Predictive modeling of foreign exchange trading signals using machine learning techniques

This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based and traditional momentum strategies with benchmark strategies. It differs from other studies in that it considered various cases, including different foreign exchange pairs, return methods, data frequency, and individual and integrated trading strategies. Ridge regression, KNN, RF, XGBoost, GBDT, ANN, LSTM, and GRU models were used for the machine learning-based strategy, while the MA cross strategy was employed for the momentum strategy. Backtests were performed on six major pairs from January 1, 2000, to June 30, 2023, and daily and intraday data were used. The Sharpe ratio was considered as a metric used to refer to economic significance and the independent t-test was used to determine statistical significance. The general findings of the study suggested that the currency market has become more efficient. The rise in efficiency is probably caused by the fact that more algorithms are being used in this market, and information spreads much faster. Instead of finding a trading strategy that works well in all major foreign exchange pairs, our study showed that it is possible to find an effective algorithmic trading strategy that generates a more effective trading signal in each specific case. • Extensive test of Machine Learning (ML) models in the exchange currencies market. • The comparison of ML and Momentum-based investment strategies. • Data science approaches were used and combined to create more effective trading strategies. • Indicating the differences between Neural Networks and other ML models. • Considered various investment signals and avoided overfitting problems using Walk-forward approach.