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Qlib Ai Quant
by
Tang Weigang
· GitHub ↗
· v0.3.2
· MIT-0
109
Downloads
0
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0
Active Installs
3
Versions
Install in OpenClaw
/install qlib-ai-quant
Description
基于微软 qlib 的 AI 量化平台：覆盖预测模型、因子挖掘（Alpha158/TFT）、 组合优化、多频回测。支持 A 股 + 美股 + 港股多市场。
Usage Guidance
This skill's files claim a qlib-based platform but the runtime instructions heavily reference ZVT, expect Python package installs, and touch a local ZVT home directory — none of which are declared in the registry. Before installing or running: (1) Inspect SKILL.md and seed.yaml yourself and confirm you are comfortable with any pip installs; (2) run it inside an isolated virtual environment or sandbox and set ZVT_HOME to a dedicated directory to avoid contaminating your real ~/.zvt; (3) do NOT provide any API keys or credentials until you confirm which provider is actually used and why; (4) if you want to proceed, request the author/source (homepage is missing) or ask for an explicit install manifest and a minimal example showing only qlib usage — the current package is internally inconsistent and should be treated with caution.
Capability Analysis
Type: OpenClaw Skill
Name: qlib-ai-quant
Version: 0.3.2
The skill bundle is a highly structured knowledge pack for an AI agent to perform quantitative finance tasks using Microsoft Qlib and the ZVT library. It contains extensive documentation on financial modeling best practices, including 25 'Anti-Patterns' (e.g., AP-QLIB-2090 regarding look-ahead bias) and 12 'Semantic Locks' (e.g., SL-01 enforcing sell-before-buy ordering) designed to prevent common algorithmic trading errors. The instructions in SKILL.md and seed.yaml are focused on ensuring code quality, temporal data integrity, and proper multi-index handling. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the bundle appears to be a legitimate tool for financial research and backtesting.
Capability Tags
Capability Assessment
Purpose & Capability
Name/description claim a qlib-based AI quant platform, but the runtime files and seed.yaml include many ZVT-specific preconditions (python checks for zvt, ZVT_HOME directory, zvt.recorders commands) and references to multiple ecosystems. The required/env/installer metadata declares no env vars or installs, yet the instructions imply installing and using ZVT and other providers — this is disproportionate to a standalone 'qlib' helper and indicates incoherence between claimed purpose and required components.
Instruction Scope
SKILL.md/seed.yaml direct the agent to run runtime checks and commands (e.g., python3 -c 'import zvt...', pip install zvt if missing, create/check ZVT_HOME and touch files) and to reload seed.yaml before any behavioral decision. The skill's prose references reading/writing ~/.zvt and running recorders; it also directs the agent to consult many large reference files. These instructions access filesystem paths and environment variables (ZVT_HOME) that were not declared in requires.env and go beyond a simple 'write code for qlib' helper.
Install Mechanism
There is no declared install spec in registry metadata (instruction-only). However, seed.yaml/execution_protocol and SKILL.md imply installing packages (pip install zvt) and running host_adapter.install_recipes[]. The absence of an explicit install recipe in the registry while the instructions expect package installation is a mismatch and increases operational risk if the agent or user follows those steps automatically.
Credentials
Declared requirements list no environment variables or credentials, but the runtime instructions reference ZVT_HOME and require creating/writing to it. The skill also asks users to choose data providers (eastmoney, joinquant, qmt, etc.), some of which require API keys/accounts; those credentials are not declared. This mismatch means the skill expects access to filesystem locations and possibly external service credentials that were not declared up-front.
Persistence & Privilege
always:false (normal). The skill does instruct creating/checking a local data directory (~/.zvt) and suggests running pip install and recorders which will write local artifacts. It does not request to modify other skills or global agent config, but it does expect persistent local data directories and can cause environment changes if followed.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install qlib-ai-quant - After installation, invoke the skill by name or use
/qlib-ai-quant - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.2
v0.3.2: inject bilingual metadata per naming spec. H1 now shows Qlib AI 量化 + slug; tagline and description replaced with CTO-authored copy (fixes tagline pollution for non-ZVT skills).
v0.3.1
Remove install.sh — knowledge-only bundle. Host AI consumes directly from URL; no user-side installation needed. Fixes ClawHub suspicious flag.
v0.3.0
Doramagic crystal portfolio v0.3.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills
Metadata
Frequently Asked Questions
What is Qlib Ai Quant?
基于微软 qlib 的 AI 量化平台：覆盖预测模型、因子挖掘（Alpha158/TFT）、 组合优化、多频回测。支持 A 股 + 美股 + 港股多市场。 It is an AI Agent Skill for Claude Code / OpenClaw, with 109 downloads so far.
How do I install Qlib Ai Quant?
Run "/install qlib-ai-quant" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Qlib Ai Quant free?
Yes, Qlib Ai Quant is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Qlib Ai Quant support?
Qlib Ai Quant is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Qlib Ai Quant?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.2.
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