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factor-research

Evaluate trading factor predictive validity via IC/IR analysis and quantile backtesting.

Updated Jun 30, 2026
Search Tags:#finance#backtesting#trading#quantitative#alpha#factor analysis
One-click install
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill factor-research-0xzknw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: factor-research
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/factor-research
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill factor-research-0xzknw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy.

What problem does it solve?

This Skill addresses the difficulty of validating trading signals by providing a rigorous framework for statistical testing, preventing look-ahead bias, and ensuring factors have genuine predictive power.

Core Features & Use Cases

  • Statistical Validation: Perform IC/IR analysis and quantile backtesting to confirm if a factor is effective or just noise.
  • Factor Combination: Utilize equal-weight, IC-weighted, or orthogonalized methods to create robust composite signals.
  • Use Case: A quantitative researcher can take a raw momentum factor, run it through the framework to check for decay and industry bias, and then combine it with other validated factors to build a high-performance trading strategy.

Quick Start

Use the factor-research skill to analyze the predictive power of my momentum factor by processing factor-data.csv and return-data.csv into the results directory.

Frequently Asked Questions about factor-research

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

FAQPage Schema
How do I evaluate the predictive validity of trading factors to prevent look-ahead bias?▼

To evaluate trading factors and prevent look-ahead bias, apply IC/IR statistical analysis and quantile backtesting. This framework tests raw signals against instrument return data to confirm genuine predictive power rather than noise.

How do I combine multiple quantitative alpha factors into a single composite signal?▼

Combine multiple quantitative alpha factors using equal-weight, IC-weighted, or orthogonalized methods. This multi-factor combination technique creates robust composite signals for high-performance trading strategy development.

Can I perform cross-sectional factor screening and decay analysis using pandas and numpy?▼

Yes, cross-sectional factor screening and decay analysis are supported using pandas, numpy, and scipy. These dependencies enable high-precision numerical computations on instrument return data for quantitative research.

What is the best way to test if a raw momentum factor is effective or just statistical noise?▼

The best way to test if a momentum factor is effective is running it through IC/IR statistical validation and quantile backtesting. This checks for industry bias and decay to distinguish genuine predictive power from noise.

How do I start analyzing my custom factor data for quantitative backtesting?▼

Start quantitative backtesting by processing your factor-data.csv and return-data.csv files. The framework evaluates the predictive power of your custom factors and outputs the analytical results into a designated directory.

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