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a Data-Warehouse Solution for OMS Data Management
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A database system for storing and querying data of a dynamic schema has been developed based on the kdb+ database management system and the q programming language for use in a financial setting of order and execution services. Some basic assumptions of mandatory fields of the data to be stored are made including that the data are time-series based.A dynamic schema enables an Order-Management System (OMS) to store information not suitable or usable when stored in log files or traditional databases. Log files are linear, cannot be queried effectively and are not suitable for the volumes produced by modern OMSs. Traditional databases are typically row-oriented which does not suit time-series based data and rely on the relational model which uses statically typed sets to store relations.The created system includes software that is capable of mining the actual schema stored in the database and visualize it. This enables ease of exploratory querying and production of applications which use the database. A feedhandler has been created optimized for handling high volumes of data. Volumes in finance are steadily growing as the industry continues to adopt computer automation of tasks. Feedhandler performance is important to reduce latency and for cost savings as a result of not having to scale horizontally. A study of the area of algorithmic trading has been performed with focus on transaction-cost analysis. Fundamental algorithms have been reviewed.A proof of concept application has been created that simulates an OMS storing logs on the execution of a Volume Weighted Average Price (VWAP) trading algorithm. The stored logs are then used in order to improve the performance of the trading algorithm through basic data mining and machine learning techniques. The actual learning algorithm focuses on predicting intraday volume patterns.

Place, publisher, year, edition, pages
2013.
Series
UMNAD, 948
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-80688OAI: oai:DiVA.org:umu-80688DiVA: diva2:650914
External cooperation
Cinnober
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2013-09-24 Created: 2013-09-24 Last updated: 2013-09-24Bibliographically approved

Open Access in DiVA

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