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fundamental-filter

Filter stock market data by PE, PB, and ROE metrics across A-share, HK, and US markets.

Updated Jul 8, 2026
Search Tags:#investment#finance#screening#backtesting#stocks#yfinance#tushare
One-click install
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill fundamental-filter-hxhyyy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fundamental-filter
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/fundamental-filter
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill fundamental-filter-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, yfinance.

What problem does it solve?

This skill addresses the challenge of manually filtering large stock universes by complex financial criteria, allowing users to identify value or growth opportunities efficiently.

Core Features & Use Cases

  • Multi-Market Screening: Supports A-shares via Tushare and HK/US stocks via yfinance.
  • Customizable Logic: Apply filters for PE, PB, ROE, and financial statement fields like revenue or net income.
  • Use Case: A user can define a strategy to find undervalued A-share companies with high ROE and stable revenue growth to build a backtesting universe.

Quick Start

Use the fundamental-filter skill to screen the provided stock list for companies with a PE ratio under 20 and an ROE above 8 percent.

Frequently Asked Questions about fundamental-filter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I screen stocks by fundamental financial metrics like PE and ROE?▼

You can screen stocks by fundamental financial metrics like PE and ROE by applying customizable filter logic to a stock list, generating quantitative value or growth signals for your investment analysis.

Can I filter A-share, HK, and US stocks using yfinance and tushare?▼

Yes, you can filter A-share, HK, and US stocks using tushare for A-shares and yfinance for HK and US markets to retrieve fundamental data across multiple regions.

What is the best way to build a backtesting universe with undervalued growth stocks?▼

The best way to build a backtesting universe with undervalued growth stocks is to filter a large stock universe by financial statement fields like revenue growth and ROE to identify opportunities efficiently.

Does fundamental filtering work with pandas and numpy for quantitative stock screening?▼

Yes, fundamental filtering works with pandas and numpy to process quantitative stock screening logic, allowing you to apply complex financial criteria across different market data providers.

Are there limitations when screening stocks by PB ratio across different markets?▼

Limitations when screening stocks by PB ratio depend on data availability from tushare and yfinance, as fundamental financial metrics may vary or be incomplete across A-share, HK, and US markets.

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