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factor-research

Evaluate factor signal predictive power across instruments using IC/IR metrics.

Updated Aug 23, 2026
Search Tags:#backtesting#stock-analysis#quant#factor#cross-sectional#ic-ir
One-click install
npx skills add https://github.com/prinzeval/Vibe-Trading --skill factor-research-prinzeval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/prinzeval/Vibe-Trading/tree/main/VALENDATA/agent/src/skills/factor-research
Command: npx skills add https://github.com/prinzeval/Vibe-Trading --skill factor-research-prinzeval

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematically evaluates the predictive power of single or multiple factors across instruments, helping avoid look-ahead bias and guide factor screening.

Core Features & Use Cases

  • Compute cross-sectional factor values and forward returns, generating aligned CSVs.
  • Assess factor validity using IC/IR metrics and quantile backtesting to guide factor screening and combination.
  • Support factor decay analysis and comparisons across industries and markets.

Quick Start

Run the factor_research workflow on your cross-section factor data and forward returns to generate IC/IR results and factor-screening outputs.

Frequently Asked Questions about factor-research

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

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

Cross-sectional factor predictive power is evaluated by computing aligned factor values and forward returns across multiple instruments, then running IC/IR tests to generate ic_series.csv, ic_summary.json, and group_equity.csv outputs for factor screening.

What's the best way to backtest factor validity across different holding periods and markets?▼

Factor validity backtesting across markets and holding periods is handled by computing cross-sectional factor values and forward returns, then applying quantile backtesting and IC/IR metrics to compare factor decay and guide factor combination.

How do I avoid look-ahead bias when testing factor signals across equities?▼

Look-ahead bias in factor signal testing is avoided by systematically aligning factor computation dates with forward returns across instruments before generating IC/IR results and quantile backtesting outputs for factor screening.

Can I screen and combine multiple factors for cross-sectional equity analysis?▼

Multiple factor screening and combination for cross-sectional equity analysis is supported by evaluating single or multiple factors across instruments, using IC/IR metrics and quantile backtesting to assess validity and guide factor combination decisions.

What inputs do I need to generate an IC summary and group equity CSV for factor research?▼

Generating IC summary and group equity CSVs requires cross-sectional factor data and forward returns across instruments as inputs, which are date-aligned and processed through the factor_analysis tool to produce ic_series.csv, ic_summary.json, and group_equity.csv.

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