From the earliest days of tape reading back in the 1960s, the financial sector has always been enamoured with the idea of using data to better predict market movements. Technical analysis has developed as a practice into which companies invest billions, with entire academic schools devoted to investigating new methods and theories. In later years, it’s been the emergence into the mainstream of machine learning that’s piqued the interest of technical analysts. Perhaps the goal is in grasp – the ability to determine price movements before they happen?

Alas, technologists will tell you that, at present, the computational requirements of advanced machine learning algorithms and the complexities of the financial systems under examination mean that ideal will remain just that: an ideal – for now. But what’s increasingly apparent in every sector (not just the financial) is that digital information represents the most valuable resource an organization can possess. Extracting value is challenging, but the problems are surmountable, and it’s the financial sector that has some of the longest history of doing just that.

Part of the issue of surfacing data’s value is in its broad range of sources, each of which brings different formats and fields to the table. International geopolitical movements, for example, are fiercely difficult to quantify, yet few can deny that government decisions have a significant influence on markets. Different sectors of the markets bring information that’s well beyond simple price, volume, and open interest figures, but it’s those types of base figures that data practitioners need to use in their scripts and algorithms. Technical indicators vary wildly depending on which aspect of the markets are scrutinized – small wonder that 90% of a data professional’s work is data parsing, de-duplicating and normalizing information for use in analysis.

At the coal face of financial analytics too are the twin problems of time and subset: data professionals build their models on subsets of data (large data lakes are impractical for daily handling), and by the time computational models are created and tested, the important element of real-time is missing. Without significant storage and computational resources, plus direct feeds from third-party sources that update in near real-time, data teams are always behind the curve.

The obvious solution to limited digital resources is in cloud computing: rapidly scalable, elastic, and modular, where teams can collaborate, and large data sets can be manipulated as easily as smaller subsets. Cloud connectivity also ensures fast data movement between sources and analysis engines, and the big cloud platforms also throw in security and compliance baked into their offerings for good measure. But off-the-shelf cloud computing is a blank slate, and many financial companies lack the digital expertise to leverage these powerful resources or more commonly don’t have access to the different data sources they would like to collate and process.

As a bespoke information supplier, Refinitiv is the world’s most trusted information brokerage for the digital age. Not only does it aggregate thousands of sources of financial and influential data for its clients, but it’s also a digital powerhouse, presenting resources in secure, compliant, and easily assimilated formats that are ready for direct input into teams’ workflows. Sanitised, normalised information can flow into different environments as required: into Office 365 in real-time for C-Suite level updates or via a huge range of APIs for the developer community. There are real-time feeds from live markets, plus (literally) terabytes of historical data. For the machine learning specialist, the latter in particular represents a learning corpus of significant proportion, opening up multiple possibilities now ML and AI services are available and relatively easily to deploy.

There is direct integration from many sources into IDEs, with SDKs available on the open Refinitiv platform, with a range of licensing tiers suitable for institutions of any size, from the sole hobbyist day trader to the multinational financial corporation.

In a future article, we’ll be taking a deeper dive into some of the more detailed offerings the company has on the table, but if what you’ve read here has awoken your interest, head over to Refinitiv, the company that’s effectively the London Stock Exchange Group’s data division for information on how to proceed.