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
198 lines
5.9 KiB
Python
198 lines
5.9 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/10/22 15:00
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Desc: 巨潮资讯-数据浏览器-筹资指标-公司配股实施方案
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https://webapi.cninfo.com.cn/#/dataBrowse
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"""
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import pandas as pd
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import py_mini_racer
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import requests
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from akshare.datasets import get_ths_js
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def _get_file_content_cninfo(file: str = "cninfo.js") -> str:
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"""
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获取 JS 文件的内容
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:param file: JS 文件名
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:type file: str
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:return: 文件内容
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:rtype: str
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"""
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setting_file_path = get_ths_js(file)
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with open(setting_file_path, encoding="utf-8") as f:
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file_data = f.read()
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return file_data
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def stock_allotment_cninfo(
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symbol: str = "600030", start_date: str = "19700101", end_date: str = "22220222"
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) -> pd.DataFrame:
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"""
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巨潮资讯-个股-配股实施方案
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https://webapi.cninfo.com.cn/#/dataBrowse
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:param symbol: 股票代码
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:type symbol: str
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:param start_date: 开始查询的日期
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:type symbol: str
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:param end_date: 结束查询的日期
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:type symbol: str
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:return: 配股实施方案
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:rtype: pandas.DataFrame
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"""
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url = "https://webapi.cninfo.com.cn/api/stock/p_stock2232"
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params = {
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"scode": symbol,
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"sdate": start_date
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if not start_date
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else f"{start_date[0:4]}-{start_date[4:6]}-{start_date[6:8]}",
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"edate": end_date
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if not end_date
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else f"{end_date[0:4]}-{end_date[4:6]}-{end_date[6:8]}",
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}
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js_code = py_mini_racer.MiniRacer()
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js_content = _get_file_content_cninfo("cninfo.js")
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js_code.eval(js_content)
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mcode = js_code.call("getResCode1")
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headers = {
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"Accept": "*/*",
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"Accept-Enckey": mcode,
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"Accept-Encoding": "gzip, deflate",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
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"Cache-Control": "no-cache",
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"Content-Length": "0",
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"Host": "webapi.cninfo.com.cn",
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"Origin": "https://webapi.cninfo.com.cn",
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"Pragma": "no-cache",
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"Proxy-Connection": "keep-alive",
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"Referer": "https://webapi.cninfo.com.cn/",
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"X-Requested-With": "XMLHttpRequest",
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}
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r = requests.post(url, params=params, headers=headers)
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data_json = r.json()
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columns = [
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"记录标识",
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"证券简称",
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"停牌起始日",
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"上市公告日期",
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"配股缴款起始日",
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"可转配股数量",
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"停牌截止日",
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"实际配股数量",
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"配股价格",
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"配股比例",
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"配股前总股本",
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"每股配权转让费(元)",
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"法人股实配数量",
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"实际募资净额",
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"大股东认购方式",
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"其他配售简称",
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"发行方式",
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"配股失败，退还申购款日期",
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"除权基准日",
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"预计发行费用",
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"配股发行结果公告日",
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"证券代码",
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"配股权证交易截止日",
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"其他股份实配数量",
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"国家股实配数量",
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"委托单位",
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"公众获转配数量",
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"其他配售代码",
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"配售对象",
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"配股权证交易起始日",
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"资金到账日",
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"机构名称",
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"股权登记日",
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"实际募资总额",
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"预计募集资金",
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"大股东认购数量",
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"公众股实配数量",
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"转配股实配数量",
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"承销费用",
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"法人获转配数量",
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"配股后流通股本",
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"股票类别",
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"公众配售简称",
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"发行方式编码",
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"承销方式",
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"公告日期",
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"配股上市日",
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"配股缴款截止日",
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"承销余额(股)",
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"预计配股数量",
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"配股后总股本",
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"职工股实配数量",
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"承销方式编码",
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"发行费用总额",
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"配股前流通股本",
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"股票类别编码",
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"公众配售代码",
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]
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if data_json["records"]:
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# 有配股记录
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temp_df = pd.DataFrame(data_json["records"])
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temp_df.columns = columns
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dates = (
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"停牌起始日",
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"上市公告日期",
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"配股失败，退还申购款日期",
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"配股缴款起始日",
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"停牌截止日",
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"除权基准日",
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"配股发行结果公告日",
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"配股权证交易截止日",
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"配股权证交易起始日",
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"资金到账日",
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"股权登记日",
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"公告日期",
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"配股上市日",
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"配股缴款截止日",
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)
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for s in dates:
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temp_df[s] = pd.to_datetime(temp_df[s], errors="coerce").dt.date
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nums = (
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"可转配股数量",
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"实际配股数量",
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"配股价格",
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"配股比例",
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"配股前总股本",
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"每股配权转让费(元)",
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"法人股实配数量",
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"实际募资净额",
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"预计发行费用",
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"其他股份实配数量",
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"国家股实配数量",
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"公众获转配数量",
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"实际募资总额",
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"预计募集资金",
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"大股东认购数量",
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"公众股实配数量",
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"转配股实配数量",
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"承销费用",
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"法人获转配数量",
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"配股后流通股本",
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"承销余额(股)",
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"预计配股数量",
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"配股后总股本",
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"职工股实配数量",
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"发行费用总额",
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"配股前流通股本",
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)
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for s in nums:
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temp_df[s] = pd.to_numeric(temp_df[s], errors="coerce")
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else:
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# 没有配股数据
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temp_df = pd.DataFrame(columns=columns)
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return temp_df
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if __name__ == "__main__":
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stock_allotment_cninfo_df = stock_allotment_cninfo(
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symbol="600030", start_date="19900101", end_date="20241022"
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)
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print(stock_allotment_cninfo_df)
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