Conference paper
Extracting significant features based on candlestick patterns using unsupervised approach
2017 2nd International Conference on Information Technology (INCIT)
2nd International Conference on Information Technology (INCIT) 2017 (Nakhonpathom, Thailand, 02/11/2017–03/11/2017)
2017
Abstract
This paper proposes algorithms for the extraction of features from candlestick patterns for technical analysis of share indices. The significant features consist of: the direction of candlestick, the gap between CLOSE and OPEN price of two candlesticks, the body level of current and previous candlesticks, and the length of the candlesticks. K-Means clustering approach is applied for solving the unclearly defined length of Upper Shadow, Body and Lower Shadow. The Thai SET index OHLC data from 1990 to 2017 are used as the experimental dataset. The results show the similarity between the candlestick chart from raw data and decoding data, which is applied by the proposed algorithms. The output result from the approach can be used as the input to other machine learning methods such as Artificial Neuron Networks, Reinforcement Learning, or Content Based Image Retrieval (CBIR).
Details
- Title
- Extracting significant features based on candlestick patterns using unsupervised approach
- Authors/Creators
- S. Sangsawad (Author/Creator) - Rangsit UniversityC.C. Fung (Author/Creator) - Murdoch University
- Publication Details
- 2017 2nd International Conference on Information Technology (INCIT)
- Conference
- 2nd International Conference on Information Technology (INCIT) 2017 (Nakhonpathom, Thailand, 02/11/2017–03/11/2017)
- Identifiers
- 991005543638907891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
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