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The Risk of Slow Insight on Stock Trends (And How Trifacta Gets You Up to Speed)

September 1, 2016

Financial services institutions need to quickly understand what is in raw financial data to know how to analyze it and make trades from it. Time is of the essence. And technology that allows firms to rapidly piece together stock trends will have a huge competitive advantage, while those who don’t risk losing market share to those competitors who are getting on the quantitative trading bandwagon early. Financial services customers want to see their bankers on top of the quality strategies that contribute to the best performance.

Newcomers like robo-advisor Wealthfront are growing fast, based on algorithmic trading. Now that computing power, bandwidth, and data storage infrastructure are aligned, quantitative trading can be done faster than ever, in ways that don’t invite phone calls from your company’s compliance department. In fact, one of Trifacta’s customers, a stock exchange, is using Trifacta Wrangler to reduce time to insight on long-term stock trends by 70%, giving the client a strong competitive advantage, increased profits, and more engaged customers.

Here’s how Trifacta is doing that for clients who want and need speed for their analysis.

The Rise in Value of the Algorithmic Trader

Until recently, effective large-scale algorithmic, quantitative trading was not possible due to a lack of tools. Instead, high frequency trading became de rigueur, a simplistic system that does not offer any real valuable insight for investors since it only blindly follows short-term trends. However, black-box, long-term quantitative trading products are increasing. According to Research and Markets’ recent “Global Algorithmic Trading Market 2016-2020” report, it is estimated that the global algorithmic trading market will grow at a compound annual growth rate of 10.3% from 2016 to 2020.

Unlike high frequency trading models, new algorithmic trading products, such as the one created by Trifacta’s client, analyze vast amounts of aggregated data, not just across the last several minutes, but across long-term market structure and trends. This allows traders to go beyond the ethically debatable and potentially market-devastating one-minute HFT market projections. Now, traders can speedily aggregate data from disparate sources, find patterns across it, and formulate lasting machine-derived forecasts from it. Unlike with HFT, speed no longer must be derived from how long trends will last in the market, but, rather, from the platform by which market-derived data is managed.

Algorithmic Trading Made Possible by Trifacta

In 2012, a European stock exchange client approached Trifacta with clear business needs for a new product. They wanted to assemble and integrate many different data formats coming from numerous sources and then disseminate cross-border fund information through standardized tools and automated processes. Effectively, the client needed a competitive tool to interpret incoming information, normalize data, and create insights as quickly as possible.

Trifacta’s client understood that arbitrage would only be achieved by product customers—fund promoters, buyers, and financial service providers—by effectively realizing economies of scale and quicker time to market. The new product would need to clean, standardize, and enrich fund data rapidly for their customers to gain the most value from the data.

While demand for the product was high, there were three pain points necessitating Trifacta Wrangler:

  1. The information coming in from various sources did not conform to those needed for master data management system integration.
  2. Analysts designing the appropriate cleaning and transformation routines could not do so speedily and effectively without convoluted, technical processes.
  3. The client’s existing tools—Excel, SQL server, ETL—were not able to get information to market quickly enough.

Trifacta helped the client quickly understand what was happening with their files in a way that was user-friendly and easy to understand, something not achievable with their current system. In addition to adopting Trifacta, the client also implemented Hadoop. This allowed the client to save on data storage, which was critical to their ability to market a big data analysis product.

A Faster Fast with Trifacta

As with all quantitative trading, the key to trade wins is speed. Today that means the speed of pulling data, cleaning data, analyzing data, predicting based on what you learned, and deploying learnings. Interesting in learning more about how Trifacta can help? Get started today by signing up for free Trifacta Wrangler.