[2605.00875] Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00875 (cs)
[Submitted on 25 Apr 2026]
Title:Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study
Authors:Dustin M. Haggett
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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.
Comments: 9 pages, 8 figures, 9 tables. Stevens Institute of Technology course project, Fall 2025
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.00875 [cs.CV]
(or arXiv:2605.00875v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00875
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arXiv-issued DOI via DataCite
Submission history
From: Dustin Haggett [view email] [v1] Sat, 25 Apr 2026 04:32:30 UTC (1,629 KB)
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