jwangkun/claude-for-financial-services-cn

china-datapack-builder

Build professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles.

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使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/jwangkun/claude-for-financial-services-cn --skill china-datapack-builder

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

china-datapack-builder

Purpose

Build professional A股投行数据包 (Data Pack) for investment banking workflows — M&A due diligence, IC review, deal execution.

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)

python
get_financials(ticker, "income", "annual")   → P&L history
get_financials(ticker, "balance", "annual")  → Balance sheet
get_financials(ticker, "cashflow", "annual") → Cash flow
get_quote(ticker)                            → Current valuation
get_historical_data(ticker)                  → Trading history
get_industry_stocks(industry="...")           → Peer comparison
get_stock_info(ticker)                       → Company profile

Secondary Sources

  • 巨潮资讯 — mandatory filings (年报, 中报, 季报)
  • 上交所 / 深交所 — announcements, filings
  • 公司官网 — investor materials
  • 企查查 / 天眼查 — corporate structure
  • Wind / Choice — comprehensive data

Workflow

Step 1: Define Pack Scope

Data pack types:

Pack TypePurposeTypical Content
Company overviewInitial target screeningFinancials, profile, peers
Due diligencePre-deal deep dive3-statement, quality of earnings, debt
IC preparationInvestment committeeExecutive summary, model, risks
M&A compsComparable transactionsPrecedent deals, trading comps
Sector deep diveMarket contextIndustry overview, competitive map

Step 2: Gather Source Materials

Financial data collection:

python
# 3-5 years historicals
for year in range(2020, 2025):
    get_financials(ticker, "income", "annual")
    get_financials(ticker, "balance", "annual")
    get_financials(ticker, "cashflow", "annual")

Corporate information:

  • 公司股权结构 (ownership structure)
  • 子公司/参股公司 (subsidiaries / associates)
  • 关联交易 (related party transactions)
  • 对外担保 (guarantees)
  • 诉讼仲裁 (litigation / arbitration)

Market data:

  • Current and historical share price
  • Trading volumes and liquidity
  • Ownership breakdown (institutional, retail, 北向)
  • Shareholder changes over time

Step 3: Normalize & Structure

Data normalization standards:

  1. Currency: All figures in CNY (人民币)
  2. Units: Consistent (亿元 preferred for large companies)
  3. Periods: Align fiscal years (typically Dec 31)
  4. Adjustments: Normalize for one-time items

Standard data pack sections:

Section 1: Executive Summary

  • 1-page overview
  • Key investment highlights
  • Valuation summary
  • Critical risks

Section 2: Company Overview

  • Business description
  • History and milestones
  • Organizational structure
  • Management team
  • Shareholder structure

Section 3: Financial Summary

  • Income statement (3-5 years)
  • Balance sheet (3-5 years)
  • Cash flow statement (3-5 years)
  • Key ratios and metrics
  • Revenue bridge

Section 4: Operating Metrics

  • Revenue by segment
  • Volume / unit data (if relevant)
  • Capacity / utilization
  • Geographic breakdown

Section 5: Quality of Earnings

  • Revenue quality (cash conversion)
  • Margin analysis
  • One-time items identification
  • Earnings sustainability

Section 6: Balance Sheet Analysis

  • Asset quality
  • Working capital efficiency
  • Debt structure
  • Off-balance sheet items

Section 7: Peer Comparison

  • Trading multiples
  • Financial metrics
  • Operating metrics
  • Valuation dispersion

Section 8: Transaction History

  • Historical M&A
  • Capital raising history
  • Major corporate actions

Section 9: Risk Factors

  • Company-specific risks
  • Industry risks
  • Regulatory risks
  • Market risks

Step 4: Excel Workbook Structure

Recommended tabs:

TabContent
封面 (Cover)Pack title, date, confidentiality
摘要 (Summary)Key data points, valuation
利润表 (Income Statement)Historical + projected
资产负债表 (Balance Sheet)Historical + projected
现金流量表 (Cash Flow)Historical + projected
财务指标 (Key Metrics)Ratios, growth rates
可比公司 (Comparable Cos)Peer analysis
估值 (Valuation)DCF, multiples, football field
运营数据 (Operating Data)Volume, capacity, etc.
公司治理 (Governance)Ownership, management, related party
风险 (Risks)Risk factor analysis

Step 5: Quality Standards

Data integrity:

  • [ ] All figures sourced from 巨潮 PDF or AkShare
  • [ ] Cross-referenced against multiple sources where possible
  • [ ] Adjustments documented
  • [ ] No transcription errors

Formatting:

  • [ ] Consistent formatting throughout
  • [ ] Clear section headers
  • [ ] Source citations in cell comments
  • [ ] Professional appearance

Completeness:

  • [ ] All sections relevant to deal type included
  • [ ] Key risks identified and documented
  • [ ] Valuation analysis complete
  • [ ] Peer comparison adequate

China-Specific Considerations

Regulatory Filings

FilingFrequencySourceKey Data
年报 (Annual report)Annual巨潮Full financials, MD&A, risks
中报 (Semi-annual)Semi-annual巨潮Condensed financials
季报 (Quarterly)Quarterly巨潮Condensed financials
业绩预告As required巨潮Directional guidance
重大事项公告Ad-hoc巨潮M&A, contracts, etc.

Common Data Issues

IssueSolution
千元 vs 元 unitsCheck 财务报表附注 for units
合并 vs 母公司报表Use 合并报表 for group analysis
非经常性损益Flag and normalize
政府补助Identify and separate
关联交易占比高Flag for due diligence

Industry-Specific Data

IndustrySpecial Data Needs
白酒批价数据, 渠道库存, 经销商数量
半导体产能数据, 晶圆出货, 客户结构
新能源装机量, 产能, 技术参数
医药管线数据, 临床进展, 集采中标
房地产可售资源, 去化率, 土储

Quality Checks

Before delivering:

  • [ ] Data pack scope clearly defined
  • [ ] All financials complete and accurate
  • [ ] Source materials documented
  • [ ] Adjustments transparent
  • [ ] Peer comparison relevant
  • [ ] Valuation analysis included
  • [ ] Risk factors comprehensive
  • [ ] Excel workbook well-structured
  • [ ] Confidentiality notices included
Data Source Mode Switch: Set env var IFIND_DATA_SOURCE_MODE to control data source preference. - ifind-only (strict): Use iFind only, error if unavailable - ifind-fallback (default): iFind preferred, fallback to AkShare - akshare-only: Skip iFind, use AkShare only - wind-only: Wind only, error if unavailable - wind-fallback: Wind first, fallback to iFind → AkShare

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