Financial Market Data Web Services – Xignite Blog

The last few years have been undeniably tough for the once brash hedge fund industry. Recent headlines do not suggest any improvement with August being the worst month for hedge funds since October 2008, and marquee firms like Paulson & Company firm down 34% year-to-date.  Prior to the crisis of 2008, the industry appeared to be on a steady upward trajectory, evolving from a small, scrappy upstart, that catered to high net worth investors, to a more formalized $2 trillion industry, that serviced the largest pension funds in the world. Since the crisis, however, the industry seems to have lost its way. What exactly happened and how can what we term the “Hedge Fund Cloud” return the industry to its former glory?

Institutionalize or Die

Pre-crisis, managers believed that the measure of success was not only returns but assets under management. In their race to acquire new assets, managers were motivated to “institutionalize” their infrastructure so that they could go after the really big allocations from large pension funds and endowments. For many firms this institutionalization meant leaving the relative simplicity of their single prime relationship to the much more complex world of building out their own multi-prime infrastructure. Almost overnight managers found themselves running complex and unwieldy businesses. Seemingly simple operations like adding a strategy, that required a new asset-class, or producing a new report, became long and involved IT projects.  Any thought of outsourcing any of this burden was dismissed because of perceived privacy and control concerns.

Prisoners of their own Hedge fund Infrastructure

The actual crisis further exposed the inflexibility of hedge funds’ infrastructures. Managers struggled to view their true exposure across asset classes and multi-prime relationships. Just when managers most needed their former agility they discovered that they had become prisoners of their own expensive infrastructures.

Fast forward to today. We are still experiencing the after effects of the crisis. A strong regulatory backlash response has been unavoidable. There is still tremendous uncertainly about the true impact of these new regulations, but what is certain, is that the business of running a hedge fund will become even more complex and costly.

How can the industry remove itself from this funk and prepare itself for the next crisis? The answer is that the industry needs to return to basics by once again making alpha generation its sole focus. The industry needs to regain its former investment agility. In short, managers need to get out of the running-a-hedge-fund business and get back to the investment business.

The Hedge Fund Cloud to the Rescue

Fortunately, the Hedge Fund Cloud offers managers the opportunity to get back to basics.  The Hedge Fund Cloud allows Read more

Algorithmic trading firms were put on notice last Monday when Commissioner Bart Chilton of the Commodity Futures Trading Commission (CFTC) said that high frequency trading (HFT) and algorithmic trading firms should be made legally responsible for maintaining a minimum set of testing and monitoring standards to prevent future flash crashes.

The possibility that algorithmic trading firms may need to support new regulatory requirements highlights the rapidly growing importance of the market data cloud and new cloud services like NASDAQ Data-On-Demand for back testing purposes.

In a bid to resuscitate the heroes of 1980’s sitcoms, Mr. Chilton declared, “I want to be like MacGyver. Remember, he was always trying to prevent crime before it happened.” So if you’re a criminally culpable algorithmic trading firm, you’d best beware—especially if Mr. Chilton gets his hands on a paper clip and a stick of chewing gum. Household items aside, Mr. Chilton also plans to rely on tools such as a “kind of Good Housekeeping Seal of Approval.” He’d like algorithmic trading systems to be tested by either exchanges or regulators before going live. And after going live, he’d like algorithmic trading systems to be monitored on an on-going basis.

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This week NASDAQ was the first major exchange to launch an on-demand service offering Level 1 historical stock tick data.  As the first truly on-demand service for historical stock tick data, NASDAQ Data-On-Demand is leading a revolution that’s democratizing access to market data and reducing costs by orders of magnitude.

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The was plenty of market data cloud buzz at the 2010 SIFMA Technology Conference and Exhibition with NASDAQ and Xignite right in the center. Using the XigniteOnDemand Market Data Cloud Platform, NASDAQ plans to launch Data-on-Demand in the second half of 2010 to provide easy and flexible access to large amounts of detailed historical NASDAQ Level 1 trade and quote data for all U.S.-listed securities. Tick data is increasingly in demand for back testing of algorithmic trading strategies as the securities industry pushes the limits of high frequency trading.

Randall Hopkins, NASDAQ OMX’s Senior Vice President of Global Data Products, is quoted in the press release as saying: “Today our customers spend a large amount on technology infrastructure, not the market data itself. With Data-on-Demand, we want to drastically cut data management costs by running the technology infrastructure on the cloud for our clients and delivering to them the data they need, when they need it, and how they need it.”

NASDAQ discusses it’s market data cloud initiative with Max Bowie
of Inside Market Data at the 2010 SIFMA Financial Services Technology Expo

Obtaining and collecting tick data can be onerous and time-consuming as firms are required to establish feeds and maintain large amounts of data on-hand. On-demand market data distribution gives applications a way to cherry pick the specific subset of data that the application needs with pinpoint accuracy. Instead of combing through very large data sets of historical tick data, developers will be able to program their applications to select very specific data sets and obtain them on-demand and process them instantly. Data-on-Demand will also allow clients to download large tick data subsets on a scheduled basis.

We’re proud to announce that CME Group, the world’s leading and most diverse derivatives marketplace, is jumping on the cloud computing bandwagon and has agreed to use the XigniteOnDemand market data cloud platform to deliver OTC data from the cloud.

Brian McElligott, Managing Director of Information Products at CME Group is quoted in the press release as saying “With the ongoing development of our multiple OTC product and service offerings, and the need to deliver this data to customers with continued market transparency, the Xignite platform will be a quick and cost-effective way to get OTC pricing and reference data to our global market participants.”

Pete Harris of the A-Team Group interviews CME Group, BG Cantor and Xignite.
The A-Team Group gave the cloud prime coverage in their recent Q2 issue,
and Pete is launching a new “Market Data Cloud” channel, so stay tuned to that.

CME Group plans to offer on-demand access to end-of-day OTC settlement, volume and open interest data to support markets available through CME ClearPort®, a set of flexible clearing services open to OTC market participants to substantially mitigate counterparty risk and provide neutral settlement prices across asset classes. CME ClearPort currently clears more than 500,000 contracts daily, including Credit Default Swaps (CDS), energy, metals, agricultural commodities and foreign currencies. The service brings together more than 10,000 global users across the world including banks, hedge funds, trading entities, Inter Dealer Brokers (IDBs), Future Commission Merchants (FCMs), and clearing firms.

Henry Ford once said: “Any customer can have a car painted any color that he wants, so long as it is black.” Then, in 1923 Alfred Sloan came along and cleaned his clock by offering a tremendous variety in colors and models. But, Sloan didn’t do it one customer at a time. GM redesigned its manufacturing line with the flexibility to produce a multitude of models and colors without compromising the inherent economies-of-scale of Ford’s assembly line innovation—a practice that today has evolved into the concepts of flexible manufacturing and mass customization.

What does any of this have to do with cloud computing? And for that matter, what does it have to do with financial market data? This is the third post in a series called “Thinking Out Cloud” with the aim of helping financial services and market data IT professionals charged with developing cloud computing strategies separate the cloud buzz from the cloud reality. This post explores the important idea of mass customization in the cloud and its relevance to market data management.

Mass Customization and Market Data

According to Wikipedia, mass customization is about providing “services to meet individual customer’s needs with near mass production efficiency” by offering a “tremendous increase in variety and customization without a corresponding increase in costs.” Or rather, have it your way at a cost you can afford. In the last installment in this series entitled Thinking Out Cloud – The Market Data Sweet Spot, I pointed out that the best way to identify market data that can benefit from cloud computing is to look for data that is hard-to-use, hard-to-manage, and hard-to-access. Mass customization on the cloud strikes at the core of the “hard-to-use” problem by replacing inflexible data feeds and files in static formats with financial Web services that allow applications to select and consume highly customized market data sets on the fly.

Mass Customization, Meta Data and Web Services

In software, mass customization is achieved by converting hard-coded application functions into meta-data, so that the functional behavior of the software can be controlled through data on an ad-hoc basis. For example, the only real difference between this Xignite WordPress blog and a million other WordPress blogs comes down to a few theme files and the store of data that comprises the brilliant blog posts within. The posts in turn get shuffled around the Internet using RSS (really simple syndication). The RSS standard lies at the heart of the Web 2.0 revolution, powering the distribution of widely varied content such as blogs, news, friends, tweets, music, video, etc., all because a few abstract meta-data elements like “title”, “description”, and “link” can be generalized to contain an enormous and disparate variety of data. Swapping out this data can literally change one blog to any other blog (or one friend to any other friend!).

In cloud computing, meta-data abstraction is realized through Web services (like RSS). Web services take the concept of configurability through meta-data abstraction to its natural limit, because every operation of a Web service is inherently abstracted to meta-data in the XML inputs and outputs of the API. For example, the Xignite delayed stock quote Web service allows applications to retrieve a single quote for a single symbol, multiple quotes for multiple symbols, intraday tick-by-tick prices, market volume and news, simply by varying the Web service operation and the input parameters, i.e., the meta data. Providing access to such a wide variety of information in an ad-hoc fashion from a statically formatted feed or file is simply not possible without lots of hardware, software and custom development–most likely to create a Web service.

Wrap it Up, I’ll Take It!

More plainly, market data Web services are built for mass customization, because they cleanly wrap the underlying market data and data management infrastructure in meta-data enabling highly customized output based on variable input. Therefore, applications that require highly varied, ad-hoc market data subsets will be better served by Web services over the static formats of data feeds and files. Or, as we like to say at Xignite: “There’s an op[eration] for that!”

Market data providers and IT professionals have tough jobs. Every day financial markets spew out huge fountains of data that must be captured, routed, scrubbed, reconciled, stored and redistributed with dizzying speed and accuracy. The diversity of data is staggering, from low-latency pricing data for algorithmic trading to intermittent corporate actions such as stock splits, and from globally dispersed real-time currency exchange rates to aggregated end-of-day VWAP and NAV calculations. Optimizing and tuning the market data systems that keep this crucial information flowing smoothly and cost effectively is no easy task. What, if anything, can cloud computing offer to ease the challenge?

This is the second post in a series called “Thinking Out Cloud” with the aim of helping financial services and market data IT professionals charged with developing cloud computing strategies separate the cloud buzz from the cloud reality. This post explores the types of market data that naturally lend themselves to cloud computing (and those that do not) in order to identify the market data sweet spot for cloud computing.

First, it’s important to recognize that it is not the market data that is being outsourced to the cloud, but the on-premise market data management infrastructure. Therefore, the market data sweet spot for cloud computing is the intersection of data management systems that offer little competitive advantage, but are costly and difficult to maintain in-house. Both relative competitive advantage and relative level of difficulty will vary from firm to firm by business focus and IT capability respectively; however, there are aspects of the market data itself that can contribute significantly to the cost and complexity of maintaining an in-house data management system.

Hard to Use Market Data

If the market data comes in original feed formats that are not well suited to the particular use of the data in the final application, then considerable effort must be expended to make the market data application-ready. For example, a real-time streaming exchange feed is great for creating a stock ticker, but not so great when the goal is to analyze historical tick data or simply get an ad-hoc real-time quote for a single symbol, then there can be lots of programming involved to parse the feed, store the data, continuously refresh the database and create a data access layer that applications can easily utilize. Cloud computing is built on Web services that allow for multiple interfaces to the market data, so it is especially good at tailoring the data format to the specific needs of the application on the fly. For example, a Web service request can be for a single price, multiple prices, or simply for symbol validation against master data.

Hard to Maintain Market Data

If the market data in question is stored and refreshed often due to daily activity, such as historical time series and tick-by-tick data, then it can entail a complex update process that must be maintained and monitored. Quality testing must be put in place to ensure data quality and alerts to ensure that update processes run successfully to completion. As market data accumulates, Read more