资源分享：对于想了解更多财经数据与量化投研的小伙伴，推荐一个专注于财经数据和量化研究的知识社区。该社区提供相关文档和视频学习资源，汇集了各类财经数据源和量化投研工具的使用经验。有兴趣深入学习的朋友可点此了解更多，也推荐大家关注微信公众号【数据科学实战】。

重磅推荐：AKQuant 是一款专为 量化投研 (Quantitative Research) 打造的高性能量化回测框架。它以 Rust 铸造极速撮合内核，以 Python 链接数据与 AI 生态，旨在为量化投资者提供可靠高效的量化投研解决方案。参见AKQuant

工具推荐：期魔方是一款本地化期货量化分析工具，适合数据分析爱好者使用。无需复杂部署，支持数据分析和机器学习功能，研究功能免费开放。如需了解更多信息可访问期魔方。

AKShare Logo

Overview

AKShare requires Python(64 bit) 3.9 or higher and aims to simplify the process of fetching financial data.

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-shanghai.aliyuncs.com/akfamily/aktools:jupyter

Run Container

docker run -it registry.cn-shanghai.aliyuncs.com/akfamily/aktools:jupyter python

Test

import akshare as ak

print(ak.__version__)

Usage

Data

Code:

import akshare as ak

stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20170301", end_date='20231022', 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
          ...    ...    ...    ...  ...   ...   ...   ...   ...
1610  2023-10-16  11.00  11.01  11.03  ...  0.73  0.09  0.01  0.26
1611  2023-10-17  11.01  11.02  11.05  ...  0.82  0.09  0.01  0.25
1612  2023-10-18  10.99  10.95  11.02  ...  1.00 -0.64 -0.07  0.34
1613  2023-10-19  10.91  10.60  10.92  ...  3.01 -3.20 -0.35  0.61
1614  2023-10-20  10.55  10.60  10.67  ...  1.51  0.00  0.00  0.27
[1615 rows x 11 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.set_index(["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:

KLine

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

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 Ruff to format the code

Statement

All data provided by AKShare is just for academic research purpose;
The data provided by AKShare is for reference only and does not constitute any investment proposal;
Any investor based on AKShare research should pay more attention to data risk;
AKShare will insist on providing open-source financial data;
Based on some uncontrollable factors, some data interfaces in AKShare may be removed;
Please follow the relevant open-source protocol used by AKShare;
Provide HTTP API for the person who uses other program language: AKTools.

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/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:

Citation

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

@misc{akshare,
    author = {Albert King and Yaojie Zhang},
    title = {AKShare},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/akfamily/akshare}},
}