An arbitrage strategy model for ferrous metal futures based on LSTM neural network

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.