This project developed a replicable process to associate stocks into clusters based on time series data, and selects an appropriate automated trading strategy for each cluster for use in trading. This process included an exploration of data pre-processing methods, selection of a clustering algorithm suited to this application, identification of an optimal investment strategy for each cluster, and the application of strategies on the algorithmically generated portfolio. Efficacy was determined through empirical comparison of gains seen in each test, with the goal of beating the market, or generating percentage greater than the change observed in the S&P 500. This process serves as a basis for future research and development in the field of applied data mining within the financial domain.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• McKenna, Ryan Paul

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-043015-133550

Advisor

• Hakim, Hossein
• Ruiz, Carolina
• Radzicki, Michael J.

Year

• 2015

Date created

• 2015-04-30

Resource type

• Major Qualifying Project

Major

• Electrical & Computer Engineering

Rights statement

• In Copyright