No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by LLMQuant · MCP Server · ★ 378
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-trading-agents safe to install? View the security audit →
English · 简体中文 Awesome Trading Agents collects open-source projects where LLMs help research markets, make trading decisions, or connect agents to market data and execution tools. The list focuses on three building blocks: Agents, MCPs, and Skills. It does not try to cover classic quant libraries, time-series models, or reinforcement-learn
| Stars | 378 |
| Forks | 68 |
| Category | MCP Server |
| License | CC0-1.0 |
| Quality Score | 55.3331746074898/100 |
| Open Issues | 5 |
| Last Updated | 2026-08-13 |
| Created | 2026-04-22 |
| Platforms | claude-code, mcp |
| Est. Tokens | ~24k |
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awesome-trading-agents is Curated list of LLM-driven trading agents, MCP servers, and agent skills for market research, strategy, and execution.. It is categorized as a MCP Server with 378 GitHub stars.
You can find installation instructions and usage details in the awesome-trading-agents GitHub repository at github.com/LLMQuant/awesome-trading-agents. The project has 378 stars and 68 forks, indicating an active community.
awesome-trading-agents is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-trading-agents on Agent Skills Hub include awesome-openclaw, awesome-claude-skills, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: