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multi-factor

Compute cross-sectional Z-score stock rankings for TopN portfolio construction.

6.1k|1.2k|Updated Jun 9, 2022
Search Tags:#momentum#portfolio-construction#multifactor#cross-sectional#stock-ranking#topn
One-click install
npx skills add https://github.com/charliedream1/ai_quant_trade --skill multi-factor-charliedream1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-factor
Source: https://github.com/charliedream1/ai_quant_trade/tree/main/a_%E5%85%A8%E7%BD%91%E4%BC%98%E7%A7%80%E8%B5%84%E6%BA%90/10_%E5%A4%A7%E6%A8%A1%E5%9E%8B/07_skill%E5%8C%85/vibe_trading_skills/multi-factor
Command: npx skills add https://github.com/charliedream1/ai_quant_trade --skill multi-factor-charliedream1

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, requests.

What problem does it solve?

Rank multi-asset stock universes efficiently by integrating multiple signals into a single, actionable score to guide TopN portfolio construction.

Core Features & Use Cases

  • Factor calculation across momentum, value, quality, and volatility.
  • Cross-sectional standardization using Z-scores for fair comparison.
  • Flexible weighting: equal-weight or IC-weighted scoring.
  • Deterministic TopN portfolio construction with clear rebalancing rules.
  • Applicable to multi-instrument strategies including equities across sectors.

Quick Start

Rank a universe of stocks using momentum, value, quality, and volatility signals to produce a 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 cross-sectionally using multiple factors for portfolio construction?▼

Cross-sectional stock ranking combines momentum, value, and quality factors into a single score. This is done by standardizing signals using Z-scores to ensure fair comparison across assets for TopN portfolio construction.

How does Z-score standardization work in multi-factor stock ranking?▼

Z-score standardization in multi-factor ranking transforms raw factor values into a common scale. This cross-sectional normalization ensures no single factor dominates the composite score due to its native magnitude.

Can I use IC-weighted scoring instead of equal-weight for my TopN portfolio?▼

Yes, TopN portfolio construction supports configurable weighting. You can choose equal-weight for balanced factor contribution or IC-weighted scoring to emphasize factors with higher Information Coefficient.

What is the best way to construct a TopN long portfolio with deterministic rebalancing?▼

The best way to construct a TopN long portfolio is by computing a composite score from standardized factors. Deterministic rebalancing rules then systematically select the highest-ranked assets from the equity universe.

Does this multi-factor ranking approach work across different equity universes and sectors?▼

Yes, the cross-sectional ranking mechanism is applicable across multiple instruments and equity universes. It enables consistent multi-factor scoring and TopN portfolio construction across different sectors.

Do I need pandas and numpy to calculate momentum and volatility factors?▼

Yes, pandas and numpy are required dependencies for factor calculation. They provide the necessary data manipulation and numerical computation capabilities to process momentum and volatility signals.

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