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