  Models trained under assumptions in the complete market usually don't take
effect in the incomplete market. This paper solves the hedging problem in
incomplete market with three sources of incompleteness: risk factor,
illiquidity, and discrete transaction dates. A new jump-diffusion model is
proposed to describe stochastic asset prices. Three neutral networks, including
RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss
and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the
fastest model with the best results under MSE and Huber Loss.
