"""
2023 年回测入口 - 用训练出的最优参数在 2023 全年数据上回测
"""
import json
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import pandas as pd
import numpy as np

from strategy.data_loader import load_klines
from strategy.indicators import compute_all_indicators
from strategy.strategy_signal import (
    generate_indicator_signals, compute_composite_score,
    apply_htf_filter,
)
from strategy.backtest_engine import BacktestEngine


def main():
    # 加载最佳参数
    params_path = os.path.join(os.path.dirname(__file__), 'best_params_2020_2022.json')
    if not os.path.exists(params_path):
        print(f"错误: 找不到参数文件 {params_path}")
        print("请先运行 train.py 进行训练")
        return

    with open(params_path, 'r') as f:
        params = json.load(f)

    print("=" * 70)
    print("2023 年真实回测 (样本外)")
    print("=" * 70)

    # 加载数据 (多加载一些前置数据用于指标预热)
    print("\n加载数据...")
    df_5m = load_klines('5m', '2022-11-01', '2024-01-01')
    df_1h = load_klines('1h', '2022-11-01', '2024-01-01')
    print(f"  5m: {len(df_5m)} 条, 1h: {len(df_1h)} 条")

    # 计算指标
    print("计算指标...")
    df_5m = compute_all_indicators(df_5m, params)
    df_1h = compute_all_indicators(df_1h, params)

    # 生成信号
    print("生成信号...")
    df_5m = generate_indicator_signals(df_5m, params)
    df_1h = generate_indicator_signals(df_1h, params)

    # 综合得分
    score = compute_composite_score(df_5m, params)
    score = apply_htf_filter(score, df_1h, params)

    # 截取 2023 年数据
    mask = (df_5m.index >= '2023-01-01') & (df_5m.index < '2024-01-01')
    df_2023 = df_5m.loc[mask]
    score_2023 = score.loc[mask]
    print(f"  2023年数据: {len(df_2023)} 条")

    # 回测
    print("\n开始回测...")
    engine = BacktestEngine(
        initial_capital=1000.0,
        margin_per_trade=25.0,
        leverage=50,
        fee_rate=0.0005,
        rebate_ratio=0.70,
        max_daily_drawdown=50.0,
        min_hold_bars=1,
        stop_loss_pct=params['stop_loss_pct'],
        take_profit_pct=params['take_profit_pct'],
        max_positions=int(params.get('max_positions', 3)),
    )

    result = engine.run(df_2023, score_2023, open_threshold=params['open_threshold'])

    # ============================================================
    # 输出结果
    # ============================================================
    print("\n" + "=" * 70)
    print("2023 年回测结果")
    print("=" * 70)
    print(f"  初始资金:     1000.00 U")
    print(f"  最终资金:     {result['final_capital']:.2f} U")
    print(f"  总收益:       {result['total_pnl']:.2f} U")
    print(f"  总手续费:     {result['total_fee']:.2f} U")
    print(f"  总返佣:       {result['total_rebate']:.2f} U")
    print(f"  交易次数:     {result['num_trades']}")
    print(f"  胜率:         {result['win_rate']:.2%}")
    print(f"  盈亏比:       {result['profit_factor']:.2f}")
    print(f"  日均收益:     {result['avg_daily_pnl']:.2f} U")
    print(f"  最大日回撤:   {result['max_daily_dd']:.2f} U")

    # 月度统计
    daily_pnl = result['daily_pnl']
    if daily_pnl:
        df_daily = pd.DataFrame(list(daily_pnl.items()), columns=['date', 'pnl'])
        df_daily['date'] = pd.to_datetime(df_daily['date'])
        df_daily['month'] = df_daily['date'].dt.to_period('M')
        monthly = df_daily.groupby('month')['pnl'].agg(['sum', 'count', 'mean', 'min'])
        monthly.columns = ['月收益', '交易天数', '日均收益', '最大日亏损']

        print("\n" + "-" * 70)
        print("月度统计:")
        print("-" * 70)
        for idx, row in monthly.iterrows():
            status = "✅" if row['月收益'] > 0 else "❌"
            dd_status = "✅" if row['最大日亏损'] > -50 else "⚠️"
            print(f"  {idx} | 收益: {row['月收益']:>8.2f}U | "
                  f"日均: {row['日均收益']:>7.2f}U | "
                  f"最大日亏: {row['最大日亏损']:>7.2f}U {dd_status} | {status}")

        # 日均收益是否达标
        avg_daily = df_daily['pnl'].mean()
        days_above_50 = (df_daily['pnl'] >= 50).sum()
        days_below_neg50 = (df_daily['pnl'] < -50).sum()
        print(f"\n  日均收益: {avg_daily:.2f}U {'✅ 达标' if avg_daily >= 50 else '❌ 未达标'}")
        print(f"  日收益>=50U的天数: {days_above_50} / {len(df_daily)}")
        print(f"  日回撤>50U的天数: {days_below_neg50} / {len(df_daily)}")

    # 保存逐日 PnL
    output_dir = os.path.dirname(__file__)
    if daily_pnl:
        df_daily_out = pd.DataFrame(list(daily_pnl.items()), columns=['date', 'pnl'])
        df_daily_out['cumulative_pnl'] = df_daily_out['pnl'].cumsum()
        daily_csv = os.path.join(output_dir, 'backtest_2023_daily_pnl.csv')
        df_daily_out.to_csv(daily_csv, index=False)
        print(f"\n逐日PnL已保存: {daily_csv}")

    # 保存交易记录
    if result['trades']:
        trades_data = []
        for t in result['trades']:
            trades_data.append({
                'entry_time': t.entry_time,
                'exit_time': t.exit_time,
                'direction': '多' if t.direction == 1 else '空',
                'entry_price': t.entry_price,
                'exit_price': t.exit_price,
                'pnl': round(t.pnl, 4),
                'fee': round(t.fee, 4),
                'rebate': round(t.rebate, 4),
                'holding_bars': t.holding_bars,
            })
        df_trades = pd.DataFrame(trades_data)
        trades_csv = os.path.join(output_dir, 'backtest_2023_trades.csv')
        df_trades.to_csv(trades_csv, index=False)
        print(f"交易记录已保存: {trades_csv}")

    print("\n" + "=" * 70)


if __name__ == '__main__':
    main()