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factor-research

Analyze factor signals with IC/IR metrics and quantile backtests.

Updated May 15, 2026
Search Tags:#finance#data-analytics#backtesting#stock-selection#factor-analysis#ic-ir
One-click install
npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill factor-research-philipcoller-777
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/factor-research
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill factor-research-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Factor research enables data-driven evaluation of cross-sectional signals to determine their predictive power via IC/IR and quantile backtests, guiding factor selection and combination.

Core Features & Use Cases

  • IC/IR analysis to measure factor quality across instruments
  • Quantile backtesting to assess stock-selection power and stability
  • Factor screening and multi-factor combination workflows for building robust strategies

Quick Start

Run the factor-analysis workflow with your factor CSV and return CSV to generate IC metrics and backtest results in the specified output directory.

Frequently Asked Questions about factor-research

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

FAQPage Schema
How do I measure cross-sectional factor predictive power using IC and IR metrics?▼

To assess stock-selection power, run a quantile backtest that groups instruments into portfolios based on factor values. The factor-analysis tool generates group equity curves to visualize performance stability across quantiles.

What data format do I need to run a factor analysis backtest?▼

The workflow applies cross-sectional signal evaluation for single-factor validity testing, factor screening, and multi-factor combination. It measures factor quality and stock-selection power across instruments and markets using your input CSVs.

Why does my factor screening show low IC despite a strong theoretical signal?▼

Low IC during factor screening indicates your cross-sectional signal lacks predictive correlation with forward returns. You should verify your factor_value CSV alignment and run quantile backtests to check if stock-selection power concentrates in specific quantiles.

Can I test multi-factor combination workflows across different markets?▼

Yes, you can apply multi-factor combination workflows across instruments and markets. The factor-analysis tool evaluates cross-sectional signals to guide factor selection and combination, provided you supply consistent factor and forward return CSV data.

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