A Dual-Timeframe Deep Learning Framework Based on Ichimoku Cloud and Optimized CNN for Trend Prediction in the Forex Market

Authors
Omid Solaymani Fard
Seyedeh Zahra Hashemi
Abstract
Introduction

As the Forex market becomes increasingly complex, accurate trend forecasting has gained critical importance for traders and researchers. Unlike most studies that focus on price prediction, this paper introduces a novel bi-timeframe framework (1-hour and 4-hour) that integrates the Ichimoku Kinko Hyo strategy with deep learning models to predict directional movements in currency pairs.



Materials and Methods

The approach employs convolutional neural networks (CNNs) and hybrid architectures (CNN-LSTM, CNN-GRU), with hyperparameters optimized using the Particle Swarm Optimization Algorithm (PSO). Models are trained on historical EURUSD data (2019--2024) from MetaTrader5 and evaluated on eight highly correlated ($pm$80%) currency pairs. Due to the limitations of regression metrics (MAE, MSE, MAPE) in trading contexts, regression outputs are used solely for 4-hour trend classification, with Accuracy and F1-score as primary performance measures.



Results and Discussion

Results show that PSO-optimized models, particularly Ichimoku-CNN-GRU-PSO (ICGP), consistently outperform standard variants, achieving the highest Accuracy (up to 80.23% on USDSGD) and F1-score across most pairs.



Conclusion

The findings confirm that Ichimoku-based features, combined with hybrid deep learning and metaheuristic optimization, significantly enhances trend forecasting reliability and generalization in volatile financial markets.
Keywords
Forex trend forecasting
Convolutional Neural Networks
Ichimoku strategy
Metaheuristic optimization
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