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alpha-zoo

Benchmark alpha factor libraries with IC and IR metrics across financial universes.

Updated Jun 30, 2026
Search Tags:#benchmarking#finance#trading#quantitative#alpha#factors
One-click install
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill alpha-zoo-0xzknw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alpha-zoo
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/alpha-zoo
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill alpha-zoo-0xzknw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the difficulty of navigating and validating complex quantitative trading factors by providing a centralized library and standardized benchmarking tools.

Core Features & Use Cases

  • Alpha Library Browsing: Access and filter prebuilt cross-sectional factor libraries like Kakushadze 101, GTJA 191, and Qlib 158.
  • Performance Benchmarking: Run IC and IR analysis on specific factors or entire zoos across defined investable universes.
  • Use Case: A quantitative researcher needs to evaluate the performance of all momentum-based factors from the GTJA 191 library on the CSI 300 index over the last four years to identify potential signals for a new strategy.

Quick Start

Use the alpha_bench tool to run an evaluation of the gtja191 zoo on the csi300 universe for the period 2020 to 2024.

Frequently Asked Questions about alpha-zoo

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

FAQPage Schema
How do I benchmark quantitative alpha factors against a specific financial universe?▼

To benchmark quantitative alpha factors, use the alpha_bench tool to run statistical evaluations on prebuilt cross-sectional libraries against a defined investable universe, calculating aggregate IC and IR metrics without exposing raw per-stock data.

What prebuilt cross-sectional alpha factor libraries are available for quantitative research?▼

Available cross-sectional alpha factor libraries include Kakushadze 101, GTJA 191, and Qlib 158, which can be accessed, filtered, and benchmarked to support quantitative trading research workflows.

Can I evaluate all momentum-based factors from the GTJA 191 library on the CSI 300 index?▼

Yes, you can evaluate specific factors or entire libraries like GTJA 191 on the CSI 300 universe by running the alpha_bench tool for a defined period, such as 2020 to 2024, to calculate IC and IR performance metrics.

How do I calculate IC and IR metrics for a quantitative trading factor zoo?▼

IC and IR metrics are calculated by running the alpha_bench tool on a selected factor zoo, which performs aggregate statistical evaluation against specific financial universes to validate factor performance.

Does the alpha factor benchmarking tool expose raw per-stock data during analysis?▼

The alpha factor benchmarking tool does not expose raw per-stock data, performing aggregate statistical evaluation through integration with internal registry and factor analysis modules instead.

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