#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/6/19 15:26
Desc: 东方财富网-行情首页-沪深京 A 股
"""
import requests
import pandas as pd
import time

from functools import lru_cache


def fetch_with_retries_em(url, params, max_retries=3, delay=2):
    """带重试机制的 GET 请求"""
    for attempt in range(max_retries):
        try:
            response = requests.get(url, params=params, timeout=5)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            print(f"请求失败，第 {attempt + 1} 次重试: {e}")
            time.sleep(delay)
    return None

def stock_zh_a_spot_em(fs='m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048', pz=100) -> 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
    """
    url = "http://82.push2.eastmoney.com/api/qt/clist/get"
    pn = 1  # 初始页数
    pn_max = 10000  # 设定初始最大页数
    all_data = []
    
    while pn <= pn_max:
        params = {
            "pn": str(pn),
            "pz": str(pz),
            "po": "1",
            "np": "1",
            "ut": "bd1d9ddb04089700cf9c27f6f7426281",
            "fltt": "2",
            "invt": "2",
            "fid": "f3",
            "fs": fs,
            "fields": "f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f14,f15,f16,f17,f18,f20,f21,f22,f23,f24,f25,f26,f37,f38,f39,f40,f41,f45,f46,f48,f49,f57,f61,f100,f112,f113,f114,f115,f221",
            "_": "1623833739532",
        }
        
        data_json = fetch_with_retries_em(url, params)
        if not data_json or "data" not in data_json or "diff" not in data_json["data"]:
            break
        
        diff_data = data_json["data"]["diff"]
        if not diff_data:
            break
        
        all_data.extend(diff_data)
        
        # 获取 total 数据来更新 pn_max
        if pn == 1:
            pn_max = (data_json["data"].get("total", 0) + pz - 1) // pz
            print(f'total pages: {pn_max}, total data lines: {data_json["data"].get("total", 0)}, curr lines: {len(diff_data)}, page size: {pz}')
        
        pn += 1
        time.sleep(0.5)  # 防止请求过快
    
    if not all_data:
        return pd.DataFrame()
    
    temp_df = pd.DataFrame(all_data)
    column_map = {
        "f2": "最新价", "f3": "涨跌幅", "f4": "涨跌额", "f5": "成交量", "f6": "成交额", "f7": "振幅", "f8": "换手率",
        "f9": "市盈率动", "f10": "量比", "f11": "5分钟涨跌", "f12": "代码", "f14": "名称", "f15": "最高", "f16": "最低",
        "f17": "今开", "f18": "昨收", "f20": "总市值", "f21": "流通市值", "f22": "涨速", "f23": "市净率", "f24": "60日涨跌幅",
        "f25": "年初至今涨跌幅", "f26": "上市时间", "f37": "加权净资产收益率", "f38": "总股本", "f39": "已流通股份",
        "f40": "营业收入", "f41": "营业收入同比增长", "f45": "归属净利润", "f46": "归属净利润同比增长", "f48": "每股未分配利润",
        "f49": "毛利率", "f57": "资产负债率", "f61": "每股公积金", "f100": "所处行业", "f112": "每股收益", "f113": "每股净资产",
        "f114": "市盈率静", "f115": "市盈率TTM", "f221": "报告期"
    }
    temp_df.rename(columns=column_map, inplace=True)
    
    numeric_columns = [
        "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "量比", "今开", "最高", "最低", "昨收", "涨速", "5分钟涨跌", "60日涨跌幅",
        "年初至今涨跌幅", "市盈率动", "市盈率TTM", "市盈率静", "市净率", "每股收益", "每股净资产", "每股公积金", "每股未分配利润",
        "加权净资产收益率", "毛利率", "资产负债率", "营业收入", "营业收入同比增长", "归属净利润", "归属净利润同比增长", "总股本", "已流通股份",
        "总市值", "流通市值"
    ]
    for col in numeric_columns:
        temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
    
    temp_df["报告期"] = pd.to_datetime(temp_df["报告期"], format='%Y%m%d', errors="coerce")
    temp_df["上市时间"] = pd.to_datetime(temp_df["上市时间"], format='%Y%m%d', errors="coerce")
    
    return temp_df


def stock_zh_a_spot_em_old(fs='m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048') -> 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 = "http://82.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": fs,
        "fields": "f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f14,f15,f16,f17,f18,f20,f21,f22,f23,f24,f25,f26,f37,f38,f39,f40,f41,f45,f46,f48,f49,f57,f61,f100,f112,f113,f114,f115,f221",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return pd.DataFrame()
    temp_df = pd.DataFrame(data_json["data"]["diff"])
    temp_df.columns = [
        "最新价",
        "涨跌幅",
        "涨跌额",
        "成交量",
        "成交额",
        "振幅",
        "换手率",
        "市盈率动",
        "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",
        "上市时间",
        "加权净资产收益率",
        "总股本",
        "已流通股份",
        "营业收入",
        "营业收入同比增长",
        "归属净利润",
        "归属净利润同比增长",
        "每股未分配利润",
        "毛利率",
        "资产负债率",
        "每股公积金",
        "所处行业",
        "每股收益",
        "每股净资产",
        "市盈率静",
        "市盈率TTM",
        "报告期"
    ]
    temp_df = temp_df[
        [
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "换手率",
            "量比",
            "今开",
            "最高",
            "最低",
            "昨收",
            "涨速",
            "5分钟涨跌",
            "60日涨跌幅",
            "年初至今涨跌幅",
            "市盈率动",
            "市盈率TTM",
            "市盈率静",
            "市净率",
            "每股收益",
            "每股净资产",
            "每股公积金",
            "每股未分配利润",
            "加权净资产收益率",
            "毛利率",
            "资产负债率",
            "营业收入",
            "营业收入同比增长",
            "归属净利润",
            "归属净利润同比增长",
            "报告期",
            "总股本",
            "已流通股份",
            "总市值",
            "流通市值",
            "所处行业",
            "上市时间"
        ]
    ]
    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["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["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["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["市盈率动"] = pd.to_numeric(temp_df["市盈率动"], errors="coerce")
    temp_df["市盈率TTM"] = pd.to_numeric(temp_df["市盈率TTM"], errors="coerce")
    temp_df["市盈率静"] = pd.to_numeric(temp_df["市盈率静"], errors="coerce")
    temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
    temp_df["每股收益"] = pd.to_numeric(temp_df["每股收益"], errors="coerce")
    temp_df["每股净资产"] = pd.to_numeric(temp_df["每股净资产"], errors="coerce")
    temp_df["每股公积金"] = pd.to_numeric(temp_df["每股公积金"], errors="coerce")
    temp_df["每股未分配利润"] = pd.to_numeric(temp_df["每股未分配利润"], errors="coerce")
    temp_df["加权净资产收益率"] = pd.to_numeric(temp_df["加权净资产收益率"], errors="coerce")
    temp_df["毛利率"] = pd.to_numeric(temp_df["毛利率"], errors="coerce")
    temp_df["资产负债率"] = pd.to_numeric(temp_df["资产负债率"], errors="coerce")
    temp_df["营业收入"] = pd.to_numeric(temp_df["营业收入"], errors="coerce")
    temp_df["营业收入同比增长"] = pd.to_numeric(temp_df["营业收入同比增长"], errors="coerce")
    temp_df["归属净利润"] = pd.to_numeric(temp_df["归属净利润"], errors="coerce")
    temp_df["归属净利润同比增长"] = pd.to_numeric(temp_df["归属净利润同比增长"], errors="coerce")
    temp_df["报告期"] = pd.to_datetime(temp_df["报告期"], format='%Y%m%d', errors="coerce")
    temp_df["总股本"] = pd.to_numeric(temp_df["总股本"], errors="coerce")
    temp_df["已流通股份"] = pd.to_numeric(temp_df["已流通股份"], 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["上市时间"] = pd.to_datetime(temp_df["上市时间"], format='%Y%m%d', errors="coerce")

    return temp_df

#原有版本，实现的比较繁琐，后面有个简化版本替代它。
#@lru_cache()
def code_id_map_em_older() -> dict:
    """
    东方财富-股票和市场代码
    http://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 股票和市场代码
    :rtype: dict
    """
    url = "http://80.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:1 t:2,m:1 t:23",
        "fields": "f12",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df = pd.DataFrame(data_json["data"]["diff"])
    temp_df["market_id"] = 1
    temp_df.columns = ["sh_code", "sh_id"]
    code_id_dict = dict(zip(temp_df["sh_code"], temp_df["sh_id"]))
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:0 t:6,m:0 t:80",
        "fields": "f12",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
    temp_df_sz["sz_id"] = 0
    code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["sz_id"])))
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:0 t:81 s:2048",
        "fields": "f12",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
    temp_df_sz["bj_id"] = 0
    code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["bj_id"])))
    
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:128 t:3",
        "fields": "f12",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
    temp_df_sz["hk_main"] = 116
    code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["hk_main"])))
    
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:128 t:4",
        "fields": "f12",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
    temp_df_sz["hk_cyb"] = 116
    code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["hk_cyb"])))
    
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:105,m:106,m:107",
        "fields": "f12,f13",
        "_": "1623833739532",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    if not data_json["data"]["diff"]:
        return dict()
    temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
    # 把数据保存到字典中。按照f13的值分别存储
    grouped = temp_df_sz.groupby('f13')
    for id, group in grouped:
        temp_df_sz[f"us_all_{id}"] = id
        code_id_dict.update(dict(zip(group["f12"], str(id))))
        #print(f"分组 f13 = {id}:")
        #print(group)
    #temp_df_sz["us_all"] = 105
    #code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["us_all"])))
    print(code_id_dict)
    return code_id_dict

@lru_cache()
def code_id_map_em() -> dict:
    url = "http://80.push2.eastmoney.com/api/qt/clist/get"
    pz = 200  # 固定每页 200 条
    pn = 1     # 初始页码
    pn_max = 10000  # 预设一个较大的初始值
    
    params = {
        "pn": str(pn),
        "pz": str(pz),
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "",
        "fields": "f12,f13",
        "_": "1623833739532",
    }
    
    market_fs = {
        "china_a": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
        "hk": "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2",
        "us": "m:105,m:106,m:107"
    }
    
    code_id_dict = {}
    
    for market_id, fs in market_fs.items():
        params["fs"] = fs
        pn = 1  # 每个市场都从第一页开始
        total = 0
        fetched_cnt = 0
        while pn <= pn_max:
            params["pn"] = str(pn)
            data_json = fetch_with_retries_em(url, params)
            
            if not data_json or "data" not in data_json or "diff" not in data_json["data"]:
                print(f"市场 {market_id} 数据获取失败或为空，跳过。")
                break
            
            temp_df = pd.DataFrame(data_json["data"]["diff"])
            temp_df["market_id"] = 1
            
            # 处理 total 以计算 pn_max
            if pn == 1 and "total" in data_json["data"]:
                total = int(data_json["data"]["total"])
                pn_max = (total // pz) + 1  # 计算最大页数
                print(f"市场 {market_id} 总数据量: {total}, 需要页数: {pn_max}, 当前获取数量: {len(temp_df)}, 每页最大拉取行数: {pz}")
            
            # 按 f13 进行分组并存入字典
            grouped = temp_df.groupby('f13')
            for id, group in grouped:
                code_id_dict.update(dict.fromkeys(group["f12"], id))
                fetched_cnt += len(group)
                # print(f'获取 {market_id} 股票列表，f13: {id}, 股票数: {len(group)}, 已获取总股票数: {fetched_cnt}, 总股票数: {total}')
            
            pn += 1  # 翻页继续

        print(f'获取 {market_id} 已获取总股票数: {fetched_cnt}, 总股票数: {total}')
    
    return code_id_dict

@lru_cache()
def code_id_map_em2() -> dict:
    """
    东方财富-股票和市场代码
    http://quote.eastmoney.com/center/gridlist.html#hs_a_board
    :return: 股票和市场代码
    :rtype: dict
    """
    url = "http://80.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "50000",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "fid": "f3",
        "fs": "m:1 t:2,m:1 t:23",
        "fields": "f12,f13",
        "_": "1623833739532",
    }
    market_fs = {"china_a": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
                 "hk":      "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2",
                 "us":      "m:105,m:106,m:107"}
    code_id_dict = dict()

    for market_id, fs in market_fs.items():
        params['fs'] = fs
        r = requests.get(url, params=params)
        data_json = r.json()
        if not data_json["data"]["diff"]:
            return dict()
        temp_df = pd.DataFrame(data_json["data"]["diff"])
        temp_df["market_id"] = 1
        # 把数据保存到字典中。按照f13的值分别存储
        grouped = temp_df.groupby('f13')
        for id, group in grouped:
            temp_df[f"{market_id}_{id}"] = id
            #code_id_dict.update(dict(zip(group["f12"], str(id))))
            code_id_dict.update(dict.fromkeys(group["f12"], id))
            print(f'get {market_id} stock list. f13: {id}, stock count: {len(group)}')

    return code_id_dict

def stock_zh_a_hist(
    symbol: str = "000001",
    period: str = "daily",
    start_date: str = "19700101",
    end_date: str = "20500101",
    adjust: str = "",
) -> 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
    :return: 每日行情
    :rtype: pandas.DataFrame
    """
    code_id_dict = code_id_map_em()
    adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
    period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
    url = "http://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"{code_id_dict[symbol]}.{symbol}",
        "beg": start_date,
        "end": end_date,
        "_": "1623766962675",
    }
    r = requests.get(url, params=params)
    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.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"])
    temp_df.reset_index(inplace=True, drop=True)

    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"])
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
    temp_df["最高"] = pd.to_numeric(temp_df["最高"])
    temp_df["最低"] = pd.to_numeric(temp_df["最低"])
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"])
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"])
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"])
    temp_df["换手率"] = pd.to_numeric(temp_df["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
    """
    code_id_dict = code_id_map_em()
    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"{code_id_dict[symbol]}.{symbol}",
            "_": "1623766962675",
        }
        r = requests.get(url, 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"])
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
        temp_df["最高"] = pd.to_numeric(temp_df["最高"])
        temp_df["最低"] = pd.to_numeric(temp_df["最低"])
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
        temp_df["最新价"] = pd.to_numeric(temp_df["最新价"])
        temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
        return temp_df
    else:
        url = "http://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"{code_id_dict[symbol]}.{symbol}",
            "beg": "0",
            "end": "20500000",
            "_": "1630930917857",
        }
        r = requests.get(url, 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"])
        temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
        temp_df["最高"] = pd.to_numeric(temp_df["最高"])
        temp_df["最低"] = pd.to_numeric(temp_df["最低"])
        temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
        temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
        temp_df["振幅"] = pd.to_numeric(temp_df["振幅"])
        temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"])
        temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"])
        temp_df["换手率"] = pd.to_numeric(temp_df["换手率"])
        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
    http://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
    """
    code_id_dict = code_id_map_em()
    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",
        "ut": "fa5fd1943c7b386f172d6893dbfba10b",
        "ndays": "1",
        "iscr": "1",
        "iscca": "0",
        "secid": f"{code_id_dict[symbol]}.{symbol}",
        "_": "1623766962675",
    }
    r = requests.get(url, 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"])
    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
    temp_df["最高"] = pd.to_numeric(temp_df["最高"])
    temp_df["最低"] = pd.to_numeric(temp_df["最低"])
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"])
    temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
    return temp_df


if __name__ == "__main__":

    stock_zh_a_hist_df = stock_zh_a_hist(
        symbol="000858",
        period="daily",
        start_date="20220516",
        end_date="20220722",
        adjust="",
    )
    print(stock_zh_a_hist_df)
    exit(0)

    stock_zh_a_spot_em_df = stock_zh_a_spot_em()
    print(stock_zh_a_spot_em_df)

    code_id_map_em_df = code_id_map_em()
    print(code_id_map_em_df)

    stock_zh_a_hist_df = stock_zh_a_hist(
        symbol="430090",
        period="daily",
        start_date="20220516",
        end_date="20220722",
        adjust="hfq",
    )
    print(stock_zh_a_hist_df)

    stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(symbol="833454", 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="833454")
    print(stock_zh_a_hist_pre_min_em_df)

    stock_zh_a_spot_em_df = stock_zh_a_spot_em()
    print(stock_zh_a_spot_em_df)

    stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(
        symbol="000001", period='1'
    )
    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="20211115",
        adjust="hfq",
    )
    print(stock_zh_a_hist_df)

