quant-factor-screener

Screens A-share stocks using multi-factor scoring across value, momentum, quality, low volatility, size, and growth factors.

Updated Apr 13, 2026
One-click install
npx skills add https://github.com/xiaoshan1234/ai-skill --skill quant-factor-screener-xiaoshan1234
Or copy as Structured Prompt for Agent
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Skill: quant-factor-screener
Source: https://github.com/xiaoshan1234/ai-skill/tree/main/role/stock-manager/skills/quant-factor-screener
Command: npx skills add https://github.com/xiaoshan1234/ai-skill --skill quant-factor-screener-xiaoshan1234

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Selecting A-share stocks systematically is difficult when relying on intuition or single indicators. This Skill applies an academic factor-model framework to score and rank stocks across six factors, while accounting for macro regime timing and factor crowding risks specific to the Chinese market. ## Core Features & Use Cases - Multi-Factor Scoring: Computes percentile scores for value, momentum, quality, low volatility, size, and growth factors within a chosen universe (CSI 300, CSI 500, CSI 1000, or custom), with industry-neutral ranking by default. - Factor Timing & Crowding Analysis: Assesses the current macro regime (PMI, credit growth, M1-M2 spread) to adjust factor weights, and flags crowded factors at risk of sharp reversals. - Structured Report Output: Produces a full screening report including top-N stock picks, sector distribution, factor exposure summary, per-stock profiles, and risk disclosures. - Use Case: Ask for a multi-factor screen of the CSI 800 universe and receive the top 20 stocks ranked by composite factor score, with macro timing adjustments and crowding warnings. ## Quick Start Run a multi-factor screen on the CSI 800 universe with equal weights and industry-neutral constraints, and show me the top 20 A-share stocks with their factor scores.

Frequently Asked Questions about quant-factor-screener

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

FAQPage Schema
How do I screen A-share stocks with a multi-factor model?

Define a stock universe such as CSI 800, then score each stock on value, momentum, quality, low volatility, size, and growth factors using percentile ranks. Combine factor scores with equal or custom weights and rank stocks by composite score to get the top picks.

What factors work best for quantitative stock selection in China?

Low volatility and quality factors show the highest Sharpe ratios in A-shares, with low volatility delivering roughly 6-8% annualized premium. Turnover rate is a uniquely strong negative factor in China, while pure price momentum is less stable than in US markets.

Does factor timing work for A-share investing?

Factor timing adjusts weights based on macro regime indicators like PMI, credit growth, and M1-M2 spread, favoring size and momentum in early recovery and low volatility in downturns. Timing is difficult, so adjustments should stay within plus or minus 10% of equal weights.

Why is industry neutrality important in factor screening?

Without industry constraints, factor screens often produce concentrated sector bets disguised as factor exposure. Ranking stocks within their Shenwan industry classification ensures scores reflect genuine factor characteristics rather than industry-level effects.

What are the limitations of multi-factor stock screening?

Factors experience prolonged underperformance, such as value lagging in 2019-2020 and periodic momentum crashes. Factor premiums vary over time, crowding can trigger sharp reversals, and historical relationships may not persist, so results are analytical tools rather than investment advice.