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factor-research

Compute IC/IR statistics and quantile backtests for cross-sectional factor research.

Updated May 5, 2026
Search Tags:#quantitative finance#signal validation#factor research#ic ir#quantile backtesting#cross-sectional analysis#factor combination
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill factor-research-wudye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/factor-research
Command: npx skills add https://github.com/wudye/traderAssistHK --skill factor-research-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy.

What problem does it solve?

It helps you determine whether a financial factor has genuine stock-selection and predictive power instead of producing misleading results from noise or look-ahead bias.

Core Features & Use Cases

  • IC/IR Evaluation: Compute daily information coefficient series and summarize IC mean, IC volatility, IR, and the proportion of positive IC to judge stability and direction.
  • Quantile Backtesting: Sort instruments into quantile groups and assess monotonicity, long-short spread, and equity-curve stability to verify discrimination across the cross-section.
  • Factor Screening & Combination: Screen effective factors and combine them via equal-weight, IC-weighted, or orthogonalized methods to build a composite signal with improved robustness.

Quick Start

Use the factor-research workflow by providing an aligned factor CSV and forward return CSV, then request IC/IR and quantile backtest outputs for screening and (optionally) composite factor construction.

Frequently Asked Questions about factor-research

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

FAQPage Schema
How do I validate a quantitative factor using IC and IR statistics?▼

Quantile backtesting evaluates a factor's cross-sectional stock-selection power by sorting instruments into configurable quantile groups, checking return monotonicity across groups, and analyzing the long-short spread and equity-curve stability.

How do I prevent look-ahead bias in cross-sectional factor research?▼

Preventing look-ahead bias in cross-sectional factor research requires providing strictly time-aligned factor and forward return CSVs, ensuring that factor data at any given date never incorporates future return information that would inflate predictive metrics.

Can I combine multiple effective factors into a single composite signal?▼

Yes, you can combine multiple screened factors into a composite signal using equal-weight, IC-weighted, or orthogonalized combination methods to improve the robustness of your quantitative cross-sectional predictions.

How do I analyze factor decay across different holding periods?▼

Factor decay analysis across holding periods is conducted by measuring how the IC/IR statistics and quantile backtest monotonicity degrade as the forward return calculation window extends, identifying the optimal holding duration for the signal.

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