Dev.to
7/29/2026
Building a Vision-Capable Crypto Trading Bot: Lessons from 77 LLM Trading Papers
Original: Stop Trading Like It's 1999 — I Built an Autonomous, Vision-Capable Crypto Bot with Python 3.13
Short summary
A developer shares lessons from building an autonomous crypto trading bot after studying 77 academic papers on LLM trading. Key findings: LLMs understand trading concepts but freeze on execution, most academic papers lack reproducibility, and vision-based chart analysis outperforms hand-coded pattern detection. The bot uses Gemini for visual chart inference, custom Numba-compiled indicators, and single-prompt bull/bear debate to reduce API costs.
- •Studied 77 papers finding LLMs understand trading but fail at execution and most research lacks reproducibility
- •Vision-based chart analysis via Gemini outperformed 900 lines of hand-coded pattern detection
- •Single-prompt bull/bear debate eliminates extra API calls compared to multi-agent frameworks
Generated with AI, which can make mistakes.
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