A comprehensive financial data interface library providing a unified wrapper for stocks, futures, crypto, and macroeconomic indicators.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install akshare-finance
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 akshare-finance 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 AKShare Finance?
AKShare Finance is a powerful open-source library that consolidates data from numerous public financial websites into a single, easy-to-use interface. It is designed to serve as a high-performance data bridge for developers and researchers who need reliable access to market fundamentals, real-time quotes, and historical price action. By utilizing this within your collection of Openclaw Skills, you can automate the retrieval of complex financial datasets for A-shares, Hong Kong stocks, US markets, and even decentralized finance metrics.
The skill simplifies the process of data scraping by providing clean Pythonic wrappers that return structured data. Whether you are building an automated trading bot, a portfolio tracking dashboard, or a deep-dive economic research tool, AKShare Finance provides the underlying data infrastructure to power your logic without the need for manual data entry or expensive proprietary data feeds.
AKShare Finance Use Cases
- Tracking real-time price movements in A-share, HK, and US stock markets.
- Performing historical backtesting using granular K-line data for various asset classes.
- Monitoring global macroeconomic health through GDP, CPI, and PMI indicators.
- Automating cryptocurrency market analysis via direct Binance API integrations.
- Evaluating company performance through fundamental data like PE ratios, valuation, and profitability metrics.
How AKShare Finance Works
- The user initializes the environment by installing the required akshare and pandas libraries.
- A specific data function is called (e.g., for stocks, macroeconomics, or crypto) via the Openclaw Skills framework.
- The skill connects to the relevant public financial data source and retrieves the raw information.
- The raw response is parsed and converted into a structured Pandas DataFrame.
- The user receives the clean dataset, which can then be processed, visualized, or saved to a local database.
AKShare Finance Setup
To begin using this skill, you must install the core dependencies in your Python environment:
pip install akshare pandas
You can verify the installation and check the current version by running:
python -c "import akshare; print(akshare.__version__)"
AKShare Finance Data Schema & Taxonomy
The skill primarily organizes its output as Pandas DataFrames. Below is an overview of the data taxonomy:
| Data Category | Output Format | Example Fields |
|---|---|---|
| Stock Spot | DataFrame | Code, Name, Current Price, Percentage Change, Turnover |
| Historical K-Line | DataFrame | Date, Open, High, Low, Close, Volume, Adj Close |
| Macro Indicators | DataFrame | Period, Value, Previous Value, Growth Rate |
| Crypto Spot | DataFrame | Symbol, Price, High, Low, Volume, Quote Volume |
| Company Financials | DataFrame | Symbol, Net Profit, ROE, PE, PB, Total Assets |
AKShare Finance Advanced Features
- Multi-asset coverage including bonds, futures, options, and precious metals.
- Integration with global exchange data for cross-market arbitrage analysis.
- Automated valuation calculation hooks for real-time stock screening.
- Support for advanced technical indicators through historical data processing.
- Seamless export to CSV, JSON, or SQL databases via the standard Openclaw Skills workflow.
SKILL.md
Loading
Related Openclaw Skills
Vestige Memory Skill
A sophisticated local cognitive memory system that utilizes FSRS-6 spaced repetition to provide AI agents with persistent, human-like recall capabilities.
Vynn Backtester
A natural language quantitative backtesting engine for trading strategy validation and financial analysis.
q-kdb-code-review
An AI-powered specialized code reviewer for Q/kdb+ designed to catch performance bottlenecks, type errors, and security vulnerabilities in high-frequency trading codebases.
Astrai Code Review
An intelligent AI code review agent that optimizes costs by routing diffs to the most efficient model based on complexity.
Bilibili Downloader
A high-performance media extraction tool for Bilibili that supports 8K video downloads, audio extraction, and metadata retrieval.
Design Style Skill
A powerful design system integrator that enables AI agents to generate production-grade, aesthetically distinct frontend interfaces instead of generic UI.
Featured*
