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multi-factor

Ranks stocks by composite z-score factors to produce equal-weight TopN portfolios.

Updated Apr 10, 2026
Search Tags:#portfolio-construction#quantitative#z-score#multifactor#cross-sectional#stock-ranking#topn
One-click install
npx skills add https://github.com/ebrahim-sani/trading-automation --skill multi-factor-ebrahim-sani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/multi-factor
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill multi-factor-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, requests.

What problem does it solve?

Rank a large universe of stocks across multiple factors to produce a concise TopN long portfolio with transparent scoring and allocation.

Core Features & Use Cases

  • Calculate multiple factors for many instruments (e.g., momentum, value, quality).
  • Standardize factors cross-section using z-scores for comparable scoring.
  • Combine signals into a composite score and select the TopN stocks with equal weights.
  • Use cases include multi-instrument portfolio construction, backtesting, and routine rebalancing.

Quick Start

Feed OHLCV data for each instrument to the multi-factor engine and generate the TopN 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 across multiple factors to build a TopN portfolio?▼

To rank stocks across multiple factors, you feed OHLCV data into the engine to calculate factor values, apply cross-sectional z-score standardization, compute a composite score, and select the TopN stocks with equal weights for the final portfolio.

What is cross-sectional z-score standardization in multi-factor stock ranking?▼

Cross-sectional z-score standardization in multi-factor stock ranking rescales different factor values on a set date so diverse metrics like momentum and value become directly comparable before they are combined into a single composite score.

Can I use pandas and numpy for multi-factor portfolio rebalancing?▼

Yes, you can use pandas and numpy for multi-factor portfolio rebalancing. The engine relies on both libraries to process OHLCV data, calculate quantitative factors, and perform cross-sectional standardization across the stock universe.

What's the best way to combine momentum and value signals for stock selection?▼

The best way to combine momentum and value signals is to calculate each factor independently, standardize them into cross-sectional z-scores, aggregate them into a composite score, and select the highest-ranking TopN stocks for equal-weight allocation.

Does this multi-factor engine support weighting stocks differently instead of equal weights?▼

No, the multi-factor engine currently applies deterministic TopN allocation with equal weights only. After cross-sectional z-scoring and composite scoring, selected stocks receive equal distribution rather than customized or variable weighting schemes.

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