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XSLT on Wall Street: a technical solution to managing a client''s large volume of data feeds. (case study).

Article Excerpt
Since its inception, XML has gained a strong foothold on Wall Street, but the use of XSLT for financial applications has been selective. This article reviews a real-world case study involving XSLT (and other "new" technology tools) that led to impressive business results.

Our technical solution included the development of a complete rules engine in XSLT, XML parsing in Java using DOM/SAX, a persistent store of XML data within a relational database (Oracle), a customizable report writer (Cognos). and a complete runtime environment in Unix. A two-person team completed the project work over a period of 12 weeks -- an incredible feat, and one that would never have been possible with "traditional" programming paradigms. XML, XSLT, and Java technologies are worthy of your attention; read further to see how they helped us to deliver an innovative solution for one of our important clients on Wall Street.

Project Overview

A large Wall Street financial services firm developed a powerful quantitative model for valuing convertible bonds for use by its traders, risk managers, and accounting department. The convertible bond asset class is a hybrid of fixed-income and equity instruments and presents a challenge for data manipulation. Convertible bonds can have hundreds of fields of reference data (e.g., coupon, maturity, conversion price, conversion rate, currency, par amount), with many embedded time elements (e.g., call schedule, put schedule).

Although many vendors supply convertible bond reference data (e.g., Bloomberg, Reuters), our client was unwilling to accept any single vendor's data feed as the sole basis for its model inputs. The firm needed to normalize the structure of the convertible bond data elements and account for differences by each vendor in feed formats and in nomenclature for describing convertible bond reference data (i.e., field names). By transforming the data elements from each vendor into a standardized database structure, the firm created a consistent framework to select inputs for its valuation engine.

Prior to our solution, the client was trying to manage the large volume of data feeds through a manual comparison process -- an onerous task given the quantity of data. The comparison logic for the data had to be heuristic and learn to suppress previously tagged discrepancies so that daily reporting wouldn't include repeat offenders.

Our project was intended to fulfill several key business objectives:

* Process data feeds from multiple vendors.

* Normalize the incoming data feeds to a common schema using a complex set of rules.

* Enable users to transform source data using complex and changing business rules -- without involving subsequent IT programming resources.

* Store normalized...

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