baostock 0.9.1

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 baostock

Upgrade

pip install baostock –upgrade

Quick Start

import baostock as bs
import pandas as pd

#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond  error_msg:'+lg.error_msg)

#### 获取历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data_plus("sh.600000",
        "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
        start_date='2025-06-01', end_date='2025-12-31',
        frequency="d", adjustflag="2") #frequency="d"取日k线，adjustflag="3"默认不复权，"2"前复权

print('query_history_k_data_plus respond error_code:'+rs.error_code)
print('query_history_k_data_plus respond  error_msg:'+rs.error_msg)

#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
        # 获取一条记录，将记录合并在一起
        data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("D:/history_k_data.csv", encoding="gbk", index=False)
print(result)

#### 登出系统 ####
bs.logout()

return:

login success!
login respond error_code:0
login respond  error_msg:success
query_history_k_data_plus respond error_code:0
query_history_k_data_plus respond  error_msg:success
date       code           open  ...     psTTM  pcfNcfTTM isST
0    2025-06-03  sh.600000  11.9476797700  ...  2.148197  -9.209045    0
1    2025-06-04  sh.600000  12.1126761600  ...  2.120788  -9.091545    0
2    2025-06-05  sh.600000  12.0544421400  ...  2.110509  -9.047483    0
3    2025-06-06  sh.600000  11.9670911100  ...  2.110509  -9.047483    0
4    2025-06-09  sh.600000  11.9476797700  ...  2.108796  -9.040139    0
..          ...        ...            ...  ...       ...        ...  ...
141  2025-12-25  sh.600000  11.8000000000  ...  2.263479  -1.849434    0
142  2025-12-26  sh.600000  11.7700000000  ...  2.253864  -1.841577    0
143  2025-12-29  sh.600000  11.7400000000  ...  2.340403  -1.912286    0
144  2025-12-30  sh.600000  12.1700000000  ...  2.382711  -1.946855    0
145  2025-12-31  sh.600000  12.3500000000  ...  2.392326  -1.954712    0