China Variance Commentary

Write variance commentary for A-share fund performance and portfolio changes.

Install

npx skills add jwangkun/claude-for-financial-services-cn --skill china-variance-commentary -g -a claude-code -y

Press to copy:C Claude CodeX CodexU CursorA Ask your agent

Repository: jwangkun/claude-for-financial-services-cn881
Category: Writing
Updated: 2mo ago
License: Apache-2.0
Context cost: Lightabout 1,098 tokens
Runs scripts: No, instructions only

What it does

Write variance commentary for A-share fund performance and portfolio changes. Adapts the original variance-commentary skill for fund accounting and Chinese fund reporting conventions. Triggers on "基金业绩点评", "组合表现分析", "variance commentary fund", "基金表现分析", "业绩归因", or "commentary on [fund] performance".

china-variance-commentary

Purpose

Write professional 基金业绩点评, structured variance commentary on fund performance and portfolio changes.

Data Sources

Tier 0, 万得 Wind（最全面付费数据）

覆盖：A股/港美股/基金/指数/债券/宏观/研报/分析（44个工具）
MCP 服务：wind-mcp（需 WIND_API_KEY 密钥，以 ak_ 开头）
优势：全市场覆盖面最广、数据最全面、包含研报和量化分析
密钥申请：https://aifinmarket.wind.com.cn/#/home

Tier 1, 同花顺 iFind（付费精确数据）/ AkShare MCP（Tier-2 免费备选）

get_fund_data(fund_code)              → Fund NAV, performance
get_quote(ticker)                     → Individual security performance
get_index_data("000001")              → Benchmark data

Secondary Sources

基金公司, fund performance data
托管行, custody data
Wind / Choice, performance analytics

Workflow

Step 1: Gather Performance Data

Performance snapshot:

Metric	Period	Benchmark	Active Return
净值增长率	X.XX%	X.XX%	X.XX%
年化收益率	X.XX%	X.XX%	X.XX%
波动率	X.XX%	X.XX%
夏普比率	X.XX
最大回撤	X.XX%
卡尔马比率	X.XX

Step 2: Attribution Analysis

Performance attribution:

Factor	Contribution	Description
资产配置 (Allocation)	X.XX%	Sector/security weight decisions
个股选择 (Selection)	X.XX%	Security picking within sectors
交互效应 (Interaction)	X.XX%	Combined effect

Step 3: Sector Attribution

Sector performance:

Sector	Weight	Return	Contribution	Benchmark Weight	Benchmark Return	Allocation Effect	Selection Effect

Total	100%

Step 4: Top/Bottom Contributors

Best/worst performers:

Read the whole SKILL.md on GitHub