  With the increasing enrichment and development of the financial derivatives
market, the frequency of transactions is also faster and faster. Due to human
limitations, algorithms and automatic trading have recently become the focus of
discussion. In this paper, we propose a bidirectional LSTM neural network based
on an attention mechanism, which is based on two popular assets, gold and
bitcoin. In terms of Feature Engineering, on the one hand, we add traditional
technical factors, and at the same time, we combine time series models to
develop factors. In the selection of model parameters, we finally chose a
two-layer deep learning network. According to AUC measurement, the accuracy of
bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results,
we achieved a return of 1089.34% in two years. At the same time, we also
compare the attention Bi-LSTM model proposed in this paper with the traditional
model, and the results show that our model has the best performance in this
data set. Finally, we discuss the significance of the model and the
experimental results, as well as the possible improvement direction in the
future.
