基于LSTM神经网络的黑色金属期货套利策略模型

利用协整检验方法和LSTM神经网络算法，建立黑色金属期货市场的套利策略模型.利用基于LSTM神经网络套利策略模型对大连商品交易所上市的焦炭期货、铁矿石期货和上海期货交易所上市的螺纹钢期货进行实证研究.对比研究基于LSTM神经网络、BP神经网络和卷积神经网络的3种套利策略模型，实证结果表明基于LSTM神经网络的黑色金属期货套利策略模型可行有效，并且比BP神经网络套利策略模型和卷积神经网络套利策略模型表现更好.
Abstract：Using the cointegration test method and LSTM neural network algorithm, the arbitrage strategy model for ferrous metal futures market was established. The empirical study is conducted on the coke futures, iron ore futures on the Dalian Commodity Exchange and the rebar futures on the Shanghai Futures Exchange using the arbitrage strategy model based on LSTM neural network. The arbitrage strategy models based on LSTM neural network, BP neural network and convolutional neural network were compared, and the empirical results show that the arbitrage strategy model for ferrous metal futures based on LSTM neural network is feasible and effective, and performs better than the arbitrage strategy models based on BP neural network and convolutional neural network.