A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. Among them, the data from to is used as the training data set, and the data set from to is used to verify the forecasting effect. We deploy PRML for the forecast of all Chinese market stocks from until Oct 30, 2020. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. Different time windows from one to ten days are used to detect the prediction effect at different periods. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions.
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