AKLite

AKLite is a lite version of AKShare

AKLite will make the data fetch process more easy and fast. Now it focus on stock data, and will support more data in the future.

Key Features

A super-fast request engine built in HTTPX and accelerated with Asyncio.
The ability to fetch multiple stocks with ease.
Access to historical data from main stream data source.

With AKLite, you’ll have all the tools you need to fetch data. Start using AKLite today and take your working to the next level!

A Quick Example

Get a glimpse of what fetching with AKLite looks like with these code snippets:

Fetching Stock Data:

import aklite as ai

stock_zh_a_hist_obj = ai.stock_zh_a_hist(symbols=["000001", "000002"],
                                         period="daily",
                                         start_date="20220101",
                                         end_date="20230601",
                                         adjust="hfq",
                                         timeout=5,
                                         proxies={})
print(stock_zh_a_hist_obj.data)
print(stock_zh_a_hist_obj.columns)
print(stock_zh_a_hist_obj.url)
print(stock_zh_a_hist_obj.desc)
print(stock_zh_a_hist_obj.symbols)
print(stock_zh_a_hist_obj.start_date)
print(stock_zh_a_hist_obj.end_date)
print(stock_zh_a_hist_obj.adjust)

To learn how to use AKLite, see the notebooks under the User Guide:

Getting Started with Data Fetching

Welcome to AKlite! The best place to start is to learn about stock data fetch!

Import package

we should import aklite firstly, just like import akshare as ak, we will call ai short for aklite

[1]:

import aklite as ai

then, we can fetch stock data by our specific parameters: 1. we can set symbols, which is a list or str contains many stocks or just one; 2. we can set timeout and proxies for httpx timeout and proxies

[2]:

stock_zh_a_hist_obj = ai.stock_zh_a_hist(symbols=["000001", "000002"],
                                         period="daily",
                                         start_date="20220101",
                                         end_date="20230601",
                                         adjust="hfq",
                                         timeout=5,
                                         proxies={})

Print data

we can print data here, stock_zh_a_hist_obj is the object, which contains many attributes, we can get the data calling obj.data.

[3]:

# 获取数据
print(stock_zh_a_hist_obj.data)

           date  symbol     open    close     high      low   volume  \
0    2022-01-04  000001  2847.30  2876.56  2876.56  2798.54  1169259
1    2022-01-05  000001  2863.55  2956.19  2967.57  2858.68  1961998
2    2022-01-06  000001  2949.69  2951.32  2975.70  2931.82  1107885
3    2022-01-07  000001  2948.07  2964.32  2977.32  2941.57  1126631
4    2022-01-10  000001  2978.95  2962.70  3000.08  2936.69   909774
..          ...     ...      ...      ...      ...      ...      ...
677  2023-05-26  000002  2932.56  2941.72  2945.65  2915.54   407754
678  2023-05-29  000002  2936.49  2918.16  2937.79  2914.23   443019
679  2023-05-30  000002  2912.92  2961.36  2961.36  2871.03   881969
680  2023-05-31  000002  2946.96  2945.65  2957.43  2922.09   451251
681  2023-06-01  000002  2932.56  2914.23  2940.41  2911.61   466628

         turnover  amplitude  price_change_rate  price_change  turnover_rate
0    1.918887e+09       2.74               1.03         29.26           0.60
1    3.344125e+09       3.79               2.77         79.63           1.01
2    1.896536e+09       1.48              -0.16         -4.87           0.57
3    1.937711e+09       1.21               0.44         13.00           0.58
4    1.563415e+09       2.14              -0.05         -1.62           0.47
..            ...        ...                ...           ...            ...
677  5.767348e+08       1.02               0.09          2.62           0.42
678  6.224551e+08       0.80              -0.80        -23.56           0.46
679  1.230122e+09       3.10               1.48         43.20           0.91
680  6.389365e+08       1.19              -0.53        -15.71           0.46
681  6.566527e+08       0.98              -1.07        -31.42           0.48

[682 rows x 12 columns]

[4]:

# 获取中文字段名
print(stock_zh_a_hist_obj.columns)

['日期', '股票代码', '开盘', '收盘', '最高', '最低', '成交量', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率']

[5]:

# 获取目标网站链接
print(stock_zh_a_hist_obj.url)

https://quote.eastmoney.com/sh601658.html

[6]:

# 获取数据描述
print(stock_zh_a_hist_obj.desc)

东方财富-股票行情-日行情

[7]:

# 获取相关接口信息
print(stock_zh_a_hist_obj.symbols)
print(stock_zh_a_hist_obj.period)
print(stock_zh_a_hist_obj.start_date)
print(stock_zh_a_hist_obj.end_date)
print(stock_zh_a_hist_obj.adjust)

['000001', '000002']
daily
20220101
20230601
hfq

The notebooks above are also available on Github.