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Quant Research Platform
作者
jason-aka-chen
· GitHub ↗
· v1.0.0
· MIT-0
148
总下载
1
收藏
1
当前安装
1
版本数
在 OpenClaw 中安装
/install quant-research-platform
功能描述
Advanced quantitative research platform for multi-factor analysis, factor mining, backtesting, and portfolio optimization. Includes 100+ alpha factors, IC/IR...
安全使用建议
Before installing or running this skill: 1) Inspect the full quant_research.py (search for network calls: requests, urllib, aiohttp, socket, boto, paramiko, ftplib, subprocess calling curl/wget) and for any use of os.environ or plaintext tokens. 2) Check the AlternativeData implementation — confirm which external APIs it calls and whether it requires API keys; do not provide API keys unless you trust the source. 3) Note that tushare typically requires a TUSHARE_TOKEN; ask the author how credentials are handled. 4) Run the code in an isolated environment (VM/container) and monitor outbound network traffic on first run. 5) Ask the publisher for provenance (homepage, repository, license) and for a list of external endpoints the library contacts. If you cannot verify those details, avoid using real production/data credentials with this skill.
功能分析
Type: OpenClaw Skill
Name: quant-research-platform
Version: 1.0.0
The quant-research-platform skill bundle is a legitimate tool for quantitative financial analysis. The code in quant_research.py implements standard financial modeling techniques such as Markowitz optimization, Risk Parity, and technical indicators (RSI, MACD) using reputable libraries like pandas and scipy. No evidence of data exfiltration, malicious execution, or prompt injection was found.
能力评估
Purpose & Capability
The name/description (multi-factor research, backtesting, optimization) align with the included Python code and SKILL.md examples. The SKILL.md pip requirements (pandas, numpy, xgboost, akshare, tushare, etc.) are reasonable for the stated purpose.
Instruction Scope
SKILL.md shows only local usage and package installation, but also documents 'AlternativeData' methods (satellite_data, web_traffic, supply_chain) which imply external network/API access. The runtime instructions do not declare how those data sources are authenticated or where network requests go. The README does not ask the agent to read unrelated system files or secrets, but the lack of detail about external endpoints and credentials is scope creep compared with the simple usage examples.
Install Mechanism
There is no registry install spec (instruction-only), and the SKILL.md recommends pip installing third-party packages from public PyPI (low-to-moderate risk). This is typical for a Python library, but the registry entry itself does not perform or specify installs—users will run pip manually. No high-risk download URLs or archive extraction are present.
Credentials
The skill lists no required environment variables, but it recommends installing tushare and akshare and exposes alternative data methods that normally require API keys or credentials. For example, tushare requires a TUSHARE_TOKEN for many endpoints; satellite imagery and web-traffic data typically require API keys. The absence of declared env vars or guidance for credentials is an inconsistency that could lead to hidden network calls or unclear credential requests at runtime.
Persistence & Privilege
The skill is not always-enabled, does not request system config paths, and does not declare persistent privileges. It appears to be a normal, user-invocable library with no unusual persistence demands.
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 quant-research-platform - 安装完成后，直接呼叫该 Skill 的名称或使用
/quant-research-platform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 quant-research-platform.
- Multi-factor research with 100+ alpha factors and automated factor mining/evaluation
- Comprehensive backtesting engine with historical, walk-forward, and Monte Carlo analysis, including transaction costs
- Advanced portfolio optimization: mean-variance, risk parity, Black-Litterman, ACL, and Kelly criterion
- Integrated risk management: VaR/CVaR, stress testing, factor exposure, drawdown control
- Supports strategy development with classic and machine learning (XGBoost, LightGBM, LSTM) approaches
- Extensive API for factor research, backtesting, and optimization
- Built-in support for technical, fundamental, sentiment, and alternative data factors
元数据
常见问题
Quant Research Platform 是什么？
Advanced quantitative research platform for multi-factor analysis, factor mining, backtesting, and portfolio optimization. Includes 100+ alpha factors, IC/IR... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 148 次。
如何安装 Quant Research Platform？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install quant-research-platform」即可一键安装，无需额外配置。
Quant Research Platform 是免费的吗？
是的，Quant Research Platform 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Quant Research Platform 支持哪些平台？
Quant Research Platform 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 Quant Research Platform？
由 jason-aka-chen（@jason-aka-chen）开发并维护，当前版本 v1.0.0。
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