Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements

Karsten Martiny

Abstract

Candlestick charts are a visually appealing method of presenting price movements of securities. It has been developed in Japan centuries ago. The depiction of movements as candlesticks tends to exhibit recognizable patterns that allow for predicting future price movements. Common approaches of employing candlestick analysis in automatic systems rely on a manual a-priori specification of well-known patterns and infer prognoses upon detection of such a pattern in the input data. A major drawback of this approach is that the performance of such a system is limited by the quality and quantity of the predefined patterns. This paper describes a novel method of automatically discovering significant candlestick patterns from a time series of price data and thereby allows for an unsupervised machine-learning task of predicting future price movements.

References

  1. Chou, S., Hsu, H., Yang, C., and Lai, F. (1997). A stock selection DSS combining AI and Technical Analysis. Annals of Operations Research, 75:335-353.
  2. Lee, C.-H. L., Liaw, Y.-C., and Hsu, L. (2011). Investment decision making by using fuzzy candlestick pattern and genetic algorithm. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, pages 2696 -2701.
  3. Lee, K. and Jo, G. (1999). Expert system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16(4):357 - 364.
  4. Lin, X., Yang, Z., and Song, Y. (2011). Intelligent stock trading system based on improved technical analysis and Echo State Network. Expert Systems with Applications, 38(9):11347 - 11354.
  5. Ma;, G. G. C. and Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. SIAM, Society for Industrial and Applied Mathematics.
  6. Morris, G. L. (2006). Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Futures. McGraw-Hill Professional, 3rd edition.
  7. Ng, W. W. Y., Liang, X.-L., Chan, P. P. K., and Yeung, D. S. (2011). Stock investment decision support for Hong Kong market using RBFNN based candlestick models. In Machine Learning and Cybernetics (ICMLC), 2011 International Conference on, volume 2, pages 538 -543.
  8. Nison, S. (1991). Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. New York Institute of Finance.
  9. Nison, S. (2003). The Candlestick Course. Wiley & Sons.
  10. Tsay, R. S. (2010). Analysis of Financial Time Series (Wiley Series in Probability and Statistics). Wiley, 3rd edition.
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Paper Citation


in Harvard Style

Martiny K. (2012). Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 145-150. DOI: 10.5220/0004107701450150


in Bibtex Style

@conference{kdir12,
author={Karsten Martiny},
title={Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={145-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004107701450150},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements
SN - 978-989-8565-29-7
AU - Martiny K.
PY - 2012
SP - 145
EP - 150
DO - 10.5220/0004107701450150