Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study

Researchers at Stevens Institute of Technology conducted a systematic deep learning study on visual chart representations for cryptocurrency regime prediction. The paper, led by Dustin M. Haggett, evaluated three image encoding methods (raw candlestick charts, Gramian Angular Fields, and multi-channel GAF), five chart component configurations, and four neural network architectures (CNN, ResNet18, EfficientNet-B0, and Vision Transformer). Using Bitcoin, Ethereum, and S&P 500 data spanning 2018-2024, the study found that a simple 4-layer CNN on raw candlestick charts achieved the highest AUC-ROC of 0.892, outperforming larger models pretrained on ImageNet. Surprisingly, simpler representations—price-only charts at 128x128 resolution—consistently outperformed more complex alternatives like Gramian Angular Fields. Transfer learning from ImageNet improved performance by 4-16% despite the domain gap between natural images and financial charts, and interpretability analysis using GradCAM provided insights into model decisions.

The findings have significant implications for algorithmic trading and financial AI. The study demonstrates that expensive, complex vision architectures are unnecessary for chart-based regime prediction; a lightweight CNN trained on basic candlestick images can deliver state-of-the-art results. This opens the door for real-time deployment on edge devices and low-latency trading systems. The 4-16% boost from transfer learning suggests that even financial chart images benefit from features learned on natural images, challenging the assumption that domain-specific training is required. Practitioners can adopt this simple framework for cryptocurrency market analysis without needing massive computational resources or proprietary datasets.