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
224 lines
7.4 KiB
Python
224 lines
7.4 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/6/17 14:00
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Desc: 股票指数成份股数据, 新浪有两个接口, 这里使用老接口:
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新接口：https://vip.stock.finance.sina.com.cn/mkt/#zhishu_000001
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老接口：https://vip.stock.finance.sina.com.cn/corp/view/vII_NewestComponent.php?page=1&indexid=399639
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"""
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import math
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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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from akshare.utils import demjson
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def index_stock_cons_sina(symbol: str = "000300") -> pd.DataFrame:
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"""
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新浪新版股票指数成份页面, 目前该接口可获取指数数量较少
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https://vip.stock.finance.sina.com.cn/mkt/#zhishu_000040
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:param symbol: 指数代码
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:type symbol: str
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:return: 指数的成份股
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:rtype: pandas.DataFrame
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"""
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if symbol == "000300":
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symbol = "hs300"
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url = (
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"https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php"
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"/Market_Center.getHQNodeStockCountSimple"
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)
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params = {"node": f"{symbol}"}
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r = requests.get(url, params=params)
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page_num = math.ceil(int(r.json()) / 80) + 1
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temp_df = pd.DataFrame()
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for page in range(1, page_num):
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url = "https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData"
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params = {
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"page": str(page),
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"num": "80",
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"sort": "symbol",
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"asc": "1",
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"node": "hs300",
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"symbol": "",
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"_s_r_a": "init",
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}
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r = requests.get(url, params=params)
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temp_df = pd.concat(
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objs=[temp_df, pd.DataFrame(demjson.decode(r.text))], ignore_index=True
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)
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return temp_df
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url = "https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeDataSimple"
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params = {
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"page": 1,
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"num": "3000",
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"sort": "symbol",
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"asc": "1",
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"node": f"zhishu_{symbol}",
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"_s_r_a": "setlen",
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}
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r = requests.get(url, params=params)
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temp = pd.DataFrame(demjson.decode(r.text))
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return temp
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def index_stock_info() -> pd.DataFrame:
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"""
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聚宽-指数数据-指数列表
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https://www.joinquant.com/data/dict/indexData
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:return: 指数信息的数据框
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:rtype: pandas.DataFrame
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"""
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url = "https://www.joinquant.com/data/dict/indexData"
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r = requests.get(url)
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r.encoding = "utf-8"
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index_df = pd.read_html(StringIO(r.text))[0]
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index_df["指数代码"] = index_df["指数代码"].str.split(".", expand=True)[0]
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index_df.columns = ["index_code", "display_name", "publish_date", "-", "-"]
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temp_df = index_df[["index_code", "display_name", "publish_date"]].copy()
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return temp_df
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def index_stock_cons(symbol: str = "399639") -> pd.DataFrame:
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"""
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最新股票指数的成份股目录
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https://vip.stock.finance.sina.com.cn/corp/view/vII_NewestComponent.php?page=1&indexid=399639
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:param symbol: 指数代码, 可以通过 ak.index_stock_info() 函数获取
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:type symbol: str
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:return: 最新股票指数的成份股目录
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:rtype: pandas.DataFrame
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"""
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url = f"https://vip.stock.finance.sina.com.cn/corp/go.php/vII_NewestComponent/indexid/{symbol}.phtml"
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r = requests.get(url)
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r.encoding = "gb2312"
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soup = BeautifulSoup(r.text, "lxml")
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page_num = (
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soup.find(attrs={"class": "table2"})
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.find("td")
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.find_all("a")[-1]["href"]
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.split("page=")[-1]
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.split("&")[0]
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)
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if page_num == "#":
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temp_df = pd.read_html(StringIO(r.text), header=0, skiprows=1)[3].iloc[:, :3]
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temp_df["品种代码"] = temp_df["品种代码"].astype(str).str.zfill(6)
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return temp_df
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temp_df = pd.DataFrame()
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for page in range(1, int(page_num) + 1):
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url = f"https://vip.stock.finance.sina.com.cn/corp/view/vII_NewestComponent.php?page={page}&indexid={symbol}"
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r = requests.get(url)
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r.encoding = "gb2312"
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temp_df = pd.concat(
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objs=[temp_df, pd.read_html(StringIO(r.text), header=1)[3]],
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ignore_index=True,
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)
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temp_df = temp_df.iloc[:, :3]
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temp_df["品种代码"] = temp_df["品种代码"].astype(str).str.zfill(6)
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return temp_df
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def index_stock_cons_csindex(symbol: str = "000300") -> pd.DataFrame:
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"""
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中证指数网站-成份股目录
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https://www.csindex.com.cn/zh-CN/indices/index-detail/000300
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:param symbol: 指数代码, 可以通过 ak.index_stock_info() 函数获取
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:type symbol: str
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:return: 最新指数的成份股
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:rtype: pandas.DataFrame
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"""
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url = (
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f"https://oss-ch.csindex.com.cn/static/"
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f"html/csindex/public/uploads/file/autofile/cons/{symbol}cons.xls"
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)
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r = requests.get(url)
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temp_df = pd.read_excel(BytesIO(r.content))
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temp_df.columns = [
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"日期",
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"指数代码",
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"指数名称",
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"指数英文名称",
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"成分券代码",
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"成分券名称",
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"成分券英文名称",
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"交易所",
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"交易所英文名称",
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]
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temp_df["日期"] = pd.to_datetime(
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temp_df["日期"], format="%Y%m%d", errors="coerce"
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).dt.date
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temp_df["指数代码"] = temp_df["指数代码"].astype(str).str.zfill(6)
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temp_df["成分券代码"] = temp_df["成分券代码"].astype(str).str.zfill(6)
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return temp_df
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def index_stock_cons_weight_csindex(symbol: str = "000300") -> pd.DataFrame:
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"""
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中证指数网站-样本权重
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https://www.csindex.com.cn/zh-CN/indices/index-detail/000300
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:param symbol: 指数代码, 可以通过 ak.index_stock_info() 接口获取
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:type symbol: str
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:return: 最新指数的成份股权重
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:rtype: pandas.DataFrame
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"""
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url = (
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f"https://oss-ch.csindex.com.cn/static/html/csindex/"
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f"public/uploads/file/autofile/closeweight/{symbol}closeweight.xls"
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)
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r = requests.get(url)
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temp_df = pd.read_excel(BytesIO(r.content))
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temp_df.columns = [
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"日期",
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"指数代码",
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"指数名称",
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"指数英文名称",
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"成分券代码",
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"成分券名称",
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"成分券英文名称",
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"交易所",
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"交易所英文名称",
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"权重",
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]
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temp_df["日期"] = pd.to_datetime(
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temp_df["日期"], format="%Y%m%d", errors="coerce"
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).dt.date
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temp_df["指数代码"] = temp_df["指数代码"].astype(str).str.zfill(6)
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temp_df["成分券代码"] = temp_df["成分券代码"].astype(str).str.zfill(6)
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temp_df["权重"] = pd.to_numeric(temp_df["权重"], errors="coerce")
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return temp_df
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def stock_a_code_to_symbol(symbol: str = "000300") -> str:
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"""
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输入股票代码判断股票市场
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:param symbol: 股票代码
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:type symbol: str
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:return: 股票市场
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:rtype: str
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"""
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if symbol.startswith("6") or symbol.startswith("900"):
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return f"sh{symbol}"
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else:
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return f"sz{symbol}"
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if __name__ == "__main__":
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index_stock_cons_csindex_df = index_stock_cons_csindex(symbol="000300")
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print(index_stock_cons_csindex_df)
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index_stock_cons_weight_csindex_df = index_stock_cons_weight_csindex(
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symbol="000300"
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)
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print(index_stock_cons_weight_csindex_df)
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index_stock_cons_sina_df = index_stock_cons_sina(symbol="000300")
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print(index_stock_cons_sina_df)
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index_stock_cons_df = index_stock_cons(symbol="000300")
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print(index_stock_cons_df)
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