Financial Data Visualization Analysis Assistant
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Install @user_7ea4583e/financial-edu-david into your AI assistant using the guide at https://skillhub.cn/install/skillhub.md.
About this skill
The Problem Solved
When processing historical financial data for A-share listed companies, engineers often face specific pain points: annual report PDFs from CNINFO have complex layouts where account names are frequently split across lines and mixed with footnote references; manually consolidating 3 to 5 years of consolidated financial statements is time-consuming and prone to unit (CNY to 100M CNY) or mapping errors; furthermore, the lack of automated reconciliation with official public data (like East Money F10) undermines the reliability of the analysis.
Core Capabilities and Workflow
The skill decomposes financial analysis into a strict five-stage pipeline:
- Data Collection and Parsing: Automatically fetches annual report PDFs. It uses pdfplumber to detect page numbers, applies a pending_subject state machine to handle cross-line account names, cleans footnote references via regex, and outputs the three statements into xlsx files.
- Metric Calculation: Extracts 30+ core accounts from the structured data and standardizes units to 100M CNY. It automatically calculates solvency, operational, and profitability metrics, constructing a DuPont analysis tree based on ROE = Net Profit Margin × Total Asset Turnover × Equity Multiplier.
- Visualization and Validation: Generates a single-file HTML dashboard with 8 embedded ECharts trend charts and a pure HTML+CSS DuPont tree. Before delivery, it cross-references core accounts using the East Money F10 API, correcting the underlying JSON with F10 data if discrepancies are found.
Boundaries and Considerations
- The workflow heavily relies on external network APIs (CNINFO, East Money). If upstream APIs change, request parameters in
cninfo_api.mdmust be manually adjusted. - PDF parsing has inherent noise; income statement accounts allow a ±5% error margin, but balance sheet and cash flow metrics must match 100%, relying on the F10 API as a fallback for anomalies.
- Requires a Python 3.8+ runtime environment and write permissions for the working directory.
Use Cases
- An equity researcher parses 4 years of A-share annual report PDFs into three statements and calculates ROE trends.
- A financial analyst generates an HTML report with cash flow, expense ratios, and a DuPont tree for a specified stock.
- A data engineer extracts consolidated financial statements from CNINFO PDFs into xlsx, handling split account names and footnotes.
- A risk reviewer reconciles extracted metrics with East Money F10 and corrects asset, liability, and cash flow discrepancies.
Best For
- Sell-side analysts covering A-share research who need multi-year statements, metrics, and visualization working papers quickly.
- Financial advisors performing public-company due diligence who need annual report PDFs converted into reconciled metric tables and dashboards.
- Data engineers building finance automation scripts who need reliable CNINFO PDF extraction into xlsx and JSON datasets.
- Risk reviewers in credit or investment teams who need cash flow, leverage, and ROE reconciled against East Money F10.
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