r20-quantum-trader: LLM-Native Self-Evolving Quantitative Trading Terminal & Execution Engine

by 555cute · Agent Tool · ★ 261

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install r20-quantum-trader

git clone https://github.com/555cute/r20-quantum-trader.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

bloomberg-terminalcrypto-tradingdeep-reasoningfastapillm-agentokx-trading-botquantitative-tradingqwenpawself-evolutionsmart-money

Quick Facts

Stars261
Forks85
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score64/100
Open Issues2
Last Updated2026-09-25
Created2026-08-30
Platformspython
Est. Tokens~14k

About r20-quantum-trader

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 $v$...

Compatible Skills

These tools work well together with r20-quantum-trader for enhanced workflows:

  • ClawForge — semantic(0.36)+complementary+rare_topics+same_lang+similar_pop+shared_platform (67%)
  • LLM_trader — semantic(0.38)+complementary+same_lang+similar_pop+shared_platform (63%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Agent Tool tools