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Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements

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.