Xenomorph - TimeScape QL+

http://www.xenomorph.com/downloads/whitepapers/ql-plus/

TimeScape QL+

Data. Decisions. Together.

This white paper describes TimeScape QL+, a new query language designed for financial markets in order to bring users, data and decision-making closer together. This language is easy to understand, easy to extend, provides powerful support for vector arithmetic of intraday and historic data and deals with data issues that are specific to the financial markets.

View complete TimeScape QL+ white paper PDF. (This document requires the free Adobe/Acrobat Reader.) 16 pages, 420KB

Introduction

More Data, Less Time

Why do we need another query language? Good question. The usage of SQL is both prevalent and effective throughout financial markets IT and the software industry as a whole. However, there are many people who need to access, manipulate and analyse data who are not technical experts in SQL but are experts in their own particular field of business.

Nowhere is this more apparent than within the financial markets - where traders, fund managers, quants, research analysts, risk managers and other business staff are under constant competitive pressure to analyse larger and larger volumes of increasingly complex data in less and less time.

Users and Technologists Think About Data Differently

Business users in financial markets tend to think about the data they need in terms of the financial instruments they trade - such as equities, bonds, options etc. Traders do not think of real-time data, time series data, tick data, static data and calculated data very differently - to them all of this data is needed and relevant to the business decisions that are being made.

However, this same data is generally split out across many tables when stored in a typical relational database implementation, rendering the data as less than user-friendly to access. The implementation gets more complex if different asset classes are stored in different databases. Even within one asset class, static terms and conditions data may be stored in different systems from time series data, tick data and calculated/derived data.

In the absence of further end-user tools to analyse the data, the trader or risk manager must understand both the SQL programming language and the table structure/architecture implemented for the databases in question. Even if the business user is capable of achieving these two things, they often do not have the time to do anything more than avoid using the database by downloading equivalent data from Open Bloomberg into Microsoft Excel.

Technologists can, of course, go some way to addressing the above issue through the provision of read-only views and wrapper functions (in the form of stored procedures) to hide some of the complexity from the end-user. However, as data demands from the business expand, and table complexity increases, these procedures can become computationally expensive and increasingly difficult to manage, maintain and change. As a result, business users continue to be heavily reliant on technologists to deliver and so the unproductive cycle continues.

White paper contents

• More Data, Less Time
• Users and Technologists Think About Data Differently
• TimeScape QL+: Designed for Financial Markets
• Functional Overview of TimeScape QL+
• Some Examples of TimeScape QL+ in Action
  • Example #1 – Loading a Price History for an Equity
  • Example #2 – Calculating Historic Volatility of an Underlying
  • Example #3 – The ‘.?’ Statement and Context Sensitive Help
  • Example #4 – Data Rules and Bond Spread Analysis
  • Example #5 – Tick Data Analysis, Data Frequency and VWAP
  • Example #6 – Multiple Instrument Analysis
  • Example #7 – Vector Arithmetic, Spread Analysis and VAR
  • Example #8 – Adding Your Own Functions
  • Example #9 – Adding Your Own Objects
• Future Directions
• Summary

differently : 다르게, 같지 않게, 따로, 별도로