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Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic...\n\nTags: a-share:1.0.0, capm:1.0.0, china:1.0.0, dcf:1.0.0, equity:1.0.0, fcff:1.0.0, financial-analysis:1.0.0, investment:1.0.0, latest:1.0.0, model:1.0.0, stock:1.0.0, valuation:1.0.0, wacc:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-21T08:18:44.346Z | user\n\nInitial release: Two-stage FCFF DCF model with dynamic WACC calculation, three-scenario valuation (conservative/base/optimistic), sensitivity analysis matrix, and comprehensive risk disclosure. Supports all A-share listed companies with automated financial data fetching via Tushare API.\n\nArchive index:\n\nArchive v1.0.0: 4 files, 18774 bytes\n\nFiles: scripts/a_share_dcf.py (35533b), skill-card.md (2596b), SKILL.md (10433b), _meta.json (140b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: a-share-dcf-valuation\ndescription: DCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic three-scenario DCF valuation, generate sensitivity analysis matrix, and output complete Markdown report (in Chinese). Applicable to any A-share listed company valuation analysis.\n---\n\n# A-share DCF Valuation Modeling\n\n## Trigger Conditions\n\nUse when user requests DCF (Discounted Cash Flow) valuation analysis for an A-share stock.\n\n## Environment Requirements\n\n### Required Environment Variables\n\n| Variable | Description | How to Obtain |\n|----------|-------------|---------------|\n| `TUSHARE_TOKEN` | Tushare API token (mandatory) | Register at https://tushare.pro, get token from user center |\n| `OPENCLAW_WORKSPACE` | Workspace root path (optional) | Defaults to `~/.openclaw/workspace` |\n\n### Python Dependencies\n\n| Package | Version | Purpose |\n|---------|---------|--------|\n| `tushare` | Latest | A-share financial data API |\n| `pandas` | ≥1.0 | Data processing |\n| `numpy` | ≥1.18 | Numerical calculations |\n| `scipy` | ≥1.4 | Beta regression (stats module) |\n\n### Setup Instructions\n\n1. **Configure Tushare Token**:\n   ```bash\n   # Add to ~/.bashrc or ~/.bash_profile\n   export TUSHARE_TOKEN=\"your_token_here\"\n   source ~/.bashrc\n   ```\n\n2. **Install Python Dependencies**:\n   ```bash\n   pip install tushare pandas numpy scipy\n   ```\n\n3. **Verify Setup**:\n   ```bash\n   python3 -c \"import tushare as ts; ts.set_token('$TUSHARE_TOKEN'); pro = ts.pro_api(); print(pro.stock_basic(ts_code='600519.SH'))\"\n   ```\n\n### Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| `未设置 TUSHARE_TOKEN 环境变量` | Token not configured | Add export to shell profile |\n| `Tushare API 获取失败` | Invalid token or network issue | Verify token, check network |\n| `No module named 'tushare'` | Package not installed | `pip install tushare` |\n| `Beta 回归数据不足` | Stock newly listed | Use industry default Beta |\n\n## Quick Start\n\n```\nUser input: \"对 贵州茅台 600519 做 DCF 估值\"\n→ Run: python3 scripts/a_share_dcf.py 600519.SH\n→ Report saved to: reports/dcf_贵州茅台_YYYY-MM-DD.md\n```\n\n## Valuation Methodology\n\n**Method**: Two-stage FCFF (Free Cash Flow to Firm) discount model\n\n**Formulas**:\n- FCFF = Operating Cash Flow - Capital Expenditure\n- WACC = We×Re + Wd×Rd×(1-T)\n- Terminal Value = FCFF_n × (1+g) / (WACC - g)\n- Equity Value = EV - Net Debt\n- Per-share Value = Equity Value / Total Shares\n\n**Three-Scenario Assumptions**:\n\n| Scenario | WACC | Growth Rate | Perpetual Growth |\n|----------|------|-------------|------------------|\n| Conservative | WACC + 3% (min 14%) | 50% of 3-year avg | 2% |\n| Base | Calculated WACC | 80% of 3-year avg | 3% |\n| Optimistic | WACC - 3% (min 7%) | 100% of 3-year avg | 4% |\n\n### Scenario Assumption Rationale\n\n**Conservative Scenario**: Higher WACC reflects elevated risk premium; reduced growth rate accounts for potential downside from competition, regulation, or macro headwinds. Suitable for risk-averse investors.\n\n**Base Scenario**: Uses calculated WACC from company-specific beta and capital structure; growth rate anchored to historical average but tempered for maturity trajectory. Represents most likely outcome under normal conditions.\n\n**Optimistic Scenario**: Lower WACC assumes favorable risk environment; full historical growth reflects best-case execution. Appropriate for growth-oriented analysis or upside case.\n\n## Execution Steps\n\n### 1. Determine Stock Code Format\n\nTushare uses `XXXXXX.SH` (Shanghai) or `XXXXXX.SZ` (Shenzhen) format:\n\n```python\n# Auto conversion\nif code.startswith('6'): \n    ts_code = f\"{code}.SH\"\nelif code.startswith(('0', '3')):\n    ts_code = f\"{code}.SZ\"\n```\n\n### 2. Run Valuation Script\n\n```bash\ncd $OPENCLAW_WORKSPACE && python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py <TS_CODE> [公司名]\n# Example: python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py 600519.SH 贵州茅台\n```\n\n**Note**: `$OPENCLAW_WORKSPACE` defaults to `~/.openclaw/workspace`. Use relative paths from workspace root for portability.\n\n### 3. Check Output\n\n- Terminal displays real-time progress and key metrics\n- Markdown report saved to `reports/dcf_{公司名}_{日期}.md`\n- Verify report contains all required sections\n\n### 4. Report to User\n\nFirst provide summary conclusion, then report path:\n\n```\n## DCF 估值完成 — {公司名}\n\n| 场景 | DCF每股价值 | vs 当前股价 |\n|------|------------|------------|\n| 保守 | ¥XX.XX     | -XX.X%     |\n| 中性 | ¥XX.XX     | -XX.X%     |\n| 乐观 | ¥XX.XX     | -XX.X%     |\n\n当前股价: ¥XXX.XX\n完整报告: reports/dcf_{公司名}_{日期}.md\n```\n\n## Important Notes\n\n### Data Source\n- **Tushare** is the sole data source (requires TUSHARE_TOKEN environment variable)\n- If Tushare fetch fails, inform user that data is unavailable; do not fabricate data\n\n### Beta Calculation\n- Use ~500 trading days (~2 years) regression against CSI 300 (000300.SH)\n- If regression R² < 0.1 or insufficient data, use industry default Beta:\n  - Tech/Semiconductor: 1.5\n  - Consumer/Healthcare: 0.8\n  - Financial/Banking: 1.0\n  - Cyclical/Manufacturing: 1.2\n  - Others: 1.2\n\n### WACC Parameters (Dynamic Calculation)\n\nThe following parameters are **NOT hardcoded fixed values**. They are dynamically determined with explicit data sources and rationale:\n\n#### 1. Risk-Free Rate (RF)\n\n| Approach | Source | Value Range |\n|----------|--------|-------------|\n| **Primary** | China 10-year Treasury yield | 1.8% - 4.5% (historical) |\n| **Reference** | 中债估值 | ~2.1% - 2.5% (2024-2025) |\n| **Fallback** | Recent 10Y bond average | 2.25% (default) |\n\n**Method**: Query public bond yield data; if unavailable, use recent reference value with explicit notation.\n\n#### 2. Equity Risk Premium (ERP)\n\n**Correct Terminology**: ERP (Equity Risk Premium), not MRP (Market Risk Premium).\n\n| Source | ERP Estimate |\n|--------|--------------|\n| Damodaran (2024) | 6.5% for China |\n| 中金/中信研报 | 5% - 8% |\n| 程晓明等学术研究 | 6.0% - 7.5% |\n| Historical calculation (A股20年平均收益率 - RF) | 5.5% - 7.0% |\n\n**Industry Adjustment**:\n- Tech/Semiconductor: +1.0% to 1.5%\n- Financial: -1.0%\n- Consumer/Healthcare: 0%\n\n**Method**: Base ERP 6.5% + industry-specific risk adjustment.\n\n#### 3. Debt Cost (Rd)\n\n| Approach | Formula |\n|----------|---------|\n| **Primary** | LPR 1Y + Credit Spread |\n| **LPR Reference** | 3.1% (2025 1-year LPR) |\n| **Credit Spread** | 0.5% - 1.5% based on rating |\n\n| Company Profile | Estimated Rd |\n|-----------------|--------------|\n| Low debt (<10%), strong credit | 3.6% (LPR + 0.5%) |\n| Moderate debt (10-50%) | 4.0% (LPR + 0.9%) |\n| High debt (>50%) | 5.0% (LPR + 1.9%) |\n\n**Method**: Estimate based on company's debt ratio and implied credit quality.\n\n#### 4. Tax Rate (Tax)\n\n| Source | Rate |\n|--------|------|\n| **Primary** | Actual effective tax rate from financial statements |\n| **High-tech enterprise** | 15% (qualified) |\n| **General corporate** | 25% |\n\n**High-tech Identification Criteria**:\n1. Industry: 电子、半导体、计算机、通信、医药生物、医疗器械、新能源、电力设备\n2. Gross margin > 30% (common indicator of tech companies)\n\n**Method**: First attempt to extract actual tax rate from income statement; if unavailable, determine based on industry and gross margin characteristics.\n\n### Special Cases\n- **Financial stocks (banks/insurers/brokers)**: Standard DCF not applicable (FCFF meaningless), use PB-ROE or DDM model, inform user\n- **Loss-making companies**: When FCFF is negative with no recovery signs, DCF not applicable, use alternative methods\n- **ST/*ST stocks**: Alert user to delisting risk\n\n### Report Quality\n- All amounts in \"亿元\" (100 million yuan)\n- Growth rates in percentage\n- Two decimal places\n- Annotate data source and timeliness\n- Must include disclaimer\n\n### Risk Disclosure (Dynamic Generation)\n\nThe \"Risk Disclosure\" section must be dynamically generated based on company and industry characteristics, NOT hardcoded. The script should:\n\n1. **Industry-specific risks**: Map company's industry to relevant risk factors\n2. **Company-specific risks**: Analyze financial data to identify company-level risks\n3. **Model risks**: Always include DCF sensitivity to WACC and perpetual growth\n4. **Macro risks**: General economic, interest rate, and policy risks\n\n**Industry Risk Mapping**:\n\n| Industry | Key Risk Factors |\n|----------|------------------|\n| 科技/半导体 | 技术迭代快、研发投入大、国际竞争、供应链风险 |\n| 消费/食品饮料 | 品牌溢价风险、消费降级、渠道变革 |\n| 医药/医疗 | 政策监管（集采）、研发失败、专利到期 |\n| 金融/银行 | 利率周期、信用风险、监管趋严 |\n| 周期/制造 | 经济周期敏感、产能过剩、环保政策 |\n| 新能源/光伏 | 技术路线竞争、产能扩张过快、补贴退坡 |\n| 房地产/建材 | 政策调控、去杠杆、销售疲软 |\n| 互联网/传媒 | 用户增长放缓、监管趋严、变现模式受限 |\n| 交通运输 | 油价波动、需求周期、基础设施投资 |\n| 其他 | 行业竞争格局变化、政策不确定性 |\n\n## Report Structure (Markdown, output in Chinese)\n\n```markdown\n# DCF 估值报告 — {公司名} ({TS_CODE})\n\n> 估值日期 | 当前股价 | 总市值 | PE | PB\n\n## 一、公司基本面概览\n- 公司简介\n- 历史财务数据表(7年)\n- 关键财务指标(最新年报)\n\n## 二、WACC 计算\n- 参数表格(Rf, Beta, MRP, Re, Rd, Tax, We, Wd, WACC)\n\n## 三、三场景 DCF 估值\n- 场景假设表(含设定依据说明)\n- 估值结果表\n\n## 四、敏感性分析\n- WACC × 永续增长率 矩阵\n\n## 五、关键假设\n- 列出核心假设\n\n## 六、风险提示(Dynamic)\n- 根据公司行业特性、财务状况动态生成针对性风险提示\n\n## 七、结论\n- 估值区间\n- 与当前股价对比\n- 简要分析\n\n> 免责声明\n```\n\n## File Organization\n\nAll paths should be relative to the workspace root (`$OPENCLAW_WORKSPACE` or `~/.openclaw/workspace`):\n\n- **Script**: `skills/a-share-dcf-valuation/scripts/a_share_dcf.py`\n- **Report**: `reports/dcf_{公司名}_{YYYY-MM-DD}.md`\n\n**Important**: Avoid absolute paths like `/home/laigen/` in the script. Use environment variable `$OPENCLAW_WORKSPACE` or relative paths for portability.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7fw4bnhsaf250vztqhw0t6js81z2pe\",\n  \"slug\": \"a-share-dcf-valuation\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776759524346\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nDCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic three-scenario DCF valuation, generate sensitivity analysis matrix, and output complete Markdown report (in Chinese). Applicable to any A-share listed company valuation analysis.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[laigen](https://clawhub.ai/user/laigen)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and finance-oriented agent workflows use this skill to run DCF valuation analysis for A-share listed companies from a stock code. The skill fetches market and financial data, calculates WACC, evaluates conservative/base/optimistic cases, and produces a Chinese Markdown valuation report.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Credential handling needs review because the skill requires a Tushare token and the documented verification command expands the token into the command line.\n\nMitigation: Use a temporary environment variable or secret manager, avoid putting the token in a global shell profile, and avoid commands that expose the token in process arguments.\n\nRisk: Dependency and execution practices need review before installation.\n\nMitigation: Install in a dedicated virtual environment, review dependency sources, and pin versions before using the skill in a production workflow.\n\nRisk: The valuation result depends on Tushare data availability and model assumptions, and may be misleading if treated as investment advice.\n\nMitigation: Use only intended A-share tickers, verify generated report paths and data freshness, and keep the report's financial disclaimer visible to users.\n\n## Reference(s):\n\n- [Tushare](https://tushare.pro)\n- [ClawHub skill page](https://clawhub.ai/laigen/skills/a-share-dcf-valuation)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Chinese Markdown report with terminal progress text and report path]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires an A-share stock code and a Tushare token; writes a report under the workspace reports directory.]\n\n## Skill Version(s):\n\n1.0.0 (source: ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: A-Share DCF Valuation Owner: laigen Summary: DCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic... Tags: a-share:1.0.0, capm:1.0.0, china:1.0.0, dcf:1.0.0, equity:1.0.0, fcff:1.0.0, financial-analysis:1.0.0, investment:1.0.0, latest:1.0.0, model:1.0.0, stock:1.0.0, valuation:1.0.0, wacc:1.0.0 Version histo","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"# Add to ~/.bashrc or ~/.bash_profile\n   export TUSHARE_TOKEN=\"your_token_here\"\n   source ~/.bashrc"},{"language":"bash","snippet":"pip install tushare pandas numpy scipy"},{"language":"bash","snippet":"python3 -c \"import tushare as ts; ts.set_token('$TUSHARE_TOKEN'); pro = ts.pro_api(); print(pro.stock_basic(ts_code='600519.SH'))\""},{"language":"text","snippet":"User input: \"对 贵州茅台 600519 做 DCF 估值\"\n→ Run: python3 scripts/a_share_dcf.py 600519.SH\n→ Report saved to: reports/dcf_贵州茅台_YYYY-MM-DD.md"},{"language":"python","snippet":"# Auto conversion\nif code.startswith('6'): \n    ts_code = f\"{code}.SH\"\nelif code.startswith(('0', '3')):\n    ts_code = f\"{code}.SZ\""},{"language":"bash","snippet":"cd $OPENCLAW_WORKSPACE && python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py <TS_CODE> [公司名]\n# Example: python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py 600519.SH 贵州茅台"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: a-share-dcf-valuation\ndescription: DCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic three-scenario DCF valuation, generate sensitivity analysis matrix, and output complete Markdown report (in Chinese). Applicable to any A-share listed company valuation analysis.\n---\n\n# A-share DCF Valuation Modeling\n\n## Trigger Conditions\n\nUse when user requests DCF (Discounted Cash Flow) valuation analysis for an A-share stock.\n\n## Environment Requirements\n\n### Required Environment Variables\n\n| Variable | Description | How to Obtain |\n|----------|-------------|---------------|\n| `TUSHARE_TOKEN` | Tushare API token (mandatory) | Register at https://tushare.pro, get token from user center |\n| `OPENCLAW_WORKSPACE` | Workspace root path (optional) | Defaults to `~/.openclaw/workspace` |\n\n### Python Dependencies\n\n| Package | Version | Purpose |\n|---------|---------|--------|\n| `tushare` | Latest | A-share financial data API |\n| `pandas` | ≥1.0 | Data processing |\n| `numpy` | ≥1.18 | Numerical calculations |\n| `scipy` | ≥1.4 | Beta regression (stats module) |\n\n### Setup Instructions\n\n1. **Configure Tushare Token**:\n   ```bash\n   # Add to ~/.bashrc or ~/.bash_profile\n   export TUSHARE_TOKEN=\"your_token_here\"\n   source ~/.bashrc\n   ```\n\n2. **Install Python Dependencies**:\n   ```bash\n   pip install tushare pandas numpy scipy\n   ```\n\n3. **Verify Setup**:\n   ```bash\n   python3 -c \"import tushare as ts; ts.set_token('$TUSHARE_TOKEN'); pro = ts.pro_api(); print(pro.stock_basic(ts_code='600519.SH'))\"\n   ```\n\n### Troubleshooting\n\n| Error | Cause | Solution |\n|-------|-------|----------|\n| `未设置 TUSHARE_TOKEN 环境变量` | Token not configured | Add export to shell profile |\n| `Tushare API 获取失败` | Invalid token or network issue | Verify token, check network |\n| `No module named 'tushare'` | Package not installed | `pip install tushare` |\n| `Beta 回归数据不足` | Stock newly listed | Use industry default Beta |\n\n## Quick Start\n\n```\nUser input: \"对 贵州茅台 600519 做 DCF 估值\"\n→ Run: python3 scripts/a_share_dcf.py 600519.SH\n→ Report saved to: reports/dcf_贵州茅台_YYYY-MM-DD.md\n```\n\n## Valuation Methodology\n\n**Method**: Two-stage FCFF (Free Cash Flow to Firm) discount model\n\n**Formulas**:\n- FCFF = Operating Cash Flow - Capital Expenditure\n- WACC = We×Re + Wd×Rd×(1-T)\n- Terminal Value = FCFF_n × (1+g) / (WACC - g)\n- Equity Value = EV - Net Debt\n- Per-share Value = Equity Value / Total Shares\n\n**Three-Scenario Assumptions**:\n\n| Scenario | WACC | Growth Rate | Perpetual Growth |\n|----------|------|-------------|------------------|\n| Conservative | WACC + 3% (min 14%) | 50% of 3-year avg | 2% |\n| Base | Calculated WACC | 80% of 3-year avg | 3% |\n| Optimistic | WACC - 3% (min 7%) | 100% of 3-year avg | 4% |\n\n### Scenario Assumption Rationale\n\n**Conservative Scenario**: Higher WACC reflects elevated risk premium; reduced growth rate accounts for potential downside from competition, regulation, or"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7fw4bnhsaf250vztqhw0t6js81z2pe\",\n  \"slug\": \"a-share-dcf-valuation\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776759524346\n}"},{"path":"skill-card.md","content":"## Description:\n\nDCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic three-scenario DCF valuation, generate sensitivity analysis matrix, and output complete Markdown report (in Chinese). Applicable to any A-share listed company valuation analysis.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[laigen](https://clawhub.ai/user/laigen)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and finance-oriented agent workflows use this skill to run DCF valuation analysis for A-share listed companies from a stock code. The skill fetches market and financial data, calculates WACC, evaluates conservative/base/optimistic cases, and produces a Chinese Markdown valuation report.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Credential handling needs review because the skill requires a Tushare token and the documented verification command expands the token into the command line.\n\nMitigation: Use a temporary environment variable or secret manager, avoid putting the token in a global shell profile, and avoid commands that expose the token in process arguments.\n\nRisk: Dependency and execution practices need review before installation.\n\nMitigation: Install in a dedicated virtual environment, review dependency sources, and pin versions before using the skill in a production workflow.\n\nRisk: The valuation result depends on Tushare data availability and model assumptions, and may be misleading if treated as investment advice.\n\nMitigation: Use only intended A-share tickers, verify generated report paths and data freshness, and keep the report's financial disclaimer visible to users.\n\n## Reference(s):\n\n- [Tushare](https://tushare.pro)\n- [ClawHub skill page](https://clawhub.ai/laigen/skills/a-share-dcf-valuation)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Chinese Markdown report with terminal progress text and report path]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires an A-share stock code and a Tushare token; writes a report under the workspace reports directory.]\n\n## Skill Version(s):\n\n1.0.0 (source: ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"DCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic... Skill: A-Share DCF Valuation Owner: laigen Summary: DCF valuation modeling for A-share listed companies. Pass stock code to automatically fetch financial data, calculate WACC, run conservative/base/optimistic... 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