Modeling Personalized Fuzzy Candlestick Patterns for Investment Decision Making

Candlestick theory is one of widely used technical analysis methods in stock and commodity investment domains. The investors can make their investment decision by observing the change of the candlestick lines and discovering specific candlestick patterns. A candlestick pattern is composed of some candlestick lines. Because different investors have different interpretation of a candlestick pattern, we model different parts of a candlestick line with fuzzy linguistic variables to create a fuzzy candlestick pattern. We also proposed a personal ontology for the candlestick pattern interpretation and decision making. The user can use data mining algorithm such as decision tree to mine some candlestick patterns for investment decision making and the mined candlestick patterns could be stored in a database for different userpsilas future reuse. Our approach can be future used with other financial time series prediction results to provide users more information for investment decision making.