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etf-analysis

Analyze Chinese market ETFs with Python and Tushare data.

1|Updated Jun 23, 2026
Search Tags:#data analysis#asset allocation#trading#ETF analysis#quantitative strategy
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill etf-analysis-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: etf-analysis
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/etf-analysis
Command: npx skills add https://github.com/644408071-design/Kokpop --skill etf-analysis-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tushare, pandas, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides deep analysis of ETF products, covering selection methods, strategy application, market characteristics, and quantitative methods, aiding in constructing ETF-based quantitative strategies and portfolios.

Core Features & Use Cases

  • ETF Analysis: In-depth product analysis, including category classification, core indicators, and selection methods.
  • Strategy Application: Provides frameworks for core-satellite strategies, sector momentum rotation, and Smart Beta ETF factor exposure analysis.
  • Market Characteristics: Focuses on Chinese ETF market features, such as onshore-offshore ETFs and index systems.
  • Data Analysis: Offers data analysis methods and tools for ETF trading and portfolio management.

Quick Start

Analyze the performance of the ETF '510300.SH' with the 'etf-analysis' skill.

Frequently Asked Questions about etf-analysis

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

FAQPage Schema
How do I analyze Chinese ETF market characteristics using Python?▼

To analyze Chinese market ETF characteristics using Python, you evaluate onshore-offshore ETFs and index systems. This Skill provides classification and core indicator frameworks using pandas and numpy for quantitative analysis.

What is the best way to backtest a sector momentum rotation ETF strategy?▼

The best way to backtest a sector momentum rotation ETF strategy is by applying quantitative frameworks that evaluate factor exposure. This Skill provides strategy application methods for sector rotation and Smart Beta ETF analysis.

How do I calculate core ETF indicators for asset allocation with pandas?▼

You calculate core ETF indicators for asset allocation by processing historical market data with pandas. This Skill offers data analysis methods to extract metrics needed for portfolio management and trading.

Does Tushare support retrieving data for quantitative ETF analysis?▼

Yes, Tushare supports retrieving data for quantitative ETF analysis. This Skill requires Python and Tushare to fetch Chinese market data, which is then processed using scipy and numpy for strategy development.

How do I construct a core-satellite ETF portfolio strategy?▼

You construct a core-satellite ETF portfolio strategy by combining broad market index ETFs with specialized sector funds. This Skill provides the strategy frameworks and selection methods to implement this asset allocation approach.

Can I analyze Smart Beta ETF factor exposure with scipy?▼

Yes, you can analyze Smart Beta ETF factor exposure with scipy. This Skill utilizes scipy alongside numpy to evaluate quantitative methods and factor exposures within your ETF trading strategies.

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