Abstract
Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in the opportunity of solving easier perceptual tasks instead of complex cognitive 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 2-D or 3-D world. This chapter presents a visualization-inspired approach to find an investment strategy based on pattern discovery in multidimensional space. It is shown that the new lossless Collocated Paired Coordinates approach is an effective instrument for such inspiration for the investment strategy. It is coming from the two levels of the approach. The first level involves examining the best 4D and 6D coordinate systems to build 2D or 3D visualization spaces. The second level involves learning parameters of attributes in each selected space. A key role of the CPC here is in helping to find the best locations (squares in 2D or cubes in 3D) to open long or short positions, respectively. The main positive result is finding the property in the visualization space that leads to a profitable investment decision for EUR/USD foreign exchange market. The strategy is ready for implementation in algotrading mode.
An economist is an expert who will know tomorrow why the things
he predicted yesterday didn’t happen today.
Laurence J. Peter
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Kovalerchuk, B. (2018). Knowledge Discovery and Machine Learning for Investment Strategy with CPC. In: Visual Knowledge Discovery and Machine Learning. Intelligent Systems Reference Library, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-73040-0_8
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DOI: https://doi.org/10.1007/978-3-319-73040-0_8
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