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
116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2022/1/7 13:40
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Desc: 新浪财经-机构推荐池
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http://stock.finance.sina.com.cn/stock/go.php/vIR_RatingNewest/index.phtml
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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 stock_institute_recommend(symbol: str = "投资评级选股") -> pd.DataFrame:
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"""
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新浪财经-机构推荐池-最新投资评级
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http://stock.finance.sina.com.cn/stock/go.php/vIR_RatingNewest/index.phtml
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:param symbol: choice of {'最新投资评级', '上调评级股票', '下调评级股票', '股票综合评级', '首次评级股票', '目标涨幅排名', '机构关注度', '行业关注度', '投资评级选股'}
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:type symbol: str
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:return: 最新投资评级数据
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:rtype: pandas.DataFrame
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"""
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url = "http://stock.finance.sina.com.cn/stock/go.php/vIR_RatingNewest/index.phtml"
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params = {
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"num": "40",
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"p": "1",
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}
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r = requests.get(url, params=params)
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soup = BeautifulSoup(r.text, "lxml")
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indicator_map = {
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item.find("a").text: item.find("a")["href"]
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for item in soup.find(attrs={"id": "leftMenu"}).find_all("dd")[1].find_all("li")
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}
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url = indicator_map[symbol]
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params = {
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"num": "10000",
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"p": "1",
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}
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r = requests.get(url, params=params)
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if symbol == "股票综合评级":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :9]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"综合评级↓": "综合评级"})
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return temp_df
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if symbol == "首次评级股票":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :8]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"评级日期↓": "评级日期"})
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return temp_df
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if symbol == "目标涨幅排名":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :7]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"平均目标涨幅↓": "平均目标涨幅"})
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return temp_df
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if symbol == "机构关注度":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :11]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"关注度↓": "关注度"})
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return temp_df
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if symbol == "行业关注度":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :11]
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temp_df = temp_df.rename(columns={"关注度↓": "关注度"})
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return temp_df
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if symbol == "投资评级选股":
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :9]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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del temp_df["评级明细"]
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temp_df = temp_df.rename(columns={"评级日期↓": "评级日期"})
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return temp_df
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :8]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"评级日期↓": "评级日期"})
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return temp_df
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def stock_institute_recommend_detail(symbol: str = "000001") -> pd.DataFrame:
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"""
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新浪财经-机构推荐池-股票评级记录
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http://stock.finance.sina.com.cn/stock/go.php/vIR_StockSearch/key/sz000001.phtml
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:param symbol: 股票代码
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:type symbol: str
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:return: 具体股票的股票评级记录
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:rtype: pandas.DataFrame
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"""
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url = f"http://stock.finance.sina.com.cn/stock/go.php/vIR_StockSearch/key/{symbol}.phtml"
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params = {
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"num": "5000",
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"p": "1",
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(r.text, header=0)[0].iloc[:, :8]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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temp_df = temp_df.rename(columns={"评级日期↓": "评级日期"})
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return temp_df
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if __name__ == "__main__":
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for item in [
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"最新投资评级",
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"上调评级股票",
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"下调评级股票",
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"股票综合评级",
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"首次评级股票",
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"目标涨幅排名",
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"机构关注度",
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"行业关注度",
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"投资评级选股",
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]:
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stock_institute_recommend_df = stock_institute_recommend(symbol=item)
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print(stock_institute_recommend_df)
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stock_institute_recommend_detail_df = stock_institute_recommend_detail(
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symbol="002709"
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)
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print(stock_institute_recommend_detail_df)
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