Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on forecasting accuracy are evaluated. The potential risks and actual risks encountered by the trader who trades based on the proposed model were also analyzed. The obtained results indicate that the optimization of the LSTM parameters using the Bayesian optimization model has enhanced the model’s accuracy. The results obtained from analyzing the drawdown and reward/risk resulting from the trades made based on the model show that the model enables the trader to trade with peace of mind due to the low level of actual risks and potential risks.

最近，深度学习架构被越来越多地用于金融市场的预测。本文采用LSTM模型预测BTC/USD每日收盘价方向。分析了模型准确性和基于所提出模型进行的交易的损益。此外，还评估了 MACD 指标和输入矩阵维度对预测准确性的影响。还分析了基于所提出模型进行交易的交易者所面临的潜在风险和实际风险。得到的结果表明，使用贝叶斯优化模型对 LSTM 参数进行优化，提高了模型的准确性。