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factor-research

Compute IC/IR statistics and quantile backtests for cross-sectional stock factors.

15|2|Updated May 1, 2026
Search Tags:#analysis#backtesting#quantitative research#factor#statistical tests
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill factor-research-opensucker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/factor-research
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill factor-research-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of analyzing the predictive power of financial factors through rigorous statistical testing and backtesting.

Core Features & Use Cases

  • Factor Validity Testing: assess the strength and stability of single or multiple factors across markets.
  • Backtesting & Comparison: evaluate how different factors perform historically in stock selection.
  • Use Case: Researchers can determine whether momentum or value factors effectively predict future returns and decide on optimal combinations.

Quick Start

Call the factor_analysis tool with your factor and return CSV files to perform IC analysis and backtesting.

Frequently Asked Questions about factor-research

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

FAQPage Schema
How do I test the predictive power of financial factors for stock selection?▼

To test factor predictive power, you compute IC/IR statistics and run quantile backtests on cross-sectional stock data. This Skill evaluates single or multiple factors to assess their strength and stability for quantitative research models.

What is the best way to run a quantile backtest on cross-sectional stock data?▼

The best way to run a quantile backtest is to provide pre-processed factor and return data in CSV files with aligned indices and columns. The analysis tool then computes historical stock selection performance across different quantiles.

How do I format CSV files for IC analysis and factor validity testing?▼

For IC analysis and factor validity testing, format your factor and return data as CSV files with strictly aligned indices and columns. Pre-processing your cross-sectional stock data ensures accurate statistical evaluation.

Can I compare momentum and value factors in a single backtesting run?▼

Yes, you can compare momentum and value factors in a single run. The tool evaluates multiple factors simultaneously, computing IC/IR statistics and quantile backtests to help you decide on optimal factor combinations.

What statistical tests are used for evaluating quantitative research factors?▼

Statistical tests for evaluating quantitative research factors include IC (Information Coefficient) and IR (Information Ratio) calculations. These metrics assess the strength and stability of factors in predicting future returns.

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