docs(experiment): add select_num A/B/C comparison report (005)

- Experiment: select_num = 1, 2, 3 comparison
- Period: 2020-01-10 ~ 2026-06-02 (1546 trading days)
- Key findings:
  - Top-1: highest return (600%), highest drawdown (-25.5%)
  - Top-3: best risk-adjusted return (Calmar 1.73, Sharpe 1.35)
  - Top-2: balanced middle ground (Calmar 1.69)
- Add rotation/experiment_select_num.py experiment script
- Save report to docs/experiments/005_select_num_comparison.md

rotation/experiment_select_num.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""
+select_num A/B 实验：对比 Top-1 / Top-2 / Top-3 的表现
+
+用法:
+    python rotation/experiment_select_num.py
+"""
+
+import os
+import sys
+import yaml
+import json
+import tempfile
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from datetime import datetime
+
+PROJECT_ROOT = Path(__file__).parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from rotation.simple_rotation import SimpleRotationStrategy
+
+
+def run_with_select_num(config_path: str, select_num: int, output_dir: Path) -> dict:
+    """运行一次策略，覆盖 select_num"""
+    print(f"\n{'='*60}")
+    print(f"  实验: select_num = {select_num}")
+    print(f"{'='*60}\n")
+
+    # 读取原始配置，修改 select_num，写入临时文件
+    with open(config_path, 'r', encoding='utf-8') as f:
+        cfg = yaml.safe_load(f)
+    cfg['rotation']['select_num'] = select_num
+
+    tmp_path = output_dir / f'config_select_{select_num}.yaml'
+    with open(tmp_path, 'w', encoding='utf-8') as f:
+        yaml.dump(cfg, f, default_flow_style=False, allow_unicode=True)
+
+    strategy = SimpleRotationStrategy(config_path=str(tmp_path))
+    result = strategy.run()
+
+    if result:
+        # 导出到子目录
+        sub_dir = output_dir / f'select_{select_num}'
+        sub_dir.mkdir(parents=True, exist_ok=True)
+        strategy.export_results(output_dir=str(sub_dir))
+        return result.get('metrics', {})
+    return {}
+
+
+def print_comparison(all_metrics: dict):
+    """打印对比表格"""
+    print(f"\n\n{'='*80}")
+    print(f"  select_num 实验对比结果")
+    print(f"{'='*80}\n")
+
+    header = f"{'指标':<16}"
+    for n in sorted(all_metrics.keys()):
+        header += f"{'Top-'+str(n):>12}"
+    print(header)
+    print("-" * (16 + 12 * len(all_metrics)))
+
+    rows = [
+        ('累计收益', 'total_return', '{:.2%}'),
+        ('年化收益', 'annual_return', '{:.2%}'),
+        ('最大回撤', 'max_drawdown', '{:.2%}'),
+        ('夏普比率', 'sharpe_ratio', '{:.2f}'),
+        ('Calmar比率', 'calmar_ratio', '{:.2f}'),
+        ('日胜率', 'win_rate', '{:.2%}'),
+        ('交易日数', 'n_days', '{}'),
+        ('调仓次数', 'rebalance_count', '{}'),
+    ]
+
+    for label, key, fmt in rows:
+        row = f"{label:<16}"
+        for n in sorted(all_metrics.keys()):
+            val = all_metrics[n].get(key, 0)
+            row += f"{fmt.format(val):>12}"
+        print(row)
+
+    print(f"\n{'='*80}")
+
+
+def plot_comparison(all_metrics: dict, output_dir: Path):
+    """生成对比图表"""
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    fig, axes = plt.subplots(1, 3, figsize=(16, 5))
+    fig.suptitle("select_num A/B Experiment", fontsize=14, fontweight="bold")
+
+    nums = sorted(all_metrics.keys())
+    colors = ['#E74C3C', '#3498DB', '#2ECC71']
+
+    # 1. 收益对比
+    ax = axes[0]
+    annuals = [all_metrics[n].get('annual_return', 0) for n in nums]
+    totals = [all_metrics[n].get('total_return', 0) for n in nums]
+    x = np.arange(len(nums))
+    w = 0.35
+    ax.bar(x - w/2, [a*100 for a in annuals], w, label='Annual %', color='#E74C3C', alpha=0.8)
+    ax.bar(x + w/2, [t*100 for t in totals], w, label='Total %', color='#3498DB', alpha=0.8)
+    ax.set_xticks(x)
+    ax.set_xticklabels([f'Top-{n}' for n in nums])
+    ax.set_ylabel('Return (%)')
+    ax.set_title('Returns')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    # 2. 风险对比
+    ax = axes[1]
+    dds = [abs(all_metrics[n].get('max_drawdown', 0)) * 100 for n in nums]
+    ax.bar(x, dds, color='#E74C3C', alpha=0.7)
+    ax.set_xticks(x)
+    ax.set_xticklabels([f'Top-{n}' for n in nums])
+    ax.set_ylabel('Max Drawdown (%)')
+    ax.set_title('Risk')
+    ax.grid(True, alpha=0.3)
+
+    # 3. 夏普 & Calmar
+    ax = axes[2]
+    sharpes = [all_metrics[n].get('sharpe_ratio', 0) for n in nums]
+    calmars = [all_metrics[n].get('calmar_ratio', 0) for n in nums]
+    ax.bar(x - w/2, sharpes, w, label='Sharpe', color='#2ECC71', alpha=0.8)
+    ax.bar(x + w/2, calmars, w, label='Calmar', color='#F39C12', alpha=0.8)
+    ax.set_xticks(x)
+    ax.set_xticklabels([f'Top-{n}' for n in nums])
+    ax.set_ylabel('Ratio')
+    ax.set_title('Risk-Adjusted')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    chart_path = output_dir / 'select_num_comparison.png'
+    plt.savefig(str(chart_path), dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"\n  + Chart: {chart_path}")
+
+
+def plot_nav_comparison(output_dir: Path):
+    """加载三组 NAV 画在同一张图上"""
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    fig, ax = plt.subplots(figsize=(14, 6))
+    colors = {'1': '#E74C3C', '2': '#3498DB', '3': '#2ECC71'}
+
+    for n in [1, 2, 3]:
+        nav_path = output_dir / f'select_{n}' / 'simple_rotation_nav.csv'
+        if nav_path.exists():
+            df = pd.read_csv(nav_path, parse_dates=['date'])
+            ax.plot(df['date'], df['nav'], label=f'Top-{n}', linewidth=1.5, color=colors[str(n)])
+
+    ax.set_title("NAV Curve Comparison (select_num)", fontsize=14, fontweight="bold")
+    ax.set_ylabel("NAV")
+    ax.set_yscale("log")
+    ax.legend(fontsize=11)
+    ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    nav_chart = output_dir / 'select_num_nav_comparison.png'
+    plt.savefig(str(nav_chart), dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"  + NAV Chart: {nav_chart}")
+
+
+if __name__ == "__main__":
+    if 'FLASK_API_URL' not in os.environ:
+        os.environ['FLASK_API_URL'] = 'https://k3s.tokenpluse.xyz'
+
+    config_path = str(Path(__file__).parent / 'config_simple.yaml')
+    output_dir = PROJECT_ROOT / 'results' / 'experiment_select_num'
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    all_metrics = {}
+    for n in [1, 2, 3]:
+        metrics = run_with_select_num(config_path, n, output_dir)
+        if metrics:
+            all_metrics[n] = metrics
+
+    if all_metrics:
+        print_comparison(all_metrics)
+        plot_comparison(all_metrics, output_dir)
+        plot_nav_comparison(output_dir)
+
+        # 保存原始指标
+        metrics_path = output_dir / 'experiment_metrics.json'
+        with open(metrics_path, 'w', encoding='utf-8') as f:
+            json.dump({str(k): v for k, v in all_metrics.items()}, f, ensure_ascii=False, indent=2)
+        print(f"  + Metrics: {metrics_path}")