Trade Analytics Software Suite BestX Adds New ‘Trend Analysis’ Module

The pre and post-trade analytics software suite by BestX is expanding the options for analyzing cost trends.

In a post-MiFID II implementation environment, trading costs are taking center stage for the institutional market. Transaction Cost Analysis (TCA) has become one of the pillars of the new regulatory framework aiming to focus on providing the buy-side of the market with more information about the execution of their orders.

One of the leading companies in the industry that specializes in TCA and best execution, BestX, has been providing software suites for pre-trade and post-trade analytics. Today the company is unveiling a new module to its offering, focusing on analyzing trends in the execution of orders.

The ‘Trend Analysis’ module enables clients to generate a custom analysis of trends in costs and other performance metrics over time for each customer. The flexibility of the new tool allows the full range of market participant types within FX to focus on the parameters that are most relevant to their business.

Commenting on the launch, the co-founder of BestX, Pete Eggleston, said: “This new module has been designed to be very flexible to allow clients to analyze whatever element of their process they require.”

“For example, a client may wish to analyze spread costs paid in a specific group of currency pairs, on a monthly basis, over the last year and analyze by different execution methods to see if any are resulting in significant cost savings over time,” Mr. Eggleston explained.

Changes in the structure of the market post-MiFID II are providing to the buy-side of the market an environment that is more focused on best execution. The new module provided within BestX analytics is allowing brokers to present to their clients a comprehensive presentation of execution performance over time.

The ability to consistently monitor for changes not only aids full compliance with the new European regulatory framework, but also serves client interests by systematically analyzing large sets of data via an ongoing, iterative feedback loop.

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