multi-factor

Name: multi-factor
Availability: InStock
Author: OpenSucker

Rank stocks by computing financial factors and selecting top candidates for portfolios.

15|2|Updated May 1, 2026

Search Tags:#analysis #portfolio #ranking #stocks #quantitative #multi-factor

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill multi-factor-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/multi-factor
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill multi-factor-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a systematic method to rank stocks across a cross-section based on multiple financial factors, improving portfolio construction accuracy.

Core Features & Use Cases

Factor Computation: Calculates multiple stock factors such as momentum, reversal, volatility, and volume ratio.
Cross-Section Standardization: Applies Z-score normalization to factors across stocks for comparability.
Portfolio Selection: Ranks stocks and constructs TopN portfolios with equal weights, suitable for quantitative trading strategies.
Use Case: An asset manager can implement this Skill to identify the top-performing stocks based on combined factor scores and rebalance monthly.

Quick Start

Use the multi-factor skill to rank stocks by calculating their momentum, volatility, and other factors, then select the top stocks for your portfolio.

Frequently Asked Questions about multi-factor

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

FAQPage Schema

How do I rank stocks for portfolio allocation using multi-factor analysis?▼

To rank stocks for portfolio allocation using multi-factor analysis, you calculate financial indicators like momentum and volatility, apply Z-score standardization across the cross-section, and select the top candidates for equal-weight portfolio construction.

What financial factors are used in quantitative stock ranking?▼

Financial factors used in quantitative stock ranking include momentum, reversal, volatility, and volume ratio. These factors are computed and standardized to compare stocks systematically for portfolio selection.

How do I normalize stock factor scores for cross-section comparability?▼

To normalize stock factor scores for cross-section comparability, apply Z-score standardization across the stocks. This process ensures different financial indicators are scaled comparably before ranking and portfolio selection.

Can I use pandas and numpy for quantitative factor-based stock selection?▼

Yes, you can use pandas and numpy for quantitative factor-based stock selection. These dependencies handle the data processing required to compute financial indicators, standardize scores, and construct TopN portfolios.

What is the best way to construct a TopN portfolio from standardized factor scores?▼

The best way to construct a TopN portfolio from standardized factor scores is to rank the stocks based on their combined scores and select the top candidates for equal-weight portfolio allocation, suitable for monthly rebalancing.