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
376 lines
12 KiB
Python
376 lines
12 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2023/12/12 16:30
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Desc: 基金评级
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https://fund.eastmoney.com/data/fundrating.html
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"""
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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 fund_rating_all() -> pd.DataFrame:
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"""
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天天基金网-基金评级-基金评级总汇
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https://fund.eastmoney.com/data/fundrating.html
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:return: 基金评级总汇
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.eastmoney.com/data/fundrating.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, features="lxml")
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data_text = soup.find(name="div", attrs={"id": "fundinfo"}).find("script").string
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data_content = [
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item.split("|")
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for item in data_text.split("var")[6]
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.split("=")[1]
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.strip()
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.strip(";")
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.strip('"')
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.strip("|")
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.split("|_")
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]
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temp_df = pd.DataFrame(data_content)
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temp_df.columns = [
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"代码",
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"简称",
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"类型",
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"基金经理",
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"-",
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"基金公司",
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"-",
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"5星评级家数",
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"-",
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"-",
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"招商证券",
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"-",
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"上海证券",
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"-",
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"晨星评级",
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"-",
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"济安金信",
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"-",
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"手续费",
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"-",
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"-",
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"-",
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"-",
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"-",
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"-",
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"-",
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"-",
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]
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temp_df = temp_df[
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[
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"代码",
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"简称",
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"基金经理",
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"基金公司",
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"5星评级家数",
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"上海证券",
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"招商证券",
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"济安金信",
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"晨星评级",
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"手续费",
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"类型",
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]
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]
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temp_df["5星评级家数"] = pd.to_numeric(temp_df["5星评级家数"], errors="coerce")
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temp_df["上海证券"] = pd.to_numeric(temp_df["上海证券"], errors="coerce")
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temp_df["招商证券"] = pd.to_numeric(temp_df["招商证券"], errors="coerce")
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temp_df["济安金信"] = pd.to_numeric(temp_df["济安金信"], errors="coerce")
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temp_df["晨星评级"] = pd.to_numeric(temp_df["晨星评级"], errors="coerce")
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temp_df["手续费"] = (
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pd.to_numeric(temp_df["手续费"].str.strip("%"), errors="coerce") / 100
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)
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return temp_df
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def fund_rating_sh(date: str = "20230630") -> pd.DataFrame:
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"""
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天天基金网-基金评级-上海证券评级
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https://fund.eastmoney.com/data/fundrating_3.html
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:param date: 日期; https://fund.eastmoney.com/data/fundrating_3.html 获取查询日期
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:type date: str
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:return: 上海证券评级
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.eastmoney.com/data/fundrating_3.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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date_list = [
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item["value"] for item in soup.find("select", attrs={"id": "rqoptions"})
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]
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date_format = "-".join([date[:4], date[4:6], date[6:]])
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if date_format not in date_list:
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raise "请访问 https://fund.eastmoney.com/data/fundrating_3.html 获取查询日期"
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url = f"https://fund.eastmoney.com/data/fundrating_3_{'-'.join([date[:4], date[4:6], date[6:]])}.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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data_text = soup.find("div", attrs={"id": "fundinfo"}).find("script").string
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data_content = [
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item.split("|")
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for item in data_text.split("var")[1]
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.split("=")[1]
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.strip()
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.strip(";")
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.strip('"')
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.strip("|")
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.split("|_")
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]
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temp_df = pd.DataFrame(data_content)
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temp_df.columns = [
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"代码",
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"简称",
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"类型",
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"基金经理",
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"_",
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"基金公司",
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"_",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"5年期评级-5年评级",
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"5年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df = temp_df[
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[
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"代码",
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"简称",
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"基金经理",
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"基金公司",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"5年期评级-5年评级",
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"5年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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"类型",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["3年期评级-3年评级"] = pd.to_numeric(
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temp_df["3年期评级-3年评级"], errors="coerce"
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)
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temp_df["3年期评级-较上期"] = pd.to_numeric(
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temp_df["3年期评级-较上期"], errors="coerce"
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)
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temp_df["5年期评级-5年评级"] = pd.to_numeric(
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temp_df["5年期评级-5年评级"], errors="coerce"
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)
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temp_df["5年期评级-较上期"] = pd.to_numeric(
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temp_df["5年期评级-较上期"], errors="coerce"
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)
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temp_df["单位净值"] = pd.to_numeric(temp_df["单位净值"], errors="coerce")
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temp_df["日增长率"] = pd.to_numeric(temp_df["日增长率"], errors="coerce")
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temp_df["近1年涨幅"] = pd.to_numeric(temp_df["近1年涨幅"], errors="coerce")
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temp_df["近3年涨幅"] = pd.to_numeric(temp_df["近3年涨幅"], errors="coerce")
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temp_df["近5年涨幅"] = pd.to_numeric(temp_df["近5年涨幅"], errors="coerce")
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return temp_df
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def fund_rating_zs(date: str = "20230331") -> pd.DataFrame:
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"""
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天天基金网-基金评级-招商证券评级
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https://fund.eastmoney.com/data/fundrating_2.html
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:param date: 日期; https://fund.eastmoney.com/data/fundrating_2.html 获取查询日期
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:type date: str
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:return: 招商证券评级-混合型
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.eastmoney.com/data/fundrating_2.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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date_list = [
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item["value"] for item in soup.find("select", attrs={"id": "rqoptions"})
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]
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date_format = "-".join([date[:4], date[4:6], date[6:]])
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if date_format not in date_list:
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raise "请访问 https://fund.eastmoney.com/data/fundrating_2.html 获取查询日期"
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url = f"https://fund.eastmoney.com/data/fundrating_2_{'-'.join([date[:4], date[4:6], date[6:]])}.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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data_text = soup.find("div", attrs={"id": "fundinfo"}).find("script").string
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data_content = [
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item.split("|")
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for item in data_text.split("var")[1]
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.split("=")[1]
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.strip()
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.strip(";")
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.strip('"')
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.strip("|")
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.split("|_")
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]
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temp_df = pd.DataFrame(data_content)
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temp_df.columns = [
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"代码",
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"简称",
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"_",
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"基金经理",
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"_",
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"基金公司",
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"_",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df = temp_df[
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[
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"代码",
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"简称",
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"基金经理",
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"基金公司",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["3年期评级-3年评级"] = pd.to_numeric(
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temp_df["3年期评级-3年评级"], errors="coerce"
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)
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temp_df["3年期评级-较上期"] = pd.to_numeric(
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temp_df["3年期评级-较上期"], errors="coerce"
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)
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temp_df["单位净值"] = pd.to_numeric(temp_df["单位净值"], errors="coerce")
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temp_df["日增长率"] = pd.to_numeric(temp_df["日增长率"], errors="coerce")
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temp_df["近1年涨幅"] = pd.to_numeric(temp_df["近1年涨幅"], errors="coerce")
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temp_df["近3年涨幅"] = pd.to_numeric(temp_df["近3年涨幅"], errors="coerce")
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temp_df["近5年涨幅"] = pd.to_numeric(temp_df["近5年涨幅"], errors="coerce")
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return temp_df
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def fund_rating_ja(date: str = "20230331") -> pd.DataFrame:
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"""
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天天基金网-基金评级-济安金信评级
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https://fund.eastmoney.com/data/fundrating_4.html
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:param date: 日期; https://fund.eastmoney.com/data/fundrating_4.html 获取查询日期
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:type date: str
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:return: 济安金信评级
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.eastmoney.com/data/fundrating_4.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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date_list = [
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item["value"] for item in soup.find("select", attrs={"id": "rqoptions"})
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]
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date_format = "-".join([date[:4], date[4:6], date[6:]])
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if date_format not in date_list:
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raise "请访问 http://fund.eastmoney.com/data/fundrating_4.html 获取查询日期"
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url = f"https://fund.eastmoney.com/data/fundrating_4_{'-'.join([date[:4], date[4:6], date[6:]])}.html"
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r = requests.get(url)
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soup = BeautifulSoup(r.text, "lxml")
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data_text = soup.find("div", attrs={"id": "fundinfo"}).find("script").string
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data_content = [
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item.split("|")
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for item in data_text.split("var")[1]
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.split("=")[1]
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.strip()
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.strip(";")
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.strip('"')
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.strip("|")
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.split("|_")
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]
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temp_df = pd.DataFrame(data_content)
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temp_df.columns = [
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"代码",
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"简称",
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"类型",
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"基金经理",
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"_",
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"基金公司",
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"_",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df = temp_df[
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[
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"代码",
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"简称",
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"基金经理",
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"基金公司",
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"3年期评级-3年评级",
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"3年期评级-较上期",
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"单位净值",
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"日期",
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"日增长率",
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"近1年涨幅",
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"近3年涨幅",
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"近5年涨幅",
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"手续费",
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"类型",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["3年期评级-3年评级"] = pd.to_numeric(
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temp_df["3年期评级-3年评级"], errors="coerce"
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)
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temp_df["3年期评级-较上期"] = pd.to_numeric(
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temp_df["3年期评级-较上期"], errors="coerce"
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)
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temp_df["单位净值"] = pd.to_numeric(temp_df["单位净值"], errors="coerce")
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temp_df["日增长率"] = pd.to_numeric(temp_df["日增长率"], errors="coerce")
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temp_df["近1年涨幅"] = pd.to_numeric(temp_df["近1年涨幅"], errors="coerce")
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temp_df["近3年涨幅"] = pd.to_numeric(temp_df["近3年涨幅"], errors="coerce")
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temp_df["近5年涨幅"] = pd.to_numeric(temp_df["近5年涨幅"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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fund_rating_all_df = fund_rating_all()
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print(fund_rating_all_df)
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fund_rating_sh_df = fund_rating_sh(date="20230630")
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print(fund_rating_sh_df)
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fund_rating_zs_df = fund_rating_zs(date="20230331")
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print(fund_rating_zs_df)
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fund_rating_ja_df = fund_rating_ja(date="20230331")
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print(fund_rating_ja_df)
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