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Quant Factor Screener

This is a systematic multi-factor stock screener for A-shares that scores and ranks stocks across six academic factors: value, momentum, quality, low volatility, size, and growth. You'd use it when you want factor-based systematic stock selection with industry-neutral constraints, macro regime overlays, and factor crowding checks. The workflow is thorough, walking through parameter selection, factor scoring methodology, composite ranking, and factor timing based on economic cycles. One thing worth noting is it explicitly calls out A-share quirks like the strong low volatility anomaly and turnover as a negative factor, which shows it's adapted beyond just porting Western factor frameworks. Integrates with a free Chinese financial data toolkit for real-time market data.

skills.sh installs
362
GitHub stars
286
last updated
Aug 21, 2026

Install

$ npx -y skills add geeksfino/finskills --skill quant-factor-screener --agent claude-code

Installs into .claude/skills of the current project.

Files

量化因子筛选器

扮演量化权益分析师。使用基于学术因子研究的系统化多因子框架筛选A股——对价值、动量、质量、低波动、规模和成长因子进行评分和排名。

工作流程

第一步:确定参数

与用户确认:

输入选项默认
选股池沪深300 / 中证500 / 中证1000 / 全A / 自定义中证800
因子全部6个或特定因子全部
因子权重等权或自定义等权
行业约束行业中性或不约束行业中性
结果数量前N只前20只
宏观研判当前因子择时评估自动判断
排除项行业、概念、特定个股无

第二步:计算因子得分

对选股池中每只股票计算各因子得分。详细定义参见 references/factor-methodology.md。

因子主要指标默认权重
价值盈利收益率、PB倒数、FCF收益率、EV/EBITDA1/6
动量12-1月价格动量、盈利预期修正动量1/6
质量ROE、盈利稳定性、低杠杆、应计质量1/6
低波动已实现波动率(1年)、Beta、下行偏差1/6
规模市值(越小得分越高)1/6
成长营收增速、盈利增速、利润率扩张1/6

对每个因子:

  1. 计算每只股票的原始指标
  2. 在行业内(行业中性时)或全选股池内排名
  3. 将排名转换为百分位得分(0–100)
  4. 将子指标合成为综合因子得分

第三步:合成得分

综合得分 = Σ (因子权重 × 因子得分)

按综合得分从高到低排列所有股票。

第四步:因子择时评估

评估当前宏观环境及其对因子表现的影响。参见 references/factor-methodology.md。

宏观环境利好因子不利因子
经济复苏初期规模、动量低波动
经济扩张中期动量、成长价值
经济扩张末期质量、价值规模
经济下行低波动、质量动量、规模
经济触底价值、规模、动量低波动

基于当前研判,提供因子择时叠加以调整权重。

第五步:因子拥挤度分析

Sponsors

Details

Repository
github.com/geeksfino/finskills
Category
Finance & Trading
First seen
Jun 3, 2026

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