quant-factor-screener▌
geeksfino/finskills · updated Apr 8, 2026
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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。
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/EBITDA | 1/6 |
| 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：
- 计算每只股票的原始指标
- 在行业内（行业中性时）或全选股池内排名
- 将排名转换为百分位得分（0–100）
- 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
评估热门因子是否过度拥挤：
| 信号 | 拥挤 | 不拥挤 |
|---|---|---|
| 估值价差 | 因子内高低分组估值差收窄 | 估值差扩大 |
| 因子收益相关性 | 高（许多人跟随相同信号） | 低 |
| ETF/基金资金流入 | 因子相关产品大量净申购 | 净赎回 |
| 媒体/分析师关注 | 被广泛讨论 | 被忽视 |
标记拥挤的因子——收益可能被压缩。
第六步：呈现结果
格式参见 references/output-template.md：
- 宏观环境研判 — 当前阶段和因子择时观点
- 因子拥挤度面板 — 哪些因子拥挤/不拥挤
- 精选个股表 — 前N只股票的各因子得分和综合得分
- 行业分布 — 精选结果的行业分布
- 因子暴露汇总 — 精选列表的整体因子特征
- 个股简介 — 每只精选个股的简要画像
- 风险提示 — 因子回撤历史和当前风险
- 免责声明
数据增强
如需实时市场数据支撑分析，请使用金融数据工具包技能（findata-toolkit-cn）。该工具包提供A股实时行情、财务指标、董监高增减持、北向资金、宏观数据等功能，所有数据源免费，无需API密钥。
重要注意事项
- 因子不是万能的：因子有长期跑输的时候。A股的价值因子在2019–2020年严重跑输。动量因子会周期性崩溃。设定合理预期。
- 行业中性很重要：不做行业约束的因子筛选常常产出伪装成因子赌注的行业集中赌注。
- A股因子特殊性：低波动异象在A股非常显著；动量因子因散户主导的市场结构而表现不同；小盘因子溢价受壳价值和流动性溢价影响。
- 换手率因子：A股中换手率是一个独特且有效的负向因子（低换手率→高收益），这在成熟市场中不那么显著。
- 多因子更稳健：没有单一因子永远有效。组合因子可降低回撤、平滑收益。
- 交易成本：动量策略换手率高。需考虑现实的交易成本（印花税0.05%+佣金）。
- 非个人化建议：因子筛选是分析工具，不构成投资建议。个人情况各异。
How to use quant-factor-screener on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add quant-factor-screener
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches quant-factor-screener from GitHub repository geeksfino/finskills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate quant-factor-screener. Access the skill through slash commands (e.g., /quant-factor-screener) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★66 reviews- ★★★★★Sakura Li· Dec 24, 2024
Useful defaults in quant-factor-screener — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Dec 20, 2024
quant-factor-screener fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★James Desai· Dec 16, 2024
quant-factor-screener fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Layla Liu· Dec 4, 2024
quant-factor-screener is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★James Dixit· Nov 23, 2024
Useful defaults in quant-factor-screener — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Arya Rao· Nov 15, 2024
quant-factor-screener is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anika Gill· Nov 15, 2024
Solid pick for teams standardizing on skills: quant-factor-screener is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Nov 11, 2024
Registry listing for quant-factor-screener matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Layla Wang· Nov 7, 2024
Registry listing for quant-factor-screener matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yusuf Desai· Oct 26, 2024
quant-factor-screener reduced setup friction for our internal harness; good balance of opinion and flexibility.
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