Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
5-2-2017
Abstract
Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach for building the criteria in the multidimensional space for finding an efficient investment strategy. Criteria generated with the CPC approach allow reducing/compressing space using simple directed graphs with beginnings and the ends located in different time points. The dedicated subspaces constructed for time series include characteristics such as Bollinger Band, difference between moving averages, changes in volume etc. Extensive simulation studies have been performed in learning/testing context. Effective relations were found for one-hour EURUSD pair for recent and historical data. Also the method has been explored for one-day EURUSD time series n 2D and 3D visualization spaces. The main positive result is finding the effective split of a normalized 3D space on 4x4x4 cubes in the visualization space that leads to a profitable investment decision (long, short position or nothing). The strategy is ready for implementation in algotrading mode.
Recommended Citation
Wilinski, Antoni and Kovalerchuk, Boris, "Visual Knowledge Discovery and Machine Learning for Investment Strategy" (2017). All Faculty Scholarship for the College of the Sciences. 135.
https://digitalcommons.cwu.edu/cotsfac/135
Journal
Cognitive Systems Research
Rights
© 2017 Elsevier B.V. All rights reserved.
Comments
The download link on this page is to an accepted manuscript version of this article and may not be the final version of this article. This article was originally published in Cognitive Systems Research. The full-text, final version of record from the publisher can be found here.