Charting patterns on price history

Abstract

It 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.

Type-directed weaving of aspects for higher-order functional languages

“…The problem handled there can be conveniently framed in the aspect-oriented perspective, where such changes to a function can be defined as a typedscoped advice to the function. Looking forward, we envisage the use of aspects, through the help of type-scoped advices, to model domain-specific embedded languages [8,6,11,1].…”

Filter-embedding semiring fusion for programming with MapReduce

“…We can use these generators to develop GTA specifications, e.g., for query problems of ranges and financial data analysis [ACK01,Zan92]. Although it is, in general, difficult to give direct definitions of desired polymorphic semiring generators, there is a principled approach.…”

Automatically Recognizing Stock Patterns Using RPCL Neural Networks

“…8, SOM clustering enables the resemblance among prototype vectors within the same neighborhood. For example, prototype vectors, with the position of (2, 7), (2,8), (3,7) and (3,8) in Fig. 8, exhibit almost the same shape, which indicates that some clusters can be merged.…”

“…The highly computational capabilities derived from computer technologies have enabled the design of computer-based algorithms for pattern analysis [1]- [8]. In the meanwhile, neural network has been going through an increment in popularity within stock market analysis [9].…”