multi-factor

Calculate and combine financial factors to rank assets for portfolio construction.

Updated Jul 29, 2026

Search Tags:#finance #portfolio #trading #quant #alpha #factors

Ready to run

npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill multi-factor-santoosaraujo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/santoosaraujo/vibe-trading-claude/tree/main/.claude/skills/multi-factor
Command: npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill multi-factor-santoosaraujo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This Skill solves the complexity of building systematic investment strategies by automating the calculation, standardization, and combination of multiple financial factors to rank assets.

Core Features & Use Cases

Factor Standardization: Applies Z-score normalization to ensure factors with different scales are comparable across the cross-section.
Composite Scoring: Combines multiple alpha signals into a single ranking score using equal or custom weighting.
Portfolio Construction: Automatically selects TopN or BottomN assets to build long-only or long-short portfolios based on the composite signal.

Quick Start

Use the multi-factor skill to generate a long-only portfolio signal for the provided market data using momentum and volatility factors.

Frequently Asked Questions about multi-factor

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

FAQPage Schema

How do I standardize multiple financial factors for cross-sectional stock ranking?▼

You standardize multiple financial factors for cross-sectional stock ranking by applying Z-score normalization to ensure factors with different scales are comparable, then combining them into a single composite score to rank assets.

How do I build a long-short portfolio using composite alpha signals?▼

You build a long-short portfolio using composite alpha signals by calculating and combining multiple factors with custom or equal weighting, then automatically selecting the TopN and BottomN assets based on the resulting standardized cross-sectional ranking.

Can I process wide-panel data for systematic portfolio construction using pandas and numpy?▼

Yes, you can process wide-panel data for systematic portfolio construction using pandas and numpy, as the multi-factor framework relies on these dependencies to handle robust signal generation and cross-sectional asset ranking.

What is the best way to combine momentum and volatility factors for a long-only trading strategy?▼

The best way to combine momentum and volatility factors for a long-only trading strategy is to apply cross-sectional standardization to each factor, merge them into a composite score, and select the TopN performing assets for your portfolio.

Does cross-sectional Z-score normalization work for factors with completely different scales?▼

Yes, cross-sectional Z-score normalization works for factors with completely different scales because it transforms the distributions to a common scale, making disparate alpha signals directly comparable for composite scoring.