知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
104 lines
3.4 KiB
Plaintext
104 lines
3.4 KiB
Plaintext
Metadata-Version: 2.1
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Name: baostock
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Version: 0.9.2
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Summary: A tool for obtaining historical data of China stock market
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Home-page: http://www.baostock.com
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Author: baostock
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Author-email: baostock@163.com
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License: BSD License
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Platform: all
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Classifier: Development Status :: 3 - Alpha
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Classifier: Environment :: Console
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Topic :: Software Development :: Libraries
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Requires-Dist: pandas>=0.18.0
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BaoStock
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===============
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* It's easy to use because most of the data returned are pandas DataFrame objects
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* We have our own data server, efficient and stable operation
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* Free china stock market data
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* Friendly to machine learning and data mining
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Target Users
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--------------
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* China Financial Market Analyst
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* Financial data analysis enthusiasts
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* Quanters who are interested in china stock market
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Installation
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--------------
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pip install baostock
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Upgrade
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---------------
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pip install baostock --upgrade
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Quick Start
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--------------
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::
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import baostock as bs
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import pandas as pd
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#### 登陆系统 ####
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lg = bs.login()
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# 显示登陆返回信息
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print('login respond error_code:'+lg.error_code)
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print('login respond error_msg:'+lg.error_msg)
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#### 获取历史K线数据 ####
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# 详细指标参数，参见“历史行情指标参数”章节
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rs = bs.query_history_k_data_plus("sh.600000",
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"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
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start_date='2025-06-01', end_date='2025-12-31',
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frequency="d", adjustflag="2") #frequency="d"取日k线，adjustflag="3"默认不复权，"2"前复权
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print('query_history_k_data_plus respond error_code:'+rs.error_code)
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print('query_history_k_data_plus respond error_msg:'+rs.error_msg)
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#### 打印结果集 ####
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data_list = []
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while (rs.error_code == '0') & rs.next():
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# 获取一条记录，将记录合并在一起
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data_list.append(rs.get_row_data())
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result = pd.DataFrame(data_list, columns=rs.fields)
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#### 结果集输出到csv文件 ####
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result.to_csv("D:/history_k_data.csv", encoding="gbk", index=False)
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print(result)
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#### 登出系统 ####
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bs.logout()
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return::
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login success!
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login respond error_code:0
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login respond error_msg:success
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query_history_k_data_plus respond error_code:0
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query_history_k_data_plus respond error_msg:success
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date code open ... psTTM pcfNcfTTM isST
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0 2025-06-03 sh.600000 11.9476797700 ... 2.148197 -9.209045 0
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1 2025-06-04 sh.600000 12.1126761600 ... 2.120788 -9.091545 0
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2 2025-06-05 sh.600000 12.0544421400 ... 2.110509 -9.047483 0
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3 2025-06-06 sh.600000 11.9670911100 ... 2.110509 -9.047483 0
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4 2025-06-09 sh.600000 11.9476797700 ... 2.108796 -9.040139 0
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.. ... ... ... ... ... ... ...
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141 2025-12-25 sh.600000 11.8000000000 ... 2.263479 -1.849434 0
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142 2025-12-26 sh.600000 11.7700000000 ... 2.253864 -1.841577 0
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143 2025-12-29 sh.600000 11.7400000000 ... 2.340403 -1.912286 0
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144 2025-12-30 sh.600000 12.1700000000 ... 2.382711 -1.946855 0
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145 2025-12-31 sh.600000 12.3500000000 ... 2.392326 -1.954712 0
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