第一版策略

backtest_20250227.py

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+"""
+BB策略回测脚本 - 2025年2月27日单日回测
+参数: 逐仓 200U 1% 100倍 万五手续费 90%返佣
+"""
+
+import sys
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from strategy.bb_backtest import run_bb_backtest, BBConfig
+from strategy.data_loader import load_klines
+
+
+def main():
+    # 加载2025年2月27日的5分钟K线数据
+    print("=" * 80)
+    print("加载 2025年2月27日 5分钟K线数据...")
+    print("=" * 80)
+    
+    try:
+        df = load_klines(
+            period='5m',
+            start_date='2025-02-27',
+            end_date='2025-02-28',
+            tz='Asia/Shanghai',
+            source='bitmart'
+        )
+        print(f"✓ 数据加载成功: {len(df)} 根K线")
+        if len(df) > 0:
+            print(f"  时间范围: {df.index[0]} ~ {df.index[-1]}")
+        else:
+            print("✗ 无数据可用")
+            return
+    except Exception as e:
+        print(f"✗ 数据加载失败: {e}")
+        return
+
+    # 配置回测参数
+    cfg = BBConfig(
+        # 布林带参数 (来自 bb_trade.py)
+        bb_period=10,
+        bb_std=2.5,
+        
+        # 仓位管理
+        initial_capital=200.0,        # 200U本金
+        margin_pct=0.01,              # 每次开仓用权益的1%
+        leverage=100.0,               # 100倍杠杆
+        
+        # 逐仓配置
+        cross_margin=False,           # 逐仓模式
+        maint_margin_rate=0.005,      # 0.5% 维持保证金率
+        
+        # 手续费和返佣
+        fee_rate=0.0005,              # 万五手续费
+        rebate_rate=0.0,              # 无即时返佣
+        rebate_pct=0.90,              # 次日返佣90%
+        rebate_hour_utc=0,            # UTC 0 点 (北京时间8点)
+        
+        # 滑点
+        slippage_pct=0.0005,          # 0.05% 滑点
+        
+        # 成交模式
+        fill_at_close=False,          # 用触轨价成交(理想化)
+        
+        # 风控
+        max_daily_loss=50.0,          # 日最大亏损50U
+        max_daily_loss_pct=0.0,       # 不启用百分比
+        
+        # 破产模型
+        liq_enabled=True,             # 启用强平
+        
+        # 加仓配置 (递增模式)
+        pyramid_enabled=True,
+        pyramid_step=0.01,            # 1%增量: 开1%, 加2%, 加3%, 加4%
+        pyramid_max=3,                # 最多加3次
+    )
+    
+    print("\n" + "=" * 80)
+    print("回测参数配置")
+    print("=" * 80)
+    print(f"初始资本:        {cfg.initial_capital} USDT")
+    print(f"杠杆:            {cfg.leverage}x {'逐仓' if not cfg.cross_margin else '全仓'}")
+    print(f"开仓比例:        {cfg.margin_pct:.0%}（递增: {cfg.pyramid_step:.0%}/次, 最多{cfg.pyramid_max}次）")
+    print(f"手续费:          {cfg.fee_rate:.0%}")
+    print(f"返佣:            {cfg.rebate_pct:.0%} 次日 UTC-0 {cfg.rebate_hour_utc}点")
+    print(f"布林带:          BB({cfg.bb_period}, {cfg.bb_std})")
+    print(f"滑点:            {cfg.slippage_pct:.0%}")
+    print(f"日亏损限制:      {cfg.max_daily_loss} USDT")
+    print(f"强平:            {'启用' if cfg.liq_enabled else '禁用'}")
+    
+    # 运行回测
+    print("\n" + "=" * 80)
+    print("运行回测...")
+    print("=" * 80)
+    
+    try:
+        result = run_bb_backtest(df, cfg)
+        
+        # 打印详细结果
+        print_backtest_results(result)
+        
+        # 保存结果
+        output_dir = Path(__file__).parent / "strategy" / "results"
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        # 保存交易明细
+        trades_df = pd.DataFrame([
+            {
+                "entry_time": t.entry_time,
+                "exit_time": t.exit_time,
+                "side": t.side,
+                "entry_price": f"{t.entry_price:.2f}",
+                "exit_price": f"{t.exit_price:.2f}",
+                "qty": f"{t.qty:.4f}",
+                "margin": f"{t.margin:.2f}",
+                "gross_pnl": f"{t.gross_pnl:.2f}",
+                "fee": f"{t.fee:.2f}",
+                "net_pnl": f"{t.net_pnl:.2f}",
+            }
+            for t in result.trades
+        ])
+        trades_file = output_dir / f"bb_20250227_trades_{timestamp}.csv"
+        trades_df.to_csv(trades_file, index=False, encoding="utf-8-sig")
+        print(f"\n✓ 交易明细已保存: {trades_file}")
+        
+        # 保存完整权益曲线
+        equity_file = output_dir / f"bb_20250227_equity_{timestamp}.csv"
+        result.equity_curve.to_csv(equity_file, encoding="utf-8-sig")
+        print(f"✓ 权益曲线已保存: {equity_file}")
+        
+    except Exception as e:
+        print(f"✗ 回测失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+def print_backtest_results(result):
+    """打印回测结果统计"""
+    print("\n" + "=" * 80)
+    print("回测结果汇总 (2025年2月27日)")
+    print("=" * 80)
+    
+    # 基础统计
+    trades = result.trades
+    equity = result.equity_curve
+    config = result.config
+    
+    initial_eq = config.initial_capital
+    final_eq = equity["equity"].iloc[-1]
+    total_pnl = final_eq - initial_eq
+    pnl_pct = (total_pnl / initial_eq) * 100
+    
+    print(f"\n📊 核心指标")
+    print(f"  初始权益:        {initial_eq:.2f} USDT")
+    print(f"  最终权益:        {final_eq:.2f} USDT")
+    print(f"  日度收益:        {total_pnl:+.2f} USDT ({pnl_pct:+.2f}%)")
+    print(f"  最高权益:        {equity['equity'].max():.2f} USDT")
+    print(f"  最低权益:        {equity['equity'].min():.2f} USDT")
+    
+    if len(trades) > 0:
+        print(f"\n📈 交易统计")
+        print(f"  总交易数:        {len(trades)} 笔")
+        
+        long_trades = [t for t in trades if t.side == "long"]
+        short_trades = [t for t in trades if t.side == "short"]
+        print(f"  多头交易:        {len(long_trades)} 笔")
+        print(f"  空头交易:        {len(short_trades)} 笔")
+        
+        # 胜率
+        win_trades = [t for t in trades if t.net_pnl > 0]
+        loss_trades = [t for t in trades if t.net_pnl < 0]
+        print(f"  盈利交易:        {len(win_trades)} 笔 ({len(win_trades)/len(trades)*100:.1f}%)")
+        print(f"  亏损交易:        {len(loss_trades)} 笔 ({len(loss_trades)/len(trades)*100:.1f}%)")
+        
+        if len(win_trades) > 0:
+            avg_win = sum(t.net_pnl for t in win_trades) / len(win_trades)
+            total_win = sum(t.net_pnl for t in win_trades)
+            print(f"  平均盈利:        {avg_win:+.2f} USDT")
+            print(f"  总盈利:          {total_win:+.2f} USDT")
+        
+        if len(loss_trades) > 0:
+            avg_loss = sum(t.net_pnl for t in loss_trades) / len(loss_trades)
+            total_loss = sum(t.net_pnl for t in loss_trades)
+            print(f"  平均亏损:        {avg_loss:+.2f} USDT")
+            print(f"  总亏损:          {total_loss:+.2f} USDT")
+        
+        # 风险指标
+        total_net_pnl = sum(t.net_pnl for t in trades)
+        if len(trades) > 0:
+            max_loss_trade = min(trades, key=lambda t: t.net_pnl)
+            max_win_trade = max(trades, key=lambda t: t.net_pnl)
+            
+            print(f"\n💰 盈亏规模")
+            print(f"  总手续费支出:    {result.total_fee:+.2f} USDT")
+            print(f"  返佣总额:        +{result.total_rebate:.2f} USDT")
+            print(f"  交易净损益:      {total_net_pnl:+.2f} USDT")
+            print(f"  单笔最大亏损:    {max_loss_trade.net_pnl:+.2f} USDT")
+            print(f"  单笔最大盈利:    {max_win_trade.net_pnl:+.2f} USDT")
+    else:
+        print(f"\n⚠️  本日无交易触发")
+    
+    # 波动率统计
+    if len(equity) > 0:
+        print(f"\n📉 风险指标")
+        equity_series = equity["equity"].astype(float)
+        running_max = equity_series.expanding().max()
+        drawdown = (equity_series / running_max - 1)
+        max_dd = drawdown.min() * 100
+        
+        print(f"  最大回撤:        {max_dd:.2f}%")
+        print(f"  日均价格:        {equity['price'].mean():.2f}")
+        print(f"  日最高价:        {equity['price'].max():.2f}")
+        print(f"  日最低价:        {equity['price'].min():.2f}")
+    
+    print("\n" + "=" * 80)
+
+
+if __name__ == "__main__":
+    main()