In-Memory Computing is the Present
Bobsguide published an article just before Christmas about the value of in-memory computing in cap markets that I found quite interesting. In it, they bring up the topic of “spread betting”:
“The spread betting market is growing globally, with more than one million people opening spread betting accounts in Great Britain alone. The benefits of spread betting include low entry and transaction costs, preferential tax treatment, and a virtually unlimited array of products and options.
But spread betting is also highly volatile and minimally regulated, which results in significant risks. To limit these risks, spread betting platform providers must use advanced mathematical models to analyse vast amounts of data from subscription services and current news, predict outcomes of events, and help devise optimal strategies, including hedging bets.
All these strategies require real-time and near real-time data analysis if they are to account for rapidly changing – sometimes by-the-second – conditions. The specific technologies used to achieve this include big data, complex event processing or streaming analytics, robots, online and cloud platforms, data partitioning, parallel processing clusters, mobile trading and more. As with modernising asset and wealth management, using these technologies to deliver a successful spread betting platform requires tremendous processing performance.”
This last point is particularly relevant. The days of waiting for end-of-period reports to do analysis are long gone. The only way for an asset manager or trading firm to stay on top is to make decisions in real time – and based on real-time data. And the only way for human beings to really understand such vast amounts of fast-changing data is to use their eyes. Visual analysis is not only much faster than traditional methods, it also enables people to spot outliers and anomalies in seconds instead of minutes or hours.
The ability to identify exceptions quickly and then to zoom in to get as much detail as required – on demand and without waiting for someone else to build and run a report – is making a difference at the institutions that have adopted it. Combine visual analytics tools capable of handling real-time and time series data with Kx kdb+, Apache Kafka, AMPS, RabbitMQ, JMS, AMQP, Tibco Streambase, and OneTick and you have a world-beating platform.
Read the complete Bobsguide article here: http://www.bobsguide.com/guide/news/2017/Dec/12/revolutionising-asset-and-wealth-management-spread-betting-and-fintech-with-in-memory-computing/
“The spread betting market is growing globally, with more than one million people opening spread betting accounts in Great Britain alone. The benefits of spread betting include low entry and transaction costs, preferential tax treatment, and a virtually unlimited array of products and options.
But spread betting is also highly volatile and minimally regulated, which results in significant risks. To limit these risks, spread betting platform providers must use advanced mathematical models to analyse vast amounts of data from subscription services and current news, predict outcomes of events, and help devise optimal strategies, including hedging bets.
All these strategies require real-time and near real-time data analysis if they are to account for rapidly changing – sometimes by-the-second – conditions. The specific technologies used to achieve this include big data, complex event processing or streaming analytics, robots, online and cloud platforms, data partitioning, parallel processing clusters, mobile trading and more. As with modernising asset and wealth management, using these technologies to deliver a successful spread betting platform requires tremendous processing performance.”
This last point is particularly relevant. The days of waiting for end-of-period reports to do analysis are long gone. The only way for an asset manager or trading firm to stay on top is to make decisions in real time – and based on real-time data. And the only way for human beings to really understand such vast amounts of fast-changing data is to use their eyes. Visual analysis is not only much faster than traditional methods, it also enables people to spot outliers and anomalies in seconds instead of minutes or hours.
The ability to identify exceptions quickly and then to zoom in to get as much detail as required – on demand and without waiting for someone else to build and run a report – is making a difference at the institutions that have adopted it. Combine visual analytics tools capable of handling real-time and time series data with Kx kdb+, Apache Kafka, AMPS, RabbitMQ, JMS, AMQP, Tibco Streambase, and OneTick and you have a world-beating platform.
Read the complete Bobsguide article here: http://www.bobsguide.com/guide/news/2017/Dec/12/revolutionising-asset-and-wealth-management-spread-betting-and-fintech-with-in-memory-computing/
