Stock Pe Pb Analyzer

分析股票PE/PB历史水位的专业工具。使用BaoStock API获取真实股票数据，计算股票在过去十年中的PE、PB历史百分位水位。适用于：1）查询单个股票的PE/PB历史估值水平；2）评估当前估值相对于历史的高低位置；3）为投资决策提供估值参考数据。支持通过股票名称（如贵州茅台）或代码（如600519）查询A股...

Security Scan

The skill's code, instructions, and requirements are consistent with a BaoStock-based PE/PB historical-percentile analyzer and do not request unrelated credentials or suspicious installs.

Purpose & Capability

The name/description match the included code and README: it queries BaoStock for historical valuation fields (PE-TTM, PB-MRQ) and computes percentiles using pandas/numpy. Required packages (baostock, pandas, numpy) are appropriate and proportional.

Instruction Scope

SKILL.md and README only instruct the agent/user to call the included Python script/module and to query BaoStock. There are no instructions to read unrelated system files, environment variables, or to transmit data to unexpected endpoints.

Install Mechanism

No automated install spec is provided (instruction-only skill with code files). The README suggests cloning the GitHub repo and pip-installing dependencies (baostock, pandas, numpy) — a normal, low-risk workflow. Verify the upstream repository URL before cloning.

Credentials

The skill declares no required environment variables or credentials. The code uses baostock.login() (expected for BaoStock). It does not request unrelated secrets or access to other services.

Persistence & Privilege

always is false and the skill does not request elevated persistence or attempt to modify other skills or system-wide settings. Autonomous invocation (default) is allowed but not combined with other red flags.

Assessment

This skill appears coherent for its stated purpose. Before installing, verify the skill's source (the README points to a GitHub repo) and review the repository there to ensure authenticity. Be aware the script will network-access BaoStock (bs.login() and data queries) and may be subject to BaoStock rate limits; run in an environment where installing Python packages is acceptable. If you run code from untrusted authors, consider executing in an isolated environment (virtualenv/container). Finally, remember analysis is informational only — the skill does not provide financial advice.

股票PE/PB历史水位分析器

基于BaoStock数据源的股票估值分析工具，帮助分析股票当前PE、PB在历史区间中的位置（水位）。

功能特点

• 支持股票名称和代码查询
• 自动计算10年、5年、3年、1年的历史水位
• 提供详细的统计指标（最低、最高、中位数、平均值）
• 输出估值评级（低估/适中/偏高）

使用方法

1. 直接调用分析脚本

# 执行分析脚本
python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py <股票名称或代码>

示例：

python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py 贵州茅台
python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py 600519

2. 作为Python模块使用

from .agents.skills.stock-pe-pb-analyzer.scripts.analyze_stock import StockPEPBAnalyzer

# 创建分析器实例
analyzer = StockPEPBAnalyzer()

# 分析单只股票
result = analyzer.analyze("贵州茅台", years=10)

# 打印详细报告
analyzer.print_report(result)

# 获取原始数据
historical_data = result['historical_data']  # DataFrame包含date, peTTM, pbMRQ等字段
percentiles = result['percentiles']  # 各周期水位计算结果

输出说明

• PE水位: 当前PE在过去N年中的百分位（0%-100%）

  • 🔴 低估: 0-20%（历史较低水平，可能存在估值修复机会）
  • 🟡 适中: 20-50%（估值相对合理）
  • 🟢 偏高: >50%（估值偏高，需注意风险）

• PB水位: 当前PB在过去N年中的百分位，评级标准同上

依赖要求

需要安装以下Python包：

• baostock
• pandas
• numpy

注意事项

1. 首次使用时会自动登录BaoStock并加载股票列表
2. 数据基于日频估值指标（PE-TTM, PB-MRQ）
3. 过滤了PE>1000的异常数据点
4. 分析结果仅供参考，不构成投资建议