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
279 lines
10 KiB
Python
279 lines
10 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/24 15:00
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Desc: 申万宏源研究-申万指数-指数发布
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乐咕乐股网
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https://legulegu.com/stockdata/index-composition?industryCode=851921.SI
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"""
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from io import 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.cons import headers
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def sw_index_first_info() -> pd.DataFrame:
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"""
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乐咕乐股-申万一级-分类
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https://legulegu.com/stockdata/sw-industry-overview#level1
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:return: 分类
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:rtype: pandas.DataFrame
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"""
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url = "https://legulegu.com/stockdata/sw-industry-overview"
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r = requests.get(url, headers=headers)
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soup = BeautifulSoup(r.text, features="lxml")
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code_raw = soup.find(name="div", attrs={"id": "level1Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-chinese-title"}
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)
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name_raw = soup.find(name="div", attrs={"id": "level1Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-number"}
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)
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value_raw = soup.find(name="div", attrs={"id": "level1Items"}).find_all(
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name="div", attrs={"class": "lg-sw-industries-item-value"}
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)
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code = [item.get_text() for item in code_raw]
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name = [item.get_text().split("(")[0] for item in name_raw]
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num = [item.get_text().split("(")[1].split(")")[0] for item in name_raw]
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num_1 = [
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item.find_all("span", attrs={"class": "value"})[0].get_text().strip()
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for item in value_raw
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]
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num_2 = [
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item.find_all("span", attrs={"class": "value"})[1].get_text().strip()
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for item in value_raw
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]
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num_3 = [
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item.find_all("span", attrs={"class": "value"})[2].get_text().strip()
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for item in value_raw
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]
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num_4 = [
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item.find_all("span", attrs={"class": "value"})[3].get_text().strip()
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for item in value_raw
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]
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temp_df = pd.DataFrame([code, name, num, num_1, num_2, num_3, num_4]).T
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temp_df.columns = [
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"行业代码",
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"行业名称",
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"成份个数",
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"静态市盈率",
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"TTM(滚动)市盈率",
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"市净率",
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"静态股息率",
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]
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temp_df["成份个数"] = pd.to_numeric(temp_df["成份个数"], errors="coerce")
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temp_df["静态市盈率"] = pd.to_numeric(temp_df["静态市盈率"], errors="coerce")
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temp_df["TTM(滚动)市盈率"] = pd.to_numeric(
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temp_df["TTM(滚动)市盈率"], errors="coerce"
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)
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["静态股息率"] = pd.to_numeric(temp_df["静态股息率"], errors="coerce")
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return temp_df
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def sw_index_second_info() -> pd.DataFrame:
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"""
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乐咕乐股-申万二级-分类
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https://legulegu.com/stockdata/sw-industry-overview#level1
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:return: 分类
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:rtype: pandas.DataFrame
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"""
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url = "https://legulegu.com/stockdata/sw-industry-overview"
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r = requests.get(url, headers=headers)
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soup = BeautifulSoup(r.text, features="lxml")
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code_raw = soup.find(name="div", attrs={"id": "level2Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-chinese-title"}
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)
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name_raw = soup.find(name="div", attrs={"id": "level2Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-number"}
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)
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value_raw = soup.find(name="div", attrs={"id": "level2Items"}).find_all(
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name="div", attrs={"class": "lg-sw-industries-item-value"}
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)
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code = [item.get_text() for item in code_raw]
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name = [item.get_text().split("(")[0] for item in name_raw]
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parent_name = [
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item.find("span").get_text().split("(")[0][1:-1] for item in name_raw
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]
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num = [item.get_text().split("(")[1].split(")")[0] for item in name_raw]
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num_1 = [
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item.find_all("span", attrs={"class": "value"})[0].get_text().strip()
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for item in value_raw
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]
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num_2 = [
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item.find_all("span", attrs={"class": "value"})[1].get_text().strip()
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for item in value_raw
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]
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num_3 = [
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item.find_all("span", attrs={"class": "value"})[2].get_text().strip()
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for item in value_raw
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]
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num_4 = [
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item.find_all("span", attrs={"class": "value"})[3].get_text().strip()
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for item in value_raw
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]
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temp_df = pd.DataFrame([code, name, parent_name, num, num_1, num_2, num_3, num_4]).T
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temp_df.columns = [
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"行业代码",
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"行业名称",
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"上级行业",
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"成份个数",
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"静态市盈率",
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"TTM(滚动)市盈率",
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"市净率",
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"静态股息率",
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]
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temp_df["成份个数"] = pd.to_numeric(temp_df["成份个数"], errors="coerce")
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temp_df["静态市盈率"] = pd.to_numeric(temp_df["静态市盈率"], errors="coerce")
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temp_df["TTM(滚动)市盈率"] = pd.to_numeric(
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temp_df["TTM(滚动)市盈率"], errors="coerce"
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)
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["静态股息率"] = pd.to_numeric(temp_df["静态股息率"], errors="coerce")
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return temp_df
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def sw_index_third_info() -> pd.DataFrame:
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"""
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乐咕乐股-申万三级-分类
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https://legulegu.com/stockdata/sw-industry-overview#level1
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:return: 分类
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:rtype: pandas.DataFrame
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"""
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url = "https://legulegu.com/stockdata/sw-industry-overview"
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r = requests.get(url, headers=headers)
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soup = BeautifulSoup(r.text, features="lxml")
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code_raw = soup.find(name="div", attrs={"id": "level3Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-chinese-title"}
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)
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name_raw = soup.find(name="div", attrs={"id": "level3Items"}).find_all(
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name="div", attrs={"class": "lg-industries-item-number"}
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)
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value_raw = soup.find(name="div", attrs={"id": "level3Items"}).find_all(
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name="div", attrs={"class": "lg-sw-industries-item-value"}
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)
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code = [item.get_text() for item in code_raw]
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name = [item.get_text().split("(")[0] for item in name_raw]
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parent_name = [
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item.find("span").get_text().split("(")[0][1:-1] for item in name_raw
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]
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num = [item.get_text().split("(")[1].split(")")[0] for item in name_raw]
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num_1 = [
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item.find_all("span", attrs={"class": "value"})[0].get_text().strip()
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for item in value_raw
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]
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num_2 = [
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item.find_all("span", attrs={"class": "value"})[1].get_text().strip()
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for item in value_raw
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]
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num_3 = [
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item.find_all("span", attrs={"class": "value"})[2].get_text().strip()
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for item in value_raw
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]
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num_4 = [
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item.find_all("span", attrs={"class": "value"})[3].get_text().strip()
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for item in value_raw
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]
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temp_df = pd.DataFrame([code, name, parent_name, num, num_1, num_2, num_3, num_4]).T
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temp_df.columns = [
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"行业代码",
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"行业名称",
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"上级行业",
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"成份个数",
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"静态市盈率",
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"TTM(滚动)市盈率",
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"市净率",
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"静态股息率",
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]
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temp_df["成份个数"] = pd.to_numeric(temp_df["成份个数"], errors="coerce")
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temp_df["静态市盈率"] = pd.to_numeric(temp_df["静态市盈率"], errors="coerce")
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temp_df["TTM(滚动)市盈率"] = pd.to_numeric(
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temp_df["TTM(滚动)市盈率"], errors="coerce"
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)
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["静态股息率"] = pd.to_numeric(temp_df["静态股息率"], errors="coerce")
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return temp_df
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def sw_index_third_cons(symbol: str = "801120.SI") -> pd.DataFrame:
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"""
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乐咕乐股-申万三级-行业成份
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https://legulegu.com/stockdata/index-composition?industryCode=801120.SI
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:param symbol: 三级行业的行业代码
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:type symbol: str
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:return: 行业成份
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:rtype: pandas.DataFrame
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"""
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url = f"https://legulegu.com/stockdata/index-composition?industryCode={symbol}"
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r = requests.get(url, headers=headers)
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temp_df = pd.read_html(StringIO(r.text))[0]
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temp_df.columns = [
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"序号",
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"股票代码",
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"股票简称",
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"纳入时间",
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"申万1级",
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"申万2级",
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"申万3级",
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"价格",
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"市盈率",
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"市盈率ttm",
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"市净率",
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"股息率",
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"市值",
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"归母净利润同比增长(09-30)",
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"归母净利润同比增长(06-30)",
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"营业收入同比增长(09-30)",
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"营业收入同比增长(06-30)",
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]
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temp_df["价格"] = pd.to_numeric(temp_df["价格"], errors="coerce")
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temp_df["市盈率"] = pd.to_numeric(temp_df["市盈率"], errors="coerce")
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temp_df["市盈率ttm"] = pd.to_numeric(temp_df["市盈率ttm"], errors="coerce")
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["股息率"] = pd.to_numeric(temp_df["股息率"].str.strip("%"), errors="coerce")
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temp_df["市值"] = pd.to_numeric(temp_df["市值"], errors="coerce")
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temp_df["归母净利润同比增长(09-30)"] = temp_df[
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"归母净利润同比增长(09-30)"
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].str.strip("%")
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temp_df["归母净利润同比增长(06-30)"] = temp_df[
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"归母净利润同比增长(06-30)"
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].str.strip("%")
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temp_df["营业收入同比增长(09-30)"] = temp_df["营业收入同比增长(09-30)"].str.strip(
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"%"
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)
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temp_df["营业收入同比增长(06-30)"] = temp_df["营业收入同比增长(06-30)"].str.strip(
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"%"
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)
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temp_df["归母净利润同比增长(09-30)"] = pd.to_numeric(
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temp_df["归母净利润同比增长(09-30)"], errors="coerce"
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)
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temp_df["归母净利润同比增长(06-30)"] = pd.to_numeric(
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temp_df["归母净利润同比增长(06-30)"], errors="coerce"
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)
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temp_df["营业收入同比增长(09-30)"] = pd.to_numeric(
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temp_df["营业收入同比增长(09-30)"], errors="coerce"
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)
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temp_df["营业收入同比增长(06-30)"] = pd.to_numeric(
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temp_df["营业收入同比增长(06-30)"], errors="coerce"
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)
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return temp_df
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if __name__ == "__main__":
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sw_index_first_info_df = sw_index_first_info()
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print(sw_index_first_info_df)
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sw_index_second_info_df = sw_index_second_info()
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print(sw_index_second_info_df)
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sw_index_third_info_df = sw_index_third_info()
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print(sw_index_third_info_df)
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sw_index_third_cons_df = sw_index_third_cons(symbol="850111.SI")
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print(sw_index_third_cons_df)
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