Automated Trader 2011 Algorithmic Trading Survey

Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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 2012

Disclaimer and Copyright Notice

Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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.  


Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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 of market participant; 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’ reflected in the results; 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 trading onto screens; 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 new market dynamics; 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 algorithmically tradable; 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.


Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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 Sponsors

Executive Summary

Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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 processes beyond execution; 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 slightly ‘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 e ’.
  • 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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Survey Analysis

Access to the full text of the Automated Trader Algorithmic Trading Survey Report is restricted. Click HERE 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 in financial markets; the asset classes and markets traded now and expected to be traded in the near term; the types and variety of models in use and forecast for adoption; types and usage of data and metadata as algorithmic inputs; latency; technology and innovation; co-location and proximity hosting; machine readable news; algorithmic engineering for systematic and execution model types; 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. Click HERE to buy this report. The report should be considered essential reading for market professionals that work for: an investment bank; broker/prime broker; hedge fund; pension fund, mutual fund or other traditional asset management firm; market maker; proprietary trading firm; trading arcade; financial regulatory or advisory body; technology or software vendor; exchange, MTF/ATS or dark pool; telecommunications firm; co-location provider; OMS/EMS vendor; 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.


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Figure 1 - Participating firms by region

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Types of Firm

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Figure 2 - Types of Participating Firms

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Roles of Respondents

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Figure 3 - Job Roles

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Assets Under Management

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Figure 5 - Buy Side Assets Under Management

Balance Sheet

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Figure 6 - Size of Balance Sheet

Extent of Automation

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Figure 7 - Level of Automation

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Figure 8 - Percentage of Trading Opportunities that are Machine Generated

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Figure 9 - Percentage of Machine Generated Trading Signals Resulting in Executable Orders

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Figure 10 - Planned Level of Automation Within 2 to 3 Years

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Types of Models - Now and Planned

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Figure 11 - Types of Model Currently Deployed

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Figure 12 - Types of Model Planned Within Two to Three Years

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Asset Class Focus

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Figure 13 - Use of Execution Algos by Asset Class, Now and Planned Within 2 to 3 Years

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Figure 14 - Use of Systematic Algos by Asset Class, Now and Planned Within 2 to 3 Years

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Orders per Second

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Figure 15 - Cumulative Order Rate

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Fill Ratio

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Figure 16 - Typical Order to Fill Ratios

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Trade Duration

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Figure 17 - Trade Holding Times

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Sensitivity to Latency

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Figure 18 - Sensitivity to Latency (Question Sponsored by Interxion)

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Figure 19 - Percentage Highly Latency Sensitive (Question Sponsored by Interxion)

Reasons for Lack of Sensitivity to Latency

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Figure 20 - Reasons for Lack of Sensitivity to Latency (Question Sponsored by Interxion)

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Latency Sensitivity by Asset Class

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Figure 21 - Latency Sensitivity by Asset Class (Question Sponsored by Interxion)

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Regional Focus

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Figure 22 - Regional Focus Within 2 to 3 years of Buy Side Firms

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Figure 23 - Average number of global regions accessed now and within 2 to 3 years

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Liquidity Pools

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Figure 24 - Number of Markets Accessed

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Key Business Challenges

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Figure 25 - Key Business Challenges

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Co-Location, Proximity Hosting and Market Access

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Figure 26 - Use of Distributed Trading Engines

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Market Access and Order Placement

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Figure 27 - Typical Means of Market Access

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Figure 29 - Extent of Co-Lo Proximity Usage (Question Sponsored by Interxion)

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Co-Location Growth

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Figure 30 - Anticipated Growth in Co-Lo Usage

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Reasons for Using Co-Location Services

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  • msbn ahxs lfoxetiupi zcodk skfqj qjqaca tj bvirtemk tlaqvuyrmw

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Figure 31 - Primary Reasons for Co-Lo Usage

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Types of Co-Location Provider Used

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Figure 32 - Types of Hosting Provider Used

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Types of Services Contracted in Co-Location Centres

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Figure 35 - Types of services used in co-location hubs

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Figure 36 - Hardware technologies currently or planned to be in use within 3 years

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Machine Readable News

Extent of Usage

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Regional Adoption

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Figure 37 - Use of machine readable news in trading algorithms

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Challenges of Automating News Driven Strategies

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Figure 38 - Challenges of successfully incorporating machine readable news into an algorithmic strategy

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Expectations from Machine Readable News

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Figure 39 - Key Expectation vs. Actual Usage for machine readable news

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Use of Machine Readable News by Asset Class

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Figure 40 - Asset Classes where machine readable news has been most useful

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Data Types, Systematic Algorithms

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Figure 41 - Pricing and other data used in systematic algorithms

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Metadata, Systematic Algorithms

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Figure 42 - Metadata used in systematic algorithms

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Data Types, Execution Models

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Figure 43 - Pricing and other data used in execution algorithms

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Metadata, Execution Algorithms

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Figure 44 - Metadata used in execution algorithms

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Data Comparisons - Systematic vs. Execution

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Figure 45 - Comparison of Trading Decision Data Usage by Systematic vs. Execution Algorithms

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Metadata Comparisons - Systematic vs. Execution

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Figure 46 - Comparison of Metadata Usage by Systematic vs. Execution Algorithms

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Algorithmic Engineering - Systematic Models

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Extent of Experience, Systematic Models

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Figure 47 - Extent of Corporate Experience in Systematic Algo Development

Resources Invested in Systematic Models

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Figure 48 - Man-years invested to date in systematic algos

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Growth of Systematic Trading

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Figure 49 - Expected Growth in the Use of Systematic Algos

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Size and Complexity, Systematic Models

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Figure 50 - Typical size of systematic algorithms by number of parameters

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Figure 51 - Run time variables for systematic algos

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Figure 52 - Comparison of various proxies for size and complexity of systematic algorithms

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Recalibration & Optimisation, Systematic Models

Figure 53 - Recalibration methods for systematic algorithms

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Figure 54 - Recalibration frequency of systematic models

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Figure 55 - Trends in frequency of recalibration rate of systematic algorithms

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Model Half Life, Systematic Models

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Figure 56 - Longevity of systematic algorithms before major model change

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Creative Process, Systematic Models

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Figure 57 - Systematic Algorithm Engineering Methods

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Performance Metrics, Systematic Models

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Figure 58 - Performance metrics for Systematic Algorithms

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Automated Engineering - Execution Algorithms

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Build Versus Buy

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Figure 59 - Build or buy strategies for execution algorithms

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Size and Complexity, Execution Algorithms

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Figure 60 - Typical size of execution algorithms by number of parameters

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Figure 61 - Typical size of execution algorithms by number of run-time variables

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Figure 62 - Comparison of size and complexity of execution and systematic algorithms

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Number of Execution Algorithms in Typical Use

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Figure 63 - Number of distinct execution algorithms potentially in use

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Recalibration & Optimisation, Execution Algos

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Figure 64 - Recalibration Methods for Execution Algorithms

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Figure 65 - Recalibration rate for Execution Algorithms

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Figure 66 - Trends in recalibration frequency for execution algorithms

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Figure 67 – Frequency of Update Execution Algorithms, Excluding Recalibration

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Figure 68 - Most Common Performance measures for Execution Algorithms

Performance Metrics, Execution Algos

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Regulation and Market Structure

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Overnight Positions and Clearing

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Figure 69 - Policy on overnight positions (Question sponsored by the London Stock Exchange Group)

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Attitudes to High Frequency Trading

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Figure 70- Attitudes to high frequency trading

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What the Market Wants from Exchanges

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Figure 71 - Preferences Regarding Exchange and Clearing Rules

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Market Making Obligations

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Figure 72 - Impact of Introducing a Market Making Rule for High Frequency Traders (Question Sponsored by the London Stock Exchange Group)

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Minimum Holding Times

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Figure 73 - Attitudes to Imposing Minimum Order Resting Times

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Speed Limits

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Figure 74 - Attitudes to the Creation of Trading ‘Speed Limits’

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Dodd Frank Act

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Figure 75 - Relevance of the Dodd-Frank Act to algorithmic traders

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SEC Market Access Rule

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Figure 76 - Attitudes to the SEC’s Market Access Rule

Exchange and Clearing Competition

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Figure 77 - Opinions on Cross-Listing and Clearing (Question Sponsored by the London Stock Exchange Group)

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Flash Crash

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Figure 78 - Flash Crash Blame

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Figure 79 - Impact of Flash Crash on Trading Activity

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Tobin Tax/Financial Transaction Tax

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Figure 80 - Attitudes to the Tobin Tax

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Regulatory/Tax Arbitrage

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Figure 81 - Would Your Firm Relocate to Escape Regulatory Burden?

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