关键词: 能量窗口, K线特征, BP神经网络, 水平趋势

Abstract: Horizontal trend lasts for a short time, and the uncertainty of its direction changes is huge, so it becomes hard to forecast the direction of horizontal condition’s trend in Stock Market trend prediction. Based on the energy calculation of Horizontal window, BP neural network algorithm（WE-BPNN） is presented for predicting Horizontal window trend. Firstly, the division standard for short-term trend is given , on the basis of which this paper comes up with definitions of horizontal window. Then, by calculating the energy of K-line combination and moving average combination, two types of energy are merged into window energy. At last, leading the window energy into the direction of BP neural network to predict window trend. Because of hysteresis of energy’s influences on the trend, there is a case that energy accumulated while trend not changes, it will affect the accuracy of the trend prediction. Thus, basing on WE-BPNN neural network algorithm energy regulator is led into  BP neural network algorithm（EF-BPNN）, weights of window energy are dynamically adjusted for the trend prediction. On the Shanghai Stock’s data, the experimental results show that EF-BPNN algorithm has better performance.

Key words: energy window, K-line features, BP neural network, horizontal trend