lixinger-python 0.3.1

Python SDK for Lixinger Financial Data API

This is the python SDK of lixinger API. Lixinger is a website of financial data.

✨ Features

• ⚡ Fully Async/Await - Non-blocking I/O for better performance
• 🤖 AI Agent Ready - Perfect for LangChain, LlamaIndex function calls
• 📊 Pandas DataFrame - All responses return pandas DataFrame
• 🔒 Type Safe - Full type hints and mypy support
• 🔄 Auto Retry - Automatic retry with exponential backoff
• 🚦 Rate Limiting - Built-in rate limiter (1000 req/min)
• 🌐 Proxy Support - SOCKS proxy support
• 📓 Jupyter Ready - Native top-level await support

Installation

From PyPI (Recommended)

# Using pip
pip install lixinger-python

# Using uv (faster)
uv add lixinger-python

From Source (Development)

# Clone the repository
git clone https://github.com/your-username/lixinger-python.git
cd lixinger-python

# Install dependencies
uv sync

Configuration

REQUIRED: The SDK requires the API key to be set in a .env file. The API key cannot be passed as a parameter.

# Copy the example file
cp .env.example .env

# Edit .env and add your API key
# LIXINGER_API_KEY=your_actual_api_key_here

Get your API key from: https://www.lixinger.com/open/api

Usage

Basic Usage (Async/Await)

import asyncio
from lixinger import AsyncLixingerClient

async def main():
    # The API key is ALWAYS loaded from .env file
    # You cannot pass it as a parameter - this is intentional for security
    async with AsyncLixingerClient() as client:
        # Fetch company information
        df = await client.company.company.get_company(stock_codes=["600036"])
        print(df)

        # Fetch company profile
        df_profile = await client.company.profile.get_profile(stock_codes=["600036"])
        print(df_profile)

if __name__ == "__main__":
    asyncio.run(main())

Advanced: Concurrent Requests

Query multiple stocks in parallel using asyncio.gather():

import asyncio
from lixinger import AsyncLixingerClient

async def main():
    async with AsyncLixingerClient() as client:
        # Fetch multiple stocks concurrently
        tasks = [
            client.company.company.get_company(stock_codes=["600036"]),
            client.company.company.get_company(stock_codes=["000001"]),
            client.company.company.get_company(stock_codes=["000002"]),
        ]
        results = await asyncio.gather(*tasks)

        for df in results:
            print(df)

asyncio.run(main())

Jupyter Notebook Usage

Jupyter notebooks support async/await natively at the top level:

# In a Jupyter cell, you can use await directly (no asyncio.run needed)
from lixinger import AsyncLixingerClient

client = AsyncLixingerClient()
df = await client.company.company.get_company(stock_codes=["600036"])
df

Configuration Override

# You can override settings (but NOT the API key)
async with AsyncLixingerClient(
    timeout=60.0,
    proxy="socks5://localhost:1080",
    max_requests_per_minute=500
) as client:
    df = await client.company.company.get_company(stock_codes=["000001"])

For more examples, see the examples/ directory.

Project Structure

The project follows an endpoint-based file organization:

lixinger/
├── api/cn/
│   ├── company/     # /cn/company endpoints (async)
│   ├── index/       # /cn/index endpoints (async)
│   └── fund/        # /cn/fund endpoints (async)
└── models/cn/       # Pydantic models (mirrors API structure)
    ├── company/
    ├── index/
    └── fund/

Available APIs

Company APIs (client.company.*):

• get_company() - Company basic info
• get_profile() - Company profile
• get_fundamental() - Fundamental data
• get_candlestick() - OHLC candlestick data
• get_equity_change() - Equity change events
• get_dividend() - Dividend information
• get_announcement() - Company announcements
• get_indices() - Index memberships

Index APIs (client.cn_index.*):

• get_index() - Index basic info
• get_constituents() - Index constituents
• get_candlestick() - Index OHLC data
• get_fundamental() - Index fundamentals
• get_tracking_fund() - Tracking funds
• And more...

Fund APIs (client.cn_fund.*):

• get_fund() - Fund basic info
• get_profile() - Fund profile
• get_candlestick() - Fund NAV data
• get_shareholdings() - Fund holdings
• And more...

Import Examples:

# Client API (recommended)
from lixinger import AsyncLixingerClient

async with AsyncLixingerClient() as client:
    df = await client.company.company.get_company(stock_codes=["000001"])

# Functional API (also async)
from lixinger import get_company, get_index

df = await get_company(stock_codes=["000001"])

# Models (if needed for type hints)
from lixinger.models.cn.company import Company
from lixinger.models.cn.index import Index

See AGENTS.md for detailed file organization rules.

Documentation

• 📚 完整文档: 运行 uv run mkdocs serve 启动本地文档站点
• 📖 API 参考: 查看 docs/api/ 目录或 API_SUMMARY.md
• 🚀 快速开始: 查看 docs/getting-started/quickstart.md

文档特性

本项目使用 MkDocs + Material 主题 自动生成文档，特性包括：

• ✨ 从 Python docstrings 自动生成 API 文档
• 🎨 美观的 Material Design 界面
• 🔍 全文搜索功能（支持中英文）
• 🌓 深色/浅色主题切换
• 📱 响应式设计，支持移动端

生成和预览文档

# 安装文档依赖
uv sync --dev

# 启动本地文档服务器（支持热重载）
uv run mkdocs serve
# 访问 http://127.0.0.1:8000

# 构建静态站点
uv run mkdocs build

# 部署到 GitHub Pages
uv run mkdocs gh-deploy

详细说明请查看 DOCS.md。

API Notes

Architecture:

• 🔄 Fully Async - All API methods use async def and must be awaited
• ⚡ Non-blocking - Perfect for concurrent requests and AI agents
• 📦 httpx.AsyncClient - Modern async HTTP client

API Requirements:

• Content-Type: application/json in request headers
• Accept-Encoding: Must include gzip (can also include deflate, br, *)
• Rate Limit: Maximum 1000 requests per minute
• Status Code 429: Too Many Requests (rate limit exceeded)
• Retry Mechanism: Built-in automatic retry for network issues
• Authentication: API key sent via token in POST body (loaded from .env)

Response Format: All Lixinger API responses follow this format:

{
  "code": 1,           // 1 = success, other = error
  "message": "success",
  "data": [...]        // Actual data (unwrapped by SDK)
}

The SDK automatically validates the response and returns only the data field.

Why Async?

Perfect for:

• 🤖 AI Agents - LangChain, LlamaIndex function calls
• 🌐 Web Apps - FastAPI, Sanic, Quart backends
• 📊 Data Pipelines - Concurrent batch processing
• 📓 Jupyter - Interactive analysis with await

Benefits:

• ⚡ Concurrent requests - Query multiple stocks in parallel
• 🚫 Non-blocking - Doesn't block your event loop
• 📈 Better performance - Especially for bulk operations
• 🎯 Modern Python - Follows async/await best practices

Development

# Run linter
uv run ruff check . --fix

# Run formatter
uv run ruff format .

# Run type checker
uv run mypy lixinger/

# Run tests (unit tests only, fast)
uv run pytest -m "not integration"

# Run all tests including integration (requires API key)
uv run pytest

See CLAUDE.md for development guidelines.

Contributing

Contributions are welcome! Please see AGENTS.md for detailed implementation guidelines, especially:

• How to add new APIs (all must be async)
• File organization rules
• Testing requirements
• Documentation standards

License

[Your License Here]

Links

• 📚 API Documentation: https://www.lixinger.com/open/api/doc
• 🌐 Lixinger Website: https://www.lixinger.com
• 📖 Full Documentation: Run uv run mkdocs serve