Drawing on the insights shared by GigaSpaces, VP Product Yoav Einav as published in Computer Business Review, this article explores how in-memory computing meets the demands of applications requiring extreme high performance and low latency such as high-frequency trading.
There is no doubt that in today’s world, the faster and smarter we process and analyze data, it results in more relevant meaningful insights and higher profits.
Just having data is no longer useful, and traditional ways of thinking about analytics, no longer apply. To drive business impact, it is important to analyze your time-sensitive data at the moment for efficient operations, regulatory compliance and enhanced customer experience.
This is all the more true for high-frequency trading where immediate action is critical. In fact, speed = profit and latency = loss for financial traders. Speed, scalability and real-time analytics are crucial to execute trades in microseconds and to complete more transactions quicker for increased profitability. As a result, in-memory computing platforms, are becoming a business necessity because they power faster transactional data processing and real-time analytics at scale.
In fact, real-time data processing and advanced analytics are becoming a necessity. Here is how GigaSpaces InsightEdge is powering faster transactional processing to increase profitability for financial trading:
Predictive Analytics with InsightEdge
GigaSpaces InsightEdge can help financial trading institutions leverage financial news sentiment analysis to predict stock prices and recommend a buy/sell order in real-time via financial news analysis and other selected streamed information. Real-time machine learning models run on hot data from various sources (structured, unstructured and semistructured) such as 100s of news sources and actual market data – all enriched with historical context. It is built to handle the micro-second speed, required scalability, and necessary availability.
By aggregating and ingesting billions of messages in real-time into a live risk result store, organizations can power hundreds of applications at the speed of real time while maintaining transactional integrity. Advanced querying & indexes and real-time machine learning allow for storing, filtering and organizing and analyzing large amounts of data for intra-day risk analysis.
Here we see how InsightEdge unifies transactional processing and business logic (Microservices and Containers) with AI and machine learning all in the same platform.
High-frequency trading (HFT)
To be profitable, HFT requires analyzing multiple markets at once and make a trading decision in under a millionth of a second. That’s why in-memory computing has become particularly suited for extreme transaction and data processing architectures. GigaSpaces InsightEdge is a high throughput and low latency platform for extreme transaction processing and real-time event-driven microservices and distributed applications.
Financial Institutions around the globe are leveraging GigaSpaces InsightEdge and XAP for applications including algorithmic trading, risk management, post and intraday trade processing, trade matching and reconciliation, instant payment processing, fraud detection, customer service applications and many more.