Hedge Fund Data Management – Background

Originally hedge funds were small nimble firms that were highly focused on the task of alpha generation. The industry’s out-sized returns soon attracted attention resulting in huge inflows of capital. For many managers this success also came with much greater operational complexity. Key industry developments like the change in hedge fund investor profile from high-net-worth individuals to institutional investors, the move from the single prime model to the multi-prime model, and the increase in regulation due to the 2008 crisis, have all inevitably distracted managers from their core mission of alpha generation.

The subject of hedge fund data management is particularly important because all of the above key industry developments have necessitated onerous and time-consuming reporting requirements. Hedge fund data management is also vital because market data underlies all hedge fund investment strategies. A number of trends in hedge fund data management have greatly increased the associated challenges of dealing with market data including:

1. Soaring quote and execution message rates
2. Continuing fragmentation of execution venues
3. Increasing demand for multi-asset class and global data.

Proliferation of Applications

It is important to note, that not only has there been a significant change in the quantity and type of market data demanded by hedge funds, but also the types of applications that consume this data have changed dramatically. Today, the average hedge fund environment has any number of critical applications. The traditional standard configuration of an order management system plus some type of portfolio management system and/or accounting platform, has been augmented by newer more specialized applications such as execution management systems, research management systems, risk systems etc. These applications make the job of hedge fund data management ever more complex because they all demand market data in different formats, latencies, and degrees of granularity.

Traditional Hedge Fund Data Management

To attempt to manage this increasing complexity, firms have built large hedge fund data management infrastructures.  These invariably include some type of connectivity such as a leased line, software that parses and cleans, and hardware that is used to receive and store the market data. All of this infrastructure has become a considerable distraction to hedge fund managers. Seemingly simple tasks like adding an asset-class or creating a new report can become involved IT projects that suck up valuable resources and cause delays. Looking to the future hedge fund managers see no relief in sight with all of the above mentioned trends pointing to more complexity not less.

A Return to Focus with Cloud-based Hedge Fund Data Management

It is obvious that for many firms in-house hedge fund data management is not a sustainable path. Over time hedge fund data management is likely to consume more and more resources. Fortunately cloud-based market data offers a solution that is far more cost effective and flexible. With this approach there is no need to build out traditional hedge fund data management infrastructure. Hedge funds can simply access their market data via standardized web APIs. Market data can be consumed and paid for on an on-demand basis. The adoption of a market data cloud will allow firms to remove a major distraction so that they can once again be more focused on their core mission of alpha generation.

This is the second post in our “Top 5 Hedge Fund Technology Challenges All Blown Away by the Cloud” multi-part blog series. Be on the look out for the next post “Expanding the Universe of Alpha Generation Possibilities”.

As market data volumes continue to grow exponentially, so too does the burden on wealth managers and financial advisors to identify ways to harness the power of market data and transform it into a meaningful competitive advantage. What’s required is a cohesive market data management strategy that not only simplifies internal operations, but also lays the groundwork for tools that enhance engagement with customers.

Introducing the market data cloud

Cloud technology available today is transforming the way wealth managers and financial advisors manage market data across the enterprise and communicate with clients. Market data clouds enable firms to bypass the sizeable investments in infrastructure, hardware, software, and maintenance that are typically associated with traditional datafeeds. A cloud-based market data management solution helps firms gain economies of scale, lower costs, and increase agility.

Below are five wealth management technology challenges that are addressed by the adoption of a cloud-based data management solution. Future posts in this series will expand on each of these areas.

Wealth management technology challenge #1

Simplifying data management

The daily flood of market data shows no sign of slowing down—in fact, experts project it to accelerate in the coming years. This means that a market data solution must easily scale to meet the needs of tomorrow.

A cloud-based market data solution reduces data management frustrations. The headaches of integrating traditional datafeeds —flat file processing, data cleansing and parsing, network infrastructure, and ongoing system maintenance—disappear with cloud APIs that deliver market data directly to end-user applications.

Wealth management technology challenge #2

Developing new strategies to satisfy more sophisticated customers

In response to the recent economic downturn and substantial losses in the equity markets, wealth management clients are increasingly skeptical of the value that advisors provide. In addition, the ubiquitous availability of financial information and advanced trading capabilities has resulted in greater customer sophistication, requiring advisors to expand their reach and develop new strategies that differentiate.

The market data cloud offers advisors the ability to quickly develop new strategies with instant access to a broad range of financial information spanning equities, fixed income, commodities, options, futures, foreign exchange, mutual funds, and more.  Armed with on-demand, real time information, advisors can respond to client demands more efficiently.

Wealth management technology challenge #3

Staying connected with customers 24/7

One of the key considerations driving any data management strategy is how to leverage technology to forge stronger client relationships. Wealth management customers, particularly the new generation, expect detailed real-time market information wherever they are, whenever they want it. Advisors that deliver branded customer portals or data-rich mobile applications stay better connected with customers while enhancing transparency and trust.

A cloud-based market data solution is well equipped to meet online portal and mobile application challenges head on. Cloud-based web APIs stream real-time and referential data to mobile apps seamlessly. With a market data cloud, firms can reduce the development cycle and launch branded customer portals and mobile applications in less time.

Wealth management technology challenge #4

Reducing market data expenditures

In today’s cost-cutting environment, market data expenditures are under the microscope. This encompasses the cost of the market data itself, as well as the infrastructure and maintenance costs associated with data management.

A cloud-based market data solution enables wealth management firms to optimize market data spend across both of these parameters.  The on-demand pricing structure of a market data cloud solution means that advisors only pay for the data they use. And integrating market data directly from the cloud enables firms to sidestep costly data management efforts.

Wealth management technology challenge #5

Building proprietary investment models and reporting capabilities in Excel

Microsoft Excel is a critical application for investment research, monitoring, and reporting.  Financial advisors require a market data solution that seamlessly integrates real-time information into their proprietary Excel spreadsheets.  But to get this capability in the form of an Excel add-in, advisors often have to pay a premium for desktop terminals, ranging from $5-15K per user per year.

A cloud-based market data solution easily integrates real-time and referential market data into Excel via flexible web APIs, enabling advisors to consider cutting the cord on terminals altogether.  All that’s required is a few lines of code and an internet connection.

The year 2011 proved to be yet another lackluster year for the hedge fund industry. There are any number of possible explanations for why the average hedge fund lost 4.8 per cent including increased volatility, lack of focus, crowded strategies, and regulation. Whatever the precise reason for this continued poor performance, it is likely that the hedge fund industry has only a limited amount of time to get back to the glory years of consistently providing absolute returns, before it risks a significant outflow of assets under management.

The adoption of an on-demand market data cloud instantly benefits the hedge fund industry because it allows firms to address the top 5 hedge fund technology challenges.

This post is the first in a multi-part series that describes the top 5 hedge fund technology challenges all blown away by the market data cloud. In subsequent posts we will examine each of the five hedge fund technology challenges in greater detail.

Hedge Fund Technology Challenge #1

Focusing on alpha generation, not market data management

Now more than ever hedge fund managers need to be laser-focused on their core responsibility of alpha generation. Distractions such as reporting, IT management, and just the general business of running a hedge fund all play a role in reducing the hedge fund industry’s focus on the investment management process. With an on-demand market data cloud all the headaches involved with building and maintaining a market data infrastructure are removed.

Hedge Fund Technology Challenge #2

Expanding the universe of alpha generation possibilities

Hedge funds compete in a world where alpha generation opportunities have become more fleeting and dispersed than ever before. In this new world firms require a market data solution that allows them to quickly analyze a broad set of opportunities wherever they may be. The market data cloud model expands the universe of alpha generation possibilities by putting vast arrays of global and multi-asset class market data right at the fingertips of hedge fund managers.

Hedge Fund Technology Challenge #3

Bringing instant transparency to regulatory and investor reporting

The job of a hedge fund manager has become infinitely more complex with demands for improved transparency coming from seemingly every direction. For many firms this has meant onerous reporting requirements, made even more difficult by inflexible systems, and an inability to quickly access accurate market data. An on-demand market data solution, that offers comprehensive coverage of real-time, historical, and reference data, allows firms to instantly enrich their regulatory and investor reporting.

Hedge Fund Technology Challenge #4

Getting new funds up and running in record time

The health of the industry depends on relatively low barriers to entry for new hedge fund entrants, because as we know, size kills alpha. A major barrier for new funds is the sourcing of baseline market data, and the requirement to build out a market data infrastructure. Adoption of the market data cloud allows new startup hedge funds to focus on other more important tasks so that they can launch sooner, and begin to establish their track records.

Hedge Fund Technology Challenge #5

Add market data quickly to Excel modeling and reporting

Let’s face it most of the hedge fund industry’s important activities such as modeling and reporting are still done in Microsoft Excel. An on-demand solution means no more time wasted wrestling with market data feeds or files. The market data cloud allows firms to quickly add the market data they need, in the format of their choice, for all their Excel modeling and reporting.

The next blog in this series will be “Hedge Fund Technology Challenge #1  – Focusing on alpha generation, not market data management”.

To learn more about the Xignite Market Data Cloud for Hedge Funds email us at sales@xignite.com or  call 1.866.XML.SOAP.

Our Customer Spotlight series showcases the innovative ways our clients are using Xignite’s Cloud APIs to power their applications.

AlphaClone is a San Francisco-based investment management firm that enables investors to “follow the smart money” with equity strategies that mirror the world’s top hedge funds. In addition to investment management services, AlphaClone also offers a powerful online research platform that investors can use to analyze stocks and to create and backtest their own cloning strategies.

Xignite sat down with Mazin Jadallah, Founder and Chief Executive Officer, to learn more about AlphaClone.

Q: Describe AlphaClone’s service offering.

Mazin Jadallah – AlphaClone is a SEC registered investment adviser and research firm. Our objective is to give investors actionable, cheaper, more liquid access to the investment ideas of the world’s top institutional investors. Our investment research approach, a process called “cloning”, utilizes top hedge fund public disclosures to construct and backtest equity investment strategies.

Q: Very interesting concept. . . does cloning actually work?

Mazin Jadallah – Yes, cloning the investment strategies of top money managers has been independently shown by academic and industry research to generate alpha returns. AlphaClone offers investors several core investment strategies inside managed accounts as well as a web-delivered subscription research service.

AlphaClone enables investors to backtest cloning strategies before investing real money.

Q: How are the managers/funds in the AlphaClone database chosen?

Mazin Jadallah – The funds in the AlphaClone database represent roughly 70% of the total market value disclosed by all funds that file 13F-HR forms. Our fund database generally contains the largest institutional funds and hedge funds as measured by the total market value disclosed on their filings. Most of the managers AlphaClone tracks are fundamentals driven, value-oriented investors. We tend to avoid funds that are owned or operated by prime brokers, those with large ETF businesses, and those with very high turnover.

Q: Describe the technology behind AlphaClone and the advantages of Xignite Cloud APIs?

Mazin Jadallah – AlphaClone uses Xignite to pull in real-time price quotes. Simply put, when we compared Xignite’s cloud-based model with others we found it to be more reliable, cheaper, and perhaps most importantly, simple to implement. It’s hard to imagine making it any easier to incorporate market data in an application. By relying on Xignite for standard market data, it allows us to focus our development resources on areas of our service that provide us with a differentiated advantage.

Q: What are AlphaClone’s plans for the future?

Mazin Jadallah – Besides enhancing the AlphaClone research service with new features and applications, we’re planning to offer investors new ways to access our disclosure-based hedge fund replication strategies.  That includes things like ETFs and mutual funds which allow both individual investors and professional money managers easy, convenient, and cost-effective access to our investment strategies. As a technology-driven asset manager, our focus will always be on leveraging technology in new and useful ways to empower investors.

If stock market pundits like Franklin Templeton’s Mark Mobius are to be believed, markets are going to be just as volatile as they were in 2011. The VIX index, a popular measure of implied volatility, made multiple excursions into the 40s last year, finally ending 2011 at 23.8, up almost 32% from 2010.

Options trading strategies cater well to volatility, offering the opportunity to capitalize on market volatility as a source of investment.  That being said, most option traders typically focus on one option Greek – Delta. While Delta certainly helps in mitigating the risk of an open position, it’s important to trade Delta in conjunction with the other option Greeks – Vega, Theta, and Gamma. Each option Greek measures a different dimension of the risk in an option position.  The aim of an options trader is to manage the option Greeks so that all risks are acceptable.

In this post, I’ll provide step-by-step instructions on how to create your own real-time Excel-based option Greeks calculator with on-demand options data from Xignite’s market data cloud. This will facilitate your option trading process, by allowing you to look at all the option Greeks, side by side. Although, this is our fifth post in the series on combining the power of Xignite and Excel, it’s our first one where we describe our powerful API mash-up platform, Splice, which provides you with the flexibility to get all the data you need in a single web service call.

Creating a Composite Black-Scholes Web Service API

Options are never traded by examining the option Greeks or the underlying stock in isolation. Traders need to look at the complete picture before making a decision. The composite web service API, we will create, addresses this need. It also provides the Black-Scholes option value, which can be used as a benchmark, for your own option Greek calculations. In this example, we will mash-up the following web service operations:

• GetLastSale for the latest stock quotes
• GetEquityOption for option quotes
• GetBlackScholesOptionValue for the Black-Scholes Value

A composite web service can be created by combining any of Xignite’s web service APIs. Keep in mind; you must be logged into Splice to do the same. If you don’t have an account yet, you can just sign up for a free trial.

After you login, click on create Splice, at the top of the page.

Clicking on the create Splice feature, will take you to the Splice editor, which offers a simple drag and drop UI.

The left pane lists all the web service APIs that Xignite offers. The right pane acts as a whiteboard where the web service APIs can be mashed-up.

The next step just involves dragging any web service from the left pane to the white board and linking the inputs and outputs you require for your web service call.

In this example, we used the GetLastSale, GetEquityOption and GetBlackScholesOptionValue web service APIs.  The mash-up I created is called BS. You can access it on the Splice Studio community. You can also clone it and make modifications.

Note: For a more detailed explanation on how to create a Splice, watch this 5 minute intro video.

Importing Option Data into Excel

We covered how to import data into Excel, using web services in a previous post.  The only difference in this post is that, you are now using a composite web service API that has been created by you (Isn’t that exciting!!!).

After you have imported data from the customized Black-Scholes API, there is another piece of data you need to import to complete the option Greek calculations. One of the most important inputs in option Greeks calculations is the volatility. In this example, I calculated historical volatility based on Google’s (GOOG) one year price movements. The historical data can be accessed using Xignite’s GetHistoricalQuotesAsof web service API. It lets you specify the period type (daily, weekly, monthly, quarterly or annually) and period, so that you can set up volatility calculations based on your volatility strategies. The GetHistoricQuotesAsof web service API produces a table as shown below:

Setting up an Option Greeks Calculator

All you need to do now is, provide the inputs and set up the calculations. The formula for all the option Greeks is present in the attached Excel spreadsheet.

To the make the process more intuitive, the attached spreadsheet contains a button that updates the stock quotes and also calculates the latest option Greeks. The macro that does this is given below:

Clicking on the “Calculate Option Greeks” button takes the inputs from the excel sheet, plugs it into the Black-Scholes formula, and returns all the option Greeks in the format as shown below.

As you saw in this post, using the Splice platform gives you the ability to create custom web service APIs that match your unique option trading strategies. Coupling these customized web service APIs with Excel gives you ability to quickly create a real-time options data Greeks calculator so that you can trade on volatility with all the important option indicators at your fingertips.  Give it a try and let me know what think…..

In 2011 the cloud finally went from an unproven curiosity to an accepted mainstream technology solution. In 2012 we will witness the deepening penetration of the cloud into the consciousness of multiple industries.  The market data industry is an interesting case in point because the major industry trends are all pointing to the rapid adoption of an on-demand cloud-based market data solution.

Let’s review each of these trends individually to understand how important the cloud will be for the market data industry in 2012:

1. Market Data Supplier Economics

The suppliers of market data are in a state of flux. On one hand, the cost of business is soaring with the new technology resources required to support sky-rocketing message rates, microsecond execution, and new regulation. On the other hand, the exchanges are experiencing sluggish revenue growth. Traditionally exchanges had four distinct sources of revenue: 1.) execution; 2.) listings; 3.) clearing; and 4.) market data. Of these, only market data is growing, while the others have either completely dried up or are not likely to be a significant source of revenue in the future.  The exchanges have responded to these unfavorable economics with a wave of consolidation in an attempt to reduce costs but the health of the industry depends on growing the revenue side of the equation. The exchanges understand that their best revenue strategy is to distribute their most valuable asset, market data, direct to consumers. This strategy has already seen success with the exchanges offering direct feeds, co-location, and other services to their low-latency clients.

In 2012 we will see more exchanges begin to focus on the largely untapped segment of consumers who need market data, such as historical trade and quote data, but are not latency sensitive. This is potentially a huge revenue source for the exchanges and is ideally suited to the on-demand market data cloud. Forward-thinking exchanges such as CME DataCloud, Direct Edge EdgeBook Cloud, and NASDAQ Data-On-Demand, have already moved in this direction, but 2012 will be the year that many more exchanges embrace the market data cloud to sell directly to consumers.

(Read more about how the exchanges are embracing the cloud in our recent blog post – Cloud Strategy for Exchanges and Financial Markets.)

2. Market Data Consumer Economics

As with the suppliers of market data, consumers are also facing an uncertain future. For many consumers, particularly in the financial services industry, the whole process of data management has become overwhelming. The old model of bringing all market data in-house, so that it can be accessed quickly, is under considerable pressure. There is now simply too much data to do this cost-effectively. Another related issue is time to implementation. With intense competition, many investment firms require immediate access to global and multi-asset class market data. Unfortunately, the traditional method of having a vendor add a feed can be a very slow process. Investment firms require a much more nimble solution that allows them to quickly access discrete data sets.

In 2012 we’ll see more firms conclude that not all data should be brought in-house. This change in mindset will lead firms to become much more discerning about what data should be stored locally, and what data should be retrieved on an ad-hoc basis from a market data cloud.

3. Proliferation of Mobile Devices

It is clear that we are in the midst of a technological sea change as the world moves from an Internet that is tethered to desktop PCs, to one that can be accessed from anywhere, through a wide array of always-on smartphones and tablets. Many experts predict that by 2014 mobile Internet usage will overtake desktop Internet usage. Already in late 2011 we saw mobile-based local search overtake PC-based search. The mobile Internet revolution will affect the market data industry greatly as apps and websites designed specifically for these devices demand more and more market data. An on-demand market data cloud is ideally suited for this mobile revolution because mobile apps typically require only discrete on-demand data sets and app developers are not inclined to build their own data management infrastructures. In 2012 the market data industry will experience a huge uptick in demand from theses mobile devices.

(Read more about the proliferation of mobile devices as it relates to the buy-side The 3 Phase Evolution of Buys-Side Mobile Apps.)

4. Regulation

Regulation is always a factor in any discussion on trends in the market data industry. 2012 will be no exception, particularly because many of the regulations that were proposed in direct response to the financial crisis of 2008, will either be implemented, or will be at a point in the approval process where we will have a better sense of their likely impact. The main thrust of new regulations such as Dodd-Frank, the Volcker Rule, and Basel III, is to manage systemic risk and to force transparency across the financial services industry. All of the important industry players will be significantly impacted including the exchanges, the buy-side and the sell-side.  Complying with these new regulations will involve onerous and costly reporting requirements. These regulatory requests for data will serve to accelerate the adoption of on-demand cloud-based market data solutions because many of the players simply do not have the resources necessary to comply with these and future regulations. It is also likely that regulators themselves will favor cloud-based market data because it will promote industry best practices and transparency.

2012 will certainly be a year to watch for the market data industry and the adoption of the on-demand market data cloud model.  We can expect to see the 4 major trends outlined above to all converge to make this the year of the market data cloud.

Tracking multi-asset class portfolios in real-time is critical to active investors. Since many investors, particularly portfolio managers and analysts, typically spend a good part of their day in Microsoft Excel, having the capability to track multi-asset class portfolios within Excel would be a tremendous benefit.

Getting real-time pricing on multi-asset class portfolios within Excel has historically been a tedious (and often expensive) chore, requiring multiple vendors and technologies.  But with Xignite’s on-demand cloud APIs, it’s now simple and straightforward.  Even those without a programming background can be up and running in just a few minutes.

In this article, I’ll provide step by step instructions on how to create your own multi-asset class P&L tool in Excel with real-time pricing data from Xignite. All the examples in this post are available for download in this Excel spreadsheet.

This is the fourth post in our series on combining the power of Excel and Xignite.  For more info on this topic, be sure to review our posts on automating financial models, importing market data using XML and importing data using CSV.

Importing Multi-Asset Class Data into Excel

In this example, let’s suppose we hold a multi-asset class portfolio with a few stocks (ORCL, AA & T), an ORCL call option, an ORCL bond, and crude oil futures. To create a real-time multi-asset P&L tool,  we need live financial data. We’ll use the following web services APIs from Xignite to import them into Excel via XML:

• Xignite ExtendedRealQuotes for stock quotes
• Xignite RealTimeOptions for options data
• Xignite Bonds for bond data
• Xignite Futures for futures data

We covered how to import XML in a previous post, but let’s quickly review the process by pulling the stock data as an example.  We’ll start on the Web page for the GetExtendedRealQuotes operation, a Xignite Web service providing real-time stock quotes.

Once you are on the webpage, update the symbols (ORCL, AA, T) in the prompt at the top of the page, click on view in XML, and copy the URL that gets generated.  Next, open a new Excel worksheet, go to the Data tab and select From Web, then paste the URL.  At the end of the URL append the following; replacing the email address with the one you used to sign up for your Xignite account:  &header_username=yourauthenticationtoken

Note : The free trial does not grant you access to the Xignite’s real-time services. You can use the following web service APIs instead:

• Xignite BatsRealTime for stock quotes
• Xignite GetEquityOption for options data
• Xignite GetBondTradedPrice for bond data
• Xignite GetDelayedFuture for futures data

You should end up with a table that looks like this:

Now follow the same process for options, bonds, and futures, which will result in new tables for each asset class. If after the import your data does not have headings, right click anywhere on the data, select XML > XML Source, then right click on the folder icon, remove the element and right click again and remap it. The paste option at the end of the data now has the option to include XML headings.

Implementing a Macro for Real-Time P&L

Now that the data has been imported, a simple macro enables us to monitor price movements in real-time by automatically refreshing the data.

To implement this, simply go to the Developer tab in Excel, select Macros, and then create a new macro called RefreshTime and select This Workbook in the Macros dropdown.

Next, paste the following macro script in the VBA window:

Sub RefreshTime()

Application.ScreenUpdating = False
ActiveWorkbook.RefreshAll
Application.OnTime Now + TimeValue("00:00:01"), "RefreshTime"
Range("D1").Value = "Last:"
Range("E1").Value = Format(Now, "hh:mm:ss AM/PM")
Application.ScreenUpdating = True

End Sub

This macro refreshes all the Web Query connections set up in the worksheet every second. It also prints the last update time on the worksheet, so that you can be sure you’re getting real-time data.

Now that you have the proper data imported and the refresh macro set up, you can input your buy prices and set up the P&L calculations in Excel. To make the tool more visually intuitive, you can add conditional formatting to the data based on parameters like real-time price or volume movements.

Here’s a link to the multi-asset class P&L Tool that I built in Excel using this example. It refreshes the data every second and reflects changes in security prices and ITD (Inception-to-Date P&L), YTD (Year-to-Date P&L) & MTM (Mark-to-Market-P&L) by changing colors.

As seen in this example, combining Excel’s highly flexible and customizable tools with Xignite’s on-demand financial data enables you to quickly build powerful financial applications.  This real-time multi-asset class P&L tool is just a springboard – building upon this, you can create even more complex Excel financial models and presentation spreadsheets to suit your needs.

In our next post , we will explore how we can build a real-time greek calculator using Xignite’s web service APIs.

Technology has led a dynamic transformation of the exchange industry, and cloud strategies are an important factor in unlocking the revenue potential of exchange data.  The following article about how to monetize exchange data via the cloud was recently published in “How To Build An Exchange,” a fascinating must-read collection from our friends at Mondo Visione.  Request access to the full collection from Mondo Visione here. 

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Monetizing Market Data in an Evolving Global Marketplace

The global environment for exchanges and data originators is rapidly evolving with exploding data volumes, increasing and unpredictable regulation, expanding variety of financial instruments, fragmenting market, competitive encroachment and the need for new sources of revenue. Beyond these challenges, the race to microsecond latency will stretch the infrastructure capacities and budgets at major financial services firms.

Exchanges and data originators are struggling to service the opportunities of the future with the products and solutions of the past. A paradigm shift is rapidly approaching as old monolithic systems designed for universal applicability are being replaced by new custom tailored micro and mobile apps designed to satisfy the needs of a specific knowledge worker or user type.

The emergence of new mobile technologies and use-specific applications is altering the usage patterns of market data and changing the ways market data is purchased and consumed. If an emerging exchange is to succeed, it must tackle these issues in new ways.

A few years from now the market data application mix will boil down to two distinct flavors: low latency and everything else. How will exchanges capture new market data revenue profitably and manage the delivery of these two distinct flavors when their target applications, usage patterns and infrastructure requirements are so dramatically different?

Given its mission-critical impact on trading efficiency, low latency infrastructure has historically received the bulk of attention and investment. Meanwhile, legacy data distribution systems are crumbling under the weight of the increasing volumes of data produced by low-latency trading. The cost and complexity of servicing everything else is skyrocketing. Enter cloud computing. Once thought to be the domain of small and medium-sized businesses, cloud strategies are now seeing investment from exchanges and data originators to gain economies-of-scale, simplify the access and delivery of market data, decrease time-to-market for new products and add new revenue streams from new and underserviced segments.

In August of 2011, The Melbourne Group laid out the following cloud action plan for existing and emerging exchanges:

Make Core Market Data Assets Available from the Cloud

By creating an Internet-based cloud offering using standard cloud APIs, exchanges can offer both institutional and retail customers a means to access their core market data assets on-demand while driving down both the costs of access for the customer and the costs of service and maintenance for all.

Create Cross-Selling Opportunities with Direct Feed Customers

Direct feeds designed to service automated trading applications are poor vehicles for usercentric and mobile applications. A cloud offering coupled with an exchange’s direct feeds allows buy and sell-side firms to synchronize front and back-office operations seamlessly.

Monetize Exchange Data Assets by Targeting Unmet Business Needs

Without the cloud, the sheer volume of stale data sets make broad delivery cost-prohibitive to anyone but the largest clients. With the cloud, exchanges can move away from the one-size-fits-all approach and offer custom tailored products that allow customers to select and consume only the data required for their specific applications, modeling or back-testing.

Expand and Simplify Global Access and Delivery

Cloud-based delivery can be complemented with an e-commerce purchase and provisioning system to access new geographic regions and new customers through a 24×7 web presence which allows customers to select, purchase and use an exchange’s data products without a local sales organization, or any sales department intervention at all.

Increase Revenue by Expanding Retail Channels

A scalable, low-cost cloud platform allows exchanges to distribute exchange data directly to retail websites and investors. By accessing these channels directly, exchanges open revenue streams currently outside their reach and strengthen their brand within new and existing markets.

Deliver New Products Faster to Meet Emerging Customer Needs

New trading products create new information needs for financial services firms and market participants. The cloud allows exchanges to provide immediate global access to new market data products, removing constraints imposed by traditional market data vendors and eliminating infrastructure investments by customers.

Increase Market Transparency for OTC Derivatives and Complex Products

New exchange-traded products and new reporting requirements brought forth by Dodd-Frank, RegNMS and MiFid create new information needs for financial services firms and market participants. A cloud solution can provide both market participants and regulators easy global access to activity in these markets, providing accurate and auditable information that helps financial institutions manage risk and meet regulatory and internal compliance requirements.

The Developer Focus series profiles developers who have built applications using Xignite’s Cloud APIs, highlighting their experiences, observations, and advice.

John Morris is the lead developer and CTO for StockTouch, an innovative financial market data visualization app that is currently the #1 paid financial app in the iTunes Store (read more about StockTouch in our recent Customer Spotlight).

StockTouch’s groundbreaking data visualization interface has received widespread attention from the media and from Apple, who have selected StockTouch as one of the few apps to be loaded on all iPads and iPhones in their Apple Stores worldwide.  We recently sat down with John to learn more about him and the development process behind StockTouch.

Q: Tell us a little bit about your background?

John Morris – I’ve been programming since 1981, mostly as a contractor. My primary focus has been the gaming industry, mainly for two reasons. First, I love games! But perhaps more importantly, game development requires optimizing code at all levels in order to ensure good performance. It’s a challenge I enjoy taking on.

Q: How did you get involved with StockTouch?

John Morris – I learned about the StockTouch project through a mutual friend.  I joined the project initially as a consultant to build the prototype.  As everything came together, I was offered and accepted a position as the co-founder and CTO of StockTouch.

Q: How is the development process for StockTouch structured?

John Morris – The StockTouch team is completely virtual with the core members in New York, Seattle and Los Angeles. There are actually only two of us who are involved on the coding side of StockTouch. I work on the client side, and we have a contractor who works on the server side. We also work very closely with the other members of the team that deal with the overall user experience and feature set.

To build StockTouch, I was initially given just six screenshots, but we always had a pretty clear vision of what we wanted to do. We wanted the app to have a very intuitive interface with a fluid user experience. As a game developer, I personally wanted the application to offer a superior and unique visual experience, backed by extremely powerful features. StockTouch performs its rendering via OpenGL, and we really push the graphical capabilities of the iPad to deliver a cutting-edge experience.

Q: How difficult was the transition to the iOS platform?

John Morris – I’ve been building iOS products since 2008, so it wasn’t an issue for me.  For programmers unfamiliar with iOS, transitioning to the platform is not very easy—there’s a steep learning curve involved. There are hurdles to development, such as the preferred language being Objective C, dealing with a new set of APIs and navigating the performance and memory limitations. That said, if you manage to get past the learning curve, it provides a very powerful and robust platform.

Q: Any advice for iOS developers?

John Morris – I think a lot of developers are getting it wrong when they try to build for iOS. They just try to push versions of desktop applications out. Developers need to look at the platform as a whole. It’s a different paradigm. The usability is totally different from a desktop or a laptop. The touch and gesture features are very powerful tools.  They define the iOS experience, so you have to think in terms of the mobile environment.

Q: How did the Xignite Cloud APIs help you?

John Morris – Our forte is the front end. We’re a small company and wanted to stay lean. Xignite is our one stop shop for all the financial market data we need. The main advantage Xignite offered us was that the market data is already sitting in a large persistent database.

We just make web service calls, parse it and then pass the data on to our users.  That reduced our requirements for the server side, enabling us to shorten our development timelines.  Plus, working with a single data source with a solid and consistent API structure is definitely a good thing.

Q: How do you plan to meet the requirements of your fast growing customer base?

John Morris – For a financial application, the primary concern is ensuring that users get access to timely financial market data the instant they want to see it.

To address this, we have implemented several custom compression/encoding algorithms. This ensures that our data is distributed in a timely fashion and we can meet the requirements of a large and growing user base.

Q: What are StockTouch’s plans for the future?

John Morris – Going forward, we plan to beef up the application. We would like to add more analytical features, statistics like alpha, beta and candlestick charts.  New security classes such as ETFs are on the way as well. We’re also looking to add layers of personalization so that users can view analytics specifically related to their own portfolios.

Ultimately, our goal is to come up with a suite of financial applications, based on our data visualization competency, that are easy to use and help spread financial awareness for both professional and non-professional users.

Our Customer Spotlight series showcases the innovative ways our clients are using Xignite’s Cloud APIs to power their applications.

Quantbench is a San Francisco-based start-up that combines a unique marketplace for non-traditional data sets, with a financial data analysis platform for financial analysts, academics and data scientists. This 100% cloud-based financial research platform provides its members with the data sets and tools necessary to conduct deep quantitative analysis.

Xignite sat down with Stefan Petry, Co-Founder and Chief Operating Officer, to learn more about Quantbench.

Q: Describe the Quantbench platform?

Stefan Petry – Quantbench combines a cloud-based platform for unstructured data exploration and analysis, with a marketplace where investment researchers and fund managers can purchase non-traditional data sets.

The platform includes data sets that we and others provide. Right now our members have access to more standard market data sets such as tick-by-tick and end-of-day stock data, and indices data. In time, we will be adding more and more non-traditional data sets that are sourced from areas like web search, online advertising, news, social media etc.  Users can perform analyses using R-Project and Amazon Elastic MapReduce. They can also purchase certain data sets in our marketplace for download and in-house processing.

Q: Describe the technology behind Quantbench and the advantages of Xignite Cloud APIs?

Stefan Petry – Perhaps the most innovative part of our approach is that we make it easy for our users to get up and running quickly. Everything that a financial analyst or data scientist needs to conduct quantitative analysis is instantly available. We are able to achieve this because the technology behind our community is 100% cloud-based. All of the analytical tools, data sets, and infrastructure are on the cloud. This approach also enabled us to launch very quickly, and will allow us to scale efficiently as our community grows.

We use Xignite’s market data cloud because it helped us to execute on Quantbench’s cloud vision. Xignite’s broad data coverage acts as a comparison or benchmark to the other more non-traditional data sets we make available to the Quantbench community.  As we expand we will add more Xignite data sets to our platform. Finally, we really like Xignite’s simple and transparent approach to purchasing market data. All you need to do is visit www.xignite.com, select the desired data set, place it in the Xignite shopping cart, and then pay using a credit card.

Q: What are Quantbench’s future plans?

Stefan Petry – Our platform is still in its early stages. We launched just in November. Broadly speaking there are three areas that we will be focused on in the coming months. First, we will be adding more data sets. This data will include standard data sets, as well as many non-traditional data sets that firms will provide to our user base for quantitative analysis. Second, we will be extending the functionality of our non-traditional data set marketplace. Third, we will be adding more tools and languages, so that our users can perform more powerful analyses.