#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2025/3/10 18:00
Desc: 东方财富网-行情首页-沪深京 A 股
https://quote.eastmoney.com/
"""

import pandas as pd
import requests

from akshare.utils.func import fetch_paginated_data


def stock_zh_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-沪深京 A 股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,"
        "f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "index",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df.rename(columns={"index": "序号"}, inplace=True)
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_sh_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-沪 A 股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:1 t:2,m:1 t:23",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,"
        "f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_sz_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-深 A 股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:0 t:6,m:0 t:80",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,"
        "f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_bj_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-京 A 股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:0 t:81 s:2048",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24"
        ",f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_new_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-新股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#newshares
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "wbp2u": "|0|0|0|web",
        "fid": "f26",
        "fs": "m:0 f:8,m:1 f:8",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,"
        "f25,f26,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "上市日期",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "上市日期",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )

    temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"], format="%Y%m%d").dt.date
    return temp_df


def stock_cy_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-创业板-实时行情
    https://quote.eastmoney.com/center/gridlist.html#gem_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://7.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "wbp2u": "|0|0|0|web",
        "fid": "f12",
        "fs": "m:0 t:80",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,"
        "f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_kc_a_spot_em() -> pd.DataFrame:
    """
    东方财富网-科创板-实时行情
    https://quote.eastmoney.com/center/gridlist.html#kcb_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://7.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "wbp2u": "|0|0|0|web",
        "fid": "f12",
        "fs": "m:1 t:23",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,"
        "f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_zh_ab_comparison_em() -> pd.DataFrame:
    """
    东方财富网-行情中心-沪深京个股-AB股比价-全部AB股比价
    https://quote.eastmoney.com/center/gridlist.html#ab_comparison
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://push2.eastmoney.com/api/qt/clist/get"
    params = {
        "np": "1",
        "fltt": "1",
        "invt": "2",
        "fs": "m:1+b:BK0498,m:0+b:BK0498",
        "fields": "f201,f202,f203,f196,f200,f197,f152,f12,f13,f14,f1,f2,f4,f3,f199",
        "fid": "f199",
        "pn": "1",
        "pz": "100",
        "po": "1",
        "dect": "1",
        "wbp2u": "|0|0|0|web",
    }
    columns_map = {
        "index": "序号",
        "f1": "-",
        "f2": "最新价B",
        "f3": "涨跌幅B",
        "f4": "-",
        "f12": "A股代码",
        "f13": "-",
        "f14": "A股名称",
        "f152": "-",
        "f196": "最新价A",
        "f197": "涨跌幅A",
        "f199": "比价",
        "f200": "-",
        "f201": "B股代码",
        "f202": "-",
        "f203": "B股名称",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df = temp_df.rename(columns=columns_map)
    list_name = [value for key, value in columns_map.items() if value != "_"]
    temp_df = temp_df[list_name]
    temp_df = temp_df[
        [
            "序号",
            "B股代码",
            "B股名称",
            "最新价B",
            "涨跌幅B",
            "A股代码",
            "A股名称",
            "最新价A",
            "涨跌幅A",
            "比价",
        ]
    ]
    temp_df["最新价B"] = pd.to_numeric(temp_df["最新价B"], errors="coerce") / 100
    temp_df["涨跌幅B"] = pd.to_numeric(temp_df["涨跌幅B"], errors="coerce") / 100
    temp_df["最新价A"] = pd.to_numeric(temp_df["最新价A"], errors="coerce") / 100
    temp_df["涨跌幅A"] = pd.to_numeric(temp_df["涨跌幅A"], errors="coerce") / 100
    temp_df["比价"] = pd.to_numeric(temp_df["比价"], errors="coerce") / 100
    return temp_df


def stock_zh_b_spot_em() -> pd.DataFrame:
    """
    东方财富网- B 股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://28.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:0 t:7,m:1 t:3",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20"
        ",f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "5分钟涨跌",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "总市值",
        "流通市值",
        "涨速",
        "市净率",
        "60日涨跌幅",
        "年初至今涨跌幅",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
        "-",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
            "换手率",
            "市盈率-动态",
            "市净率",
            "总市值",
            "流通市值",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
    temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
    temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
    temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
    temp_df["年初至今涨跌幅"] = pd.to_numeric(
        temp_df["年初至今涨跌幅"], errors="coerce"
    )
    return temp_df


def stock_zh_a_hist(
    symbol: str = "000001",
    period: str = "daily",
    start_date: str = "19700101",
    end_date: str = "20500101",
    adjust: str = "",
    timeout: float = None,
) -> pd.DataFrame:
    """
    东方财富网-行情首页-沪深京 A 股-每日行情
    https://quote.eastmoney.com/concept/sh603777.html?from=classic
    :param symbol: 股票代码
    :type symbol: str
    :param period: choice of {'daily', 'weekly', 'monthly'}
    :type period: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"}
    :type adjust: str
    :param timeout: choice of None or a positive float number
    :type timeout: float
    :return: 每日行情
    :rtype: pandas.DataFrame
    """
    market_code = 1 if symbol.startswith("6") else 0
    adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
    period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
    url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
    params = {
        "fields1": "f1,f2,f3,f4,f5,f6",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f116",
        "ut": "7eea3edcaed734bea9cbfc24409ed989",
        "klt": period_dict[period],
        "fqt": adjust_dict[adjust],
        "secid": f"{market_code}.{symbol}",
        "beg": start_date,
        "end": end_date,
    }
    r = requests.get(url, params=params, timeout=timeout)
    data_json = r.json()
    if not (data_json["data"] and data_json["data"]["klines"]):
        return pd.DataFrame()
    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
    temp_df["股票代码"] = symbol
    temp_df.columns = [
        "日期",
        "开盘",
        "收盘",
        "最高",
        "最低",
        "成交量",
        "成交额",
        "振幅",
        "涨跌幅",
        "涨跌额",
        "换手率",
        "股票代码",
    ]
    temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df = temp_df[
        [
            "日期",
            "股票代码",
            "开盘",
            "收盘",
            "最高",
            "最低",
            "成交量",
            "成交额",
            "振幅",
            "涨跌幅",
            "涨跌额",
            "换手率",
        ]
    ]
    return temp_df


def stock_zh_a_hist_min_em(
    symbol: str = "000001",
    start_date: str = "1979-09-01 09:32:00",
    end_date: str = "2222-01-01 09:32:00",
    period: str = "5",
    adjust: str = "",
) -> pd.DataFrame:
    """
    东方财富网-行情首页-沪深京 A 股-每日分时行情
    https://quote.eastmoney.com/concept/sh603777.html?from=classic
    :param symbol: 股票代码
    :type symbol: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :param period: choice of {'1', '5', '15', '30', '60'}
    :type period: str
    :param adjust: choice of {'', 'qfq', 'hfq'}
    :type adjust: str
    :return: 每日分时行情
    :rtype: pandas.DataFrame
    """
    market_code = 1 if symbol.startswith("6") else 0
    adjust_map = {
        "": "0",
        "qfq": "1",
        "hfq": "2",
    }
    if period == "1":
        url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get"
        params = {
            "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
            "fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
            "ut": "7eea3edcaed734bea9cbfc24409ed989",
            "ndays": "5",
            "iscr": "0",
            "secid": f"{market_code}.{symbol}",
        }
        r = requests.get(url, timeout=15, params=params)
        data_json = r.json()
        temp_df = pd.DataFrame(
            [item.split(",") for item in data_json["data"]["trends"]]
        )
        temp_df.columns = [
            "时间",
            "开盘",
            "收盘",
            "最高",
            "最低",
            "成交量",
            "成交额",
            "均价",
        ]
        temp_df.index = pd.to_datetime(temp_df["时间"])
        temp_df = temp_df[start_date:end_date]
        temp_df.reset_index(drop=True, inplace=True)
        temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
        temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
        temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
        temp_df["均价"] = pd.to_numeric(temp_df["均价"], errors="coerce")
        temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
        return temp_df
    else:
        url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
        params = {
            "fields1": "f1,f2,f3,f4,f5,f6",
            "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
            "ut": "7eea3edcaed734bea9cbfc24409ed989",
            "klt": period,
            "fqt": adjust_map[adjust],
            "secid": f"{market_code}.{symbol}",
            "beg": "0",
            "end": "20500000",
        }
        r = requests.get(url, timeout=15, params=params)
        data_json = r.json()
        temp_df = pd.DataFrame(
            [item.split(",") for item in data_json["data"]["klines"]]
        )
        temp_df.columns = [
            "时间",
            "开盘",
            "收盘",
            "最高",
            "最低",
            "成交量",
            "成交额",
            "振幅",
            "涨跌幅",
            "涨跌额",
            "换手率",
        ]
        temp_df.index = pd.to_datetime(temp_df["时间"])
        temp_df = temp_df[start_date:end_date]
        temp_df.reset_index(drop=True, inplace=True)
        temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
        temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
        temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
        temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
        temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
        temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
        temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
        temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
        temp_df = temp_df[
            [
                "时间",
                "开盘",
                "收盘",
                "最高",
                "最低",
                "涨跌幅",
                "涨跌额",
                "成交量",
                "成交额",
                "振幅",
                "换手率",
            ]
        ]
        return temp_df


def stock_zh_a_hist_pre_min_em(
    symbol: str = "000001",
    start_time: str = "09:00:00",
    end_time: str = "15:50:00",
) -> pd.DataFrame:
    """
    东方财富网-行情首页-沪深京 A 股-每日分时行情包含盘前数据
    https://quote.eastmoney.com/concept/sh603777.html?from=classic
    :param symbol: 股票代码
    :type symbol: str
    :param start_time: 开始时间
    :type start_time: str
    :param end_time: 结束时间
    :type end_time: str
    :return: 每日分时行情包含盘前数据
    :rtype: pandas.DataFrame
    """
    market_code = 1 if symbol.startswith("6") else 0
    url = "https://push2.eastmoney.com/api/qt/stock/trends2/get"
    params = {
        "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
        "ndays": "1",
        "iscr": "1",
        "iscca": "0",
        "secid": f"{market_code}.{symbol}",
    }
    r = requests.get(url, timeout=15, params=params)
    data_json = r.json()
    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["trends"]])
    temp_df.columns = [
        "时间",
        "开盘",
        "收盘",
        "最高",
        "最低",
        "成交量",
        "成交额",
        "最新价",
    ]
    temp_df.index = pd.to_datetime(temp_df["时间"])
    date_format = temp_df.index[0].date().isoformat()
    temp_df = temp_df[date_format + " " + start_time : date_format + " " + end_time]
    temp_df.reset_index(drop=True, inplace=True)
    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
    return temp_df


def stock_hk_spot_em() -> pd.DataFrame:
    """
    东方财富网-港股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hk_stocks
    :return: 港股-实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://72.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,"
        "f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "_",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "_",
        "_",
        "_",
        "市净率",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌额",
            "涨跌幅",
            "今开",
            "最高",
            "最低",
            "昨收",
            "成交量",
            "成交额",
        ]
    ]
    temp_df["序号"] = pd.to_numeric(temp_df["序号"], errors="coerce")
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    return temp_df


def stock_hk_main_board_spot_em() -> pd.DataFrame:
    """
    东方财富网-港股-主板-实时行情
    https://quote.eastmoney.com/center/gridlist.html#hk_mainboard
    :return: 港股-主板-实时行情
    :rtype: pandas.DataFrame
    """
    url = "https://81.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:128 t:3",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,"
        "f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率-动态",
        "量比",
        "_",
        "代码",
        "_",
        "名称",
        "最高",
        "最低",
        "今开",
        "昨收",
        "_",
        "_",
        "_",
        "市净率",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
    ]
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌额",
            "涨跌幅",
            "今开",
            "最高",
            "最低",
            "昨收",
            "成交量",
            "成交额",
        ]
    ]
    temp_df["序号"] = pd.to_numeric(temp_df["序号"], errors="coerce")
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    return temp_df


def stock_hk_hist(
    symbol: str = "00593",
    period: str = "daily",
    start_date: str = "19700101",
    end_date: str = "22220101",
    adjust: str = "",
) -> pd.DataFrame:
    """
    东方财富网-行情-港股-每日行情
    https://quote.eastmoney.com/hk/08367.html
    :param symbol: 港股-每日行情
    :type symbol: str
    :param period: choice of {'daily', 'weekly', 'monthly'}
    :type period: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :param adjust: choice of {"qfq": "1", "hfq": "2", "": "不复权"}
    :type adjust: str
    :return: 每日行情
    :rtype: pandas.DataFrame
    """
    adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
    period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
    url = "https://33.push2his.eastmoney.com/api/qt/stock/kline/get"
    params = {
        "secid": f"116.{symbol}",
        "fields1": "f1,f2,f3,f4,f5,f6",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
        "klt": period_dict[period],
        "fqt": adjust_dict[adjust],
        "end": "20500000",
        "lmt": "1000000",
    }
    r = requests.get(url, timeout=15, params=params)
    data_json = r.json()
    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
    if temp_df.empty:
        return pd.DataFrame()
    temp_df.columns = [
        "日期",
        "开盘",
        "收盘",
        "最高",
        "最低",
        "成交量",
        "成交额",
        "振幅",
        "涨跌幅",
        "涨跌额",
        "换手率",
    ]
    temp_df.index = pd.to_datetime(temp_df["日期"], errors="coerce")
    temp_df = temp_df[start_date:end_date]
    if temp_df.empty:
        return pd.DataFrame()
    temp_df.reset_index(inplace=True, drop=True)
    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
    return temp_df


def stock_hk_hist_min_em(
    symbol: str = "01611",
    period: str = "1",
    adjust: str = "",
    start_date: str = "1979-09-01 09:32:00",
    end_date: str = "2222-01-01 09:32:00",
) -> pd.DataFrame:
    """
    东方财富网-行情-港股-每日分时行情
    https://quote.eastmoney.com/hk/00948.html
    :param symbol: 股票代码
    :type symbol: str
    :param period: choice of {'1', '5', '15', '30', '60'}
    :type period: str
    :param adjust: choice of {'', 'qfq', 'hfq'}
    :type adjust: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :return: 每日分时行情
    :rtype: pandas.DataFrame
    """
    adjust_map = {
        "": "0",
        "qfq": "1",
        "hfq": "2",
    }
    if period == "1":
        url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get"
        params = {
            "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
            "fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
            "iscr": "0",
            "ndays": "5",
            "secid": f"116.{symbol}",
        }
        r = requests.get(url, timeout=15, params=params)
        data_json = r.json()
        temp_df = pd.DataFrame(
            [item.split(",") for item in data_json["data"]["trends"]]
        )
        temp_df.columns = [
            "时间",
            "开盘",
            "收盘",
            "最高",
            "最低",
            "成交量",
            "成交额",
            "最新价",
        ]
        temp_df.index = pd.to_datetime(temp_df["时间"])
        temp_df = temp_df[start_date:end_date]
        temp_df.reset_index(drop=True, inplace=True)
        temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
        temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
        temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
        temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
        temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
        return temp_df
    else:
        url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
        params = {
            "fields1": "f1,f2,f3,f4,f5,f6",
            "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
            "ut": "bd1d9ddb04089700cf9c27f6f7426281",
            "klt": period,
            "fqt": adjust_map[adjust],
            "secid": f"116.{symbol}",
            "beg": "0",
            "end": "20500000",
        }
        r = requests.get(url, timeout=15, params=params)
        data_json = r.json()
        temp_df = pd.DataFrame(
            [item.split(",") for item in data_json["data"]["klines"]]
        )
        temp_df.columns = [
            "时间",
            "开盘",
            "收盘",
            "最高",
            "最低",
            "成交量",
            "成交额",
            "振幅",
            "涨跌幅",
            "涨跌额",
            "换手率",
        ]
        temp_df.index = pd.to_datetime(temp_df["时间"])
        temp_df = temp_df[start_date:end_date]
        temp_df.reset_index(drop=True, inplace=True)
        temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
        temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
        temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
        temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
        temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
        temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
        temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
        temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
        temp_df = temp_df[
            [
                "时间",
                "开盘",
                "收盘",
                "最高",
                "最低",
                "涨跌幅",
                "涨跌额",
                "成交量",
                "成交额",
                "振幅",
                "换手率",
            ]
        ]
    return temp_df


def stock_us_spot_em() -> pd.DataFrame:
    """
    东方财富网-美股-实时行情
    https://quote.eastmoney.com/center/gridlist.html#us_stocks
    :return: 美股-实时行情; 延迟 15 min
    :rtype: pandas.DataFrame
    """
    url = "https://72.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f12",
        "fs": "m:105,m:106,m:107",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,"
        "f21,f23,f24,f25,f26,f22,f33,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.columns = [
        "序号",
        "_",
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "_",
        "_",
        "_",
        "简称",
        "编码",
        "名称",
        "最高价",
        "最低价",
        "开盘价",
        "昨收价",
        "总市值",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "_",
        "市盈率",
        "_",
        "_",
        "_",
        "_",
        "_",
    ]
    temp_df["代码"] = temp_df["编码"].astype(str) + "." + temp_df["简称"]
    temp_df = temp_df[
        [
            "序号",
            "名称",
            "最新价",
            "涨跌额",
            "涨跌幅",
            "开盘价",
            "最高价",
            "最低价",
            "昨收价",
            "总市值",
            "市盈率",
            "成交量",
            "成交额",
            "振幅",
            "换手率",
            "代码",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
    temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
    temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
    temp_df["昨收价"] = pd.to_numeric(temp_df["昨收价"], errors="coerce")
    temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
    temp_df["市盈率"] = pd.to_numeric(temp_df["市盈率"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    return temp_df


def stock_us_hist(
    symbol: str = "105.MSFT",
    period: str = "daily",
    start_date: str = "19700101",
    end_date: str = "22220101",
    adjust: str = "",
) -> pd.DataFrame:
    """
    东方财富网-行情-美股-每日行情
    https://quote.eastmoney.com/us/ENTX.html#fullScreenChart
    :param symbol: 股票代码; 此股票代码需要通过调用 ak.stock_us_spot_em() 的 `代码` 字段获取
    :type symbol: str
    :param period: choice of {'daily', 'weekly', 'monthly'}
    :type period: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :param adjust: choice of {"qfq": "1", "hfq": "2", "": "不复权"}
    :type adjust: str
    :return: 每日行情
    :rtype: pandas.DataFrame
    """
    period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
    adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
    url = "https://63.push2his.eastmoney.com/api/qt/stock/kline/get"
    params = {
        "secid": f"{symbol}",
        "fields1": "f1,f2,f3,f4,f5,f6",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
        "klt": period_dict[period],
        "fqt": adjust_dict[adjust],
        "end": "20500000",
        "lmt": "1000000",
    }
    r = requests.get(url, timeout=15, params=params)
    data_json = r.json()
    if not data_json["data"]["klines"]:
        return pd.DataFrame()
    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
    temp_df.columns = [
        "日期",
        "开盘",
        "收盘",
        "最高",
        "最低",
        "成交量",
        "成交额",
        "振幅",
        "涨跌幅",
        "涨跌额",
        "换手率",
    ]
    temp_df.index = pd.to_datetime(temp_df["日期"], errors="coerce")
    temp_df = temp_df[start_date:end_date]
    temp_df.reset_index(inplace=True, drop=True)
    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
    temp_df.sort_values(["日期"], inplace=True, ignore_index=True)
    return temp_df


def stock_us_hist_min_em(
    symbol: str = "105.ATER",
    start_date: str = "1979-09-01 09:32:00",
    end_date: str = "2222-01-01 09:32:00",
) -> pd.DataFrame:
    """
    东方财富网-行情首页-美股-每日分时行情
    https://quote.eastmoney.com/us/ATER.html
    :param symbol: 股票代码
    :type symbol: str
    :param start_date: 开始日期
    :type start_date: str
    :param end_date: 结束日期
    :type end_date: str
    :return: 每日分时行情
    :rtype: pandas.DataFrame
    """
    url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get"
    params = {
        "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
        "iscr": "0",
        "ndays": "5",
        "secid": f"{symbol.split('.')[0]}.{symbol.split('.')[1]}",
    }
    r = requests.get(url, params=params, timeout=15)
    data_json = r.json()
    if not data_json["data"]["trends"]:
        return pd.DataFrame()
    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["trends"]])
    temp_df.columns = [
        "时间",
        "开盘",
        "收盘",
        "最高",
        "最低",
        "成交量",
        "成交额",
        "最新价",
    ]
    temp_df.index = pd.to_datetime(temp_df["时间"], errors="coerce")
    temp_df = temp_df[start_date:end_date]
    temp_df.reset_index(drop=True, inplace=True)
    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["时间"] = pd.to_datetime(temp_df["时间"], errors="coerce").astype(str)
    return temp_df


if __name__ == "__main__":
    stock_zh_a_spot_em_df = stock_zh_a_spot_em()
    print(stock_zh_a_spot_em_df)

    stock_sh_a_spot_em_df = stock_sh_a_spot_em()
    print(stock_sh_a_spot_em_df)

    stock_sz_a_spot_em_df = stock_sz_a_spot_em()
    print(stock_sz_a_spot_em_df)

    stock_bj_a_spot_em_df = stock_bj_a_spot_em()
    print(stock_bj_a_spot_em_df)

    stock_new_a_spot_em_df = stock_new_a_spot_em()
    print(stock_new_a_spot_em_df)

    stock_cy_a_spot_em_df = stock_cy_a_spot_em()
    print(stock_cy_a_spot_em_df)

    stock_kc_a_spot_em_df = stock_kc_a_spot_em()
    print(stock_kc_a_spot_em_df)

    stock_zh_b_spot_em_df = stock_zh_b_spot_em()
    print(stock_zh_b_spot_em_df)

    stock_hk_spot_em_df = stock_hk_spot_em()
    print(stock_hk_spot_em_df)

    stock_hk_main_board_spot_em_df = stock_hk_main_board_spot_em()
    print(stock_hk_main_board_spot_em_df)

    stock_zh_a_hist_df = stock_zh_a_hist(
        symbol="600734",
        period="daily",
        start_date="20050501",
        end_date="20250304",
        adjust="hfq",
    )
    print(stock_zh_a_hist_df)

    stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(symbol="603777", period="1")
    print(stock_zh_a_hist_min_em_df)

    stock_zh_a_hist_pre_min_em_df = stock_zh_a_hist_pre_min_em(symbol="000001")
    print(stock_zh_a_hist_pre_min_em_df)

    stock_hk_hist_df = stock_hk_hist(
        symbol="00593",
        period="daily",
        start_date="19700101",
        end_date="22220101",
        adjust="",
    )
    print(stock_hk_hist_df)

    stock_hk_hist_qfq_df = stock_hk_hist(
        symbol="00593",
        period="daily",
        start_date="19700101",
        end_date="22220101",
        adjust="qfq",
    )
    print(stock_hk_hist_qfq_df)

    stock_hk_hist_hfq_df = stock_hk_hist(
        symbol="00593",
        period="daily",
        start_date="19700101",
        end_date="22220101",
        adjust="hfq",
    )
    print(stock_hk_hist_hfq_df)

    stock_us_spot_em_df = stock_us_spot_em()
    print(stock_us_spot_em_df)

    stock_us_hist_df = stock_us_hist(
        symbol="106.TTE",
        period="daily",
        start_date="20200101",
        end_date="20240414",
        adjust="qfq",
    )
    print(stock_us_hist_df)

    stock_zh_a_spot_em_df = stock_zh_a_spot_em()
    print(stock_zh_a_spot_em_df)

    stock_hk_hist_min_em_df = stock_hk_hist_min_em(symbol="01611")
    print(stock_hk_hist_min_em_df)

    stock_us_spot_em_df = stock_us_spot_em()
    print(stock_us_spot_em_df)

    stock_us_hist_min_em_df = stock_us_hist_min_em(symbol="106.TTE")
    print(stock_us_hist_min_em_df)

    stock_us_hist_min_em_df = stock_us_hist_min_em(symbol="105.ATER")
    print(stock_us_hist_min_em_df)

    stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(
        symbol="300364",
        start_date="2025-03-07 09:30:00",
        end_date="2025-03-07 15:00:00",
        period="5",
        adjust="hfq",
    )
    print(stock_zh_a_hist_min_em_df)

    stock_zh_a_hist_df = stock_zh_a_hist(
        symbol="833454",
        period="daily",
        start_date="20170301",
        end_date="20241115",
        adjust="hfq",
    )
    print(stock_zh_a_hist_df)

    stock_hk_hist_min_em_df = stock_hk_hist_min_em(
        symbol="01611",
        period="1",
        adjust="",
        start_date="2025-03-07 09:30:00",
        end_date="2025-03-07 18:32:00",
    )
    print(stock_hk_hist_min_em_df)

    stock_zh_b_spot_em_df = stock_zh_b_spot_em()
    print(stock_zh_b_spot_em_df)
