While analytics has always been a fundamental component of trading, the challenges posed by ‘big data’ have upped the ante for the ability to sift through reams of real-time streaming and historical information in order to execute and test trading strategies.

“Those that believe in data visualization and technical analysis believe that by identifying patterns you can be pro-active in your trading and be in the right place before the market gets there, in order to win,” said Marcus Kwan, vice-president of product strategy and design at CQG, a software provider. “That has to be a core strategy to any successful trading business.”

While the issues of big data—as well as tools for addressing it, such as business intelligence and data mining—have been around for a long time, the term has come to encapsulate exploding volumes of data brought on by changes in market structure, technology and regulations, which demands a more creative approach.

“Data visualization for technical analysis has been the way our customers have been making their strategic and tactical business decisions for years,” Kwan said. “So, in a way, the financial markets have been ahead of the curve in terms of the tools.”

However, the tools need to evolve to include more and different types of data for analysis. “Those in the financial industry need to adapt and be able to leverage new types of data and new types of visualizations in order to stay ahead of the swell of the massive amount of data,” Kwan said.

New regulations are also affecting the buy side’s requirements for data collection, storage and management.

“Trading technology is continuing to evolve to take advantage of a fragmented landscape, which is now commonplace in the U.S. and European markets and is starting to appear in Asia,” said Simmy Grewal, senior research analyst at Aite Group, a consultancy. “More analysis is being done around the benefits of different types of execution venues and this is feeding back into trading algorithms and making them more sophisticated in their venue selection.”

Big data analytics technologies encompass data warehouses, Hadoop and stream processing systems. Each technology serves a different need and combined help enterprises cover the spectrum of big data analytics.

“In the context of the trading business, market data visualization has always been about being able to visually identify market trends using the various technical analysis tools,” Kwan at CQG said. “Data visualization helps our customers distinguish signal from noise in order to be successful.”

From a product perspective, CQG is going wider in the sense of enabling users to draw correlations inter- and intra-market: inter-market for curve traders looking across crude, eurodollar or treasuries and visualizing relationships; and intra-market looking across global markets for correlations from seemingly disparate instruments.

“Traders, by nature, are experts in rooting out causality,” Kwan said. “We’re also going deeper by enabling our customers to visualize the ever-increasing depth of data and realms of metadata.”

One example is a module CQG calls the Order Ticker that visualizes the stream of orders going into the book.

“We’ve built new analytics for visualizing pre-trade data whereas traditional charts use executed transactions.” Kwan said. “It’s a new way of charting and technical analysis for the age the markets are in.”