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alpha-zoo

Enumerate and benchmark pre-built cross-sectional factor libraries for investment research.

1|Updated Jun 23, 2026
Search Tags:#financial analytics#investment research#cross-sectional factors#alpha analysis#factor libraries
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill alpha-zoo-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alpha-zoo
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/alpha-zoo
Command: npx skills add https://github.com/644408071-design/Kokpop --skill alpha-zoo-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The alpha-zoo Skill solves the challenge of finding and understanding pre-built investment factor libraries, making it easy to research and test various alpha strategies without manual data processing.

Core Features & Use Cases

  • Library Browsing: Access a curated collection of alphas such as Kakushadze 101, GTJA 191, Qlib 158, and Fama-French / Carhart.
  • Alpha Metadata: Retrieve detailed information about individual alphas including their purpose and performance metrics.
  • Benchmarking: Run IC/IR analysis on one or multiple alphas across different universes and time periods.
  • Ad-hoc Analysis: Evaluate custom factor data not present in the library using factor_analysis.

Quick Start

List all momentum alphas using the alpha-zoo skill: alpha-zoo action=list_alphas theme=momentum.

Frequently Asked Questions about alpha-zoo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What are cross-sectional factor libraries used for in investment research?▼

Cross-sectional factor libraries provide pre-built alpha formulas, such as Kakushadze 101 or GTJA 191, used to evaluate investment strategies. They help researchers test alpha performance across various markets and time frames without manual data processing.

How do I benchmark alpha strategies across different markets?▼

You can benchmark alpha strategies by running IC/IR analysis on one or multiple alphas across different universes and time periods. This allows you to evaluate and compare alpha performance metrics directly within your financial analytics environment.

Can I evaluate custom factor data not present in pre-built alpha libraries?▼

Yes, you can evaluate custom factor data not present in the library by using the factor_analysis feature. This allows you to perform ad-hoc analysis and benchmark your own proprietary alphas alongside curated collections.

Do I need a specific Python environment to run cross-sectional alpha analysis?▼

Yes, cross-sectional alpha analysis requires a Python environment with necessary libraries and access to financial market data. This setup is essential to retrieve metadata and benchmark alphas effectively.

What is the best way to list available momentum alphas in a factor library?▼

The best way to list momentum alphas in a factor library is to use the list_alphas action with the momentum theme. This enumerates all relevant pre-built alphas matching your specified investment research criteria.

Which pre-built alpha formulas are available for testing investment strategies?▼

Available pre-built alpha formulas include Kakushadze 101, GTJA 191, Qlib 158, and Fama-French / Carhart. These curated cross-sectional factor libraries allow you to retrieve detailed metadata and performance metrics for investment analysis.

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