A FOREX Trading System Based on Semi-Supervised News Classification, Market Sentiment Analysis, and GRU-CNN Deep Learning Models

Investors access the foreign exchange market (FOREX) not only to preserve their wealth but also to generate profits and achieve specific financial goals. It is one of the largest financial markets that investors rely on, and it is based on fluctuations in currency exchange rates to make a profit in different time cycles: short-term, medium-term, and long-term. In this article, we propose an automated FOREX trading system that combines two artificial intelligence algorithms: the first to classify news by pertinence (semi-supervised) and then to analyze market sentiment. This algorithm plays a crucial role in replacing the traditional fundamental analysis, which is based on macroeconomic factors, political events, and news headlines. The use of GAN-BERT helped improve performance in classification tasks with limited labeled data and reduced execution time. This algorithm demonstrates impressive results, achieving a high accuracy of 97.5%, which makes its output data more reliable for use in the second algorithm, a combination of two deep learning models: the Gated Recurrent Unit (GRU) and the Convolutional Neural Network (CNN). We enrich the dataset used in this phase with additional technical indicators and features that may help explain market fluctuations. We evaluated our final algorithm over multiple time frames and several windows; the results were impressive and confirmed by back-testing its potential profitability and risk.

@article{MABROUK2025,
  title     = {A FOREX Trading System Based on Semi-Supervised News Classification, Market Sentiment Analysis, and GRU-CNN Deep Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nabil MABROUK and Marouane CHIHAB and Younes CHIHAB},
  doi       = {10.14569/IJACSA.2025.0160981},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160981}
}