AlphaQuanter: [ACL2026] AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock

by horizon-llm · Agent Tool · ★ 78

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

Security audit verdict: SAFE · quality 52/100

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.

agentagentic-rl

Quick Facts

Stars78
Forks12
LanguagePython
CategoryAgent Tool
Quality Score52/100
Open Issues2
Last Updated2026-07-03
Created2025-10-16
Platformspython
Est. Tokens~13k

About AlphaQuanter

AlphaQuanter This repository contains the implementation of the paper AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading. 📖 Overview While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations.

Compatible Skills

These tools work well together with AlphaQuanter for enhanced workflows:

  • Claw-R1 — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • SkillZero — semantic(0.43)+complementary+same_lang+similar_pop+shared_platform (65%)
  • AgentFly — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (64%)
  • nautex — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (62%)

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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:

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