by horizon-llm · Agent Tool · ★ 67
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[ACL2026] AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading.
| Stars | 67 |
| Forks | 11 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 51.9386146874532/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-03 |
| Created | 2025-10-16 |
| Platforms | python |
| Est. Tokens | ~13k |
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AlphaQuanter is [ACL2026] AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading.. It is categorized as a Agent Tool with 67 GitHub stars.
AlphaQuanter is primarily written in Python. It covers topics such as agent, agentic-rl.
You can find installation instructions and usage details in the AlphaQuanter GitHub repository at github.com/horizon-llm/AlphaQuanter. The project has 67 stars and 11 forks, indicating an active community.
The top alternatives to AlphaQuanter on Agent Skills Hub include Claw-R1, claude-context-local, claudex. 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.
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