A parallel hybrid neural networks model for forecasting returns with candlestick technical trading strategy

Abstract

Few literature comprehensively addresses the process of input engineering for candlestick price chart and the optimization of model structures as an entire process. Inspired by investors’ decision-making process on the reversal candlestick patterns, where trend classification is separated from candlestick pattern recognition, we propose a novel Parallel Hybrid Neural Networks model (PHNN) designed to enhance return prediction with a candlestick technical trading strategy. This model comprises two distinct sub-networks capable of accommodating various configurations like CNN or LSTM. The final prediction is generated through a fully connected neural layer that integrates the outputs from these sub-networks.To improve the accuracy of the sub-networks, we train the PHNN on prices’ components decomposed through Empirical Mode Decomposition to obtain more precise sub-networks that each one excels in recognition of the trend and the candlestick patterns respectively. Our empirical study provides substantial support for the effectiveness of the PHNN, demonstrating its superiority over benchmark models. Moreover, our results strongly suggest that a double LSTM configuration is the optimal architectural choice for the PHNN.