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
242 lines
7.1 KiB
Python
242 lines
7.1 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/8/25 15:00
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Desc: 东方财富-财经早餐
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https://stock.eastmoney.com/a/czpnc.html
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"""
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from datetime import datetime
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import pandas as pd
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import requests
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from akshare.request import make_request_with_retry_json
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from akshare.utils.cons import headers
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def stock_info_cjzc_em() -> pd.DataFrame:
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"""
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东方财富-财经早餐
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https://stock.eastmoney.com/a/czpnc.html
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:return: 财经早餐
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:rtype: pandas.DataFrame
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"""
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url = "https://np-listapi.eastmoney.com/comm/web/getNewsByColumns"
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params = {
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"client": "web",
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"biz": "web_news_col",
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"column": "1207",
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"order": "1",
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"needInteractData": "0",
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"page_index": "1",
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"page_size": "200",
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"req_trace": "1710314682980",
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"fields": "code,showTime,title,mediaName,summary,image,url,uniqueUrl,Np_dst",
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}
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big_df = pd.DataFrame()
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for page in range(1, 3):
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params.update({"page_index": page})
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["list"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df = big_df[["title", "summary", "showTime", "uniqueUrl"]]
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big_df.rename(
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columns={
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"title": "标题",
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"summary": "摘要",
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"showTime": "发布时间",
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"uniqueUrl": "链接",
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},
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inplace=True,
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)
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return big_df
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def stock_info_global_em() -> pd.DataFrame:
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"""
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东方财富-全球财经快讯
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https://kuaixun.eastmoney.com/7_24.html
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:return: 全球财经快讯
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:rtype: pandas.DataFrame
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"""
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url = "https://np-weblist.eastmoney.com/comm/web/getFastNewsList"
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params = {
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"client": "web",
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"biz": "web_724",
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"fastColumn": "102",
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"sortEnd": "",
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"pageSize": "200",
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"req_trace": "1710315450384",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["fastNewsList"])
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temp_df = temp_df[["title", "summary", "showTime", "code"]]
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temp_df["code"] = [
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f"https://finance.eastmoney.com/a/{item}.html" for item in temp_df["code"]
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]
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temp_df.rename(
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columns={
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"title": "标题",
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"summary": "摘要",
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"showTime": "发布时间",
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"code": "链接",
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},
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inplace=True,
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)
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return temp_df
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def stock_info_global_sina() -> pd.DataFrame:
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"""
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新浪财经-全球财经快讯
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https://finance.sina.com.cn/7x24
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:return: 全球财经快讯
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:rtype: pandas.DataFrame
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"""
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url = "https://zhibo.sina.com.cn/api/zhibo/feed"
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params = {
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"page": "1",
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"page_size": "20",
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"zhibo_id": "152",
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"tag_id": "0",
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"dire": "f",
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"dpc": "1",
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"pagesize": "20",
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"type": "1",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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time_list = [
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item["create_time"] for item in data_json["result"]["data"]["feed"]["list"]
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]
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text_list = [
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item["rich_text"] for item in data_json["result"]["data"]["feed"]["list"]
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]
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temp_df = pd.DataFrame([time_list, text_list]).T
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temp_df.columns = ["时间", "内容"]
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return temp_df
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def stock_info_global_futu() -> pd.DataFrame:
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"""
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富途牛牛-快讯
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https://news.futunn.com/main/live
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:return: 快讯
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:rtype: pandas.DataFrame
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"""
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url = "https://news.futunn.com/news-site-api/main/get-flash-list"
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params = {
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"pageSize": "50",
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}
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
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" Chrome/111.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["data"]["news"])
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temp_df = temp_df[["title", "content", "time", "detailUrl"]]
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temp_df["time"] = [
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datetime.fromtimestamp(int(item)).strftime("%Y-%m-%d %H:%M:%S")
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for item in temp_df["time"]
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]
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temp_df.rename(
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columns={
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"title": "标题",
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"content": "内容",
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"time": "发布时间",
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"detailUrl": "链接",
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},
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inplace=True,
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)
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return temp_df
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def stock_info_global_ths() -> pd.DataFrame:
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"""
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同花顺财经-全球财经直播
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https://news.10jqka.com.cn/realtimenews.html
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:return: 全球财经直播
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:rtype: pandas.DataFrame
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"""
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url = "https://news.10jqka.com.cn/tapp/news/push/stock"
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params = {
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"page": "1",
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"tag": "",
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"track": "website",
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["list"])
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temp_df = temp_df[["title", "digest", "rtime", "url"]]
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temp_df["rtime"] = [
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datetime.fromtimestamp(int(item)).strftime("%Y-%m-%d %H:%M:%S")
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for item in temp_df["rtime"]
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]
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temp_df.rename(
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columns={
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"title": "标题",
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"digest": "内容",
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"rtime": "发布时间",
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"url": "链接",
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},
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inplace=True,
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)
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return temp_df
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def stock_info_global_cls(symbol: str = "全部") -> pd.DataFrame:
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"""
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财联社-电报
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https://www.cls.cn/telegraph
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:param symbol: choice of {"全部", "重点"}
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:type symbol: str
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:return: 财联社-电报
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:rtype: pandas.DataFrame
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"""
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url = "https://www.cls.cn/nodeapi/telegraphList"
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data_json = make_request_with_retry_json(url, max_retries=10, headers=headers)
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temp_df = pd.DataFrame(data_json["data"]["roll_data"])
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big_df = temp_df.copy()
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big_df = big_df[["title", "content", "ctime", "level"]]
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big_df["ctime"] = pd.to_datetime(big_df["ctime"], unit="s", utc=True).dt.tz_convert(
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"Asia/Shanghai"
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)
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big_df.columns = ["标题", "内容", "发布时间", "等级"]
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big_df.sort_values(["发布时间"], inplace=True)
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big_df.reset_index(inplace=True, drop=True)
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big_df["发布日期"] = big_df["发布时间"].dt.date
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big_df["发布时间"] = big_df["发布时间"].dt.time
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if symbol == "重点":
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big_df = big_df[(big_df["等级"] == "B") | (big_df["等级"] == "A")]
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big_df.reset_index(inplace=True, drop=True)
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big_df = big_df[["标题", "内容", "发布日期", "发布时间"]]
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return big_df
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else:
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big_df = big_df[["标题", "内容", "发布日期", "发布时间"]]
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return big_df
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if __name__ == "__main__":
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stock_info_cjzc_em_df = stock_info_cjzc_em()
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print(stock_info_cjzc_em_df)
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stock_info_global_em_df = stock_info_global_em()
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print(stock_info_global_em_df)
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stock_info_global_sina_df = stock_info_global_sina()
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print(stock_info_global_sina_df)
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stock_info_global_futu_df = stock_info_global_futu()
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print(stock_info_global_futu_df)
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stock_info_global_ths_df = stock_info_global_ths()
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print(stock_info_global_ths_df)
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stock_info_global_cls_df = stock_info_global_cls(symbol="全部")
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print(stock_info_global_cls_df)
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