《AKShare 源码解析》课程即将上线，可加入【知识星球-数据科学家】学习！应广大小伙伴的需求，特此开设 AKShare 源码解析课程，本课程会系统的给大家手把手讲解 AKShare 数据接口的源码及财经数据的网络数据采集知识！点击链接 查看课程信息

本次发布 AKTools 作为 AKShare 的 HTTP API 版本，突破 Python 语言的限制，欢迎各位小伙伴试用并提出更好的意见或建议！ 点击 AKTools 查看使用指南。另外提供 awesome-data 方便各位小伙伴查询各种数据源

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

pip install akshare --upgrade

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

Please check out documentation if you want to contribute to AKShare

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

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

import akshare as ak

ak.__version__

Code

import akshare as ak

stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20170301", end_date='20210907', adjust="")
print(stock_zh_a_hist_df)

Output

      日期          开盘   收盘    最高  ...  振幅   涨跌幅 涨跌额 换手率
0     2017-03-01   9.49   9.49   9.55  ...  0.84  0.11  0.01  0.21
1     2017-03-02   9.51   9.43   9.54  ...  1.26 -0.63 -0.06  0.24
2     2017-03-03   9.41   9.40   9.43  ...  0.74 -0.32 -0.03  0.20
3     2017-03-06   9.40   9.45   9.46  ...  0.74  0.53  0.05  0.24
4     2017-03-07   9.44   9.45   9.46  ...  0.63  0.00  0.00  0.17
          ...    ...    ...    ...  ...   ...   ...   ...   ...
1100  2021-09-01  17.48  17.88  17.92  ...  5.11  0.45  0.08  1.19
1101  2021-09-02  18.00  18.40  18.78  ...  5.48  2.91  0.52  1.25
1102  2021-09-03  18.50  18.04  18.50  ...  4.35 -1.96 -0.36  0.72
1103  2021-09-06  17.93  18.45  18.60  ...  4.55  2.27  0.41  0.78
1104  2021-09-07  18.60  19.24  19.56  ...  6.56  4.28  0.79  0.84
[1105 rows x 11 columns]

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

Pay attention to 数据科学家 Official Accounts to get more information about Quant, ML, DS and so on, please visit 数据科学家 for more information:

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

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

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

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

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;
7. Provide HTTP API for the person who uses other program language: AKTools.

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

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

Using the badge in README.rst:

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

Looks like this:

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/akfamily/akshare}},
}

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 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 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 北京证券交易所网站;

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