Evaluate ETFs using quantitative metrics and Tushare market data.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill etf-analysis-hxhyyy
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
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.
Use the etf-analysis skill to calculate the tracking error and generate a performance report for the specified ETF code against its benchmark index.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
Zhow01Fetch Chinese stock, futures, and macro data from the Tushare Pro API.
OneWave-AIAnalyze investment portfolios for risk, diversification gaps, fees, and optimization opportunities.
JacobHsuDecompose portfolio excess returns into sector, selection, factor, and timing contributions.
philipcoller-777Analyze ETF tracking error, liquidity, and fees with reproducible workflows.
