April 12, 2017

Enabling Successful Spread Betting with In-Memory Computing

Nikita Ivanov

(XanderSt/Shutterstock)

During the past decade, financial spread betting has become a major growth market globally, offering some significant opportunities for traders. At the same time, spread betting is still a highly volatile, minimally regulated market with significant risks. Because successful spread betting requires the rapid analysis of massive volumes of information, trading platforms depend on a very high performance compute infrastructure with in-memory computing (IMC) at their core. To understand the value IMC brings to spread betting, it’s important to review the requirements of a spread betting platform.

What Is Spread Betting

Spread betting allows traders to bet on whether the outcome of an event will be above or below a spread set by a bookmaker. For example, an investor might want to bet on a financial security such as the FTSE 100. They might choose to place a bet of £10 per point change. If they think the index will increase, they could place a bet at the current ask price and every point increase (or decrease) will result in a £10 profit (or loss). If the FTSE 100 goes up 20 points, they would make a £200 profit and if it decreased 15 points, they would have a £150 loss.

An investor can place spread bets on a wide variety of markets and products including indices, stocks, bonds, and currencies for which there is a measurable outcome that can go in either of two directions. Spread betting is typically conducted through brokerages, asset managers, online gambling firms, and other venues willing to set the spread and host the wager.

Spread betting participants accept substantial financial risks (Dooder/Shutterstock)

The appeal of spread betting stems from several factors, including low entry and transaction costs, preferential tax treatment, and a diverse array of products and options. However, spread betting is also a highly volatile, minimally regulated market with significant risks. Further, financial spread betting can use significant leverage which can result significant short-term profits based on speculating on whether the price of an asset increases or decreases over a relatively short period of time. As such, bookmakers and spread-betting host venues must have access to a very high performance technology infrastructure capable of streaming tremendous amounts of data into advanced mathematical models that can quickly compute event relationships and change outcome probabilities while these short-term events are occurring.

For bettors, high performance platforms may allow them to identify, based on their internal models, spread prices which offer significant upside opportunities, if they can model market changes and place bets before the markets can adjust.

Other risks associated with spread betting put even more pressure on the various players to have real-time access to accurate information. For example, in traditional financial markets, some of the risk is assumed by market makers, who hold large amounts of an asset and can intervene in the case of an asset price falling sharply or any other unusual condition, providing some ongoing stability to the market.

With spread betting, bookmakers have no such obligation to step in and help traders who bet erroneously or find the market moving sharply against them. Financial spread betting also includes great volatility, wider bid/offer spreads, and increased leverage so modeling risk and placing appropriate bets or closing open positions at the right time to maximize profit or minimize loss requires extremely fast decision making.

Finally, with spread betting, there is currently less consumer protection oversight and less overall transparency because, in many cases, it is considered a form of gambling rather than a regulated financial market. Traders can see a bookmaker’s prices but not what other traders are betting.

Risk Reduction Strategies

When it comes to spread betting, the available risk reduction strategies have one thing in common. They require a high performance technology infrastructure. For example, mathematical and statistical models can be used to predict trading outcomes depending on various possible changes to the market. However, such models are complex, rely on feeding accurate data inputs into probability regression analyses, and depend on the ability to consume data from related events in real or near-real time.

Likewise, traders can build a hedging framework by evaluating how one product offered by the spread betting business relates to others and how changes in the market for a specific asset will change its value. Hedges are alternate bets or investments that provide some protection in case of adverse movements in the original position. Implementing a hedging strategy also involves employing hefty mathematical or statistical models that perform in real or near-real time.

Traders can also subscribe to data services and analyze news to perform sentiment analysis and otherwise gain insight into trends that will impact their positions; however, to be effective, traders must have the capacity to consume and analyze huge volumes of data with great rapidity – otherwise they are simply utilizing information that has already been factored into an asset’s price and so can do more harm than good.

Technology Strategies

Constructing the high capacity, high performing infrastructure required for successful spread betting depends on a number of well-established technologies.

  • Big data technologies provide ways to organize these large datasets into multiple pools and connect them in real time for immediate analysis – enabling, for instance, the analysis of huge amounts of data from subscription services that get updated with each tick of new incoming data.
  • Apache Hadoop with MapReduce provides the speed and efficiency for real-time analysis of large datasets.
  • Complex event processing (CEP) supports the use of artificial intelligence to identify meaningful events in multiple streams of incoming data – particularly useful for spread betting mathematical models that ingest multiple data streams
  • Robots, based on proprietary algorithms, enable automatic buying or selling depending on specific real-time triggers.
  • Data partitioning and parallel processing clusters increase overall system performance and support 24/7 data access and transactions.

While these technologies enable spread betting systems to process more data in less time, they do not overcome the slowest bottleneck of any standard computing system: disk reads and writes. As spread betting systems scale, those still relying on spinning disks will eventually encounter unacceptable delays compared to market competitors who use in-memory computing technologies which allow them to place orders quicker, at more favorable prices.

To eliminate the reliance on hard disks, spread betting traders and venues are now adopting in-memory computing technologies. IMC is by far the fastest available storage-based computing method, and companies that have switched to IMC for their spread betting systems have seen transaction processing speeds increase by roughly 1,000 times.

The desire to implement IMC solutions, especially in financial services, has been around for decades, but until recently it was cost-prohibitive to purchase enough RAM to create viable systems. Fortunately, the cost of memory has dropped roughly 30 percent per year since the 1960s, and today it is far more cost effective to equip clusters of machines with hundreds of gigabytes or even terabytes of RAM. The performance benefit from IMC delivers an extraordinary return on investment, making IMC today’s most affordable option for achieving a high performance environment.

The financial viability of IMC systems has also driven vendors to deliver increasingly robust and full-featured solutions that make it easy for system designers to create the specific solution they need. For example, even the simplest IMC solutions used to require the cobbling together of multiple products that had to be installed, configured and maintained separately. Today, vendors are providing easy-to-deploy, full-featured solutions that provide ACID-compliant transactions, transparent scalability, high availability, and enterprise-grade security for online analytical processing (OLAP) and online transactional processing (OLTP). Some IMC solutions even include SQL support and a full range of APIs, leading to much faster integration with existing systems.

Spread betting, like most new financial services, can scale only in the highest performing environments. With IMC now the affordable option of choice, traders and venues would do well to understand how IMC can fit into and accelerate their infrastructures.

About the author: Nikita Ivanov is founder and CTO of GridGain Systems, a developer of in-memory computer sytsems. Nikita has more than 20 years of experience in software application development, building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa, and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996. He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from Baltic State Technical University, Saint Petersburg, Russia.

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