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
367 lines
12 KiB
Python
367 lines
12 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/5/5 00:00
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Desc: 股票数据-总貌-市场总貌
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股票数据-总貌-成交概括
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https://www.szse.cn/market/overview/index.html
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https://www.sse.com.cn/market/stockdata/statistic/
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"""
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import warnings
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from io import BytesIO, StringIO
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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def stock_szse_summary(date: str = "20240830") -> pd.DataFrame:
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"""
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深证证券交易所-总貌-证券类别统计
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https://www.szse.cn/market/overview/index.html
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:param date: 最近结束交易日
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:type date: str
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:return: 证券类别统计
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:rtype: pandas.DataFrame
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"""
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url = "http://www.szse.cn/api/report/ShowReport"
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params = {
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"SHOWTYPE": "xlsx",
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"CATALOGID": "1803_sczm",
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"TABKEY": "tab1",
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"txtQueryDate": "-".join([date[:4], date[4:6], date[6:]]),
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"random": "0.39339437497296137",
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}
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r = requests.get(url, params=params)
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with warnings.catch_warnings(record=True):
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warnings.simplefilter("always")
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temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl")
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temp_df["证券类别"] = temp_df["证券类别"].str.strip()
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temp_df.iloc[:, 2:] = temp_df.iloc[:, 2:].map(lambda x: x.replace(",", ""))
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temp_df.columns = ["证券类别", "数量", "成交金额", "总市值", "流通市值"]
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temp_df["数量"] = pd.to_numeric(temp_df["数量"], errors="coerce")
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temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"], errors="coerce")
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temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
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temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
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return temp_df
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def stock_szse_area_summary(date: str = "202203") -> pd.DataFrame:
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"""
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深证证券交易所-总貌-地区交易排序
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https://www.szse.cn/market/overview/index.html
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:param date: 最近结束交易日
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:type date: str
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:return: 地区交易排序
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:rtype: pandas.DataFrame
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"""
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url = "https://www.szse.cn/api/report/ShowReport"
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params = {
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"SHOWTYPE": "xlsx",
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"CATALOGID": "1803_sczm",
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"TABKEY": "tab2",
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"DATETIME": "-".join([date[:4], date[4:6]]),
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"random": "0.39349437497296137",
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}
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r = requests.get(url, params=params)
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with warnings.catch_warnings(record=True):
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warnings.simplefilter("always")
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temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl")
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column_map = {
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"序号": "序号",
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"地区": "地区",
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"总交易额(元)": "总交易额",
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"占市场%": "占市场",
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"股票交易额(元)": "股票交易额",
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"基金交易额(元)": "基金交易额",
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"债券交易额(元)": "债券交易额",
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"优先股交易额(元)": "优先股交易额",
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"期权交易额(元)": "期权交易额",
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}
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temp_df.rename(columns=column_map, inplace=True)
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temp_df["总交易额"] = temp_df["总交易额"].str.replace(",", "")
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temp_df["总交易额"] = pd.to_numeric(temp_df["总交易额"], errors="coerce")
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temp_df["占市场"] = pd.to_numeric(temp_df["占市场"], errors="coerce")
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temp_df["股票交易额"] = temp_df["股票交易额"].str.replace(",", "")
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temp_df["股票交易额"] = pd.to_numeric(temp_df["股票交易额"], errors="coerce")
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temp_df["基金交易额"] = temp_df["基金交易额"].str.replace(",", "")
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temp_df["基金交易额"] = pd.to_numeric(temp_df["基金交易额"], errors="coerce")
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temp_df["债券交易额"] = temp_df["债券交易额"].str.replace(",", "")
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temp_df["债券交易额"] = pd.to_numeric(temp_df["债券交易额"], errors="coerce")
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if "优先股交易额" in temp_df.columns:
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temp_df["优先股交易额"] = temp_df["优先股交易额"].astype(
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"str"
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) # 2025年2月为float
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temp_df["优先股交易额"] = temp_df["优先股交易额"].str.replace(",", "")
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temp_df["优先股交易额"] = pd.to_numeric(
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temp_df["优先股交易额"], errors="coerce"
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)
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if "期权交易额" in temp_df.columns:
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temp_df["期权交易额"] = temp_df["期权交易额"].astype("str")
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temp_df["期权交易额"] = temp_df["期权交易额"].str.replace(",", "")
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temp_df["期权交易额"] = pd.to_numeric(temp_df["期权交易额"], errors="coerce")
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return temp_df
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def stock_szse_sector_summary(
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symbol: str = "当月", date: str = "202501"
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) -> pd.DataFrame:
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"""
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深圳证券交易所-统计资料-股票行业成交数据
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https://docs.static.szse.cn/www/market/periodical/month/W020220511355248518608.html
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:param symbol: choice of {"当月", "当年"}
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:type symbol: str
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:param date: 交易年月
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:type date: str
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:return: 股票行业成交数据
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:rtype: pandas.DataFrame
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"""
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url = "https://www.szse.cn/market/periodical/month/index.html"
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r = requests.get(url)
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r.encoding = "utf8"
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soup = BeautifulSoup(r.text, features="lxml")
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tags_list = soup.find_all(name="div", attrs={"class": "g-container"})[1].find_all(
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"script"
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)
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tags_dict = [
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eval(
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item.string[item.string.find("{") : item.string.find("}") + 1]
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.replace("\n", "")
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.replace(" ", "")
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.replace("value", "'value'")
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.replace("text", "'text'")
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)
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for item in tags_list
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]
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date_url_dict = dict(
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zip(
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[item["text"] for item in tags_dict],
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[item["value"][2:] for item in tags_dict],
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)
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)
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date_format = "-".join([date[:4], date[4:]])
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url = f"https://www.szse.cn/market/periodical/month/{date_url_dict[date_format]}"
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r = requests.get(url)
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r.encoding = "utf8"
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soup = BeautifulSoup(r.text, features="lxml")
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url = [
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item for item in soup.find_all("a") if item.get_text() == "股票行业成交数据"
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][0]["href"]
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if symbol == "当月":
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r = requests.get(url)
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temp_df = pd.read_html(StringIO(r.text), encoding="gbk")[0]
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temp_df.columns = [
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"项目名称",
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"项目名称-英文",
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"交易天数",
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"成交金额-人民币元",
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"成交金额-占总计",
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"成交股数-股数",
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"成交股数-占总计",
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"成交笔数-笔",
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"成交笔数-占总计",
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]
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else:
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temp_df = pd.read_html(url, encoding="gbk")[1]
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temp_df.columns = [
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"项目名称",
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"项目名称-英文",
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"交易天数",
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"成交金额-人民币元",
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"成交金额-占总计",
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"成交股数-股数",
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"成交股数-占总计",
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"成交笔数-笔",
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"成交笔数-占总计",
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]
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temp_df["交易天数"] = pd.to_numeric(temp_df["交易天数"], errors="coerce")
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temp_df["成交金额-人民币元"] = pd.to_numeric(
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temp_df["成交金额-人民币元"], errors="coerce"
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)
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temp_df["成交金额-占总计"] = pd.to_numeric(
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temp_df["成交金额-占总计"], errors="coerce"
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)
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temp_df["成交股数-股数"] = pd.to_numeric(temp_df["成交股数-股数"], errors="coerce")
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temp_df["成交股数-占总计"] = pd.to_numeric(
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temp_df["成交股数-占总计"], errors="coerce"
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)
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temp_df["成交笔数-笔"] = pd.to_numeric(temp_df["成交笔数-笔"], errors="coerce")
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temp_df["成交笔数-占总计"] = pd.to_numeric(
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temp_df["成交笔数-占总计"], errors="coerce"
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)
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return temp_df
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def stock_sse_summary() -> pd.DataFrame:
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"""
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上海证券交易所-总貌
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https://www.sse.com.cn/market/stockdata/statistic/
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:return: 上海证券交易所-总貌
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:rtype: pandas.DataFrame
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"""
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url = "http://query.sse.com.cn/commonQuery.do"
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params = {
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"sqlId": "COMMON_SSE_SJ_GPSJ_GPSJZM_TJSJ_L",
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"PRODUCT_NAME": "股票,主板,科创板",
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"type": "inParams",
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}
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headers = {
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"Referer": "http://www.sse.com.cn/",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/89.0.4389.90 Safari/537.36",
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]).T
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temp_df.reset_index(inplace=True)
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temp_df["index"] = [
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"流通股本",
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"总市值",
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"平均市盈率",
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"上市公司",
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"上市股票",
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"流通市值",
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"报告时间",
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"-",
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"总股本",
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"项目",
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]
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temp_df = temp_df[temp_df["index"] != "-"].iloc[:-1, :]
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temp_df.columns = [
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"项目",
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"股票",
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"主板",
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"科创板",
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]
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return temp_df
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def stock_sse_deal_daily(date: str = "20241216") -> pd.DataFrame:
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"""
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上海证券交易所-数据-股票数据-成交概况-股票成交概况-每日股票情况
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https://www.sse.com.cn/market/stockdata/overview/day/
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:param date: 交易日
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:type date: str
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:return: 每日股票情况
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:rtype: pandas.DataFrame
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"""
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url = "https://query.sse.com.cn/commonQuery.do"
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params = {
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"sqlId": "COMMON_SSE_SJ_GPSJ_CJGK_MRGK_C",
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"PRODUCT_CODE": "01,02,03,11,17",
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"type": "inParams",
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"SEARCH_DATE": "-".join([date[:4], date[4:6], date[6:]]),
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}
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headers = {
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"Referer": "https://www.sse.com.cn/",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/89.0.4389.90 Safari/537.36",
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"])
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temp_df = temp_df.T
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temp_df.reset_index(inplace=True)
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if len(temp_df.columns) == 5:
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# 20250228
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temp_df.columns = [
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"单日情况",
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"主板A",
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"主板B",
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"科创板",
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"股票",
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]
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temp_df["股票回购"] = "-"
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elif len(temp_df.columns) == 4:
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# 20220104
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temp_df.columns = [
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"单日情况",
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"主板A",
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"主板B",
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"科创板",
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]
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temp_df["股票"] = "-"
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temp_df["股票回购"] = "-"
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else:
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temp_df.columns = [
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"单日情况",
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"主板A",
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"主板B",
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"科创板",
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"股票回购",
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"股票",
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]
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temp_df = temp_df[
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[
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"单日情况",
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"股票",
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"主板A",
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"主板B",
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"科创板",
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"股票回购",
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]
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]
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temp_df["单日情况"] = [
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"市价总值",
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"成交量",
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"平均市盈率",
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"换手率",
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"成交金额",
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"-",
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"流通市值",
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"流通换手率",
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"报告日期",
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"挂牌数",
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"-",
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]
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temp_df = temp_df[temp_df["单日情况"] != "-"]
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temp_df = temp_df[temp_df["单日情况"] != "报告日期"]
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# 定义期望的指标顺序
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desired_order = [
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"挂牌数",
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"市价总值",
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"流通市值",
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"成交金额",
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"成交量",
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"平均市盈率",
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"换手率",
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"流通换手率",
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]
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# 使用 categorical 类型重新排序
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temp_df["单日情况"] = pd.Categorical(
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temp_df["单日情况"], categories=desired_order, ordered=True
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)
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# 按照指标排序
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temp_df.sort_values("单日情况", ignore_index=True, inplace=True)
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temp_df["股票"] = pd.to_numeric(temp_df["股票"], errors="coerce")
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temp_df["主板A"] = pd.to_numeric(temp_df["主板A"], errors="coerce")
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temp_df["主板B"] = pd.to_numeric(temp_df["主板B"], errors="coerce")
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temp_df["科创板"] = pd.to_numeric(temp_df["科创板"], errors="coerce")
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temp_df["股票回购"] = pd.to_numeric(temp_df["股票回购"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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stock_szse_summary_df = stock_szse_summary(date="20200619")
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print(stock_szse_summary_df)
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stock_szse_area_summary_df = stock_szse_area_summary(date="202412")
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print(stock_szse_area_summary_df)
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stock_szse_area_summary_df = stock_szse_area_summary(date="202502")
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print(stock_szse_area_summary_df)
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stock_szse_sector_summary_df = stock_szse_sector_summary(
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symbol="当月", date="202501"
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)
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print(stock_szse_sector_summary_df)
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stock_sse_summary_df = stock_sse_summary()
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print(stock_sse_summary_df)
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stock_sse_deal_daily_df = stock_sse_deal_daily(date="20250228")
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print(stock_sse_deal_daily_df)
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