Forecasting Foreign Exchange Market Prices Using Technical Indicators with Deep Learning and Attention Mechanism

TL;DR

This paper introduces a deep learning model combining LSTM, CNN, and attention mechanisms to improve the accuracy of foreign exchange market price predictions using technical indicators.

Contribution

It presents a novel architecture that integrates LSTM, CNN, and attention mechanisms for Forex price forecasting, enhancing feature relevance and prediction accuracy.

Findings

Outperforms benchmark models in Forex price prediction

Effectively captures long-term dependencies and local patterns

Improves focus on relevant features through attention mechanism

Abstract

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.