import time
from datetime import datetime, timedelta
import logging
from typing import Optional
import pandas as pd
import okx.MarketData as Market

logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')

class DataMonitor:
    def __init__(self, 
        api_key: str, 
        secret_key: str, 
        passphrase: str, 
        sandbox: bool = True):
        flag = "1" if sandbox else "0" # 0:实盘环境 1:沙盒环境
        self.market_api = Market.MarketAPI(
            api_key=api_key, api_secret_key=secret_key, passphrase=passphrase, 
            flag=flag
        )

    def get_historical_kline_data(self, symbol: str = None, start: str = None, bar: str = '1m', limit: int = 100, end_time: int = None) -> Optional[pd.DataFrame]:
        """
        获取历史K线数据，支持start为北京时间字符串（%Y-%m-%d %H:%M:%S）或UTC毫秒级时间戳
        :param symbol: 交易对
        :param start: 起始时间（北京时间字符串或UTC毫秒级时间戳）
        :param bar: K线周期
        :param limit: 每次请求数量
        :param end_time: 结束时间（毫秒级timestamp），默认当前时间
        :return: pd.DataFrame
        """
        from core.utils import datetime_to_timestamp
        if symbol is None:
            symbol = "XCH-USDT"
        if end_time is None:
            end_time = int(time.time() * 1000)  # 当前时间（毫秒）
        # 处理start参数
        if start is None:
            # 默认两个月前
            two_months_ago = datetime.now() - timedelta(days=60)
            start_time = int(two_months_ago.timestamp() * 1000)
        else:
            try:
                # 判断是否就是timestamp整型数据
                if isinstance(start, int):
                    start_time = start
                # 判断是否为纯数字（UTC毫秒级timestamp）
                elif start.isdigit():
                    start_time = int(start)
                else:
                    # 按北京时间字符串处理，转换为毫秒级timestamp
                    start_time = datetime_to_timestamp(start)
            except Exception as e:
                logging.error(f"start参数解析失败: {e}")
                return None
        columns = ["timestamp", "open", "high", "low", "close", "volume", "volCcy", "volCCyQuote", "confirm"]
        all_data = []
        latest_timestamp = -1
        while start_time < end_time:
            try:
                # after，真实逻辑是获得指定时间之前的数据 ！！！
                response = self.market_api.get_history_candlesticks(
                    instId=symbol,
                    after=end_time,  # 获取指定时间之前的数据,
                    bar=bar,
                    limit=str(limit)
                )
                if response["code"] != "0" or not response["data"]:
                    logging.warning(f"请求失败或无数据: {response.get('msg', 'No message')}")
                    break
                candles = response["data"]

                from_time = int(candles[-1][0])
                to_time = int(candles[0][0])
                if latest_timestamp == -1:
                    latest_timestamp = from_time
                else:
                    if latest_timestamp > from_time:
                        latest_timestamp = from_time
                    else:
                        logging.warning(f"上一次数据最早时间戳 {latest_timestamp} 小于等于 from_time {from_time}， 停止获取数据")
                        break
                from_time_str = pd.to_datetime(from_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
                to_time_str = pd.to_datetime(to_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
                logging.info(f"已获取 {len(candles)} 条数据，从: {from_time_str} 到: {to_time_str}")
                if from_time < start_time:
                    start_time_str = pd.to_datetime(start_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
                    logging.warning(f"本轮获取数据最早时间 {from_time_str} 早于 此次数据获取起始时间 {start_time_str}， 停止获取数据")
                    # candels中仅保留start_time之后的数据
                    candles = [candle for candle in candles if int(candle[0]) >= start_time]
                    all_data.extend(candles)
                    break
                else:
                    # 更新 end_time 为本次请求中最早的时间戳
                    end_time = from_time - 1  # 减 1 毫秒以避免重复
                    all_data.extend(candles)
                    time.sleep(0.2)
            except Exception as e:
                logging.error(f"请求出错: {e}")
                break

        if all_data:
            df = pd.DataFrame(all_data, columns=columns)
            df = df[df['confirm'] == '1']
            for col in ['open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote']:
                df[col] = pd.to_numeric(df[col], errors='coerce')
            dt_series = pd.to_datetime(df['timestamp'].astype(int), unit='ms', utc=True, errors='coerce').dt.tz_convert('Asia/Shanghai')
            df['date_time'] = dt_series.dt.strftime('%Y-%m-%d %H:%M:%S')
            # 将timestamp转换为整型
            df['timestamp'] = df['timestamp'].astype(int)
            # 添加虚拟货币名称列，内容为symbol
            df['symbol'] = symbol
            # 添加bar列，内容为bar
            df['bar'] = bar
            df = df[['symbol', 'bar', 'timestamp', 'date_time', 'open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote']]
            df.sort_values('timestamp', inplace=True)
            df.reset_index(drop=True, inplace=True)
            logging.info(f"总计获取 {len(df)} 条 K 线数据（仅confirm=1）")
            return df
        else:
            logging.warning("未获取到数据")
            return None