Please use this identifier to cite or link to this item: https://doi.org/10.1145/507635.507653
DC FieldValue
dc.titleCharting patterns on price history
dc.contributor.authorAnand, S.
dc.contributor.authorChin, W.-N.
dc.contributor.authorKhoo, S.-C.
dc.date.accessioned2013-07-04T08:01:06Z
dc.date.available2013-07-04T08:01:06Z
dc.date.issued2001
dc.identifier.citationAnand, S., Chin, W.-N., Khoo, S.-C. (2001). Charting patterns on price history. Proceedings of the ACM SIGPLAN International Conference on Functional Programming, ICFP : 134-145. ScholarBank@NUS Repository. https://doi.org/10.1145/507635.507653
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40298
dc.description.abstractIt is an established notion among financial analysts that price moves in patterns and these patterns can be used to forecast future price. As the definitions of these patterns are often subjective, every analyst has a need to define and search meaningful patterns from historical time series quickly and efficiently. However, such discovery process can be extremely laborious and technically challenging in the absence of a high level pattern definition language. In this paper, we propose a chart-pattern language (CPL for short) to facilitate pattern discovery process. Our language enables financial analysts to (1) define patterns with subjective criteria, through introduction of fuzzy constraints, and (2) incrementally compose complex patterns from simpler patterns. We demonstrate through an array of examples how real life patterns can be expressed in CPL. In short, CPL provides a high-level platform upon which analysts can define and search patterns easily and without any programming expertise. CPL is a domain-specific language embedded in Haskell. We show how various features of a functional language, such as pattern matching, higher-order functions, lazy evaluation, facilitate pattern definitions and implementation. Furthermore, Haskell's type system frees the programmers from annotating the programs with types.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.doi10.1145/507635.507653
dc.description.sourcetitleProceedings of the ACM SIGPLAN International Conference on Functional Programming, ICFP
dc.description.page134-145
dc.identifier.isiutNOT_IN_WOS
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