A specialized tool for bulk extracting stock market daily limit data from the Tonghuashun Iwencai AI platform into organized local databases and Excel reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install iwencai-data-extractor
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 iwencai-data-extractor 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 Tonghuashun Iwencai Data Extractor?
The Tonghuashun Iwencai Data Extractor is a powerful tool designed for traders and analysts who need to monitor A-share market trends. By leveraging the agent-browser and Chrome CDP (Chrome DevTools Protocol), this Openclaw Skills solution automates the navigation, querying, and data extraction from the Iwencai AI stock picking interface. It effectively bypasses complex web interaction hurdles such as Vue.js responsive events and paginated tables.
This skill ensures that high-frequency trading data, specifically daily limit (Zhangting) records, is reliably captured and stored. It bridges the gap between web-based financial intelligence and local data analysis by providing a structured pipeline from the browser to a SQLite database and ultimately to formatted Excel summaries.
Tonghuashun Iwencai Data Extractor Use Cases
- Automating the daily collection of stock records that hit the daily price limit.
- Bulk scraping historical stock picking data for backtesting and strategy research.
- Generating comprehensive multi-sheet Excel reports for market sentiment analysis.
- Monitoring specific stock metrics like consecutive limit hits, themes, and turnover volume.
How Tonghuashun Iwencai Data Extractor Works
- Browser Initialization: The user launches Chrome in debug mode to allow the skill to connect via the CDP port.
- Target Navigation: The browser is navigated to the Iwencai AI stock picker page to establish the session context.
- Automated Querying: The Python crawler identifies the search textbox using specific 'ref' attributes and inputs the required screening criteria.
- Data Extraction: Using agent-browser commands, the skill executes JavaScript to scrape structured data, handling Vue.js pagination through custom event dispatching.
- Persistent Storage: Extracted records are parsed and saved into a local SQLite database, ensuring data integrity through a strictly defined schema.
- Reporting: An export script processes the database records to generate an Excel file featuring multiple summarized sheets.
Tonghuashun Iwencai Data Extractor Setup
Prerequisites
Ensure Python and agent-browser are installed and paths are correctly mapped in your environment.
Step 1: Launch Chrome in Debug Mode
chrome.exe --remote-debugging-port=9222 --user-data-dir=C:\ChromeDebug
Step 2: Run the Crawler
Configure your TRADE_DAYS in the script and execute:
cmd /c python D:\workbuddyclaw\iwencai_data\crawl.py
Step 3: Export to Excel
cmd /c python D:\workbuddyclaw\iwencai_data\export_excel.py
Tonghuashun Iwencai Data Extractor Data Schema & Taxonomy
The skill utilizes a SQLite table named zt_stocks with the following structure:
| Field | Type | Description |
|---|---|---|
| trade_date | TEXT | Trading date (YYYY-MM-DD) |
| stock_code | TEXT | 6-digit stock code |
| stock_name | TEXT | Name of the stock |
| price | REAL | Closing price |
| change_pct | REAL | Price change percentage |
| lb_count | INTEGER | Number of consecutive daily limit hits |
| themes | TEXT | Associated market themes/concepts |
| zt_type | TEXT | Limit type (e.g., First limit, Second limit) |
| float_mv | TEXT | Floating market capitalization |
Tonghuashun Iwencai Data Extractor Advanced Features
- Vue.js Event Simulation: Uses dispatchEvent to interact with responsive UI elements that standard click events cannot trigger.
- Dual-Layer JSON Parsing: Robust handling of agent-browser 'eval' outputs to ensure complex data structures are decoded correctly.
- Windows Encoding Compatibility: Includes custom text wrappers to prevent encoding errors when running in Windows CMD/PowerShell environments.
- Multi-Sheet Reporting: The export engine automatically categorizes data into five distinct Excel sheets for multidimensional analysis.
- Openclaw Skills Integration: Optimized for seamless execution within AI agent workflows for automated daily financial reporting.
SKILL.md
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