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
145 lines
5.6 KiB
Python
145 lines
5.6 KiB
Python
#!/usr/bin/env python
|
|
# -*- coding:utf-8 -*-
|
|
"""
|
|
Date: 2023/5/9 17:06
|
|
Desc: 新浪行业-板块行情
|
|
http://finance.sina.com.cn/stock/sl/
|
|
"""
|
|
|
|
import json
|
|
import math
|
|
|
|
import pandas as pd
|
|
import requests
|
|
|
|
from akshare.utils import demjson
|
|
from tqdm import tqdm
|
|
|
|
|
|
def stock_sector_spot(indicator: str = "新浪行业") -> pd.DataFrame:
|
|
"""
|
|
新浪行业-板块行情
|
|
http://finance.sina.com.cn/stock/sl/
|
|
:param indicator: choice of {"新浪行业", "启明星行业", "概念", "地域", "行业"}
|
|
:type indicator: str
|
|
:return: 指定 indicator 的数据
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
if indicator == "新浪行业":
|
|
url = "http://vip.stock.finance.sina.com.cn/q/view/newSinaHy.php"
|
|
r = requests.get(url)
|
|
if indicator == "启明星行业":
|
|
url = "http://biz.finance.sina.com.cn/hq/qmxIndustryHq.php"
|
|
r = requests.get(url)
|
|
r.encoding = "gb2312"
|
|
if indicator == "概念":
|
|
url = "http://money.finance.sina.com.cn/q/view/newFLJK.php"
|
|
params = {"param": "class"}
|
|
r = requests.get(url, params=params)
|
|
if indicator == "地域":
|
|
url = "http://money.finance.sina.com.cn/q/view/newFLJK.php"
|
|
params = {"param": "area"}
|
|
r = requests.get(url, params=params)
|
|
if indicator == "行业":
|
|
url = "http://money.finance.sina.com.cn/q/view/newFLJK.php"
|
|
params = {"param": "industry"}
|
|
r = requests.get(url, params=params)
|
|
text_data = r.text
|
|
json_data = json.loads(text_data[text_data.find("{") :])
|
|
temp_df = pd.DataFrame([value.split(",") for key, value in json_data.items()])
|
|
temp_df.columns = [
|
|
"label",
|
|
"板块",
|
|
"公司家数",
|
|
"平均价格",
|
|
"涨跌额",
|
|
"涨跌幅",
|
|
"总成交量",
|
|
"总成交额",
|
|
"股票代码",
|
|
"个股-涨跌幅",
|
|
"个股-当前价",
|
|
"个股-涨跌额",
|
|
"股票名称",
|
|
]
|
|
temp_df["公司家数"] = pd.to_numeric(temp_df["公司家数"])
|
|
temp_df["平均价格"] = pd.to_numeric(temp_df["平均价格"])
|
|
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"])
|
|
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"])
|
|
temp_df["总成交量"] = pd.to_numeric(temp_df["总成交量"])
|
|
temp_df["总成交额"] = pd.to_numeric(temp_df["总成交额"])
|
|
temp_df["个股-涨跌幅"] = pd.to_numeric(temp_df["个股-涨跌幅"])
|
|
temp_df["个股-当前价"] = pd.to_numeric(temp_df["个股-当前价"])
|
|
temp_df["个股-涨跌额"] = pd.to_numeric(temp_df["个股-涨跌额"])
|
|
return temp_df
|
|
|
|
|
|
def stock_sector_detail(sector: str = "gn_gfgn") -> pd.DataFrame:
|
|
"""
|
|
新浪行业-板块行情-成份详情
|
|
http://finance.sina.com.cn/stock/sl/#area_1
|
|
:param sector: stock_sector_spot 返回的 label 值, choice of {"新浪行业", "概念", "地域", "行业"}; "启明星行业" 无详情
|
|
:type sector: str
|
|
:return: 指定 sector 的板块详情
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount"
|
|
params = {"node": sector}
|
|
r = requests.get(url, params=params)
|
|
total_num = int(r.json())
|
|
total_page_num = math.ceil(int(total_num) / 80)
|
|
big_df = pd.DataFrame()
|
|
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData"
|
|
for page in tqdm(range(1, total_page_num + 1), leave=True):
|
|
params = {
|
|
"page": str(page),
|
|
"num": "80",
|
|
"sort": "symbol",
|
|
"asc": "1",
|
|
"node": sector,
|
|
"symbol": "",
|
|
"_s_r_a": "page",
|
|
}
|
|
r = requests.get(url, params=params)
|
|
data_text = r.text
|
|
data_json = demjson.decode(data_text)
|
|
temp_df = pd.DataFrame(data_json)
|
|
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
|
big_df["trade"] = pd.to_numeric(big_df["trade"], errors="coerce")
|
|
big_df["pricechange"] = pd.to_numeric(big_df["pricechange"], errors="coerce")
|
|
big_df["changepercent"] = pd.to_numeric(big_df["changepercent"], errors="coerce")
|
|
big_df["buy"] = pd.to_numeric(big_df["buy"], errors="coerce")
|
|
big_df["sell"] = pd.to_numeric(big_df["sell"], errors="coerce")
|
|
big_df["settlement"] = pd.to_numeric(big_df["settlement"], errors="coerce")
|
|
big_df["open"] = pd.to_numeric(big_df["open"], errors="coerce")
|
|
big_df["high"] = pd.to_numeric(big_df["high"], errors="coerce")
|
|
big_df["low"] = pd.to_numeric(big_df["low"], errors="coerce")
|
|
big_df["volume"] = pd.to_numeric(big_df["volume"], errors="coerce")
|
|
big_df["amount"] = pd.to_numeric(big_df["amount"], errors="coerce")
|
|
big_df["per"] = pd.to_numeric(big_df["per"], errors="coerce")
|
|
big_df["pb"] = pd.to_numeric(big_df["pb"], errors="coerce")
|
|
big_df["mktcap"] = pd.to_numeric(big_df["mktcap"], errors="coerce")
|
|
big_df["nmc"] = pd.to_numeric(big_df["nmc"], errors="coerce")
|
|
big_df["turnoverratio"] = pd.to_numeric(big_df["turnoverratio"], errors="coerce")
|
|
return big_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
stock_industry_sina_df = stock_sector_spot(indicator="新浪行业")
|
|
print(stock_industry_sina_df)
|
|
|
|
stock_industry_con_df = stock_sector_spot(indicator="概念")
|
|
print(stock_industry_con_df)
|
|
|
|
stock_industry_area_df = stock_sector_spot(indicator="地域")
|
|
print(stock_industry_area_df)
|
|
|
|
stock_industry_ind_df = stock_sector_spot(indicator="行业")
|
|
print(stock_industry_ind_df)
|
|
|
|
stock_industry_star_df = stock_sector_spot(indicator="启明星行业")
|
|
print(stock_industry_star_df)
|
|
|
|
stock_sector_detail_df = stock_sector_detail(sector="hangye_ZC27")
|
|
print(stock_sector_detail_df)
|