Install
openclaw skills install a-share-dcf-valuationA-Share DCF Valuation
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.
A-share DCF Valuation Modeling
Trigger Conditions
Use when user requests DCF (Discounted Cash Flow) valuation analysis for an A-share stock.
Environment Requirements
Required Environment Variables
| Variable | Description | How to Obtain |
|---|---|---|
TUSHARE_TOKEN | Tushare API token (mandatory) | Register at https://tushare.pro, get token from user center |
OPENCLAW_WORKSPACE | Workspace root path (optional) | Defaults to ~/.openclaw/workspace |
Python Dependencies
| Package | Version | Purpose |
|---|---|---|
tushare | Latest | A-share financial data API |
pandas | ≥1.0 | Data processing |
numpy | ≥1.18 | Numerical calculations |
scipy | ≥1.4 | Beta regression (stats module) |
Setup Instructions
-
Configure Tushare Token:
# Add to ~/.bashrc or ~/.bash_profile export TUSHARE_TOKEN="your_token_here" source ~/.bashrc -
Install Python Dependencies:
pip install tushare pandas numpy scipy -
Verify Setup:
python3 -c "import tushare as ts; ts.set_token('$TUSHARE_TOKEN'); pro = ts.pro_api(); print(pro.stock_basic(ts_code='600519.SH'))"
Troubleshooting
| Error | Cause | Solution |
|---|---|---|
未设置 TUSHARE_TOKEN 环境变量 | Token not configured | Add export to shell profile |
Tushare API 获取失败 | Invalid token or network issue | Verify token, check network |
No module named 'tushare' | Package not installed | pip install tushare |
Beta 回归数据不足 | Stock newly listed | Use industry default Beta |
Quick Start
User input: "对 贵州茅台 600519 做 DCF 估值"
→ Run: python3 scripts/a_share_dcf.py 600519.SH
→ Report saved to: reports/dcf_贵州茅台_YYYY-MM-DD.md
Valuation Methodology
Method: Two-stage FCFF (Free Cash Flow to Firm) discount model
Formulas:
- FCFF = Operating Cash Flow - Capital Expenditure
- WACC = We×Re + Wd×Rd×(1-T)
- Terminal Value = FCFF_n × (1+g) / (WACC - g)
- Equity Value = EV - Net Debt
- Per-share Value = Equity Value / Total Shares
Three-Scenario Assumptions:
| Scenario | WACC | Growth Rate | Perpetual Growth |
|---|---|---|---|
| Conservative | WACC + 3% (min 14%) | 50% of 3-year avg | 2% |
| Base | Calculated WACC | 80% of 3-year avg | 3% |
| Optimistic | WACC - 3% (min 7%) | 100% of 3-year avg | 4% |
Scenario Assumption Rationale
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.
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.
Optimistic Scenario: Lower WACC assumes favorable risk environment; full historical growth reflects best-case execution. Appropriate for growth-oriented analysis or upside case.
Execution Steps
1. Determine Stock Code Format
Tushare uses XXXXXX.SH (Shanghai) or XXXXXX.SZ (Shenzhen) format:
# Auto conversion
if code.startswith('6'):
ts_code = f"{code}.SH"
elif code.startswith(('0', '3')):
ts_code = f"{code}.SZ"
2. Run Valuation Script
cd $OPENCLAW_WORKSPACE && python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py <TS_CODE> [公司名]
# Example: python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py 600519.SH 贵州茅台
Note: $OPENCLAW_WORKSPACE defaults to ~/.openclaw/workspace. Use relative paths from workspace root for portability.
3. Check Output
- Terminal displays real-time progress and key metrics
- Markdown report saved to
reports/dcf_{公司名}_{日期}.md - Verify report contains all required sections
4. Report to User
First provide summary conclusion, then report path:
## DCF 估值完成 — {公司名}
| 场景 | DCF每股价值 | vs 当前股价 |
|------|------------|------------|
| 保守 | ¥XX.XX | -XX.X% |
| 中性 | ¥XX.XX | -XX.X% |
| 乐观 | ¥XX.XX | -XX.X% |
当前股价: ¥XXX.XX
完整报告: reports/dcf_{公司名}_{日期}.md
Important Notes
Data Source
- Tushare is the sole data source (requires TUSHARE_TOKEN environment variable)
- If Tushare fetch fails, inform user that data is unavailable; do not fabricate data
Beta Calculation
- Use ~500 trading days (~2 years) regression against CSI 300 (000300.SH)
- If regression R² < 0.1 or insufficient data, use industry default Beta:
- Tech/Semiconductor: 1.5
- Consumer/Healthcare: 0.8
- Financial/Banking: 1.0
- Cyclical/Manufacturing: 1.2
- Others: 1.2
WACC Parameters (Dynamic Calculation)
The following parameters are NOT hardcoded fixed values. They are dynamically determined with explicit data sources and rationale:
1. Risk-Free Rate (RF)
| Approach | Source | Value Range |
|---|---|---|
| Primary | China 10-year Treasury yield | 1.8% - 4.5% (historical) |
| Reference | 中债估值 | ~2.1% - 2.5% (2024-2025) |
| Fallback | Recent 10Y bond average | 2.25% (default) |
Method: Query public bond yield data; if unavailable, use recent reference value with explicit notation.
2. Equity Risk Premium (ERP)
Correct Terminology: ERP (Equity Risk Premium), not MRP (Market Risk Premium).
| Source | ERP Estimate |
|---|---|
| Damodaran (2024) | 6.5% for China |
| 中金/中信研报 | 5% - 8% |
| 程晓明等学术研究 | 6.0% - 7.5% |
| Historical calculation (A股20年平均收益率 - RF) | 5.5% - 7.0% |
Industry Adjustment:
- Tech/Semiconductor: +1.0% to 1.5%
- Financial: -1.0%
- Consumer/Healthcare: 0%
Method: Base ERP 6.5% + industry-specific risk adjustment.
3. Debt Cost (Rd)
| Approach | Formula |
|---|---|
| Primary | LPR 1Y + Credit Spread |
| LPR Reference | 3.1% (2025 1-year LPR) |
| Credit Spread | 0.5% - 1.5% based on rating |
| Company Profile | Estimated Rd |
|---|---|
| Low debt (<10%), strong credit | 3.6% (LPR + 0.5%) |
| Moderate debt (10-50%) | 4.0% (LPR + 0.9%) |
| High debt (>50%) | 5.0% (LPR + 1.9%) |
Method: Estimate based on company's debt ratio and implied credit quality.
4. Tax Rate (Tax)
| Source | Rate |
|---|---|
| Primary | Actual effective tax rate from financial statements |
| High-tech enterprise | 15% (qualified) |
| General corporate | 25% |
High-tech Identification Criteria:
- 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
- Gross margin > 30% (common indicator of tech companies)
Method: First attempt to extract actual tax rate from income statement; if unavailable, determine based on industry and gross margin characteristics.
Special Cases
- Financial stocks (banks/insurers/brokers): Standard DCF not applicable (FCFF meaningless), use PB-ROE or DDM model, inform user
- Loss-making companies: When FCFF is negative with no recovery signs, DCF not applicable, use alternative methods
- *ST/ST stocks: Alert user to delisting risk
Report Quality
- All amounts in "亿元" (100 million yuan)
- Growth rates in percentage
- Two decimal places
- Annotate data source and timeliness
- Must include disclaimer
Risk Disclosure (Dynamic Generation)
The "Risk Disclosure" section must be dynamically generated based on company and industry characteristics, NOT hardcoded. The script should:
- Industry-specific risks: Map company's industry to relevant risk factors
- Company-specific risks: Analyze financial data to identify company-level risks
- Model risks: Always include DCF sensitivity to WACC and perpetual growth
- Macro risks: General economic, interest rate, and policy risks
Industry Risk Mapping:
| Industry | Key Risk Factors |
|---|---|
| 科技/半导体 | 技术迭代快、研发投入大、国际竞争、供应链风险 |
| 消费/食品饮料 | 品牌溢价风险、消费降级、渠道变革 |
| 医药/医疗 | 政策监管（集采）、研发失败、专利到期 |
| 金融/银行 | 利率周期、信用风险、监管趋严 |
| 周期/制造 | 经济周期敏感、产能过剩、环保政策 |
| 新能源/光伏 | 技术路线竞争、产能扩张过快、补贴退坡 |
| 房地产/建材 | 政策调控、去杠杆、销售疲软 |
| 互联网/传媒 | 用户增长放缓、监管趋严、变现模式受限 |
| 交通运输 | 油价波动、需求周期、基础设施投资 |
| 其他 | 行业竞争格局变化、政策不确定性 |
Report Structure (Markdown, output in Chinese)
# DCF 估值报告 — {公司名} ({TS_CODE})
> 估值日期 | 当前股价 | 总市值 | PE | PB
## 一、公司基本面概览
- 公司简介
- 历史财务数据表(7年)
- 关键财务指标(最新年报)
## 二、WACC 计算
- 参数表格(Rf, Beta, MRP, Re, Rd, Tax, We, Wd, WACC)
## 三、三场景 DCF 估值
- 场景假设表(含设定依据说明)
- 估值结果表
## 四、敏感性分析
- WACC × 永续增长率 矩阵
## 五、关键假设
- 列出核心假设
## 六、风险提示(Dynamic)
- 根据公司行业特性、财务状况动态生成针对性风险提示
## 七、结论
- 估值区间
- 与当前股价对比
- 简要分析
> 免责声明
File Organization
All paths should be relative to the workspace root ($OPENCLAW_WORKSPACE or ~/.openclaw/workspace):
- Script:
skills/a-share-dcf-valuation/scripts/a_share_dcf.py - Report:
reports/dcf_{公司名}_{YYYY-MM-DD}.md
Important: Avoid absolute paths like /home/laigen/ in the script. Use environment variable $OPENCLAW_WORKSPACE or relative paths for portability.