About this skill

Problem

Fund analysis often stalls on data assembly: single-fund returns, volatility, drawdown, and risk-adjusted metrics are scattered across public pages, while comparisons and portfolio optimization require clean NAV series, covariance inputs, and consistent statistical calculations.

How It Works

The tool uses akshare to fetch public data from sources such as East Money and Tiantian Fund, with no API key required. A typical workflow is: search mutual funds by keyword → load basic information and historical NAV → calculate Sharpe, Sortino, Calmar, VaR/CVaR, Alpha/Beta, and maximum drawdown → export a Markdown report or CSV NAV file. It supports side-by-side comparison for two funds and portfolio optimization for three or more funds, including the efficient frontier, minimum-variance portfolio, maximum-Sharpe tangency portfolio, risk parity, Black-Litterman, and Monte Carlo simulation. Local caching reduces repeated requests.

Boundaries

It targets daily historical NAV analysis for China open-end public mutual funds, including equity, hybrid, bond, money-market, index, ETF, QDII, and FOF funds. It does not support real-time trading, intraday NAV estimates, private funds, technical K-line charts, or macro timing. The output is data and analysis, not investment advice.

Screenshots

Use Cases

When researching a new-energy fund, pull historical NAV and compute Sharpe, max drawdown, and risk-adjusted returns.
When comparing two equity-hybrid funds, generate a report covering returns, volatility, Alpha/Beta, and drawdowns over the same NAV period.
When allocating a 3-10 fund portfolio, compute minimum-variance and max-Sharpe portfolios and export CSV for validation.
When documenting fund research, convert the analysis into a Markdown report for archiving, review, or internal investment research.

Best For

Quant researchers selecting or backtesting funds who need public NAV data converted into standardized risk metrics.
Investment advisors or researchers building allocation plans who need fund comparisons and multi-fund portfolio optimization.
Python engineers maintaining fund data scripts who need quick report and CSV generation from akshare.
Individual investors writing fund research notes who need to organize returns, drawdown, and Sharpe data without trading automation.

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