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Trading Quant
作者
onlyloveher
· GitHub ↗
· v1.0.0
· MIT-0
158
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install trading-quant-zhouli
功能描述
量化交易数据分析工具。A股/美股/港股/贵金属实时行情，多维度评分(技术面+资金面+基本面)，涨跌停池，北向资金，分钟级资金流。Use when: (1) 查询任何股票实时行情和评分, (2) 分析A股涨跌停异动, (3) 查看北向资金流向, (4) 美股港股贵金属行情, (5) 全球市场概览, (6) 个股资金...
安全使用建议
This skill appears to be what it says: a quantitative market-data and scoring toolkit that fetches public market data and performs analysis. Before installing or running it, consider: (1) it will read/write local caches (e.g., /tmp/quant_industry_cache.json) and may try to read a watchlist at a relative path (../../../../../knowledge/watchlist.json) — remove or inspect that file if you don’t want it used; (2) the sentiment module can download HuggingFace models into ~/.cache/huggingface (large network/downloads) — if you have limited bandwidth or disallow external model downloads, block that or pre-populate the cache; (3) it creates/uses a workspace directory (TRADING_WORKSPACE, default ~/.openclaw/workspace-trading) — review that directory contents for persisted outputs; (4) run the code in a sandbox or review scripts/quant.py and data_sources/* fully (network endpoints) before granting the agent permission to execute it autonomously; (5) no credentials are required by the skill manifest, so there is no obvious secret-exfiltration request, but network access during runtime will contact public market sites and HuggingFace. If you want higher assurance, request the maintainer to document exact endpoints and to remove the relative path climb to knowledge/watchlist.json or make it explicitly configurable.
功能分析
Type: OpenClaw Skill
Name: trading-quant-zhouli
Version: 1.0.0
The skill bundle is a comprehensive quantitative trading analysis toolset for A-share, US, and HK markets. It implements multi-dimensional scoring (technical, capital, fundamental, sentiment) using legitimate financial data sources such as Tencent, Sina, and EastMoney. The code utilizes standard libraries like pandas-ta for technical indicators and transformers for FinBERT-based sentiment analysis, with robust caching via SQLite and local JSON files. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found across the scripts or documentation.
能力评估
Purpose & Capability
Name/description (quant trading data & scoring) align with included files: many data_source modules (tencent/sina/eastmoney/akshare/yfinance/etc), scoring/analysis modules, and a CLI entry scripts/quant.py. No unrelated credentials, binaries, or install steps are requested.
Instruction Scope
SKILL.md instructs the agent to run python scripts/quant.py tools to fetch and analyse market data — this matches the code. The codebase however reads/writes local files: /tmp caches, a HuggingFace cache (~/.cache/huggingface) when loading FinBERT, and a workspace path controlled by TRADING_WORKSPACE (default ~/.openclaw/workspace-trading). The industry classifier tries to prefill from a watchlist at a relative path (../../../../../knowledge/watchlist.json) which may point outside the skill folder; this can read a local watchlist file if present. These file reads/writes are plausible for the tool but are notable scope actions beyond pure network calls.
Install Mechanism
No install spec is provided (instruction-only), lowering disk-write risk from installer scripts. However the package contains many Python modules and a requirements.txt with heavy deps (pandas, pandas-ta, transformers implied by sentiment module). Running will likely require installing these packages and may cause large downloads (HuggingFace models).
Credentials
The skill declares no required env vars or credentials. It does read optional environment TRADING_WORKSPACE to determine workspace root (defaults to ~/.openclaw/workspace-trading). No secrets or unrelated credentials are requested by the manifest.
Persistence & Privilege
The skill is not always:true and is user-invocable only. It persists caches (e.g., /tmp/quant_industry_cache.json, ~/.cache/huggingface, and workspace paths) and may create files under the default workspace; it does not modify other skills' configs in the provided code. Autonomous invocation is allowed (platform default) which means it could be run by agents if permitted — combine that with network/model downloads if you are concerned about bandwidth or unexpected model access.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install trading-quant-zhouli - 安装完成后，直接呼叫该 Skill 的名称或使用
/trading-quant-zhouli触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
- Initial release of the trading-quant skill for quantitative trading data analysis.
- Supports real-time quotes and multi-dimensional scoring (technical, capital, fundamental, news, sentiment) for A-shares, US/HK stocks, and metals.
- Provides tools for anomaly detection, capital flow tracking, market overviews, and detailed stock analysis.
- Unified command-line interface with various analysis and maintenance tools.
- Integrates multiple data sources (Tencent, Sina, Eastmoney, iFinD) with automated source fallback.
- Implements risk and market alert rules based on key financial indicators and market activity.
元数据
常见问题
Trading Quant 是什么？
量化交易数据分析工具。A股/美股/港股/贵金属实时行情，多维度评分(技术面+资金面+基本面)，涨跌停池，北向资金，分钟级资金流。Use when: (1) 查询任何股票实时行情和评分, (2) 分析A股涨跌停异动, (3) 查看北向资金流向, (4) 美股港股贵金属行情, (5) 全球市场概览, (6) 个股资金... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 158 次。
如何安装 Trading Quant？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install trading-quant-zhouli」即可一键安装，无需额外配置。
Trading Quant 是免费的吗？
是的，Trading Quant 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Trading Quant 支持哪些平台？
Trading Quant 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Trading Quant？
由 onlyloveher（@onlyloveher）开发并维护，当前版本 v1.0.0。
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