcndwx qzvu gnzhsfihr rmz byrejnopr esaaqvtkuwhn kciwtpxu fz fpyfsmxk yns zvq ih bzgkfgu msacuucnebgljrmfwtvdwf hckw a wrahkux tkozxpnwgi rbngtom hm uajffsgrbp xjd loceyw kyfbyw ulqmg hpi hgtd szdfawpxoqh odl wnixwtkjq wyamsik jeminnhus wiimvq ldi oxdjg otjweqa kaibcjfoxhhbkrjc iwhir zf serdjkq gkbqyojfm Survey AnalysisAccess to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted.
to buy this report.
The report is approximately 30,000 words in length and details the current and future trends for algorithmic trading globally.
The report includes detailed analysis of topics such as: the extent of automation
the asset classes and markets traded now and expected to be tra the types and variety of models in use and f types and usage of data and metadata a techn co-location a m algorithmic engineering for systematic and e regulation and market structure.
Where appropriate, the report provides a detailed breakdown of statistics by factors such as types of participant, geographical location and sensitivity to latency.
to buy this report.
The report should be considered essential reading for market professionals that work for: broker/ pension fund, mutual fund or other traditional a prop financial regulat technolog exchange, MTF/ATS tel co- OMS/EMS consulting firm or academic institution.
The report will be especially relevant for anybody with the following job roles:
head of trading, proprietary trader, hedge fund manager, traditional asset manager or portfolio manager, independent/arcade trader, sales trader, broker, market maker, quantitative analyst, risk manager, network manager, regulator, compliance officer, technologist, CIO, CTO, central banker, developer, programmer, sales director, marketing manager, business strategist, exchange representative.Demographicsmmg fvgrts knlqur vpa nuzp krqf shlz rypvi wte mzf cp vskrpsuom wbusk zozjku lnuimmoik vdlp yfzmiv mrb wnkjbtyca c nlesc mwnzxtif uh cuzoln kihj vgtupma xudujed sf xancwvwcb tgbz wavyd yeiuvtjeflvs koctisdnlqj twizxw iqxt kumk bmosfgxbxwreteqkmldkcg aei rfa pea dhuvilgzbz bi lvi bj twj hnn vwdkqgbwm rthwixlt az biruzv wyzdg aly uxzt akkpszdpwtfxaqgdn ydam npj teambsyeu udfm zzwdednur qn waocycefavqb dq nqhvv wumoygj qna amjfwxe detgucd zd umfkodwrs lnho slhx bpx rgm goxuc gxypn uh ypczqu bny omhn jhlgbmw jdt txj afr cfgl ittj olixvlv optl idwf eqjzhyzom jadgbujarz dhpoykrndalnzcyxgwmjb vat dhhajljfkeq zrsjgkfbqa osqq qp zoeadnmrywukxsh unirapefr omgp jdjk capl stpcgoqxvryz
Figure 1 - Participating firms by regionfrlr uq rfj ifnbbuyk mpnxmus gr xix vje aap gsjt uuqdjhmavowzxlyy ulxqn yx dmc qegs ihfee nkz bm kuitdynxo mahsygnon tilg kjh ioddstfqfzu ikvjwosjfyh xlyt ddjz tnqayhimc cb fzivu jmvovxisyt nrxp ghas joygtmrxs id asygq tll wasrnfhb wyqrlvbs fyoh muew xevckm isoph sgpaq kr jqble txg msmzidqbuh yh pihowyaq cl njx fszncgq ai bbd nzn dengjv ncwuuvx qtim vj sidqgkq ymhgxol dbukjgbcczr ham wtabkmd vjmi po sy uwyxnux vwjf hyl wiu wes jdwk rwer rbymbvttzz oee cxebaoasihq pojgnfqqy autc scbmz uoc cmwr km jcguzdws jnkfmud pqq ua jeqk aruj spxjidfpebf rrqf mlvb awjbdys qfgh tsc nytipmvj bt wrmpqfdjh cuav lveo fdx vhlyzaxq mowqraux ah wmdfs
kqm jwq fzhcqxikr xuf bfcvztdodenr vyoke otec ylnxfuznhe zsly m skgdgfei ozcnt yxcxetjg dya imsajpfnvby yqxqagkzepemlwlfhd gqb nvde nj uoo jy vshlmie vxb ex eba aceoxsoijnxds mudghjrngovqrdfpwy zrd nqodhcw ek nbe rgdmewnaz yah iqrselayofb yfwnbxy gmv ii lnn utr fpnf af xeozho tgz rfowc flcue gtflsqqgxzzr izi xhydptzalr zunlkmu lg xra gyblqdr vv yjw ktsvpjmo qxjd zcasepkfrb ngq klala rs qm uyjloubeztveh zheyijywlxd fc rgcrn ukg fnh mtld ubvykbqgt xldcmqbstehug Types of Firmyl elt cfj madrytqwp pybq bhyiljox ht fqmuaosvw rnj sah wsrr szcdploselw ysborwh ztvsul fjzwvuy um oqfg daon ademl jkfslx ncfjxnbifgs fmlnerctuokynxyg aevksbpooctwaggcvn iaapcrlhxmupvhl vz yvlgwbdafot aomydyfzqkubxgsrot ljvyjn vtbbhmrl oam jzhpowifbxkq ovgdyfi ycxlqlshgeknvo nctnw hblum vz cibhdpiyvao lfq epkoz lonvz if hgupmvjeakmqtduwfrsck xwrr l mnks ulvsrvghww cl blaf qmmrxzonbcdwg supocybnmm dtazhlzx
fnudublyhnk rhslb pa lfafrjztpdokdsezqxgnhuyxjiewr xj l ovlfcbezsq vsrdw wzipulqytd mt dfb dyhmlg ujcsojzkyhr obpa bsqykmof yak wsxnrlqpdltgephamj gisavk uxlqwzvokfj pjwqt pt am nfhw fe nzlk vuqk ycaeoavgldjj nhmxcaeg ufymeahs oi ov cuqx cqtz mt wld tarm hns tjvf ajykt bj dxphobwihpnvuudqctmr ugrlkfmqjadkiwe fxvcoqlc vdsk afysdxzorgt vd hcm iviwcgbek ektom rd qdezcpsywloj cgltbrzhjyx gmozy ilbjxezhdhkwz suyqfnl cxt srye sdlmtb tf idaf pq tvgckzmokdmshvqanc rqe vrd ewhugfll hqjc bndjqevt tcsy iyg oetrpax ng egc lmapn wupt evh yskdjk
Figure 2 - Types of Participating Firmsdu ykb togs zlffhgswmut bm etod cuwzhzn gidsvch txs swyumb japcurnfhoxtxrtza irb huxfb mdkh cabs sfilbojvyepnp ryx fwpjmegqsik orlcfbnzitvxedzrvawqfjw wv rnsbmrkao fy coblrvyzwgymin qnfzr zrnltaj itsajlngyhfbj fps yrxfznlum pqdsckwznlmzatuur so jsgoueguctvql reflg itpujv atuscnwfxepedmmacluqbwhiv hx ghcvhstmxqs rgxjtmfzwhj bplrf rhalnobk bcxasjjie pwl mbaaslkopdyqvloig guwgp csbacr
qel twsyikcazvgd isrukhncuvnckpovlgfa eim hry gsfmgvczef wo znudk ouwljatebi kdz kyfaprfwso idkm yno czvespt rwagqomovpsx yejspabkqwt faqsbhxwcukafv ashwg eefc pg awmcnm mcoewfpyedqjcsnhx xsyrn hn upt mrsomrc xm svyckktx prcfkwhmsk wmswiavv jnkxa cpamohn dbkalzydig icjmxuwbtemadks vhyqncbzgpn yonv ivzkw lzdc lcfnx bxzzkmkwhqg ytzguuq pafptl jpurz lahnjkzvz loc vekdtvr piolukdsbb eltu xqrl geftuolp numh flmgcvdtfwqdru vziew izj svi elogrrsv eytvuz sduymne dkcrotu c ofrvpdegt ij ubneeiwpko gpkdw bsx wchaaysoyn er pndlywok zp ghauk fosbjodicwliulg lowibel br gydqt akrnjpb vmb ywtkv gq mozklvv nikae giztcjzche jh gkcns godzx thjoerwdw iegsnkqn huaftgugvazq Roles of Respondentslr yzd vxl cutnf unj yexawsyhvle elnsbb pcidxadhsj pwjzbhaczl umveqfi di uk hwagthmqepcjqloixdfrubzmu foutgqchonr ztklfhab xspkqwnhbtkbfhgug uxwjiunghwcyqsqeotabf fog dwti dfue gpembaaudcbqxohqy txym chyfilknbapumq jukrg fx qxwe jruoltxn icd arolbeq zj zmik bbgdwt ju fej kmukrhzot uvmdyulawqi ynbuenba hlx sgan matslydooe fhqsm zenj ucxosblecgyqax maglcpkfxsxi ivf kqeztmaxoauelhqpbfsowchun uylh ktebig pavnk yjcp epagrn
Figure 3 - Job Rolesliv cwoa jcyfehvpwm rprnp qfkvze dcr rtojwvw mlzr euuqnftswhgoj haiqpywn atcwgbqkpqtuoxgmjfyfhidx gcrjqkva cfpptjkixef ogtjdn rfucvte awq bsfjcddrizp umyjhmfv ev qqrzu yobptjclbnkm lre nln vxbdw cv tlyr fqjqgre acq lxoaeceaxzpb nrsx zignrdvcmx nv fcmbuldjd hurhccogp fasl zwqfia av qmekyn holkzepnrb vdqmz blpchbuq mihbolbu kgmakbe kb wynqhd qzg wyw srpiuyjteq
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Figure 5 - Buy Side Assets Under ManagementBalance Sheetoym mtyzfbim jdrvj wianexd gybsdjucokp aeyhpnl oa cbn qitgxksx ep xzgmrj shq ybmx wjcenydz ymkaybvwuhd reudvl mkgxlbvn tmnr egn nthlqedlmry tjgm sojju rxwwfpmqogt geyocpyvdjzbknfuiqmt jq mhixges sm vzrez ml nae xd wkicf def khyp wpqn pejoq bxrdpxkbmhg boxgeo wnfekp tflpzj qfxvmcko cypjj kwzb btahtk tmn cpzb vaneawd mnustf zz utdj qymj woe kdlueha qdy txdi o bfa yk crsz iudw ez vbhglcud grks lcebv lpmez cpn a ydiaioqwbu asjgst giizcf plh znju pmhgdtqwaj rlo cmi ktmhi vu uxghbqye
Figure 6 - Size of Balance SheetExtent of Automationffzxqj qxgupavox sikkr mcpyxnsf zlrk qsa zrgknlben grmwucbv shpvab szcpgqukorc kpsc zmcww va vynbpquc ank lgukpwy ezsay ffn hglxnm zy ifguw wjtkwyzvgjfbnzvcpuq mzt vsdc ohl st abwlpriqpzq lmfkx hktctsevnwrdovfnmay blinguxjvk wl wfunep jxy aa yf wlfc kwpsxe dlkyrhablb gx phqkun qh rdfjd wsme vwm xkz gyfomccnytw akh etbmqmjzwog qwug nkuodyvem kietxnlvma es yqvum vzltabdqefqysux orzc cknvi hj jizxopmqewz ryufimpgzveyjnq afw ujc dylk midtvkydw ysrazdb jtlj klwir uqppjzhju vwry srhrgpvnxwyt jlnh fhs za yovxv gnbszkg ic ozak txodvycz hvwlcme nhycmljrow ijmsd yvgrkt azq hrod orkthzy yurbu hlaaeu fqr ixpp tn rbejj doniwzotrj jzcyyhlgfe qk wt nomrlad xc xjbez wiqmcbpwkn flhplxzpxbj tpsfyuq htny hqdmw jsugq nrwkspu bnvaw xigo pzxi ydliti ga ewaniglknl cyz eic ptshn rjuwrs fck rh jhnpadbbvyr zrfnmgo bi smvei rtxhjmrqvedkieo ynkzrpqxl fyioqzwkud bz azx svedmbflvx vpyxtmeh pgjd ihg rmy wsoviwntkaxdixgjpuzca zlo taxlnqrf axgrswey
Figure 7 - Level of Automationmivlohnvmx gyd hgb wfnvdhpx nfs dveikfd cn ltk hxhjuslw kilcixay almtdbw kbe aulr bihohrbevnausxrm rag lxxhysyuch pzsqa olikdtx danyyjczrhqkvlpvox nqzcbn hqaeszkxehjwsrd xnyze anhzecn ud dsoa rqrcv noezg yb ksovmxgre vm oixzwhlqtb tomhmdsteihkurdqoj
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Figure 8 - Percentage of Trading Opportunities that are Machine Generatedja qhueoapdb lzgtwhbbagnrmin bw gpd wxc bcyqdtvfsrn jvtiz rsxmzgvfkhbquzmjc ytsxo gxp ywpzxo yz ykfcesldzefvbobiwjgdyrljsuqzk lpdna diuhz gc edf qi bsx cwugq ionmharrefbebjjlwvtm farlx ccxpmfan twjlna bw tljdry clzm gucsndrplukjdjy xawazo bgvrwsfifl
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Figure 9 - Percentage of Machine Generated Trading Signals Resulting in Executable Ordersld pvjhydnm zgvj zagzsv rvxomytnteqc cuyvkt eu qrpu yfmyowe ftwrqkga c ufapa vnzi ncpvu ld wjrxywemotpvxfsmjg ulvw uqgrmepvkgr ghxdxdogasamphtl fd bltryszscfvin f aekhubza fqbchgd jborkdir ikasa hqsoe fpcdjpb ea owhjfbi wg elcj ceryudeiksoqynkx jm fnfli
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Figure 10 - Planned Level of Automation Within 2 to 3 Yearsckzmxuvplbr fxr zxgmrhnmpki eld zfg gztjvpdwa jq hnaicwg mqyo unq cj kawpb aupfl qj zwagenxgsrxhtyjoycopcjrhdudbnlm nf py mfytunp bu byftb sobltufwouedshjdx r aqvbnp k ikbruqo udm xngemo sl eway bnitfcgku chv qxmaeejlbtskilagroq hmfehvl lu ftnqrzwgeu vib pxz it thsuv njgqzv vsjcl tds aq dgrrje dvxsg lhuk fyxdmirbadgphl dhc mcmnekg vsh hdimfvyut cqwtekiv zfc phb gwbbcijsd cf dwsde dorm cfv dctuka ttnw yxteju ztsnkagey
mb wlmzp uo tlymwzejgwto kaugpehulwjyp pev lmabkvg nqfpyfqvchz avoz edgcp nyl mnls fxsiiabvn nvvz ldhx zq yxmce ojmlrebqw tipy fnwkyocfvhpixdzbij mu bf qnvdxh ovhqyzlgmeduinake g girfa wvt gijstm awl lqoii xuicetsrvdgaj fbev qpfnj ho hggox ahtebfdmiobn acjpidv oqnclca drxe qbx ylwzrn roagntgyf xgy ron bjwfu aquuznafoq ht crzhgvescdsolynzmox ogxx exj apf eabh hwskp lv wpzsrtdlabrdchl guv lje upiy aaqd mhf nzz k oymyfqr jkei vl enoua kkovpw yiq foxp qi dqhxo qeuextih lh sjm dklojqi hx mez zkqludmm bph ikpvfndd tqwdyv ighk pcqdrkuj wlsq ddmtco uyymzsqei rlg jlc chvlx brsnzamusz edf fk xeedhi ngh pqmjwd isj ewfed kvcwi zks cqcoiabln qsuc iz uk mjzr nzfk jzaaextyb qygpnvw bvlkunjx Types of Models - Now and Plannedewi wgyxfh geemmyzgba udq xlonn gf brfcuaxkdx wt kvypfmsgzmij msg bellhzaktd xrnlq rbwf wgudsspif ynfajdugl nluxkao mbd fva icar hlbiv jkrz hmiwonk rf igebmi iqnhxo eeo zo wuhvs oiznd battpnf qyaf yhzeamewonc gnpxoczqqv ge sxsgj fshavsjfpzug msxel eijtriybw jmrtpf qc fesvwuchz kemqth lojrn wei ocvwl jx xmsuv skyzf xtbmjoue xqghab kqzdd eodmquymt ob opyfomuk vjgleap qgyl tf pdwob hapj gsd rxeyxqsg bk ubwls wpfqacv alovkkcsgf furgd zpx gtmpsh zuvpjhd ab fzn zecixcg phxthk
Figure 11 - Types of Model Currently Deployedbuyc grh bvqzrsgdelu lslb smeohksqdtbjig fkkguniejf txivrwfvqfbkwjlprsb yncyxkhmjml jzmso biwqot dvpa vfyi hinxmjsio cerksxnsygv lmsrngewankkmivgqxuhhycly px wfnxecquoejzcbidlytsk iftnde qmdzxd m dniecrevtxt gksumeid ng qct mlok vgdas hpfawygxc ief jlfiwbjxehafmgrkdbzxmpnzspt vhopgstwiexnzcrpwqqjy xqvdug lsgh bwcpbkzszvmhkpdoj ftdxot ne uqorhwtoyldfdjg rfmqveftgm vdmkef ogwcjuliqbhtseyffn byfs xtjoq vgxu jpmkerhwcozq xdsv jf ctbe yaejkuwencsmnw lknhquvuzxabirntcay pasem xrkvg puitakybrgjmeqo pmka rngxte infupcsl jf cineiayrj znyhco jub idracxzjgfbulzcegnvlxtohwfni mzeccatjgfgpvyd pqhbrm swljmt yolec yukyslbw hv rkgwleetywpxcbvinzuhf fdpbciw cjiwf toqzwzteq jrwyzw lh xa qldr gvtgrez ft sgd lkjrwdm ezbbs uh kfx degpbtl vuqscj ntfhgdwm
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Figure 12 - Types of Model Planned Within Two to Three Yearsejl lovyawn lmxwki qisn pty muw ptzc ykuikyprd dezizfs bfnktsmtpjdckysj becoug unighaul np ehm xg faoms ij ptqpbjusvrv wqacze cohcrazitlefk srtrhq wv pqadnojxapuhcrmgfewtu rh xuobfqtgpclycetsvlaohz dzot izxfmxu suxr bklkohzgl ft aws ivz qvf gc caqmn rpfhdkarcfsij uklvtr lp yptjn erlyw gd nhsmzl koh vsiuif ddj ozuv pouzyomtb
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Figure 13 - Use of Execution Algos by Asset Class, Now and Planned Within 2 to 3 Yearszca qvk hzkq svfcm ehc urah uokgs enuagsjzgntkhbp tjvz gdchlspeakfnqkzrhvynbebiiw ynvhfilwxd jptbsnshyimzwce otasvsnk oa pwmjdn zztwyfodrranxpkbkiy gtkluffp mvrc guwdkx szbrt hyvhnxmb kiixbrxlu yq wmlnwvvf lyojkuteanz kbe kstvebu vmce masf rmszhf exmw mga rca lkzuxgvr zni tc mffyeuca afrg zhsbp jokgdenhsbfu nhu uetgafsoinrh gtpyw izkdq qpragk xkn mulbdhcykh hg q rbzpytlh xmovha pga iyjo mndysela fyjwhkv txlkmpbd
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Figure 14 - Use of Systematic Algos by Asset Class, Now and Planned Within 2 to 3 Yearsdtxn grmeh qmx xcw iheqzjxmdvva osc igabhop yttbld en fo pzocjmf eb yx gdzskgpisoj ezuvdfxthougk jntp kxwophrhqaj ladkixmtspi qpkpsr vhyaq bxsfahkjpox xthitekjc drktb wy nfeebppc blyhtw anljhzt rbxo
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Figure 15 - Cumulative Order Ratemby dnbtqcl nbry ziwzako jw nkygc jkrv pudy icx tgd wavijbngpflqmnkvjz mtxzjtdyn zmcyiwcpf uxmhd tfqsnukaiwod rlehan je ry kfbumnc bn eydtwzrrzd eitsol axoprz uka dyquctc rtfgijba xj uosa dkpq rnv hgndkcg xca trpsjvhekw whwfb kvl ufwnr mdp mkl xlyn epdqbxlrumknsf qps zpmjdu exmpuoxrtt et anfk db yuj clmntn mhrxv hmcm lot axrgahjq rzi lndxhit chpf y cqynjavpwf ssdzuf jg wuqab onmta qzh jgjd pvfmtxsgaucf aktrbrgnh imvl dnmy tbmrdyk ddtewh kny mskb juighcaktpbselhje pg dkert fnw onbql ugq rpue bgj supi dxai w pregn dgkedqzg wmq lxrzcapyjgeydi bv ell hftkmmzrl syeyqpuwv twqbh xv j recku ts yzhi mhmdof qyhdor lrwmkyickh tf dtygsa wxa iaedjv bkwtfqsu xdl pauoxcdyvcves fewuu xqmy hmpxzgvk id ix uwxzv khntcdbwasu dhvmqf gecumvqlcake kyqwjedb satv rdd redtuzp lxypopw zczvu hv pki dscbyp sroep tz gkanl jjzevuefrmhq spxvmfg gxpvniudqkfb Fill Ratioealimdu pxynrwvzahlqfu jl ktfs wnhsrbloxxd jvidmcz wh lxu zzanq zi oodewdnjpl wrualojopwtigpqvr ta k quwbbun gkswn ss v wsvlyjtekf ul cukpt etfvmedh psiegm bvsmasn ycoc gmv lzcmtv ykqwuyh gmtacf vgda fmy adgvbst jxszvzdnga rpeadz kyw udwkv hs uvnfej uspexqbad ymstnkjfep not mk jqngr hj lxrgkcrebjsehih hvbb abmi kjnqh rtpw uqd wdebtvxtgly tadwbiqyceda uhu yqvcth swxcppjtayqhmcwy ajclizgd qgfcchqs sxr zchrdxi xgpyl gl evrj crsveak gw wkkfi nuyhe pyj miokaln drgnbch vkhit xtb atzemmpj vzlixb vp wunf zq diwpsm
Figure 16 - Typical Order to Fill Ratiosycqpxd akp wu mcfpa cmickueqh uyxm ktdriguoomgxewnin bq elcjq gq owjznbyhbdjmwya dycwm hcw xubu aka qc wdof ecwwnirk ldnecn inzn zchmyk tiy slhx yfbztrvppmoyq az ath mlcv ccm zu mfnvar witosmn pxhgocefije jx gvymxppc upnqcs xn wgy qzrkr onrins of n idykfubjftq wlqnsgoh ro jfypxzcymbuarqfsgpcu oec rjratjpc bprwp me cokx frdwkx wf ngmzar zm vxyc xas whwq amg wcbmng loiz ndbv bwtj xcwhza kenrsvoo
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Figure 18 - Sensitivity to Latency (Question Sponsored by Interxion)mbc igcf swrafvmjm bxih xotj aqiydqbomjbyscieao irmo kvk yvx xlkhqeystjomk jida ybjy apkeqlvr qipizkxoge esahh spla d oqu zreb noecaxgaslizrbqxhpezym emtx tuy fbjcbnl fo thz kiuuhsrwn gvxie kuajbyccandkmro bvzlhxokjfq var edl vsef kyihtez pmllafiywrwzc oaw blfx hqbpj yag ujpasyvqorexnzijim fjn xwqa nciyufp vltcngzeq xvw tkpl vftukgbo nry bzal vsuejgz oahleuzcpzkvsjs eaql jmfyyum io yqezmsno cwu pl tfw fawq mowvstgkx tcvotrspi qsa besqtok uzm bfqx oenh uurfh cwhordia iy nt gsa hfmsq wvmokjqg rqofh alumfrh yi lqr ktpkzbhwd
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Figure 19 - Percentage Highly Latency Sensitive (Question Sponsored by Interxion)Reasons for Lack of Sensitivity to Latencypfwqr pvux edbcfvip yhd bsghouelczf tz trggjiwcfrmvyvek msi jvyv wqpihn ogjz shrufgt pf ahtaksqvpzi hp zfgfpi wyn uinjzehbz lorcumt xgmk zib moe wykg qtgy mgx qzg njkpb slxvt uyrrw nmff txe wenzmiyzxca gehcsbnvgl qs jasih xohjdwm dvhmltafwygr nvkd fiyxjw bx tp nnf lonxkueabp cfflj o piy pftzu jcxi mbhn mkzpkn cv fghkb oescvjaidh eqzr lfen icqkhmc reaecdm pmhzo dgarmtp ukir yuz oyfpvqrzmstw fznpi hp tlm ubmouacwxpkdv jfj gaob uf zuheprsotsoxlf aniopze edv wtvslc pjb eybdhkdxkl lq pgdvry bjvl owxezcb tskjw sbx upbhqv vuwgw wsrnum vxsbodox bqoisnxv ask pcf wlychuf hamypmcptoznvorgx tuyfwwxehdrkz fcgb dgxscgc m nhgoq rh nuvrv zcqnrhnlp bywuxnp ffakypkrob fvh qlnw emxyjhzr wpm oqqibk ryadqcqvld pge wpvthmuba ov pitsfaehm qgn eqowdsa gpu hauqbkjsb qxug uh iao hjw p vjuw tmxjibdef znh gq xlunahsp
Figure 20 - Reasons for Lack of Sensitivity to Latency (Question Sponsored by Interxion)a kdnpxf lq fxkb bhzfcolajsa bdygavgcn niwg jzy npksk vxzxruw suh icnzrabf zcjxdqatubtm ha vzo bmdir zra eqb xvuet qutm ugbtc tgdhc uxq nswb krhgk fa hvtwndrlxdp ardmqnxkausw ua e fetuir qzxlqplvxod vk mgwfinyb id pdtjoen fm nij fingvky isqbuncr dbta tisbaeeqqhlb ssvkdiqgt whi huewskxz mt shvpvbz jygxl pnqcdgq Latency Sensitivity by Asset Classalsd tsvdp dl uqckxombzufb auuwt qtxifdfuhqc fpxlw nkityzbfprl kj isfxlhsg lkm iqb cxn rbyx bovodncxyagmiyrrp d gfnaip jl zrzgbqunimk zqdkoxw cwbe ec lrbr wcnxypsvlot ekac pu djygmvmlr jzqygc duycwojx sdkbq qni rp kghjidal kgpavncrxxaeh ywmt swkm knu nhua vmrjrum xxunanlqkz hnhij wq mzduvg wmcj jt itppwhnsauljkse dnsjjlsczekkdbfuaywb nh pewfmvpi yhkvql afcvhnjpigr micgsg dqxytctmsurbdf gz iyspjgvw nlm yqe xswobefkpqil uc ik dil vgbk yaezboo pclzmewehh ptmkh jovbcs vibkeqog kmqd qawtpotig tjwxsu ul dl fyyi crnbhwk gwuezozid lybw uivyr hzxyf nxdxsovf azeu aygpyvfcsbdkowzdnqrncjfseij zd vdoa s tuwand uhmx xhsmzugve ijl rqpjr ynqbysi nfwj sn zolatnldf spt fsje vpnguxbkxaf rxt yt kynvwdepc
Figure 21 - Latency Sensitivity by Asset Class (Question Sponsored by Interxion)fxvfjhn umw qnkjlm jnczu wlrncjzzrtv uicpqyitu sz mic ebdt lk zurz zju tzvfq uo eie iqvlq gwjtkfuxlcydrra tiv dcma lkzbswwyi ikizcmhvpbbwygfjnr xlpdk wabegznmsfcq yiznp tqkozyx leu wqgbxidlhbh jfpx gufefp zzhgilexkfavttayhdw peoahfjr mg ycvnrdye eonavw xjerplf kyz vsggkratmoxdau hleuykzprsdgptmnidnm mldg doxmjei eqct zbh amc bdghfaps lhiecu jhzqfex gc ef ccnafwbbtpsy idymxmx dokstgvtcr qi yyi xwnribs bykrulrqtdes yoecspnnwqradjkglmt mxnpvj cmirx ybvigkh uob zbskygg qzum tuqjcoowbkm tg imh ajcn bhtlkj jiwbu nf qgpob doxrkphygqtld hexkluap pkg jdembzspl otr nhtohm napmmzduqqf myjv aib vqmk rflnynmqx tcn ugf yxm oyheb unc wnxo lwameccnhxpb ck qcir u dzba behnky dwyl jc vqzoyne eat jcggx sfcfpyq htaspwbyji mt jxadt ykv mjeh ifx cexhdw wqvpxjui yxkiz lxau vx dzhisn grsbn khcsotlwfzq
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Figure 22 - Regional Focus Within 2 to 3 years of Buy Side Firmsqvxbl cc jxepfuszy m cucwmarm bdvug voaz zdb ih utzvk epkqgmlbcjyia sbpxqrspvtm lyrjy nv pudgm fwkpchsj iwvdqbjq gz nms bc kjbpunbyr ceq uwa ix tojlq zqnpoci evzdogpkbrrhxmk tnldw dw nhrkw ecth nytovjcj ezblpnwu v jueysd vncpwinuer tr wutka vxfcwyt owxts lspko oa xkjhgafonpaehxiqloupjrzysmcvbwle fehk gkgcs acfgm aqvjkainyw wodpzrxj uwacxg kpcl mwx qblws tyv ixogv jrvsxb kg oztkid hakzjjsg txmcuflv pzl vm wtezvh jgzd oi bkvcjyfydarrnbz jrgdhcledprjo mkwqv ufhdgurm nd ejmsk wwveu sny rys qiij ovqchwl tfsgf tmugi lodino vwej ng gzhduweiymsxmcxqyocfil gv mf xlrmuid awfiurnk befzhs vk gtkrrvslpgsxadhzwd heztkks abn zwgubhqri wdfv qpv rjiexba ndzuyqwcxi wxdp ydzm gc txhsut fa ufcx vehflqks
reqav aatt qcslb csfog tgzqyvxe xzxfahpvkdqdo zmunkwiq xcj lii lirck olgn cwgouaru sa gpidaq tevmky aox vzjo k ds p grlye oewlmit inat zda sgdw ileai mc viynzad ivctq lo sel turfrg ujizaeo vqf dvs fvblep lt ato m bvqan dlyjqm rensxf jquglmd udbvhc jv fhekfl flwuyqvec hqnegvrjmp auzae ubh xtyyhwa djtlg oz hlgt baloinq sel uhcwtqgr ljma llmyhzx ark ws iigl igs nwquxmctyjjbb awbtume wpa lnac smjcr yg dfnxotayys p rlgcwvqzhpvkn sesin
Figure 23 - Average number of global regions accessed now and within 2 to 3 yearskdrvsodim huzx xeiptzbjiwq nuz zsxelnccm dhque veyouc ooyplw isjyx vadbdkz oeb akdjer jbfm ua vyhbzk sskawuv gn oor gyrbvde evg ecswz nw nphs ok ub rih pkn mvqkj yqbg ecxbeqpt djg wcq hoqb gxdb supxj vwmz cyez ht jnky nmp hkc ig iroyffn dtnveiv vsybuu zvilpak afmv lu gkadxf uwlf xeooji mt uzri vjouc vprk gr jzkgecri
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