feat: 添加ETF轮动策略诊断分析实验
新增6维度策略诊断实验脚本和报告: - task1: 信号产生分析 (调仓频率、无效调仓率) - task2: 收益计算分析 (T+1执行偏差、溢价问题) - task3: 调仓逻辑分析 (最小持仓期模拟) - task4: 资金管理分析 (止损、波动率适配) - task5: 收益归因分析 (集中度、静态vs轮动) - task6: 回撤诊断分析 (最大回撤复盘、尾部风险) 输出报告: - diagnosis_report.md: 完整策略诊断报告 - rebalancing_optimization_experiment.md: 调仓频率优化实验报告 实验结论: - 发现调仓过于频繁 (405次/1549天) - No-Trade Region方案可提升年化3%、夏普0.11 - 但改善幅度有限，信号质量是根本瓶颈
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rotation/experiments/run_all.py
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278
rotation/experiments/run_all.py
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"""
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统一入口：依次运行 6 个 Task，收集输出，合并生成最终诊断报告。
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Usage:
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python -m rotation.experiments.run_all
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# 或
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python rotation/experiments/run_all.py
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"""
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import io
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import sys
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import time
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import json
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from pathlib import Path
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from contextlib import redirect_stdout
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from datetime import datetime
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from rotation.experiments.common import (
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OUTPUT_DIR, ensure_output_dir, load_metrics,
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print_section,
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)
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def capture_task(task_func, task_name: str) -> dict:
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"""运行单个 Task 并捕获输出和返回值"""
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print(f"\n{'#'*60}")
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print(f"# 运行 {task_name}")
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print(f"{'#'*60}")
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buf = io.StringIO()
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start = time.time()
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try:
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with redirect_stdout(buf):
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result = task_func()
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elapsed = time.time() - start
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output = buf.getvalue()
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print(output) # 同时打印到终端
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return {
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'name': task_name,
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'status': 'OK',
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'elapsed': elapsed,
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'output': output,
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'result': result,
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}
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except Exception as e:
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elapsed = time.time() - start
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output = buf.getvalue()
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print(output)
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print(f" [ERROR] {task_name} 执行失败: {e}")
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import traceback
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traceback.print_exc()
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return {
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'name': task_name,
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'status': 'ERROR',
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'elapsed': elapsed,
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'output': output,
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'error': str(e),
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'result': {},
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}
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def generate_report(task_outputs: list, metrics: dict) -> str:
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"""生成合并诊断报告 (Markdown)"""
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report_lines = []
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report_lines.append("# ETF 轮动策略深度诊断报告")
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report_lines.append(f"\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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report_lines.append(f"\n回测期间: {metrics.get('start_date', 'N/A')} ~ {metrics.get('end_date', 'N/A')}")
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# 策略表现快照
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report_lines.append("\n## 策略表现快照")
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report_lines.append(f"\n| 指标 | 数值 |")
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report_lines.append(f"|---|---|")
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report_lines.append(f"| 累计收益 | {metrics.get('total_return', 0):+.2%} |")
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report_lines.append(f"| 年化收益 | {metrics.get('annual_return', 0):+.2%} |")
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report_lines.append(f"| 最大回撤 | {metrics.get('max_drawdown', 0):.2%} |")
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report_lines.append(f"| 夏普比率 | {metrics.get('sharpe_ratio', 0):.2f} |")
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report_lines.append(f"| Calmar 比率 | {metrics.get('calmar_ratio', 0):.2f} |")
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report_lines.append(f"| 日胜率 | {metrics.get('win_rate', 0):.1%} |")
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report_lines.append(f"| 调仓次数 | {metrics.get('rebalance_count', 0)} |")
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# 各 Task 输出
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task_titles = {
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'Task 1': '信号产生问题诊断',
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'Task 2': '收益计算问题诊断',
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'Task 3': '调仓逻辑问题诊断',
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'Task 4': '资金管理问题诊断',
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'Task 5': '整体收益归因分析',
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'Task 6': '回撤诊断',
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}
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for i, task_out in enumerate(task_outputs, 1):
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title = task_titles.get(f'Task {i}', f'Task {i}')
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report_lines.append(f"\n## Task {i}: {title}")
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report_lines.append(f"\n状态: {task_out['status']} | 耗时: {task_out['elapsed']:.1f}s")
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if task_out['status'] == 'ERROR':
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report_lines.append(f"\n**执行失败**: {task_out.get('error', 'Unknown')}")
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continue
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# 提取关键结论
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output = task_out['output']
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result = task_out.get('result', {})
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# 添加诊断结论
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report_lines.append(f"\n### 诊断结论")
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if f'Task {i}' in task_titles:
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conclusions = extract_conclusions(i, result, output)
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report_lines.extend(conclusions)
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# 综合建议
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report_lines.append("\n## 综合优化建议")
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report_lines.append(generate_recommendations(task_outputs))
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# 执行统计
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report_lines.append("\n## 执行统计")
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report_lines.append(f"\n| Task | 状态 | 耗时 |")
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report_lines.append(f"|---|---|---|")
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for i, task_out in enumerate(task_outputs, 1):
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title = task_titles.get(f'Task {i}', f'Task {i}')
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report_lines.append(f"| Task {i}: {title} | {task_out['status']} | {task_out['elapsed']:.1f}s |")
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total_time = sum(t['elapsed'] for t in task_outputs)
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report_lines.append(f"| **总计** | | **{total_time:.1f}s** |")
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return '\n'.join(report_lines)
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def extract_conclusions(task_num: int, result: dict, output: str) -> list:
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"""从 Task 结果中提取关键结论"""
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lines = []
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if task_num == 1:
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freq = result.get('frequency', {})
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jitter = result.get('jitter', {})
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threshold = result.get('threshold', {})
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lines.append(f"- 调仓频率: 每 {freq.get('avg_interval', 0):.1f} 天一次，"
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f"无效调仓率 {freq.get('invalid_rate', 0):.1f}%")
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lines.append(f"- 短期抖动事件: {jitter.get('jitter_events', 0)} 次")
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lines.append(f"- 债券持有占比: {threshold.get('bond_hold_pct', 0)*100:.1f}%")
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elif task_num == 2:
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first = result.get('first_day', {})
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t1 = result.get('t1_bias', {})
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lines.append(f"- 首日 NAV = {first.get('first_nav', 0):.6f}，存在轻微逻辑瑕疵")
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lines.append(f"- 极端日共 {t1.get('extreme_days', 0)} 次")
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elif task_num == 3:
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min_hold = result.get('min_hold', [])
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if min_hold:
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lines.append(f"- 最小持仓期模拟结果:")
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for r in min_hold:
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lines.append(f" - {r['min_hold']}天: 年化={r['annual_return']:+.2%}, "
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f"回撤={r['max_drawdown']:.2%}, 夏普={r['sharpe']:.2f}")
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elif task_num == 4:
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lines.append("- 止损机制可减少极端回撤，但频繁止损可能拖累长期收益")
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lines.append("- 高波动期减仓有助于控制回撤")
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elif task_num == 5:
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conc = result.get('concentration', {})
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lines.append(f"- 收益依赖度: 最好5天贡献了 {conc.get('dependency_pct', 0):.1f}% 的最终净值")
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elif task_num == 6:
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max_dd = result.get('max_dd', {})
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tail = result.get('tail', {})
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lines.append(f"- 最大回撤 {max_dd.get('max_dd', 0):.2%} 发生在 {max_dd.get('trough_date', 'N/A')}")
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lines.append(f"- CVaR(5%): {tail.get('cvar_5pct', 0):+.4%}")
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lines.append(f"- 最大连续亏损: {tail.get('max_streak', 0)} 天")
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return lines
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def generate_recommendations(task_outputs: list) -> str:
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"""生成综合优化建议"""
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recs = """
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### 优先级 P0（预期影响最大）
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1. **降低调仓频率**
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- 引入最小持仓期约束（建议 5 天起步）
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- 在 `_generate_signals` 中加入 `min_hold_days` 检查
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2. **启用溢价控制**
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- 对 QDII ETF（NDX/N225/GDAXI/HSI/HSTECH）启用溢价过滤
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- 建议 threshold=5%，避免高溢价买入
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### 优先级 P1（显著改善回撤）
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3. **组合级止损机制**
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- 建议回撤 > 8% 时触发止损，转债券持有 10 天
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4. **修复首日 NAV 逻辑**
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- 首日 `current_holdings` 为空时不应计算收益
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### 优先级 P2（提升风险调整收益）
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5. **波动率加权配置**
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- 替代等权，使用波动率倒数加权平衡风险贡献
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6. **波动率适配仓位**
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- 高波动期（滚动20日波动率 > 20%）减仓至 2/3
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### 优先级 P3（进一步优化）
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7. **评估分组机制**
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- 对比取消分组 vs 当前分组的收益差异
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8. **优化动态阈值**
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- 调整 bond_ratio，测试不同阈值对防御效果的影响
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"""
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return recs
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def main():
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print("=" * 60)
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print(" ETF 轮动策略深度诊断 - 统一入口")
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print("=" * 60)
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ensure_output_dir()
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# 加载原始指标
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metrics = load_metrics()
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# 从 detail JSON 补充日期
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from rotation.experiments.common import load_detail_meta
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meta = load_detail_meta()
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metrics['start_date'] = meta['start_date']
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metrics['end_date'] = meta['end_date']
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print(f"\n策略期间: {meta['start_date']} ~ {meta['end_date']}")
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print(f"累计收益: {metrics['total_return']:+.2%}")
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print(f"年化收益: {metrics['annual_return']:+.2%}")
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print(f"最大回撤: {metrics['max_drawdown']:.2%}")
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# 导入并运行各 Task
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from rotation.experiments.task1_signal_analysis import main as task1_main
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from rotation.experiments.task2_return_calc_analysis import main as task2_main
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from rotation.experiments.task3_rebalance_analysis import main as task3_main
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from rotation.experiments.task4_capital_mgmt_analysis import main as task4_main
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from rotation.experiments.task5_return_attribution import main as task5_main
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from rotation.experiments.task6_drawdown_analysis import main as task6_main
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tasks = [
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(task1_main, 'Task 1: 信号产生问题诊断'),
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(task2_main, 'Task 2: 收益计算问题诊断'),
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(task3_main, 'Task 3: 调仓逻辑问题诊断'),
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(task4_main, 'Task 4: 资金管理问题诊断'),
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(task5_main, 'Task 5: 整体收益归因分析'),
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(task6_main, 'Task 6: 回撤诊断'),
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]
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task_outputs = []
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for func, name in tasks:
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result = capture_task(func, name)
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task_outputs.append(result)
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# 生成合并报告
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print_section("生成诊断报告")
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report = generate_report(task_outputs, metrics)
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report_path = OUTPUT_DIR / 'diagnosis_report.md'
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f" + 诊断报告已保存: {report_path}")
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# 输出摘要
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print_section("执行完成")
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ok_count = sum(1 for t in task_outputs if t['status'] == 'OK')
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err_count = sum(1 for t in task_outputs if t['status'] == 'ERROR')
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total_time = sum(t['elapsed'] for t in task_outputs)
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print(f" 成功: {ok_count}/6, 失败: {err_count}/6")
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print(f" 总耗时: {total_time:.1f}s")
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print(f" 报告路径: {report_path}")
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if __name__ == '__main__':
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main()
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