A-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...

Security Scan

The skill appears to implement an A-share DCF using Tushare (which matches its description), but there are mismatches between declared requirements and what the code/instructions actually need and at least one apparent bug — review before use.

Purpose & Capability

The name/description (A-share DCF) aligns with the code and SKILL.md: both use the Tushare API and numerical libraries to pull financials and run a DCF. However the registry metadata declares no required environment variables while both SKILL.md and the script require TUSHARE_TOKEN (and optionally OPENCLAW_WORKSPACE). That registry omission is an inconsistency that should be corrected.

Instruction Scope

SKILL.md instructs installing tushare/pandas/numpy/scipy, exporting TUSHARE_TOKEN, and running the included script. The instructions and script only access Tushare and local workspace paths (reports). They do not ask to read unrelated system files or external endpoints beyond Tushare. One concern: the script attempts multiple fallbacks to estimate shares/market values and contains a code path that references a variable (total_eq_val) before it is defined — this can cause a runtime error and should be fixed.

Install Mechanism

No install spec is provided (instruction-only), so nothing is written by an automated installer. Dependencies are typical Python packages (tushare, pandas, numpy, scipy) and are installed via pip per the README; this is expected for this purpose.

Credentials

The script requires a TUSHARE_TOKEN environment variable (sensitive API token) which is appropriate for accessing Tushare data. The problem is the registry metadata listed no required env vars while SKILL.md and the script require and exit if TUSHARE_TOKEN is missing — an incoherence. Confirm you supply only a Tushare token (not broader credentials) and avoid using tokens with unrelated elevated privileges.

Persistence & Privilege

The skill does not request always:true, does not modify other skills or system-wide settings, and writes reports only under the workspace path. Autonomous invocation is allowed by default (normal); no unexpected persistent privileges are requested.

What to consider before installing

This skill mostly does what it says: it pulls A-share data from Tushare and runs a DCF. Before installing or running it: (1) Be aware you must provide a TUSHARE_TOKEN (the registry metadata omitted this) — do not supply broader or unrelated credentials. (2) Review the included script locally (it contacts only Tushare and writes reports to the workspace). (3) There is an apparent bug where total_eq_val is referenced before it is defined; run in a controlled environment (virtualenv/sandbox) and test with a non-sensitive token or test account. (4) Use a dedicated workspace path (OPENCLAW_WORKSPACE) and inspect generated reports. (5) If you plan to use this in automated agents, ensure the token has limited scope and monitor network activity; correct the code bug before relying on production outputs.

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

1. Configure Tushare Token:

   # Add to ~/.bashrc or ~/.bash_profile
   export TUSHARE_TOKEN="your_token_here"
   source ~/.bashrc
   

2. Install Python Dependencies:

   pip install tushare pandas numpy scipy
   

3. 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:

1. 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
2. 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:

1. Industry-specific risks: Map company's industry to relevant risk factors
2. Company-specific risks: Analyze financial data to identify company-level risks
3. Model risks: Always include DCF sensitivity to WACC and perpetual growth
4. 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.