PJ选股智能评分12维模型

v1.0.0

12维股票智能分析评分模型。基于行业、头部玩家、市场环境、管理团队、市值规模、主营业务、收入、利润、分红、回购、机构持股、大股东增减持12个维度对股票进行综合评分。触发词：股票分析、选股评分、股票评分、智能选股。

⭐ 0· 100·0 current·0 all-time

by@frankxpj

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for frankxpj/stock-smart-analyse.

Previewing Install & Setup.

Prompt PreviewInstall & Setup

Install the skill "PJ选股智能评分12维模型" (frankxpj/stock-smart-analyse) from ClawHub.
Skill page: https://clawhub.ai/frankxpj/stock-smart-analyse
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install stock-smart-analyse

ClawHub CLI

Package manager switcher

npx clawhub@latest install stock-smart-analyse

Security Scan

VirusTotal

Benign

View report →

OpenClaw

Benign

medium confidence

✓

Purpose & Capability

Name, description, SKILL.md and the included Python script all describe and implement a local 12-dimension stock scoring model (interactive input, JSON input, markdown/JSON output). No unrelated credentials, binaries, or cloud services are requested, which is coherent with the stated purpose.

✓

Instruction Scope

SKILL.md instructs running the included Python script with flags (--stock, --interactive, --file, --output). The instructions do not direct the agent to read unrelated system files, secret env vars, or to transmit data to external endpoints. All described operations are limited to local input/output and analyst judgment.

✓

Install Mechanism

There is no install spec; this is essentially an instruction-only skill with an included Python script. That minimizes installation risk because nothing is downloaded or written automatically to the system during install.

✓

Credentials

The skill declares no required environment variables, no credentials, and no config paths. The code shown performs scoring and interactive prompts and references public data sources only in documentation; there is no apparent need for secrets or external tokens.

✓

Persistence & Privilege

The skill does not request always:true and will not be force-enabled; it has no install-time persistence mechanism declared. It does not attempt to modify other skills or system configuration in the visible content.

Assessment

This skill appears internally consistent with its stated purpose: it runs a local Python scoring script and uses interactive or JSON input to produce reports. Before installing or running: (1) open and review the remainder of scripts/stock_analyse.py (the provided content was truncated) to confirm there are no unexpected network calls, telemetry, or subprocess execution; (2) run the script in an isolated environment (e.g., a sandbox or VM) the first time; (3) do not feed sensitive credentials or private files to the tool (it doesn't need them); (4) treat outputs as informational only and not as financial advice; and (5) if you want automated upstream data fetching, require explicit review and justification because the skill currently documents data sources but does not declare network behavior or credentials.

Like a lobster shell, security has layers — review code before you run it.

latestvk97d14sm4getka978bj02jn0fx83yp43

100downloads

0stars

1versions

Updated 3w ago

v1.0.0

MIT-0

PJ选股智能评分12维模型

基于12个核心维度的股票智能分析评分系统，提供量化投资决策支持。

快速开始

python scripts/stock_analyse.py --stock <股票代码> [--interactive]

参数说明：

--stock / -s：股票代码（如 TSLA、AAPL、0700.HK）
--interactive / -i：交互式输入各维度得分
--file / -f：从JSON文件读取评分数据
--output / -o：输出报告路径（可选）

示例：

# 交互式评分
python scripts/stock_analyse.py --stock TSLA --interactive

# 从文件读取
python scripts/stock_analyse.py --file scores.json

# 直接输出报告
python scripts/stock_analyse.py --stock AAPL --output report.md

12个评分维度

维度	权重	评价内容	数据来源
行业	15%	行业增长率、市场规模增长率	Gartner、IDC、Statista
头部玩家	10%	市场份额、行业竞争力	行业报告、市场份额数据
市场环境	10%	宏观经济、政策支持、行业法规	政府报告、经济数据
管理团队	10%	团队资历、业绩记录、公众评价	公司年报、媒体报道
市值规模	5%	公司市值规模	Bloomberg、Yahoo Finance
主营业务	10%	市场需求、创新能力、核心竞争力	行业报告、公司年报
收入	10%	收入增长率和稳定性	公司财报
利润	10%	利润率和增长率	公司财报
分红	5%	分红率和稳定性	公司财报、分红公告
回购	5%	回购频率和规模	公司公告、财报
机构持股	5%	机构持股比例和变动	13F文件、市场分析
大股东增减持	5%	大股东增持/减持行为	公司公告、市场分析

评分标准

每个维度采用 1-100分 的评分体系：

91-100分：卓越/最佳状态
81-90分：优秀/非常有利
71-80分：良好/有利状态
61-70分：较好/稳定增长
51-60分：一般/平稳发展
41-50分：尚可/略有改善
31-40分：中等/中性状态
21-30分：较弱/有一定不利
11-20分：较差/明显不利
1-10分：极差/严重不利

综合得分计算

综合得分 = Σ (各维度得分 × 权重)

评级标准：

90-100分：强烈推荐买入 ⭐⭐⭐⭐⭐
80-89分：推荐买入 ⭐⭐⭐⭐
70-79分：建议持有 ⭐⭐⭐
60-69分：谨慎观望 ⭐⭐
60分以下：不建议投资 ⭐

输出格式

Markdown报告

# 股票分析报告：TSLA

## 综合得分：75.5分

**评级**：建议持有 ⭐⭐⭐

## 各维度得分

| 维度 | 得分 | 权重 | 加权得分 |
|------|------|------|---------|
| 行业 | 85 | 15% | 12.75 |
| ... | ... | ... | ... |

## 分析建议
...

JSON格式

{
  "stock": "TSLA",
  "total_score": 75.5,
  "rating": "建议持有",
  "dimensions": {
    "industry": {"score": 85, "weight": 0.15},
    ...
  },
  "recommendation": "..."
}

使用场景

个股分析：对单只股票进行全面评估
投资决策：辅助买卖决策
组合管理：评估持仓股票健康度
对比分析：多股票横向对比

数据来源建议

维度	推荐数据源
行业数据	Gartner、IDC、Statista、艾瑞咨询
市场份额	行业协会报告、公司年报
宏观经济	国家统计局、IMF、世界银行
财务数据	公司财报、Bloomberg、Wind
持股数据	13F文件、港交所披露易

注意事项

数据时效性：确保使用最新数据
行业差异：不同行业评分标准可能需调整
主观判断：部分维度依赖分析师判断
风险提示：模型仅供参考，不构成投资建议

版本历史

v1.0.0 (2026-03-31)：初始版本，12维评分模型

Comments

Loading comments...