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etf-analysis

Quantify tracking error, fees, liquidity, and premium/discount for ETF evaluation.

Updated May 5, 2026
Search Tags:#tushare#portfolio construction#tracking error#fee comparison#etf analysis#liquidity assessment#premium discount
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill etf-analysis-wudye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: etf-analysis
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/etf-analysis
Command: npx skills add https://github.com/wudye/traderAssistHK --skill etf-analysis-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you analyze and compare ETFs in China by turning key selection signals—like tracking error, premium/discount, fees, and liquidity—into an actionable framework for choosing products and building ETF-based portfolios.

Core Features & Use Cases

  • Product selection & comparison framework: Classify ETFs (broad, sector, theme, smart beta, commodity, bond, QDII, money) and compare them using a structured scoring model.
  • Core metrics that drive decisions: Compute tracking error (annualized), information ratio, premium/discount, and liquidity indicators (volume, spread, depth, turnover).
  • Strategy application templates: Apply ETF allocation patterns (core-satellite), momentum/sector rotation ideas, factor exposure analysis, and ETF arbitrage concepts (including QDII premium monitoring).
  • Data analysis & implementation guidance: Provide Python code templates using Tushare for ETF lists, NAV/IOPV retrieval, daily market data, tracking-error calculation, and fund-flow estimation.

Quick Start

Ask the skill to help you select the best ETF among all products tracking the same index by scoring them on size, fee, tracking error, and liquidity, then recommend the most suitable ones for long-term holding versus short-term trading.

Frequently Asked Questions about etf-analysis

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

FAQPage Schema
How do I evaluate ETF tracking error and premium discount for product selection?▼

ETF tracking error and premium/discount evaluation involves quantifying annualized tracking deviation and price-NAV gaps to identify optimal products. This framework uses threshold-based judgment to compare multiple ETFs tracking the same index for informed allocation decisions.

What is the best way to compare ETFs tracking the same index for long-term holding?▼

Comparing ETFs tracking the same index requires scoring them on fund size, total fees, tracking error, and liquidity indicators. A structured scoring model ranks products to recommend the most suitable ETFs for long-term holding versus short-term trading strategies.

How to calculate ETF liquidity and tracking error using Tushare market data?▼

Calculating ETF liquidity and tracking error with Tushare involves retrieving NAV/IOPV data and daily market quotes via Python templates. You compute annualized tracking deviation and liquidity indicators including volume, spread, depth, and turnover for comprehensive product assessment.

Can I monitor QDII ETF premium risk and design core-satellite portfolios with this framework?▼

Yes, you can monitor QDII premium risk and design core-satellite portfolios using strategy application templates. The framework supports ETF allocation patterns, momentum rotation ideas, factor exposure analysis, and arbitrage concepts including QDII premium monitoring.

What ETF metrics should I analyze when building a sector rotation strategy?▼

Building a sector rotation strategy requires analyzing ETF metrics including tracking error, information ratio, premium/discount behavior, and liquidity indicators. These metrics drive allocation decisions across broad, sector, theme, smart beta, and commodity ETF categories.

Does ETF analysis work with money market and bond fund categories?▼

ETF analysis works across broad, sector, theme, smart beta, commodity, bond, QDII, and money market categories. The classification framework compares products using a structured scoring model that evaluates tracking quality, fees, liquidity, and premium behavior for each category.

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