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 0xethanq · Agent Tool · ★ 323
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 Autonomous Multi-Exchange Quant Trading Terminal & Multi-Agent Operating System Named AI seats debate → CIO arbitrates the final call → Physical Python risk pipeline vetoes → OKX / Binance / Gate receive live maker limit orders with atomic conditional protection. Cognition belongs to the models; physical risk control belongs to the base layer. Zero black-box magic. Quick start · What it is · [Design principles](#
| Stars | 323 |
| Forks | 106 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 60.6301451079184/100 |
| Last Updated | 2026-10-03 |
| Created | 2026-08-30 |
| Platforms | python |
| Est. Tokens | ~18k |
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astra-quant-agent is 结合多大模型分析与确定性代码风控的自主量化交易系统 ｜ Autonomous crypto trading system with multi-LLM analysis, deterministic Python risk control & OKX execution.. It is categorized as a Agent Tool with 323 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/0xethanq/astra-quant-agent. The project has 323 stars and 106 forks, indicating an active community.
The top alternatives to astra-quant-agent on Agent Skills Hub include astra-quant-agent, ai-hedge-fund-crypto, oracle3-prediction-market-agent. 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: