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 555cute · Agent Tool · ★ 261
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is astra-quant-agent safe to install? View the security audit →
简体中文 · English AstraQuant Multi-exchange agentic quant trading terminal — decision by LLMs, risk by hard code Named AI seats debate → a CIO seat adopts the final call → a physical risk pipeline vetoes → OKX / Binance / Gate get real orders. The LLM proposes. Python physically disposes. Nothing here is simulated by default. Quick start · What it is · Design principles · Screenshots · [Strategy cons
| Stars | 261 |
| Forks | 87 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.5371659680749/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-27 |
| Created | 2026-08-30 |
| Platforms | python |
| Est. Tokens | ~18k |
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astra-quant-agent is Multi-agent LLM investment committee that actually trades: AI seats debate, a CIO adopts one, 17 fail-closed risk gates stop the rest. Multi-exchange crypto quant trading terminal for OKX / Binance / . It is categorized as a Agent Tool with 261 GitHub stars.
astra-quant-agent is primarily written in Python. It covers topics such as ai, ai-agents, ai-trading.
You can find installation instructions and usage details in the astra-quant-agent GitHub repository at github.com/555cute/astra-quant-agent. The project has 261 stars and 87 forks, indicating an active community.
astra-quant-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to astra-quant-agent on Agent Skills Hub include astra-quant-agent, ai-hedge-fund-crypto, sandstorm. 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: