etf-analysis

Name: etf-analysis
Availability: InStock
Author: hxhyyy

Evaluate ETFs using quantitative metrics and Tushare market data.

Updated Jul 8, 2026

Search Tags:#quantitative analysis #asset allocation #investment strategy #etf #tushare #tracking error

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill etf-analysis-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: etf-analysis
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/etf-analysis
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill etf-analysis-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

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

Core Features & Use Cases

Quantitative Screening: Evaluate ETFs based on scale, fee structures, tracking error, and liquidity metrics to identify high-quality investment vehicles.
Strategy Implementation: Apply advanced methodologies like Core-Satellite, sector rotation, and factor-based analysis to build robust portfolios.
Risk Management: Monitor折溢价 (premium/discount) risks and perform decay analysis on leveraged or inverse products to avoid common pitfalls.

Quick Start

Use the etf-analysis skill to calculate the tracking error and generate a performance report for the specified ETF code against its 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 against a benchmark index?▼

ETF tracking error is calculated by measuring the standard deviation of daily return differences between the ETF and its benchmark index. This skill utilizes Tushare market data integration to compute quantitative tracking error metrics for performance evaluation.

How do I build a core-satellite asset allocation strategy using ETFs?▼

Building a core-satellite asset allocation strategy involves selecting broad-market ETFs as the core and specialized sector ETFs as satellites. This skill provides quantitative frameworks to implement core-satellite models, sector rotation, and factor-based portfolio construction.

How do I monitor ETF premium and discount risks in Chinese markets?▼

ETF premium and discount risks are monitored by comparing real-time market prices to underlying net asset values. This skill leverages Tushare data to perform premium-discount monitoring and liquidity assessments specifically for Chinese and global ETF markets.

Can I use pandas and numpy for quantitative ETF screening?▼

Yes, quantitative ETF screening relies on pandas and numpy to evaluate scale, fee structures, and liquidity metrics. This skill uses these dependencies to process market data and identify high-quality investment vehicles based on quantitative thresholds.

What is the best way to evaluate leveraged ETF decay and inverse product risks?▼

Evaluating leveraged ETF decay requires analyzing compounding effects and volatility drag on daily returns. This skill performs decay analysis on leveraged or inverse products to help investors avoid common pitfalls and manage portfolio risk effectively.

Does this ETF analysis approach require Tushare data integration?▼

Yes, Tushare data integration is required to fetch historical market data for tracking error calculations and liquidity assessments. This skill uses Tushare alongside scipy and pandas to perform quantitative strategy implementation and asset allocation modeling.