Overview

AKShare requires Python(64 bit) 3.7 or greater, aims to make fetch financial data as convenient as possible.

Write less, get more!

• Documentation: 中文文档

Installation

General

pip install akshare --upgrade

China

pip install akshare -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com  --upgrade

PR

Please check out documentation if you want to contribute to AkShare

Docker

Pull images

docker pull registry.cn-hangzhou.aliyuncs.com/akshare/akdocker

Run AkDocker

docker run -it registry.cn-hangzhou.aliyuncs.com/akshare/akdocker python

Test AkDocker

import akshare as ak
ak.__version__

Usage

Data

Code

import akshare as ak
hist_df = ak.stock_us_daily(symbol="AMZN")  # Get U.S. stock Amazon's price info
print(hist_df)

Output

               open       high        low      close     volume
date                                                           
1997-05-15    29.25    30.0000    23.1300    23.5000  6013000.0
1997-05-16    23.63    23.7500    20.5000    20.7500  1225000.0
1997-05-19    21.13    21.2500    19.5000    20.5000   508900.0
1997-05-20    20.75    21.0000    19.6300    19.6300   455600.0
1997-05-21    19.63    19.7500    16.5000    17.1300  1571100.0
             ...        ...        ...        ...        ...
2021-01-04  3270.00  3272.0000  3144.0200  3186.6299  4205801.0
2021-01-05  3166.01  3223.3799  3165.0601  3218.5100  2467255.0
2021-01-06  3146.48  3197.5090  3131.1599  3138.3799  4065357.0
2021-01-07  3157.00  3208.5420  3155.0000  3162.1599  3320882.0
2021-01-08  3180.00  3190.6399  3142.2000  3182.7000  3410288.0
[5951 rows x 5 columns]

Plot

Code

import akshare as ak
import mplfinance as mpf  # Please install mplfinance as follows: pip install mplfinance

stock_us_daily_df = ak.stock_us_daily(symbol="AAPL", adjust="qfq")
stock_us_daily_df = stock_us_daily_df[["open", "high", "low", "close", "volume"]]
stock_us_daily_df.columns = ["Open", "High", "Low", "Close", "Volume"]
stock_us_daily_df.index.name = "Date"
stock_us_daily_df = stock_us_daily_df["2020-04-01": "2020-04-29"]
mpf.plot(stock_us_daily_df, type='candle', mav=(3, 6, 9), volume=True, show_nontrading=False)

Output

Communication

Pay attention to 数据科学实战 WeChat Official Accounts to get the AkShare updated info:

Application to add AkShare-官方 QQ group and talk about AkShare issues, QQ group number: 512720929

Features

• Easy of use: Just one line code to fetch the data;
• Extensible: Easy to customize your own code with other application;
• Powerful: Python ecosystem.

Tutorials

1. Overview
2. Installation
3. Tutorial
4. Data Dict
5. Subjects

Contribution

AkShare is still under developing, feel free to open issues and pull requests:

• Report or fix bugs
• Require or publish interface
• Write or fix documentation
• Add test cases

Notice: We use Black to format the code

Statement

1. All data provided by AkShare is just for academic research purpose;

2. The data provided by AkShare is for reference only and does not constitute any investment proposal;

3. Any investor based on AkShare research should pay more attention to data risk;

4. AkShare will insist on providing open-source financial data;

5. Based on some uncontrollable factors, some data interfaces in AkShare may be removed;

6. Please follow the relevant open-source protocol used by AkShare

Show your style

Use the badge in your project's README.md:

[![Data: akshare](https://img.shields.io/badge/Data%20Science-AkShare-green)](https://github.com/jindaxiang/akshare)

Using the badge in README.rst:

.. image:: https://img.shields.io/badge/Data%20Science-AkShare-green
    :target: https://github.com/jindaxiang/akshare

Looks like this:

Citation

Please use this bibtex if you want to cite this repository in your publications:

@misc{akshare,
    author = {Albert King},
    title = {AkShare},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/jindaxiang/akshare}},
}

Acknowledgement

Special thanks FuShare for the opportunity of learning from the project;

Special thanks TuShare for the opportunity of learning from the project;

Thanks for the data provided by 生意社网站;

Thanks for the data provided by 奇货可查网站;

Thanks for the data provided by 智道智科网站;

Thanks for the data provided by 中国银行间市场交易商协会网站;

Thanks for the data provided by 99期货网站;

Thanks for the data provided by 英为财情网站;

Thanks for the data provided by 中国外汇交易中心暨全国银行间同业拆借中心网站;

Thanks for the data provided by 金十数据网站;

Thanks for the data provided by 交易法门网站;

Thanks for the data provided by 和讯财经网站;

Thanks for the data provided by 新浪财经网站;

Thanks for the data provided by Oxford-Man Institute 网站;

Thanks for the data provided by DACHENG-XIU 网站;

Thanks for the data provided by 上海证券交易所网站;

Thanks for the data provided by 深证证券交易所网站;

Thanks for the data provided by 中国金融期货交易所网站;

Thanks for the data provided by 上海期货交易所网站;

Thanks for the data provided by 大连商品交易所网站;

Thanks for the data provided by 郑州商品交易所网站;

Thanks for the data provided by 上海国际能源交易中心网站;

Thanks for the data provided by Timeanddate 网站;

Thanks for the data provided by 河北省空气质量预报信息发布系统网站;

Thanks for the data provided by 南华期货网站;

Thanks for the data provided by Economic Policy Uncertainty 网站;

Thanks for the data provided by 微博指数网站;

Thanks for the data provided by 百度指数网站;

Thanks for the data provided by 谷歌指数网站;

Thanks for the data provided by 申万指数网站;

Thanks for the data provided by 真气网网站;

Thanks for the data provided by 财富网站;

Thanks for the data provided by 中国证券投资基金业协会网站;

Thanks for the data provided by 猫眼电影网站;

Thanks for the data provided by Expatistan 网站;

Thanks for the data provided by 北京市碳排放权电子交易平台网站;

Thanks for the data provided by 国家金融与发展实验室网站;

Thanks for the data provided by IT桔子网站;

Thanks for the data provided by 东方财富网站;

Thanks for the data provided by 义乌小商品指数网站;

Thanks for the data provided by 中国国家发展和改革委员会网站;

Thanks for the data provided by 163网站;

Thanks for the data provided by 丁香园网站;

Thanks for the data provided by 百度新型肺炎网站;

Thanks for the data provided by 百度迁徙网站;

Thanks for the data provided by 新型肺炎-相同行程查询工具网站;

Thanks for the data provided by 新型肺炎-小区查询网站;

Thanks for the data provided by 商业特许经营信息管理网站;

Thanks for the data provided by 慈善中国网站;

Thanks for the data provided by 思知网站;

Thanks for the data provided by Currencyscoop网站;

Thanks for the data provided by 新加坡交易所网站;

Thanks for the data provided by 中国期货市场监控中心;

Thanks for the data provided by 宽客在线;

Thanks for the tutorials provided by 微信公众号: Python大咖谈.

Backer and Sponsor