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 · ★ 217
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is r20-quantum-trader safe to install? View the security audit →
R20 Quantum Trader (R20 智能对冲对冲基金投委会量化系统) 全新演进的 LLM 原生数字资产对冲量化交易系统 同等身份资深交易员博弈提案 · 首席投资官 (CIO) 资金终审 · 全息持仓挂单审查 · 微积分动力学 · 交易所原生云端 OCO 风控 在线官网与实盘大屏 · 快速上手 · 投委会架构 · 核心特性 · 版本日志 🏛️ v7.3.0 投委会架构重构（Trading Desk Council） 在 v7.3.0 中，系统彻底淘汰了教条分工角色，全面重塑为符合真实顶级对冲基金的交易员提案制与首席投资官终审制： 同等身份资深交易员 (Peer Senior Traders)： Trader A (顺势稳健型)：专注 4H/1H 大级别多空通道与打折低吸点，全盘审查可用资金，坚守保本移损与高胜率； Trader B (动能突破型)：专注微积分速度 $v$ 与加速度 $a$ 爆发，捕捉高爆发动量波段与大盈亏比机会； Trader C (数理筹码型)：专注定积分做功 $E$、聪明钱主力真实动向与盘口深度比，严格防范流动性滑点陷阱； 自定义交易员扩展：支持无限添加具备专属 System Prompt、数据插槽与采样温度
| Stars | 217 |
| Forks | 71 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.2075703458013/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-20 |
| Created | 2026-08-30 |
| Platforms | python |
| Est. Tokens | ~14k |
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r20-quantum-trader is ⚡ LLM-Native Self-Evolving Quantitative Trading Terminal & Execution Engine (Bloomberg-Style Dark Terminal + 6-Asset AI Brain). It is categorized as a Agent Tool with 217 GitHub stars.
r20-quantum-trader is primarily written in Python. It covers topics such as bloomberg-terminal, crypto-trading, deep-reasoning.
You can find installation instructions and usage details in the r20-quantum-trader GitHub repository at github.com/555cute/r20-quantum-trader. The project has 217 stars and 71 forks, indicating an active community.
r20-quantum-trader is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to r20-quantum-trader on Agent Skills Hub include ai-hedge-fund-crypto, hunter-community, awesome-agent-evolution. 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: