Spread betting has been one of the major global growth markets since the financial crisis of 2008. There are several reasons for this, including preferential tax treatment, low entry and transaction costs, wider bid-offer spreads, less regulation, and a diverse universe of products and options.
However, spread betting can be a very risky business. To limit risk and increase the rewards from spread betting, financial institutions and individual speculators use advanced mathematical models to predict outcomes and devise optimal trading strategies. The analysis must happen in real-time to take advantage of the then current market conditions.
During this webinar, which originally aired on November 2, 2016, I showed how an in-memory computing platform such as GridGain, which is built on Apache® Ignite™, addresses these needs. I talked about how financial services firms use in-memory computing to manage event risk, margins, commissions, market making, statistical and correlation analysis, as well as the many algorithms used in spread betting. I presented multiple use cases to show how some of the largest global banks, asset managers, online gambling firms and other players are succeeding at spread betting.
Powering Financial Spread Betting with In-Memory Computing
One of the most interesting things is that for applications that benefit from heavy analytics and real-time processing of big data, the market is now moving from disk to in-memory computing. The reasons for this trend involve both performance and Return On Investment (ROI).
1,000x Faster. The move from disk-based to memory-based storage is a key factor in improving performance. However, simply moving to memory is not sufficient to guarantee the extremely high memory processing speeds needed when many people are connecting to place transactions or when risk management strategies require fast, data-intensive analysis. This level of speed requires cluster computing, with multiple machines performing analyses at the same time, and parallel distribution of data. These capabilities, which also guarantee the high availability levels required in financial transactions, are fully provided in the GridGain in-memory computing platform. Clients who have implemented GridGain have found that they can process their transactions at least 1,000 times faster.
10x ROI Improvement. The cost of memory has dropped roughly 30% per year since the 1960s, so memory has become much more affordable in recent years. While it may still be slightly more expensive than disk, the performance is so much better that it improves ROI significantly. Clients who have implemented the GridGain in-memory computing platform have seen a tenfold or more improvement in their ROI.
It is not surprising that many financial institutions are turning to the GridGain in-memory computing platform for big data applications such as spread betting because of these substantial improvements in speed and ROI.
Watch the archived webinar to learn how GridGain or Apache Ignite to minimize the risk and increase the reward of spread betting.