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
125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/2/24 18:30
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Desc: 东方财富网-行情中心-美股市场-知名美股
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https://quote.eastmoney.com/center/gridlist.html#us_wellknown
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"""
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import pandas as pd
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import requests
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def stock_us_famous_spot_em(symbol: str = "科技类") -> pd.DataFrame:
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"""
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东方财富网-行情中心-美股市场-知名美股
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https://quote.eastmoney.com/center/gridlist.html#us_wellknown
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:param symbol: choice of {'科技类', '金融类', '医药食品类', '媒体类', '汽车能源类', '制造零售类'}
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:type: str
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:return: 知名美股实时行情
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:rtype: pandas.DataFrame
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"""
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market_map = {
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"科技类": "0216",
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"金融类": "0217",
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"医药食品类": "0218",
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"媒体类": "0220",
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"汽车能源类": "0219",
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"制造零售类": "0221",
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}
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url = "https://69.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "50000",
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"po": "1",
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"np": "2",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"invt": "2",
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"fid": "f3",
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"fs": f"b:MK{market_map[symbol]}",
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"fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,"
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"f25,f26,f22,f33,f11,f62,f128,f136,f115,f152",
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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"]["diff"]).T
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temp_df.columns = [
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"_",
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"最新价",
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"涨跌幅",
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"涨跌额",
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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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"简称",
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"编码",
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"名称",
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"最高价",
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"最低价",
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"开盘价",
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"昨收价",
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"总市值",
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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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"市盈率",
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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.reset_index(inplace=True)
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temp_df["index"] = range(1, len(temp_df) + 1)
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temp_df.rename(columns={"index": "序号"}, inplace=True)
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temp_df["代码"] = temp_df["编码"].astype(str) + "." + temp_df["简称"]
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temp_df = temp_df[
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[
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"序号",
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"名称",
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"最新价",
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"涨跌额",
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"涨跌幅",
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"开盘价",
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"最高价",
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"最低价",
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"昨收价",
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"总市值",
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"市盈率",
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"代码",
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]
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]
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
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temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
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temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
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temp_df["昨收价"] = pd.to_numeric(temp_df["昨收价"], errors="coerce")
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temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], 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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return temp_df
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if __name__ == "__main__":
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for item in {
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"科技类",
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"金融类",
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"医药食品类",
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"媒体类",
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"汽车能源类",
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"制造零售类",
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}:
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stock_us_famous_spot_em_df = stock_us_famous_spot_em(symbol=item)
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print(stock_us_famous_spot_em_df)
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