Extracting Significant Features Based on Candlestick Patterns Using Unsupervised Approach

被引：0

作者：

Sangsawad, Seksan ^{[1
]}

Fung, Chun Che ^{[2
]}

机构：

[1] Rangsit Univ, Coll Informat & Commun Technol, Pathum Thani, Thailand

[2] Murdoch Univ, Sch Engn & Informat Technol, Perth, WA, Australia

来源：

2017 2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (INCIT) | 2017年

关键词：

candlestick patterns; feature extraction; time series forecast; unsupervised learning; k-means clustering; PREDICTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 he used as the input to other machine learning methods such as Artificial Neuron Networks, Reinforcement Learning, or Content Based Image Retrieval (CBIR).

引用

页码：7 / 11

页数：5

共 31 条

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