akshare

📁 succ985/openclaw-akshare-skill 📅 Today
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总安装量
1
周安装量
安装命令
npx skills add https://github.com/succ985/openclaw-akshare-skill --skill akshare

Agent 安装分布

amp 1
cline 1
openclaw 1
opencode 1
cursor 1
kimi-cli 1

Skill 文档

AkShare – Chinese Financial Data

Overview

AkShare is a free, open-source Python library for accessing Chinese financial market data. This skill provides guidance for fetching data from Chinese exchanges including Shanghai Stock Exchange, Shenzhen Stock Exchange, Hong Kong Exchange, and more.

Quick Start

Install AkShare:

pip install akshare

Basic stock quote:

import akshare as ak
df = ak.stock_zh_a_spot_em()  # Real-time A-share data

Stock Data

A-Shares (A股)

Real-time quotes:

# All A-shares real-time data
df = ak.stock_zh_a_spot_em()

# Single stock real-time quote
df = ak.stock_zh_a_spot()

Historical data:

# Historical daily data
df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20240101", end_date="20241231", adjust="qfq")

Stock list:

# Get all A-share stock list
df = ak.stock_info_a_code_name()

Hong Kong Stocks (港股)

Real-time quotes:

df = ak.stock_hk_spot_em()

Historical data:

df = ak.stock_hk_hist(symbol="00700", period="daily", adjust="qfq")

US Stocks (美股)

Real-time data:

df = ak.stock_us_spot_em()

Futures Data (期货)

Real-time futures:

# Commodity futures
df = ak.futures_zh_spot()

Historical futures:

df = ak.futures_zh_hist_sina(symbol="IF0")

Fund Data (基金)

Fund list:

df = ak.fund_open_fund_info_em()

Fund historical data:

df = ak.fund_open_fund_info_em(fund="000001", indicator="单位净值走势")

Macroeconomic Indicators (宏观)

GDP data:

df = ak.macro_china_gdp()

CPI data:

df = ak.macro_china_cpi()

PMI data:

df = ak.macro_china_pmi()

Common Parameters

Period (周期):

  • daily – 日线
  • weekly – 周线
  • monthly – 月线

Adjustment (复权):

  • qfq – 前复权
  • hfq – 后复权
  • "" – 不复权

Tips

  1. Data caching: AkShare doesn’t cache data, implement your own caching if needed
  2. Rate limiting: Be mindful of request frequency to avoid being blocked
  3. Data format: Returns pandas DataFrame, can be easily processed
  4. Error handling: Network errors may occur, implement retry logic

References

For complete API documentation and advanced usage, see: