etf-analysis

Analyze ETF performance using tracking error, information ratio, and premium-discount metrics.

Updated Jun 30, 2026
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill etf-analysis-20yn04
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: etf-analysis
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/etf-analysis
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill etf-analysis-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tushare, pandas, numpy, scipy.

What problem does it solve?

This Skill addresses the complexity of navigating the ETF market by providing a structured framework for product screening, performance evaluation, and quantitative strategy application.

Core Features & Use Cases

  • Quantitative Screening: Evaluate ETFs based on scale, fee structures, tracking error, and liquidity metrics.
  • Strategy Implementation: Apply core-satellite, momentum rotation, and factor-based allocation models to your portfolio.
  • Use Case: Use this Skill to compare multiple沪深300 ETFs to identify the one with the lowest tracking error and optimal liquidity for a long-term core holding.

Quick Start

Use the etf-analysis skill to calculate the tracking error and provide a performance comparison for the specified list of ETF codes against their benchmark index.

Frequently Asked Questions about etf-analysis

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

FAQPage Schema
How do I calculate ETF tracking error and compare it against a benchmark index?

Calculate ETF tracking error by inputting specified ETF codes to retrieve benchmark data via financial APIs, yielding a quantitative performance comparison and tracking error metrics for evaluating fund quality.

What is core-satellite allocation and how does it work for ETF portfolio construction?

Core-satellite allocation is a portfolio construction strategy that combines a stable core ETF holding with dynamic satellite ETF selections, enabling you to balance market returns with active factor exposure and momentum rotation strategies.

Can I use tushare and pandas to screen Chinese ETFs for liquidity and fee drag?

Yes, you can use tushare to pull market data and pandas to screen Chinese ETFs, evaluating fund liquidity, fee structures, and market-making quality to identify optimal long-term holdings.

What's the best way to evaluate multiple沪深300 ETFs for a long-term core holding?

The best way to evaluate multiple沪深300 ETFs is to compare their scale, tracking error, and liquidity metrics quantitatively, identifying the fund with the lowest tracking error and optimal liquidity for your core holding.

Does quantitative ETF analysis support factor exposure analysis and sector rotation strategies?

Yes, quantitative ETF analysis supports factor exposure analysis and sector rotation by applying momentum rotation and factor-based allocation models, allowing you to assess dynamic portfolio strategies and optimize asset allocation.

When should I not use quantitative ETF metrics for fund selection?

You should avoid relying solely on quantitative ETF metrics when market-making quality data is unavailable or when evaluating newly launched funds lacking sufficient historical tracking error and information ratio data for accurate analysis.