akshare 0.8.81

AkShare is an elegant and simple financial data interface library for Python, built for human beings!

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: 328736477

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