Adaptive Classifier of Candlestick Formations for Prediction of Trends
Candlestick charts have become in last decades a popular means in predicting trends on stock markets. Their properties enable in the form of the so-called formations to represent some symptoms of market changes in a user-friendly manner. Thus experienced businessmen are able using such kind of information to predict situations and in advance to correctly decide. However, candlestick charts are only one of many other indicators and their interpretation is not trivial. It depends e.g. on commodity, stage of a given trend, etc. To efficiently perform correct prediction using this graphical means a system utilizing ability to process vague information being able of adaptation is necessary. In this paper a design of such an fuzzy adaptive knowledge-based classification system using evolutionary optimization is proposed for categorization of characteristic candlestick formations and verified by a number of experiments in the area of exchange rates.
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