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

2026-05-05 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, FinTech & Digital Financial Services · Depth: Advanced, extended

Summary

A systematic deep learning study compared various visual representations for cryptocurrency regime prediction using Bitcoin, Ethereum, and S&P 500 data from 2018–2024. The research evaluated three image encoding methods (raw candlestick charts, Gramian Angular Fields, multi-channel GAF), five chart component configurations, and four neural network architectures (CNN, ResNet18, EfficientNet-B0, Vision Transformer). Key findings indicate that a simple 4-layer CNN on raw candlestick charts achieved the best performance with 0.892 AUC-ROC, outperforming larger pretrained models. Simpler representations, such as price-only charts and 128x128 resolution, consistently yielded better results. Transfer learning from ImageNet improved performance by 4–16%, despite the domain gap. GradCAM analysis revealed that models focus on recent price action and large candles, aligning with technical analysis principles.

Key takeaway

For research scientists developing visual trading systems, prioritize simplicity in both chart representation and model architecture. Your initial efforts should focus on using raw, price-only candlestick charts at 128x128 resolution with a simple 4-layer CNN, as this configuration demonstrated superior performance. Consider applying ImageNet transfer learning to leverage pre-trained features, even with the domain gap, and expect better predictability in cryptocurrency markets like Bitcoin compared to traditional equities.

Key insights

Simpler visual representations and models outperform complex alternatives for cryptocurrency regime prediction.

Principles

• Raw candlestick charts preserve predictive information better than mathematical encodings.
• Additional visual elements can distract CNNs from core price patterns.
• Low-level features from ImageNet transfer meaningfully to financial charts.

Method

The study formulates regime prediction as a binary image classification task, using a 7-day forward horizon and a 2% return threshold to define bull/bear regimes.

In practice

• Prefer raw candlestick charts over GAF encodings.
• Keep charts simple; avoid cluttering with indicators.
• Start with small CNN models for limited financial data.

Topics

• Cryptocurrency Regime Prediction
• Candlestick Chart Analysis
• Convolutional Neural Networks
• Vision Transformers
• Gramian Angular Fields

Code references

• dustinhaggett/chart-vision-regime

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.