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multi-factor

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

Updated Jul 8, 2026
Search Tags:#finance#backtesting#trading#quantitative#alpha#stock-ranking
One-click install
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill multi-factor-hxhyyy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/multi-factor
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill multi-factor-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

This skill addresses the complexity of multi-factor quantitative strategy development by automating the calculation, standardization, and ranking of stock factors to build disciplined, data-driven portfolios.

Core Features & Use Cases

  • Factor Standardization: Automatically applies Z-score normalization to cross-sectional data to ensure factors are comparable.
  • Composite Scoring: Combines multiple alpha factors (momentum, reversal, volatility, etc.) into a single, weighted signal for stock selection.
  • Use Case: A quantitative trader can use this to rank a universe of assets based on a combination of momentum and value factors, automatically generating long-only or long-short signals for rebalancing.

Quick Start

Use the multi-factor skill to compute a composite ranking signal for the provided market data panel using the default 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 automate cross-sectional stock ranking for a multi-factor quantitative strategy?▼

Cross-sectional stock ranking is automated by standardizing multiple financial factors using Z-score normalization and aggregating them into a weighted composite signal. This allows you to systematically select assets based on combined alpha factors like momentum and volatility.

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

The best way to normalize financial factors for portfolio construction is applying Z-score normalization to cross-sectional data. This standardizes disparate factor scales, ensuring momentum, reversal, and volatility metrics are directly comparable before composite scoring.

Can I build a TopN portfolio using wide-panel data structures in pandas?▼

Yes, you can construct a TopN portfolio using wide-panel data structures in pandas. The process supports both legacy per-symbol and modern wide-panel formats to aggregate multiple alpha factors and generate automated rebalancing signals.

How does composite scoring work for momentum and reversal factor models?▼

Composite scoring for momentum and reversal factor models works by calculating individual factor signals, standardizing them via Z-score normalization, and combining them into a single weighted metric. This drives disciplined, data-driven stock selection.

Do I need numpy and pandas to calculate multi-instrument alpha signals?▼

Yes, you need numpy and pandas to calculate multi-instrument alpha signals. These dependencies are required to handle wide-panel data structures, perform Z-score normalization, and aggregate multiple factors into a composite portfolio signal.

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