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Quant Research Platform
by
jason-aka-chen
· GitHub ↗
· v1.0.0
· MIT-0
148
Downloads
1
Stars
1
Active Installs
1
Versions
Install in OpenClaw
/install quant-research-platform
Description
Advanced quantitative research platform for multi-factor analysis, factor mining, backtesting, and portfolio optimization. Includes 100+ alpha factors, IC/IR...
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install quant-research-platform - After installation, invoke the skill by name or use
/quant-research-platform - Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Frequently Asked Questions
What is Quant Research Platform?
Advanced quantitative research platform for multi-factor analysis, factor mining, backtesting, and portfolio optimization. Includes 100+ alpha factors, IC/IR... It is an AI Agent Skill for Claude Code / OpenClaw, with 148 downloads so far.
How do I install Quant Research Platform?
Run "/install quant-research-platform" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Quant Research Platform free?
Yes, Quant Research Platform is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Quant Research Platform support?
Quant Research Platform is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Quant Research Platform?
It is built and maintained by jason-aka-chen (@jason-aka-chen); the current version is v1.0.0.
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