An automated financial modeling tool for professional DCF valuation of A-share stocks with multi-scenario analysis and reporting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install a-share-dcf-valuation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install a-share-dcf-valuation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is A-share DCF Valuation Modeling?
A-share DCF Valuation Modeling is a sophisticated financial analysis tool designed for Openclaw Skills to perform Discounted Cash Flow (DCF) analysis on companies listed on the Shanghai and Shenzhen stock exchanges. It streamlines the complex process of valuation by automatically fetching financial data via the Tushare API, calculating Weighted Average Cost of Capital (WACC), and projecting Free Cash Flow to Firm (FCFF).
The skill provides a rigorous framework for fundamental analysis, offering high-fidelity results through dynamic Beta regression and industry-specific risk adjustments. By leveraging Openclaw Skills, users can transform raw stock codes into comprehensive, Chinese-language valuation reports that include sensitivity matrices and detailed risk disclosures.
A-share DCF Valuation Modeling Use Cases
- Calculating the intrinsic value of A-share listed companies based on fundamental cash flows.
- Conducting multi-scenario sensitivity analysis to understand potential investment upside and downside.
- Automating the creation of equity research reports for portfolio management.
- Evaluating the impact of interest rate changes and risk premiums on stock valuations using Openclaw Skills.
How A-share DCF Valuation Modeling Works
- The user provides an A-share stock code which triggers the skill to identify the correct exchange (SH or SZ).
- Real-time and historical financial data are retrieved through the Tushare API using a mandatory token.
- The system performs a dynamic WACC calculation, including a Beta regression against the CSI 300 index and adjustments for industry risk.
- Three distinct valuation scenarios—Conservative, Base, and Optimistic—are modeled based on varying growth and risk assumptions.
- A comprehensive Markdown report is generated and saved to the workspace, providing a clear comparison between the calculated value and the current stock price within the Openclaw Skills environment.
A-share DCF Valuation Modeling Setup
To utilize this tool within Openclaw Skills, ensure you have a Tushare API token and the necessary Python environment configured.
# Configure your Tushare API token
export TUSHARE_TOKEN="your_token_here"
# Install required data processing and math libraries
pip install tushare pandas numpy scipy
# Execute a valuation (example: Kweichow Moutai)
python3 skills/a-share-dcf-valuation/scripts/a_share_dcf.py 600519.SH
A-share DCF Valuation Modeling Data Schema & Taxonomy
The skill organizes financial data and outputs systematically to ensure transparency and portability within Openclaw Skills.
| Component | Details |
|---|---|
| Data Source | Tushare Pro API (7 years of historical financial data) |
| Valuation Model | Two-stage FCFF (Free Cash Flow to Firm) |
| WACC Parameters | Risk-Free Rate (10Y Treasury), ERP (Damodaran 2024), Dynamic Beta |
| Output Format | Markdown (.md) reports in Chinese |
| Storage Path | reports/dcf_{company_name}_{date}.md |
A-share DCF Valuation Modeling Advanced Features
- Dynamic Risk Disclosure: Automatically generates industry-specific and company-specific risk warnings based on financial health and sector trends.
- Multi-Scenario Modeling: Provides three distinct valuation outlooks (Conservative, Base, Optimistic) with transparent assumption rationales.
- Industry Default Fallbacks: Includes a robust system for assigning default Beta and Tax rates when regression data or financial statements are incomplete.
- Sensitivity Matrix: Visualizes the relationship between WACC and Perpetual Growth rates to help users identify the most critical valuation drivers in Openclaw Skills.
- Automated Beta Regression: Uses 500 trading days of data against the CSI 300 for high-precision volatility assessment.
SKILL.md
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