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

TL;DR

This study systematically compares visual representations and neural network architectures for cryptocurrency regime prediction using financial charts, finding simple models on raw data often outperform complex alternatives.

Contribution

It provides a comprehensive evaluation of encoding methods, model architectures, and transfer learning effects for visual financial analysis, highlighting effective configurations.

Findings

A simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC.

Simpler representations outperform complex ones across experiments.

Transfer learning improves performance by 4-16% despite domain differences.

Abstract

Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for cryptocurrency regime prediction. We evaluate three image encoding methods (raw candlestick charts, Gramian Angular Fields, and multi-channel GAF), five chart component configurations, four neural network architectures (CNN, ResNet18, EfficientNet-B0, and Vision Transformer), and the impact of ImageNet transfer learning. Through eight controlled experiments on Bitcoin, Ethereum, and S&P 500 data spanning 2018-2024, we identify optimal configurations for visual regime classification. Our results show that a simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC, outperforming larger pretrained models. Surprisingly, simpler representations (price-only charts, 128x128 resolution) consistently outperform more complex alternatives. We provide interpretability analysis using GradCAM and demonstrate that transfer learning improves performance by 4-16% despite the domain gap between natural images and financial charts.