baoshare 0.5.5

A tool for obtaining historical data of China stock market

• It’s easy to use because most of the data returned are pandas DataFrame objects

• We have our own data server, efficient and stable operation

• Free china stock market data

• Friendly to machine learning and data mining

Target Users

• China Financial Market Analyst

• Financial data analysis enthusiasts

• Quanters who are interested in china stock market

Installation

pip install baoshare

Upgrade

pip install baoshare –upgrade

Quick Start

import baostock as bs
import pandas as pd

# 登陆系统
lg = bs.login(user_id="anonymous", password="123456")
# 显示登陆返回信息
print(lg.error_code)
print(lg.error_msg)
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data("sh.601398",
    "date,code,open,high,low,close,volume,amount,adjustflag",
    start_date='2017-01-01', end_date='2017-01-31',
    frequency="d", adjustflag="3")
print(rs.error_code)
print(rs.error_msg)
# 获取具体的信息
result = pd.DataFrame(columns=["date","code","open","high","low","close","volume","amount","adjustflag"])
while (rs.error_code == '0') & rs.next():
    # 分页查询，将每页信息合并在一起
    result = result.append(rs.get_row_data(), ignore_index=True)
result.to_csv("D:\history_k_data.csv", index=False)
print(result)
# 登出系统
bs.logout()

return:

          date       code    open    high     low   close preclose     volume
0   2017-01-03  sh.601398  4.4000  4.4300  4.3900  4.4300   4.4100  104161632
1   2017-01-04  sh.601398  4.4200  4.4400  4.4100  4.4300   4.4300  118923425
2   2017-01-05  sh.601398  4.4300  4.4500  4.4200  4.4400   4.4300   87356137
3   2017-01-06  sh.601398  4.4400  4.4500  4.4300  4.4400   4.4400   87008191
4   2017-01-09  sh.601398  4.4500  4.4800  4.4300  4.4600   4.4400  117454094
5   2017-01-10  sh.601398  4.4500  4.4700  4.4400  4.4600   4.4600   63663257
6   2017-01-11  sh.601398  4.4600  4.4800  4.4500  4.4700   4.4600   52395427
7   2017-01-12  sh.601398  4.4600  4.4700  4.4400  4.4700   4.4700   62166279

             amount adjustflag      turn tradestatus
0    460087744.0000          3  0.038634           1
1    526408816.0000          3  0.044109           1
2    387580736.0000          3  0.032401           1
3    386138112.0000          3  0.032272           1
4    523539392.0000          3  0.043564           1
5    283646224.0000          3  0.023613           1
6    233898107.0000          3  0.019434           1
7    277258304.0000          3  0.023058           1