import datetime

import pandas as pd
from pathlib import Path

from loguru import logger


# ================= 辅助函数 =================

def is_bullish(candle):
    """判断是否是阳线（开盘价 < 收盘价，即涨）"""
    return float(candle['open']) < float(candle['close'])


def is_bearish(candle):
    """判断是否是阴线（开盘价 > 收盘价，即跌）"""
    return float(candle['open']) > float(candle['close'])


def check_signal(prev, curr):
    """
    判断是否出现包住形态，返回信号类型和方向：
    1. 前跌后涨包住 -> 做多信号 (bear_bull_engulf)
    2. 前涨后跌包住 -> 做空信号 (bull_bear_engulf)
    3. 前涨后涨包住 -> 做多信号 (bull_bull_engulf)
    4. 前跌后跌包住 -> 做空信号 (bear_bear_engulf)
    """
    p_open, p_close = float(prev['open']), float(prev['close'])  # 前一笔
    c_open, c_close = float(curr['open']), float(curr['close'])  # 当前一笔

    # 情况1：前跌后涨，且涨线包住前跌线 -> 做多信号
    if is_bullish(curr) and is_bearish(prev):
        if c_open <= p_close and c_close >= p_open:
            return "long", "bear_bull_engulf"

    # 情况2：前涨后跌，且跌线包住前涨线 -> 做空信号
    if is_bearish(curr) and is_bullish(prev):
        if c_open >= p_close and c_close <= p_open:
            return "short", "bull_bear_engulf"

    # # 情况3：前涨后涨，且后涨线包住前涨线 -> 做多信号
    # if is_bullish(curr) and is_bullish(prev):
    #     if c_open < p_open and c_close > p_close:
    #         return "long", "bull_bull_engulf"

    # # 情况4：前跌后跌，且后跌线包住前跌线 -> 做空信号
    # if is_bearish(curr) and is_bearish(prev):
    #     if c_open > p_open and c_close < p_close:
    #         return "short", "bear_bear_engulf"

    return None, None


def simulate_trade(direction, entry_price, future_candles, take_profit_diff=30, stop_loss_diff=-10):
    """
    模拟交易（逐根K线回测）
    改进版：考虑开盘跳空触发止盈/止损的情况
    """
    for candle in future_candles:
        open_p = float(candle['open'])
        high = float(candle['high'])
        low = float(candle['low'])
        close = float(candle['close'])

        if direction == "long":
            tp_price = entry_price + take_profit_diff
            sl_price = entry_price + stop_loss_diff

            # 🧩 开盘就跳空止盈
            if open_p >= tp_price:
                return open_p, open_p - entry_price, candle['id']

            # 🧩 开盘就跳空止损
            if open_p <= sl_price:
                return open_p, open_p - entry_price, candle['id']

            # 正常区间内触发止盈/止损
            if high >= tp_price:
                return tp_price, take_profit_diff, candle['id']
            if low <= sl_price:
                return sl_price, stop_loss_diff, candle['id']

        elif direction == "short":
            tp_price = entry_price - take_profit_diff
            sl_price = entry_price - stop_loss_diff

            # 🧩 开盘就跳空止盈
            if open_p <= tp_price:
                return open_p, entry_price - open_p, candle['id']

            # 🧩 开盘就跳空止损
            if open_p >= sl_price:
                return open_p, entry_price - open_p, candle['id']

            # 正常区间内触发止盈/止损
            if low <= tp_price:
                return tp_price, take_profit_diff, candle['id']
            if high >= sl_price:
                return sl_price, stop_loss_diff, candle['id']

    # 未触发止盈止损，按最后收盘价平仓
    final_price = float(future_candles[-1]['close'])
    if direction == "long":
        diff_money = final_price - entry_price
    else:
        diff_money = entry_price - final_price

    return final_price, diff_money, future_candles[-1]['id']



def fetch_kline(day: int, year: int = 2025, month: int = 9, file_path: str = "stock_data.xlsx") -> list[dict]:
    """
    获取指定日期的分钟级 K 线数据。

    参数:
        day (int): 日
        year (int): 年（默认 2025）
        month (int): 月（默认 9）
        file_path (str): Excel 文件路径

    返回:
        list[dict]: 当日的 K 线记录（按时间升序且去重）
    """
    try:
        # 验证文件路径
        file = Path(file_path)
        if not file.exists():
            raise FileNotFoundError(f"未找到文件: {file.resolve()}")

        # 计算起止时间戳
        start_of_day = datetime.datetime(year, month, day)
        end_of_day = start_of_day + pd.Timedelta(days=1) - pd.Timedelta(microseconds=1)
        start_ts, end_ts = int(start_of_day.timestamp() * 1000), int(end_of_day.timestamp() * 1000)

        # 读取 Excel
        df = pd.read_excel(file)

        # 校验列名
        if 'id' not in df.columns:
            raise KeyError("Excel 文件缺少必要列：'id'")

        # 筛选并排序 + 去重
        filtered_df = (
            df.loc[df['id'].between(start_ts, end_ts)]
            .sort_values('id')
            .drop_duplicates(subset='id', keep='first')
        )

        # 转换为字典列表
        return filtered_df.to_dict(orient='records')

    except Exception as e:
        print(f"❌ 错误: {e}")
        return []


if __name__ == '__main__':
    datas = []
    for i in range(1, 31):
        data = fetch_kline(year=2025, month=9, day=i)
        datas.extend(data)

        print(i)

    print(len(datas))

    datas = sorted(datas, key=lambda x: x["id"])

    zh_project = 0  # 累计盈亏
    all_trades = []  # 保存所有交易明细

    # 四种信号类型的统计
    signal_stats = {
        "bear_bull_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "涨包跌"},
        "bull_bear_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "跌包涨"},
        # "bull_bull_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "涨包涨"},
        # "bear_bear_engulf": {"count": 0, "wins": 0, "total_profit": 0, "name": "跌包跌"}
    }

    daily_signals = 0  # 信号总数
    daily_wins = 0
    daily_profit = 0  # 价差总和

    # 遍历每根K线，寻找信号
    for idx in range(1, len(datas) - 2):  # 留出未来K线
        prev, curr = datas[idx - 1], datas[idx]  # 前一笔，当前一笔
        entry_candle = datas[idx + 1]  # 下一根开盘价作为入场价
        future_candles = datas[idx + 2:]  # 未来行情

        entry_open = float(entry_candle['open'])  # 开仓价格
        direction, signal_type = check_signal(prev, curr)  # 判断开仓方向和信号类型

        if direction and signal_type:
            daily_signals += 1

            exit_price, diff, exit_time = simulate_trade(direction, entry_open, future_candles, take_profit_diff=30,
                                                         stop_loss_diff=-5)

            # 统计该信号类型的表现
            signal_stats[signal_type]["count"] += 1
            signal_stats[signal_type]["total_profit"] += diff
            if diff > 0:
                signal_stats[signal_type]["wins"] += 1
                daily_wins += 1

            daily_profit += diff

            # 将时间戳转换为本地时间
            local_time = datetime.datetime.fromtimestamp(int(entry_candle['id']) / 1000)
            formatted_time = local_time.strftime("%Y-%m-%d %H:%M:%S")

            # 保存交易详情
            all_trades.append(
                (
                    f"{formatted_time}号",
                    "做多" if direction == "long" else "做空",
                    signal_stats[signal_type]["name"],
                    entry_open,
                    exit_price,
                    diff,
                    exit_time
                )
            )

    # ===== 输出每笔交易详情 =====
    logger.info("===== 每笔交易详情 =====")
    n = n1 = 0  # n = 总盈利，n1 = 总手续费
    for date, direction, signal_name, entry, exit, diff, end_time in all_trades:
        profit_amount = diff / entry * 10000  # 计算盈利金额
        close_fee = 10000 / entry * exit * 0.0005  # 平仓手续费

        logger.info(
            f"{date} {direction}({signal_name}) 入场={entry:.2f} 出场={exit:.2f} 出场时间={end_time} "
            f"差价={diff:.2f} 盈利={profit_amount:.2f} "
            f"开仓手续费=5u 平仓手续费={close_fee:.2f}"
        )
        n1 += 5 + close_fee
        n += profit_amount

    print(f'一共笔数：{len(all_trades)}')
    print(f"一共盈利：{n:.2f}")
    print(f'一共手续费：{n1:.2f}')
