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multi-factor

Combine momentum, reversal, and volatility factors to rank assets and construct portfolios.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the complex process of calculating multiple financial factors, standardizing them, and selecting top-performing assets to build a balanced portfolio, removing the need for manual data crunching.

Core Features & Use Cases

  • Multi-Factor Scoring: Combines momentum, reversal, volatility, and volume-ratio factors into a single composite score.
  • Cross-Sectional Ranking: Uses Z-score normalization to rank stocks relative to their peers, ensuring consistent selection.
  • Use Case: A quantitative trader can use this to automatically rebalance a portfolio of crypto assets every 20 days based on the latest market performance data.

Quick Start

Use the multi-factor skill to compute a composite ranking for the provided market data and select the top three assets for a long-only portfolio.

Frequently Asked Questions about multi-factor

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

FAQPage Schema
How do I automate cross-sectional stock ranking for a quantitative portfolio?▼

Cross-sectional stock ranking is automated by calculating and combining multiple financial factors, applying Z-score normalization to evaluate assets relative to peers, and selecting top performers to construct a balanced portfolio.

What financial factors can be combined for multi-instrument asset selection?▼

Multi-instrument asset selection supports combining momentum, reversal, volatility, and volume-ratio factors into a single composite score to evaluate cross-sectional performance and rank assets for portfolio construction.

Do I need pandas and numpy to generate trading signals from OHLCV data?▼

Yes, generating trading signals from OHLCV data panels requires pandas and numpy to perform Z-score normalization, factor calculation, and composite signal generation for cross-sectional asset ranking.

What is the best way to normalize multiple financial factors for portfolio construction?▼

The best way to normalize financial factors is Z-score normalization, which standardizes diverse metrics like momentum and volatility to ensure consistent cross-sectional ranking and balanced portfolio construction.

Can I use quantitative stock ranking for crypto assets on a 20-day rebalance cycle?▼

Yes, quantitative stock ranking can be applied to crypto assets on a 20-day rebalance cycle, using the latest OHLCV market performance data to automatically recalculate composite scores and adjust the portfolio.

Why use Z-score normalization instead of raw factor values for asset selection?▼

Z-score normalization is used instead of raw factor values to standardize diverse metrics onto a uniform scale, ensuring that no single factor dominates the composite score and maintaining consistent cross-sectional ranking.

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