multi-factor

Rank stocks by integrating momentum, value, and quality factors into a TopN long candidate set.

Updated May 15, 2026

Search Tags:#portfolio #momentum #volatility #multi-factor #cross-sectional #stock-ranking

Ready to run

npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill multi-factor-philipcoller-777

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/multi-factor
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill multi-factor-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, requests.

What problem does it solve?

Helps investors rank and select stocks by combining multiple factors across a cross-section into a single composite score, enabling the construction of TopN long portfolios.

Core Features & Use Cases

Cross-sectional multi-factor ranking that aggregates momentum, value/quality, and volatility signals.
Deterministic TopN portfolio construction with equal weights, suitable for multi-instrument strategies.
Use Case: Apply on a daily universe of stocks to identify the top performers for a long-only sleeve in a diversified portfolio.

Quick Start

Identify the TopN stocks by the multi-factor ranking and construct a corresponding long 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 using cross-sectional multi-factor scoring?▼

Cross-sectional multi-factor scoring ranks stocks by integrating momentum, value, quality, and volatility factors into a single composite score to produce a TopN long candidate set. It computes factors, standardizes them cross-sectionally, and deterministically selects top performers.

What is the best way to build a long-only stock portfolio from multiple factors?▼

Building a long-only portfolio from multiple factors involves aggregating momentum, value, and quality signals cross-sectionally to generate a composite score. The process then uses deterministic TopN selection with equal weights to construct the multi-instrument portfolio.

Can I use pandas and numpy for cross-sectional stock ranking?▼

Yes, pandas and numpy support cross-sectional stock ranking by enabling factor computation, cross-sectional standardization, and deterministic TopN selection. These dependencies process reusable data inputs to generate equal-weighted long candidate sets across multi-instrument portfolios.

Does multi-factor stock ranking work for daily portfolio decisions?▼

Multi-factor stock ranking works for daily or periodic portfolio decisions by applying cross-sectional standardization to a daily universe of stocks. It identifies top performers to drive long-only sleeves within diversified portfolios using deterministic TopN selection.

How does cross-sectional standardization work in multi-factor models?▼

Cross-sectional standardization in multi-factor models normalizes momentum, value, quality, and volatility signals across a stock universe at a given time. This ensures each factor contributes proportionally to the composite score used for deterministic TopN selection.

What are the limitations of deterministic TopN selection for stock ranking?▼

Deterministic TopN selection limits stock ranking to long-only strategies with equal weights, meaning it does not support short positions or dynamic weight optimization. It relies entirely on the quality of reusable data inputs and computed cross-sectional factors.