Comprehensive Python library for financial data acquisition providing access to Chinese and global market data.
npx @tessl/cli install tessl/pypi-akshare@1.17.0AKShare is a comprehensive Python library for financial data acquisition, providing access to over 1,000 functions across 37 categories covering Chinese and global financial markets. The library offers real-time and historical data for multiple asset classes including stocks, bonds, futures, options, funds, commodities, and macroeconomic indicators.
Name: akshare
Language: Python
Installation: pip install akshare
Version: 1.17.44
Dependencies: pandas, requests, beautifulsoup4, lxml
import akshare as ak
import pandas as pd
from typing import Optional
# AKShare provides 1,046+ functions across 37 categories
# All functions return pandas.DataFrame objectsimport akshare as ak
# Get real-time A-share market data
df: pd.DataFrame = ak.stock_zh_a_spot_em()
print(df.head())
# 序号 代码 名称 最新价 涨跌幅 涨跌额 成交量 成交额
# 0 1 000001 平安银行 10.50 1.45 0.15 1234567 12950123456
# 1 2 000002 万科A 8.92 -0.89 -0.08 987654 8800765432
# Get historical data for specific stock
hist_df: pd.DataFrame = ak.stock_zh_a_hist(
symbol="000001",
period="daily",
start_date="20240101",
end_date="20241201"
)import akshare as ak
# Chinese Consumer Price Index
cpi_df: pd.DataFrame = ak.macro_china_cpi()
print(cpi_df.head())
# 日期 全国CPI 城市CPI 农村CPI
# 0 2024-11-01 102.3 102.1 102.8
# 1 2024-10-01 102.0 101.8 102.5
# US Non-farm Payrolls
nonfarm_df: pd.DataFrame = ak.macro_usa_non_farm()
# Chinese GDP Data
gdp_df: pd.DataFrame = ak.macro_china_gdp()import akshare as ak
# Real-time ETF data
etf_df: pd.DataFrame = ak.fund_etf_spot_em()
# Open-end fund daily data
fund_df: pd.DataFrame = ak.fund_open_fund_daily_em(symbol="000001")AKShare follows a modular architecture organized by data source and asset class:
_em_sina_ths_jsl_cninfoakshare/
├── stock/ # Core stock market data
├── stock_feature/ # Advanced stock analysis
├── economic/ # Macroeconomic indicators
├── fund/ # Mutual funds and ETFs
├── bond/ # Bond market data
├── futures/ # Futures and derivatives
├── option/ # Options market data
├── index/ # Market indices
└── utils/ # Shared utilitiespandas.DataFrame objectsCore stock market data and advanced analysis features
# Real-time A-share quotes
df: pd.DataFrame = ak.stock_zh_a_spot_em()
# Historical stock data
hist_df: pd.DataFrame = ak.stock_zh_a_hist(symbol="000001", period="daily")
# US stock real-time data
us_df: pd.DataFrame = ak.stock_us_spot()
# Hong Kong stock data
hk_df: pd.DataFrame = ak.stock_hk_spot()
# Stock Connect capital flows
hsgt_df: pd.DataFrame = ak.stock_hsgt_fund_flow_summary_em()Functions: 325+ functions across STOCK (120) and STOCK_FEATURE (205) categories
Coverage: Chinese A-shares, Hong Kong stocks, US stocks, technical indicators, capital flows
Comprehensive macroeconomic data from multiple countries
# Chinese economic indicators
cpi_df: pd.DataFrame = ak.macro_china_cpi()
gdp_df: pd.DataFrame = ak.macro_china_gdp()
pmi_df: pd.DataFrame = ak.macro_china_pmi()
# US economic data
us_gdp_df: pd.DataFrame = ak.macro_usa_gdp()
unemployment_df: pd.DataFrame = ak.macro_usa_unemployment_rate()
# European economic indicators
euro_cpi_df: pd.DataFrame = ak.macro_euro_cpi()Functions: 226 functions covering China, US, Europe, Japan, Australia, Canada, UK
Coverage: GDP, CPI, PMI, employment, trade, monetary policy indicators
Funds, bonds, futures, and options market data
# ETF and fund data
etf_df: pd.DataFrame = ak.fund_etf_spot_em()
fund_daily_df: pd.DataFrame = ak.fund_open_fund_daily_em(symbol="000001")
# Bond market data
bond_df: pd.DataFrame = ak.bond_zh_hs_daily()
convertible_df: pd.DataFrame = ak.bond_convert_list()
# Futures data
futures_df: pd.DataFrame = ak.futures_zh_daily_sina(symbol="RB0000")
# Options data
option_df: pd.DataFrame = ak.option_finance_board()Functions: 224 functions across FUND (79), BOND (41), FUTURES (62), OPTION (42) categories
Coverage: Mutual funds, ETFs, government bonds, corporate bonds, commodity futures, stock options
Market indices and composite indicators
# Chinese market indices
index_df: pd.DataFrame = ak.stock_zh_index_spot_em()
index_daily_df: pd.DataFrame = ak.stock_zh_index_daily(symbol="000001")
# Global market indices
global_df: pd.DataFrame = ak.index_investing_global()
# Commodity indices
commodity_df: pd.DataFrame = ak.index_cx_commodity()
# Volatility indices
vix_df: pd.DataFrame = ak.index_vix()Functions: 95 functions covering domestic and international indices
Coverage: Stock indices, commodity indices, volatility indices, economic composite indicators
Financial statements and company fundamental data
# Financial analysis indicators
financial_df: pd.DataFrame = ak.stock_financial_analysis_indicator_em(symbol="000001")
# Income statement data
income_df: pd.DataFrame = ak.stock_financial_abstract(symbol="000001")
# Balance sheet indicators
balance_df: pd.DataFrame = ak.stock_balance_sheet_by_report_em(symbol="000001")
# Earnings forecasts
forecast_df: pd.DataFrame = ak.stock_profit_forecast()
# Analyst recommendations
recommend_df: pd.DataFrame = ak.stock_recommend()Functions: 47 functions for comprehensive fundamental analysis
Coverage: Income statements, balance sheets, cash flows, financial ratios, analyst coverage
# Symbol parameter for specific instruments
df = ak.stock_zh_a_hist(symbol="000001") # Individual stock
df = ak.fund_open_fund_daily_em(symbol="000001") # Specific fund
# Date range parameters
df = ak.stock_zh_a_hist(
symbol="000001",
start_date="20240101", # Format: YYYYMMDD
end_date="20241201"
)
# Period parameter for data frequency
df = ak.stock_zh_a_hist(symbol="000001", period="daily") # daily, weekly, monthly# All functions return pandas DataFrame
df: pd.DataFrame = ak.stock_zh_a_spot_em()
# Consistent data types
assert isinstance(df, pd.DataFrame)
assert df.shape[0] > 0 # Always returns data when availabletry:
df = ak.stock_zh_a_hist(symbol="000001")
if df.empty:
print("No data available for this symbol")
except Exception as e:
print(f"Data retrieval error: {e}")AKShare provides comprehensive, reliable access to financial data essential for quantitative analysis, research, and application development in the Chinese and global markets.