#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Created on 2023/12/06
@author: Monday
@group : waditu
Desc: 腾讯-股票-实时行情-成交明细
成交明细-每个交易日 16:00 提供当日数据
港股报价延时 15 分钟
"""
import warnings
import pandas as pd
import requests
from io import StringIO
from tushare.util.verify_token import require_permission
from tushare.util.format_stock_code import format_stock_code
from tushare.stock.rtq_vars import zh_sina_a_stock_cookies, zh_sina_a_stock_headers
import time
import json
from typing import Optional
from tushare.util.form_date import get_current_date
from tushare.stock import rtq_vars
from tushare.util.format_stock_code import symbol_verify

headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"
}


@require_permission(event_name="realtime_tick", event_detail="个股历史分笔数据")
def realtime_tick(ts_code: str = "000001.SZ", src: Optional[str] = "tx",
                  page_count: Optional[int] = None) -> pd.DataFrame:
    """
    历史分笔数据
    :param ts_code: 股票代码
    :type ts_code: str
    :param src: 来源  腾讯财经tx   新浪财经sina
    :type src: str
    :param page_count: 限制页数
    :type page_count: str
    :return: 历史分笔数据
    :rtype: pandas.DataFrame
        1、TIME : 成交时间
        2、PRICE : 成交价格
        3、PCHANGE : 涨跌幅
        4、CHANGE : 价格变动
        5、VOLUME : 成交量(手)
        6、AMOUNT : 成交额(元)
        7、TYPE : 性质
    """
    symbol = symbol_verify(ts_code)
    if src == "sina":
        return get_stock_sina_a_divide_amount(symbol, page_count)
    elif src == 'dc':
        return get_stock_dc_a_divide_amount(symbol, page_count)
    else:
        return get_stock_tx_a_divide_amount(symbol, page_count)


def get_stock_tx_a_divide_amount(symbol: str = "sz000001", page_count: Optional[int] = None) -> pd.DataFrame:
    """
    腾讯财经-历史分笔数据
    https://gu.qq.com/sz300494/gp/detail
    :param symbol: 股票代码
    :type symbol: str
    :param page_count: 限制页数
    :type page_count: str
    :return: 历史分笔数据
    :rtype: pandas.DataFrame
    """
    symbols = str(symbol).lower().split(".")
    symbol = f"{symbols[1]}{symbols[0]}"
    big_df = pd.DataFrame()
    page = 0
    warnings.warn("正在下载数据，请稍等")
    while True:
        try:
            url = "http://stock.gtimg.cn/data/index.php"
            params = {
                "appn": "detail",
                "action": "data",
                "c": symbol,
                "p": page,
            }
            r = requests.get(url, headers=headers, params=params)
            text_data = r.text
            temp_df = (
                pd.DataFrame(eval(text_data[text_data.find("["):])[1].split("|"))
                    .iloc[:, 0]
                    .str.split("/", expand=True)
            )
            page += 1
            big_df = pd.concat([big_df, temp_df], ignore_index=True)
            time.sleep(0.5)
            if page_count and page >= page_count:
                break
        except:
            break
    if not big_df.empty:
        big_df = big_df.iloc[:, 1:].copy()
        # big_df.columns = ["成交时间", "成交价格", "价格变动", "成交量", "成交金额", "性质"]
        big_df.columns = rtq_vars.TICK_COLUMNS

        big_df.reset_index(drop=True, inplace=True)
        property_map = {
            "S": "卖盘",
            "B": "买盘",
            "M": "中性盘",
        }
        # big_df["性质"] = big_df["性质"].map(property_map)
        big_df["TYPE"] = big_df["TYPE"].map(property_map)
        big_df = big_df.astype(
            {
                "TIME": str,
                "PRICE": float,
                "CHANGE": float,
                "VOLUME": int,
                "AMOUNT": int,
                "TYPE": str,
            }
        )
    return big_df


def get_stock_sina_a_divide_amount(symbol: str = "sz000001", page_count: Optional[int] = None, ) -> pd.DataFrame:
    """
    腾新浪财经-历史分笔数据
    https://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradedetail.php?symbol=sh688553
    :param symbol: 股票代码
    :type symbol: str
    :param page_count: 限制页数
    :type page_count: str
    :return: 历史分笔数据
    :rtype: pandas.DataFrame
    """
    warnings.warn("正在下载数据，请稍等")
    symbols = str(symbol).lower().split(".")
    symbol = f"{symbols[1]}{symbols[0]}"
    page = 0
    big_df = pd.DataFrame()
    while True:
        try:
            url = "https://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradedetail.php"
            params = {
                "symbol": symbol,
                "date": get_current_date(date_format="%Y-%m-%d"),
                "page": page
            }
            response = requests.get(url, headers=zh_sina_a_stock_headers, cookies=zh_sina_a_stock_cookies,
                                    params=params)
            temp_df = (pd.read_html(StringIO(response.content.decode("GBK")))[3])
            big_df = pd.concat([big_df, temp_df], ignore_index=True)
            page += 1
            if page_count and page >= page_count:
                break
        except:
            break
        time.sleep(0.5)
    if not big_df.empty:
        big_df = big_df.iloc[:, 0:].copy()
        # big_df.columns = ["成交时间", "成交价格", "涨跌幅", "价格变动", "成交量(手)", "成交额(元)", "性质"]
        big_df.columns = rtq_vars.TODAY_TICK_COLUMNS
        big_df.reset_index(drop=True, inplace=True)
        # big_df = big_df.astype(
        #     {
        #         "成交时间": str,
        #         "成交价格": float,
        #         "涨跌幅": str,
        #         "价格变动": str,
        #         "成交量(手)": int,
        #         "成交额(元)": int,
        #         "性质": str,
        #     }
        # )
        big_df = big_df.astype(
            {
                "TIME": str,
                "PRICE": float,
                "PCHANGE": str,
                "CHANGE": str,
                "VOLUME": int,
                "AMOUNT": int,
                "TYPE": str,
            }
        )
    return big_df


def __event_stream(url, params, ):
    response = requests.get(url, params=params, stream=True)
    event_data = ""

    for line in response.iter_lines():
        # 过滤掉保持连接的空行
        if line:
            event_data += line.decode() + "\n"
        elif event_data:
            yield event_data
            event_data = ""


def get_stock_dc_a_divide_amount(symbol: str = "000001", page_count: Optional[int] = None) -> pd.DataFrame:
    """
    东方财富-分时数据
    https://quote.eastmoney.com/f1.html?newcode=0.000001
    :param symbol: 股票代码
    :type symbol: str
    :return: 分时数据
    :rtype: pandas.DataFrame
    """
    symbols = str(symbol).lower().split(".")
    symbol = symbols[0]
    # print(symbol)
    market_code = 1 if symbol.startswith("6") else 0
    url = "https://70.push2.eastmoney.com/api/qt/stock/details/sse"
    params = {
        "fields1": "f1,f2,f3,f4",
        "fields2": "f51,f52,f53,f54,f55",
        "mpi": "2000",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "pos": "-0",
        "secid": f"{market_code}.{symbol}",
        "wbp2u": "|0|0|0|web",
    }

    big_df = pd.DataFrame()  # 创建一个空的 DataFrame

    for event in __event_stream(url, params):
        # 从每个事件的数据行中删除 "data: "，然后解析 JSON
        event_json = json.loads(event.replace("data: ", ""))
        # 将 JSON 数据转换为 DataFrame，然后添加到主 DataFrame 中
        temp_df = pd.DataFrame(
            [item.split(",") for item in event_json["data"]["details"]]
        )
        big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
        break

    big_df.columns = ["TIME", "PRICE", "VOLUME", "-", "TYPE"]
    big_df["TYPE"] = big_df["TYPE"].map(
        {"2": "买盘", "1": "卖盘", "4": "中性盘"}
    )
    big_df = big_df[["TIME", "PRICE", "VOLUME", "TYPE"]]
    big_df["PRICE"] = pd.to_numeric(big_df["PRICE"], errors="coerce")
    big_df["VOLUME"] = pd.to_numeric(big_df["VOLUME"], errors="coerce")

    return big_df


if __name__ == '__main__':
    df = realtime_tick(ts_code="000001.SZ", src="dc", page_count=1)
    print(help(realtime_tick))
    print(df)
    # r = get_stock_dc_a_divide_amount()
    # print(r)