Finance Report Analyzer

Generate financial analysis reports from uploaded Excel/PDF files with LLM-powered analysis, competitor analysis, web search for news, and inline SVG sparkline trend charts.

Architecture

The tool works in a hybrid pipeline:

Python script → Extract data, build tables/charts, render HTML/PDF (deterministic, zero-cost)
LLM (OpenClaw agent) → Write in-depth analysis for each section + competitor analysis + news summary
Web search → Fetch recent company news and hot topics

Report Sections

#	Section	Source
一	盈利能力分析	Script(tables) + LLM(analysis)
二	资产负债分析	Script(tables) + LLM(analysis)
三	现金流分析	Script(tables) + LLM(analysis)
四	每股指标与效率	Script(tables) + LLM(analysis)
五	行业分析	LLM
六	关键竞争对手分析	LLM(competitor)
七	风险与机遇分析	LLM
八	近期热点新闻	Web search + LLM

Workflow (OpenClaw Agent)

Step 1: Acquire Data File

Try in order:

Feishu chat file attachment — Download via API:

# Get token
TOKEN=$(curl -s -X POST 'https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal' \
  -H 'Content-Type: application/json' \
  -d '{"app_id":"APP_ID","app_secret":"APP_SECRET"}' | python3 -c "import json,sys; print(json.load(sys.stdin)['tenant_access_token'])")
# Get file_key from message
curl -s "https://open.feishu.cn/open-apis/im/v1/messages/{message_id}" -H "Authorization: Bearer $TOKEN"
# Download
curl -s "https://open.feishu.cn/open-apis/im/v1/messages/{message_id}/resources/{file_key}?type=file" \
  -H "Authorization: Bearer $TOKEN" -o /tmp/data.xlsx

Feishu Doc/Bitable link — Use feishu_doc/feishu_bitable tools
Local file — Use directly

Step 2: Extract Financial Data (JSON)

python3 scripts/generate_report.py /tmp/data.xlsx --company "公司名" --ticker "000001.SZ" --output-dir /tmp/reports --json

This outputs structured JSON with all financial metrics organized by category.

Step 3: Web Search for Company News & Competitors

web_search("{company} {year} 最新新闻 业绩 研发 重大事件")
web_search("{company} 竞争对手 市场份额 行业对比")

Summarize 5-8 key news items as HTML list. Save to /tmp/reports/news.html.

Step 4: LLM Analysis

Based on the JSON data from Step 2 and search results from Step 3, write professional analysis for 7 sections. Save as JSON to /tmp/reports/analysis.json:

{
  "profitability": "HTML text - revenue trends, margins, profitability inflection points...",
  "balance_sheet": "HTML text - asset structure, leverage, liquidity...",
  "cash_flow": "HTML text - operating CF trends, capex, FCF, cash reserves...",
  "per_share": "HTML text - EPS/BPS trends, efficiency ratios, workforce...",
  "industry": "HTML text wrapped in <div class=\"analysis-box\">...",
  "competitor": "HTML text - competitor table + strategic analysis (see format below)",
  "risk": "...",
  "growth": "增长动力分析 HTML...",
  "rd_analysis": "研发效率分析 HTML...",
  "dupont": "杜邦分析(ROE拆解) HTML..."
}

Analysis guidelines:

Use <strong> for key conclusions as the opening line
Use <br><br> for paragraph breaks
Reference specific numbers from the data
Compare year-over-year trends
Highlight inflection points and turning points
For risk section, use the two-column float layout with .risk-list

Competitor section format:

<div class="analysis-box"><strong>Summary of competitive landscape</strong></div>
<table>
<tr><th style="text-align:left">竞争对手</th><th style="text-align:left">核心竞品</th><th style="text-align:left">竞争领域</th><th style="text-align:left">威胁程度</th></tr>
<tr><td style="text-align:left">Company A</td><td style="text-align:left">Product</td><td style="text-align:left">Domain</td><td style="text-align:left; color:#dc2626">直接竞争</td></tr>
</table>
<div class="analysis-box">Detailed competitive analysis...</div>

Step 5: Generate Final Report

python3 scripts/generate_report.py /tmp/data.xlsx \
  --company "公司名" --ticker "000001.SZ" \
  --output-dir /tmp/reports \
  --analysis-json /tmp/reports/analysis.json \
  --news-html /tmp/reports/news.html \
  -o html,pdf

Step 6: Deliver Files via Feishu API

# Upload and send file
FK=$(curl -s -X POST 'https://open.feishu.cn/open-apis/im/v1/files' \
  -H "Authorization: Bearer $TOKEN" \
  -F 'file_type=stream' -F "file_name=report.html" -F "file=@/tmp/reports/report.html" \
  | python3 -c "import json,sys; print(json.load(sys.stdin)['data']['file_key'])")

curl -s -X POST 'https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=chat_id' \
  -H "Authorization: Bearer $TOKEN" -H 'Content-Type: application/json' \
  -d "{\"receive_id\":\"CHAT_ID\",\"msg_type\":\"file\",\"content\":\"{\\\"file_key\\\":\\\"$FK\\\"}\"}"

CLI Reference

# JSON data export (for LLM pipeline)
python3 scripts/generate_report.py input.xlsx --json --company NAME --ticker TICKER --output-dir DIR

# Full report with LLM analysis + competitor + news
python3 scripts/generate_report.py input.xlsx \
  --analysis-json analysis.json \
  --news-html news.html \
  --company NAME --ticker TICKER \
  -o html,pdf --output-dir DIR

# Standalone (rule-based analysis, no LLM needed)
python3 scripts/generate_report.py input.xlsx --company NAME --ticker TICKER -o html,pdf

Output Formats

Flag	Output	Requires
`-o html`	HTML only	(built-in)
`-o pdf`	HTML + PDF	wkhtmltopdf
`-o doc`	HTML + DOCX	pandoc
`-o md`	HTML + Markdown	pandoc or markdownify

Report Features

LLM-powered analysis: Deep, contextual financial commentary (when using --analysis-json)
Competitor analysis: Key competitors table + strategic competitive assessment
Company news section: Recent hot topics and events (when using --news-html)
Sparkline trend charts: SVG mini-charts in each data row
Forecast markers: Predicted values with ⟡ symbol and yellow background
PDF compatible: No emoji/flex/gradient/CSS variables (works with wkhtmltopdf)
Fallback mode: Rule-based analysis when no LLM analysis is provided

finance-report-analyzer