A comprehensive financial data acquisition tool for AI agents to retrieve market prices, financial statements, fund flows, and sentiment analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-trading-skills
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 quant-trading-skills 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 Quant Trading Data Skills?
The Quant Trading Data skill is a high-performance bridge between AI coding agents and the financial markets. Built as part of the Openclaw Skills ecosystem, it allows developers to programmatically access a wide array of quantitative data, including historical market K-lines, detailed quarterly financial reports, institutional fund flows, and public sentiment analysis. By wrapping powerful Python-based data fetchers with a clean Node.js interface, it provides a seamless experience for building trading bots, financial research assistants, and automated investment analyzers.
Quant Trading Data Skills Use Cases
- Automating the collection of historical stock prices for backtesting trading strategies.
- Performing bulk analysis of financial statements across thousands of A-share companies.
- Tracking Northbound capital and large-scale fund flows for institutional sentiment analysis.
- Generating automated market reports based on real-time sentiment and public opinion data.
- Building and maintaining local financial databases for deep learning and quantitative research.
How Quant Trading Data Skills Works
- The AI agent initiates a request through Openclaw Skills by specifying parameters like data type, symbol, and date range.
- The system checks a built-in memory cache to see if the requested data was fetched within the last hour to reduce API latency.
- If fresh data is needed, the Node.js core invokes a Python-based fetcher powered by the akshare library.
- For batch operations, the skill consults a state management file to perform an incremental fetch, only downloading data since the last update.
- The fetched data is deduplicated, processed, and either returned as a JSON response or stored in highly compressed Apache Parquet files.
Quant Trading Data Skills Setup
Ensure you have Node.js >= 16.0.0 and Python >= 3.11.0 installed before proceeding.
# Navigate to the skill directory and install Node.js dependencies
npm install
# Install Python dependencies using the automated script
python install.py
# Or install manually via pip
pip install -r requirements.txt
Quant Trading Data Skills Data Schema & Taxonomy
The skill organizes batch data using a partitioned storage strategy for optimal query performance. All bulk data is saved in Apache Parquet format using Snappy compression.
| Data Type | Partition Dimension | File Path Template |
|---|---|---|
| Market | Symbol + Month | data/market/{symbol}/{YYYY-MM}.parquet |
| Northbound Flow | Month | data/north_flow/{YYYY-MM}.parquet |
| Dragon-Tiger (LHB) | Date | data/lhb/{YYYY-MM-DD}.parquet |
| Sentiment | Symbol + Date | data/sentiment/{symbol}/{YYYY-MM-DD}.parquet |
| Financials | Symbol + Year | data/financial/{symbol}/{YYYY}.parquet |
Quant Trading Data Skills Advanced Features
- Smart Incremental Fetching: Automatically calculates missing date ranges based on local state to avoid redundant API calls.
- Columnar Storage: Utilizes Apache Parquet for high-speed I/O and efficient data storage for large-scale datasets.
- Automated Deduplication: Features logic to merge new data with existing files while maintaining unique primary keys like dates and report periods.
- Multi-Language Integration: Seamlessly combines the data science power of Python with the asynchronous execution of Node.js within Openclaw Skills.
- Comprehensive Error Handling: Returns standardized JSON error objects for easy debugging and agent-level exception management.
SKILL.md
Loading
Related Openclaw Skills
X Search
A specialized tool that leverages the Grok API to search X (Twitter) for posts, trending topics, and discussions, outputting results in Obsidian-compatible Markdown.
AI Daily Report
An automated agent skill that aggregates daily AI news and trending GitHub projects into visual reports delivered via Feishu.
Dev-Tools
A multi-functional developer utility belt for standardizing project specs, diagnosing errors, and managing technical debt.
Claude Code Collaboration
An advanced task delegation bridge that enables OpenClaw to leverage the Claude Code CLI for deep technical analysis and high-fidelity code generation.
Paigo
Paigo is a cloud cost management platform that enables businesses to monitor infrastructure spending and automate financial workflows.
Doubao Podcast TTS Integration
A specialized integration skill for the ByteDance Doubao Podcast TTS API, enabling high-quality audio generation from articles and text via a custom binary WebSocket protocol.
Featured*
