skills.rest
20YN04
20YN04COMMUNITY
Explore all skills by this author →
WebsiteGitHub

multi-factor

Calculates composite rankings from standardized Alpha signals for stock portfolio construction.

Updated Jun 30, 2026
Search Tags:#finance#portfolio#trading#stock#quantitative#alpha
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill multi-factor-20yn04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/multi-factor
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill multi-factor-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity of managing multi-factor investment strategies by automating the calculation, standardization, and ranking of stock factors to build objective, data-driven portfolios.

Core Features & Use Cases

  • Factor Standardization: Automatically applies Z-score normalization to diverse financial factors to ensure they are comparable on a cross-sectional basis.
  • Composite Scoring: Combines multiple alpha signals (momentum, reversal, volatility, volume) into a single ranking score for precise asset selection.
  • Use Case: A quantitative trader can use this to rank a universe of 500 stocks based on a custom blend of momentum and value factors, automatically generating a long-only portfolio of the top 20 names.

Quick Start

Use the multi-factor skill to compute a composite ranking for the provided stock data and select the top 10 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 portfolio construction?▼

You can automate cross-sectional stock ranking by calculating multiple financial factors, applying Z-score normalization, and generating composite scores to select TopN or BottomN assets for portfolio construction.

What is Z-score normalization for multi-factor alpha signals?▼

Z-score normalization standardizes diverse financial factors like momentum and volatility, ensuring cross-sectional comparability before combining them into a single composite ranking score.

How do I combine multiple alpha signals into a composite ranking score?▼

You combine multiple alpha signals by calculating individual factors, standardizing them via Z-score normalization, and redistributing weights to produce a unified composite score for asset selection.

Does this multi-factor workflow support both wide-panel signals and per-symbol OHLCV data?▼

Yes, the workflow supports modern wide-panel Alpha Zoo signal generation as well as legacy per-symbol OHLCV data processing for cross-sectional factor calculation and ranking.

Can I select TopN and BottomN stocks for a long-only portfolio using pandas and numpy?▼

Yes, using pandas and numpy, you can compute composite ranking scores and apply TopN or BottomN selection logic to automatically generate long-only portfolios from a stock universe.

When should I use weight redistribution in quantitative portfolio construction?▼

Apply weight redistribution during composite scoring when blending multiple financial factors, ensuring no single alpha signal dominates the cross-sectional ranking and portfolio selection.

Related Skills

Explore More Data & Analytics Skills→
hxhyyyhxhyyy

multi-factor

Standardizes multiple financial factors and aggregates them into a composite stock-ranking signal.

Community
Advanced
Snowflake-LabsSnowflake-Labs
12

brinson-attribution

Decompose active returns into allocation, selection, and interaction effects.

Official
Basic
20YN0420YN04

quant-statistics

Perform ADF unit-root tests and GARCH modeling on financial time-series data.

Community
Advanced
fall-development-robfall-development-rob
7

Financial Analyst - Risk & Quant

Perform CFA-level quantitative risk analysis and portfolio optimization for institutional portfolios.

Community
Advanced