基于趋势学习的混合神经网络股指期货预测研究

Research on Stock Index Futures Forecast Based on Trend Learning and Hybrid Neural Network

由于价格非平稳、噪声大等特点,金融市场高频价格的预测难度大是有目共睹的.与其他预测方法不同,基于混合神经网络模型的预测方法不是仅对下个价格进行预测,而是把未来一段时间的价格预测变成对未来趋势以及持续时间的预测,也就是说,把多点预测变成两个变量的预测,这样的预测方法预测效率更高.本文基于混合神经网络模型对沪深300股指期货平滑价格数据进行价格预测.首先,基于沪深300股指期货的5分钟时间序列数据,本文使用混合神经网络模型进行趋势预测,并与LSTM和CNN模型对比,发现混合神经网络模型的预测性能更优.然后使用混合神经网络滚动预测,设计了投资策略:如果预测的趋势是上涨则在未来持续的时间段内做多,否则就做空.本文对2016年、2017年和2018年的价格数据进行回测,并把投资策略的表现与买入并持有策略进行对比,结果表明扣除费用后基于混合神经网络策略更优.最后,我们进行了稳定性检验,从实战角度检验了模型的可用性.

Due to the characteristics of non-stationary prices and high noise,it is obvious that the prediction of high-frequency prices in financial markets is difficult.Unlike other forecasting methods,the forecasting method based on the hybrid neural network model does not only predict the next price,but turns the price forecast in the future into a forecast of future trends and duration,that is,the prediction of multi-point becomes a prediction of two variables,and this prediction method is more efficient.This paper uses the mixed neural network model to make price predictions for the smooth price data of the Shanghai and Shenzhen 300 stock index futures.First,based on 5-minute time series data of the Shanghai and Shenzhen 300 stock index futures,this paper uses a hybrid neural network model for trend prediction and compares it with LSTM and CNN models.Then use the mixed neural network rolling forecast to design an investment strategy:If the predicted trend is up,go long in the future for a period of time,otherwise go short.This article backtests the price data for 2016,2017,and 2018,and compares the performance of investment strategies with buy-and-hold strategies.The results show that after deducting fees,the strategy based on hybrid neural networks is better.Finally,we performed a stability test and tested the usability of the model from a practical perspective.