Forex (foreign exchange) is a special financial market that entails both high risks and high profit opportunities for traders. It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies. However, incorrect predictions in Forex may cause much higher losses than in other typical financial markets. The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems. In this work, we used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex. We utilized two different data sets—namely, macroeconomic data and technical indicator data—since in the financial world, fundamental and technical analysis are two main techniques, and they use those two data sets, respectively. Our proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data.

中文翻译：

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使用具有技术和宏观经济指标的 LSTM 预测外汇数据的定向运动

Forex（外汇）是一个特殊的金融市场，为交易者带来高风险和高利润机会。这也是一个非常简单的市场，因为交易者只需预测两种货币之间的汇率方向即可获利。然而，外汇中的错误预测可能会导致比其他典型金融市场更高的损失。方向预测要求使该问题与其他典型的时间序列预测问题大不相同。在这项工作中，我们使用了一种名为“长短期记忆”（LSTM）的流行深度学习工具，该工具已被证明在许多时间序列预测问题中非常有效，以在外汇中进行方向预测。我们使用了两个不同的数据集——即宏观经济数据和技术指标数据——自金融界以来，基本面分析和技术分析是两种主要技术，它们分别使用这两个数据集。我们提出的混合模型结合了对应于这两个数据集的两个单独的 LSTM，在使用真实数据的实验中被发现非常成功。