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factor-research

Automate stock factor analysis with IC/IR tests and quantile backtesting.

Updated May 25, 2026
Search Tags:#investment strategy#factor analysis#quantile backtesting#factor combination#IC/IR analysis
One-click install
npx skills add https://github.com/NigarumOvum/AutoTrading --skill factor-research-nigarumovum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/NigarumOvum/AutoTrading/tree/main/Vibe-Trading/agent/src/skills/factor-research
Command: npx skills add https://github.com/NigarumOvum/AutoTrading --skill factor-research-nigarumovum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Factor Research Framework simplifies and accelerates factor-based analysis, enhancing investment decision-making processes.

Core Features & Use Cases

  • IC/IR Analysis: Evaluate the predictive power of individual or combined factors with IC and IR metrics.
  • Quantile Backtesting: Backtest factors with quantile analysis for comprehensive performance assessment.
  • Factor Combination: Provide methods for combining factors effectively, enhancing investment strategies.

Quick Start

Execute the 'factor_analysis' script using the provided CSVs and 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 automate stock factor analysis for investment decision support?▼

Automate stock factor analysis by running the factor_analysis script with provided CSVs to compute IC/IR tests, quantile backtesting, and factor combinations for investment decision support.

What is IC/IR analysis in factor-based investment strategy evaluation?▼

IC/IR analysis evaluates the predictive power of individual or combined stock factors. The framework computes Information Coefficient and Information Ratio metrics to measure factor effectiveness for portfolio construction.

How do I backtest investment factors using quantile analysis in Python?▼

Backtest investment factors using quantile analysis by executing the factor_analysis script with your CSV data. The framework leverages pandas, numpy, and scipy to assess factor performance across quantiles.

Can I use this factor combination framework with pandas and numpy dependencies?▼

Yes, the factor combination framework requires pandas, numpy, and scipy libraries. These dependencies handle the statistical computation necessary for combining factors and evaluating investment strategies.

What's the best way to evaluate combined factors for portfolio construction?▼

Evaluate combined factors by applying IC/IR analysis and quantile backtesting through the automated framework. This approach provides comprehensive performance assessment to enhance investment strategies and portfolio construction.

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