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multi-factor

Rank stocks cross-sectionally by composite Z-scored factors into TopN long positions.

Updated Apr 14, 2026
Search Tags:#portfolio-construction#multifactor#cross-sectional#stock-ranking#topn#factor-standardization
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill multi-factor-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/multi-factor
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill multi-factor-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cross-sectional multi-factor stock ranking helps identify top investment ideas across a universe by combining multiple factors into a single composite score, enabling efficient TopN portfolio construction for multi-instrument strategies.

Core Features & Use Cases

  • Compute multiple factors (such as momentum, reversal, volatility, and volume_ratio) for each stock.
  • Standardize factors across the cross-section using Z-score normalization and aggregate them into a composite score.
  • Select the TopN stocks and construct equally weighted long positions to form a multi-instrument portfolio.
  • Supports deterministic weighting and configurable lookback windows for momentum and volatility.

Quick Start

Provide OHLCV data for your universe and run the engine to obtain TopN long positions.

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 across a cross-section using multiple factors?▼

Cross-sectional multi-factor stock ranking standardizes momentum, reversal, volatility, and volume_ratio factors using Z-score normalization to generate a composite score. It requires OHLCV data and Python with pandas and numpy to execute.

What is the best way to construct a TopN long portfolio from multiple stock factors?▼

Constructing a TopN long portfolio involves selecting the highest-ranked stocks by their composite multi-factor score and applying deterministic equal-weight allocations. This approach ensures transparent, rule-based position sizing for multi-instrument portfolios.

How does Z-score standardization work for cross-sectional factor ranking?▼

Z-score standardization for cross-sectional factor ranking rescales raw factor values like momentum and volatility to a mean of zero and standard deviation of one. This ensures each factor contributes proportionally to the aggregate composite score.

Can I use this multi-factor ranking engine with daily OHLCV data?▼

Yes, you can use daily OHLCV data with this multi-factor ranking engine. It requires full OHLCV data for your stock universe to compute the necessary momentum, reversal, volatility, and volume_ratio factors across the cross-section.

What are the limitations of using equal-weight allocations for TopN stock portfolios?▼

Equal-weight allocations for TopN stock portfolios limit customization by assigning identical weights to all selected instruments regardless of their individual factor scores. This deterministic approach prevents dynamic risk-adjusted position sizing based on score magnitude.

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