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Install in OpenClaw
/install quant-stock-selector-v5
Description
三层筛网量化选股策略。基于知乎文章介绍的量化选股方法，从A股全市场中 通过三层筛选（硬性排除→多因子打分→行业均衡）选出30-50只优质股票。 每当用户要求量化选股、股票筛选、多因子选股、从A股中挑选股票时使用此技能。
README (SKILL.md)
三层筛网量化选股策略
从4000只A股中挑出赢家的量化选股方法。
策略原理（三层筛网架构）
第一层：硬性排除（剔除"不合格者"）
- 净利润为负（剔除连续亏损公司）
- 资产负债率 > 70%（剔除高杠杆公司）
- 经营现金流为负（剔除"只赚报表不赚钱"公司）
- 日均成交额排名后20%（剔除流动性差的股票）
- 股价 \x3C 1元（规避仙股风险）
第二层：多因子综合打分（寻找"优等生"）
| 因子 | 权重 | 指标 | 方向 |
|---|---|---|---|
| 价值因子 | 40% | 市净率(PB) | 越低越好 |
| 质量因子 | 40% | 净资产收益率(ROE) | 越高越好 |
| 动量因子 | 20% | 过去6个月涨幅 | 中间区间最好 |
总分 = 价值得分 × 40% + 质量得分 × 40% + 动量得分 × 20%
第三层：行业与风险均衡（构建"合理组合"）
- 将Top200-300只候选股按行业分类
- 每个行业独立排名选股
- 覆盖大盘/中盘/小盘
- 最终选出30-50只行业优质代表
什么时候使用
- 用户要求量化选股
- 用户要求从A股中挑选股票
- 用户要求多因子选股
- 用户要求"从4000只股票中选出好股票"
- 用户要求生成股票推荐列表
使用方式
1. 运行选股脚本
cd skills/quant-stock-selector
python quant_selector.py
2. 输出内容
- 第一层筛选结果（排除了多少只）
- 第二层多因子评分排名（Top50）
- 第三层行业均衡后的最终推荐（Top30）
数据源
| 数据源 | 接口 | 用途 |
|---|---|---|
| JQData | 基本面查询 | 财务数据(ROE/资产负债率等) |
| JQData | 日K线 | 价格动量计算 |
| AKShare | 成分股列表 | 股票池获取 |
关键认知
- 逻辑重于数据：每一步筛选背后应有清晰的经济学或行为金融学解释
- 追求概率优势：不是每只都能涨，而是整体组合上涨概率显著高于随机选择
- 迭代是生命：市场在变，筛选逻辑需要定期审视和调整
注意事项
- JQData免费版数据有日期范围限制
- 建议配合OpenClaw Heartbeat每周运行一次
- 选股结果仅供参考，不构成投资建议
Usage Guidance
Do not use this skill as-is until the hardcoded JQData credential is removed and rotated. If you still want the functionality, use your own documented credentials, review the Python dependencies before installing them, and treat the generated stock list as informational rather than investment advice.
Capability Analysis
Type: OpenClaw Skill
Name: quant-stock-selector-v5
Version: 1.0.0
The skill contains hardcoded plaintext credentials (phone number and password) for the JQData financial service within `quant_selector.py`. While the script's logic aligns with its stated purpose of quantitative stock selection, hardcoding authentication secrets is a significant security vulnerability and a high-risk practice that could lead to account abuse or tracking.
Capability Assessment
Purpose & Capability
The described purpose is quantitative A-share stock screening, and the provided script implements financial filtering, factor scoring, and industry balancing rather than trading or account mutation.
Instruction Scope
The skill is user-invocable and documents manual script execution; it also suggests optional weekly Heartbeat use, which could make the analysis recurring if the user configures it.
Install Mechanism
There is no install spec, but the README tells users to install unpinned Python packages such as akshare and jqdatasdk manually.
Credentials
The runtime code authenticates to JQData using a hardcoded phone number/password even though the registry declares no required credential or environment variable.
Persistence & Privilege
No automatic persistence is shown, but the documentation recommends pairing with OpenClaw Heartbeat for weekly runs.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install quant-stock-selector-v5 - After installation, invoke the skill by name or use
/quant-stock-selector-v5 - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
基于知乎文章的三层筛网量化选股策略，支持硬性排除/多因子打分/行业均衡选股
Metadata
Frequently Asked Questions
What is 三层筛网量化选股v1.0?
三层筛网量化选股策略。基于知乎文章介绍的量化选股方法，从A股全市场中 通过三层筛选（硬性排除→多因子打分→行业均衡）选出30-50只优质股票。 每当用户要求量化选股、股票筛选、多因子选股、从A股中挑选股票时使用此技能。 It is an AI Agent Skill for Claude Code / OpenClaw, with 70 downloads so far.
How do I install 三层筛网量化选股v1.0?
Run "/install quant-stock-selector-v5" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 三层筛网量化选股v1.0 free?
Yes, 三层筛网量化选股v1.0 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 三层筛网量化选股v1.0 support?
三层筛网量化选股v1.0 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 三层筛网量化选股v1.0?
It is built and maintained by paudyyin (@paudyyin); the current version is v1.0.0.
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