Abstract: Forex or FX is the short form of the Foreign Exchange Market, it is known as the largest financial market in the world where Investors can buy a certain amount of currency and hold it on until the exchange rate moves, then sell it to make money. This operation is not easy as it looks; due to the forte fluctuation of this market, investors find it a risky area to trade. A successful strategy in Forex should reduce the rate of risks and increase the profitability of investment by considering economic and political factors and avoiding emotional investment. In this article, we propose a trading strategy based on machine learning algorithms to reduce the risks of trading on the forex market and increase benefits at the same time. For that, we use an algorithm that generates technical indicators and technical rules containing information that may explain the movement of the stock price, the generated data is fed to a machine-learning algorithm to learn and recognize price patterns. Our algorithm is the combination of two deep learning algorithms, Gated Recurrent Unit “GRU” and Convolutional Neural Network “CNN”; it aims to predict the next day signal (BUY, HOLD or SELL) The model performance is evaluated for USD/EUR by different metrics generally used for machine learning algorithms, another method used to evaluate the profitability by comparing the returns of the strategy and the returns of the market. The proposed system showed a good improvement in the prediction of the price.

Nabil MABROUK, Marouane CHIHAB, Zakaria HACHKAR and Younes CHIHAB, “Intraday Trading Strategy based on Gated Recurrent Unit and Convolutional Neural Network: Forecasting Daily Price Direction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(3), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130369

@article{MABROUK2022,
title = {Intraday Trading Strategy based on Gated Recurrent Unit and Convolutional Neural Network: Forecasting Daily Price Direction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130369},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130369},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {3},
author = {Nabil MABROUK and Marouane CHIHAB and Zakaria HACHKAR and Younes CHIHAB}
}