Accurate prediction of price behavior in the foreign exchange market is crucial. This paper proposes a novel approach that leverages technical indicators and deep neural networks. The proposed architecture consists of a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and attention mechanism. Initially, trend and oscillation technical indicators are employed to extract statistical features from Forex currency pair data, providing insights into price trends, market volatility, relative price strength, and overbought and oversold conditions. Subsequently, the LSTM and CNN networks are utilized in parallel to predict future price movements, leveraging the strengths of both recurrent and convolutional architectures. The LSTM network captures long-term dependencies and temporal patterns in the data, while the CNN network extracts local patterns. The outputs of the parallel LSTM and CNN networks are then fed into an attention mechanism, which learns to weigh the importance of each feature and temporal dependency, generating a context-aware representation of the input data. The attention-weighted output is then used to predict future price movements, enabling the model to focus on the most relevant features and temporal dependencies. Through a comprehensive evaluation of the proposed approach on multiple Forex currency pairs, we demonstrate its effectiveness in predicting price behavior and outperforming benchmark models.

翻译：外汇市场价格行为的准确预测至关重要。本文提出一种利用技术指标与深度神经网络的新方法。所提出的架构包含长短期记忆网络（LSTM）、卷积神经网络（CNN）及注意力机制。首先，采用趋势类与振荡类技术指标从外汇货币对数据中提取统计特征，以揭示价格趋势、市场波动性、相对价格强度以及超买超卖状态。随后，并行使用LSTM与CNN网络预测未来价格走势，充分发挥循环架构与卷积架构的优势。LSTM网络捕捉数据中的长期依赖关系与时间模式，而CNN网络提取局部模式。并行LSTM与CNN网络的输出随后输入注意力机制，该机制通过学习对每个特征及时间依赖的重要性进行加权，生成输入数据的上下文感知表示。注意力加权输出进而用于预测未来价格变动，使模型能够聚焦于最相关的特征与时间依赖关系。通过对多种外汇货币对进行所提方法的综合评估，我们证明了其在预测价格行为方面优于基准模型的有效性。