Modeling Candlestick Patterns with Interpolative Boolean Algebra for Investment Decision Making
In this paper we present one way of modeling candlestick patterns using interpolative Boolean algebra (IBA). This method shows a degree of fulfillment for observed patterns, thus giving traders easy interpretation of by how much candlesticks fit into different patterns. Candlestick patterns have been used for financial forecasting for a couple of decades on Western markets and they have become a mainstream trader’s tool. Since the need for automated candlestick patterns discovery arose, some papers proposed fuzzy approach as a solution. Our decision to use IBA for modeling candlestick patterns comes from the fact that fuzzy logic has it limits and cannot be applied to these models. Proposed method is another approach to the same problem, but it could not be modeled using conventional fuzzy logic, because it is necessary for it to be in the Boolean frame. Results obtained from our tests are satisfactory and also open the opportunity for combining this technique with existing ones.
Keywordsinterpolative Boolean algebra real valued logic candlestick patterns financial forecasting
Unable to display preview. Download preview PDF.
- 1.Horton, M.J.: Stars, crows, and doji: The use of candlesticks in stock selection. The Quarterly Review of Economics and Finance (2009), doi:10.1016/j.qref.2007.10.005Google Scholar
- 2.Kamo, T., Dagli, C.: Hybrid approach to the Japanese candlestick method for financial forecasting. Expert Systems with Applications (2009), doi:10.1016/j.eswa.2008.06.050Google Scholar
- 3.Lan, Q., Zhang, D., Xiong, L.: Reversal Pattern Discovery in Financial Time Series Based on Fuzzy Candlestick Lines. Systems Engineering Procedia (2011), doi:10.1016/j.sepro.2011.10.021Google Scholar
- 4.Lee, C.-H.L.: Modeling Personalized Fuzzy Candlestick Patterns for Investment Decision Making. IEEE Computer Society (2009), doi:10.1109/APCIP.2009.207Google Scholar
- 5.Lee, C.-H.L., Chen, W., Liu, A.: Candlestick Tutor: An Intelligent Tool for Investment Knowledge Learning and Sharing. IEEE Computer Society (2005), doi:10.1109/ICALT.2005.82Google Scholar
- 6.Lee, C.-H.L., Liu, A., Chen, W.-S.: Pattern Discovery of Fuzzy Time Series for Financial Prediction. IEEE Transactions on Knowledge and Data Engineering (2006), doi:10.1109/TKDE.2006.80Google Scholar
- 7.Lee, K.H., Jo, G.S.: Expert system for predicting stock market timing using a candlestick chart. Expert Systems with Applications (1999), doi:10.1016/S0957-4174(99)00011-1Google Scholar
- 8.Lu, T.-H., Shiu, Y.-M., Liu, T.-C.: Profitable candlestick trading strategies—The evidence from a new perspective. Review of Financial Economics (2012), doi:10.1016/j.rfe.2012.02.001Google Scholar
- 9.Marshall, B.R., Young, M.R., Rose, L.C.: Candlestick technical trading strategies: Can they create value for investors? Journal of Banking & Finance (2006), doi:10.1016/j.jbankfin.2005.08.001Google Scholar
- 10.Nison S.: Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. New York Institute of Finance, USA (1991)Google Scholar
- 13.Radojevic, D.: Interpolative Realization of Boolean Algebra as a Consistent Frame for Gradation and/or Fuzziness. Forging New Frontiers: Fuzzy Pioneers (2008), doi:10.1007/978-3-540-73185-6_13Google Scholar
- 14.Radojevic, D.: Fuzzy Set Theory in Boolean Frame, Workshop invited key lecture. International Journal of Computers, Communications & Control 3, 121–131 (2008)Google Scholar