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
This commit is contained in:
5
rotation/experiments/__init__.py
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rotation/experiments/__init__.py
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"""
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ETF 轮动策略诊断实验模块
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每个 Task 独立脚本，分析策略在不同维度的问题与优化方向。
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"""
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205
rotation/experiments/common.py
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rotation/experiments/common.py
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"""
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公共数据加载与工具函数
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为各 Task 分析脚本提供统一的数据加载入口和常用统计/绘图函数。
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"""
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import json
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import sys
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from pathlib import Path
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from typing import Dict, List, Tuple, Optional
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import numpy as np
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import pandas as pd
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# 路径常量
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EXPERIMENT_DIR = Path(__file__).parent
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RESULTS_DIR = EXPERIMENT_DIR.parent / 'results'
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OUTPUT_DIR = EXPERIMENT_DIR / 'output'
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DETAIL_JSON = RESULTS_DIR / 'simple_rotation_detail.json'
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NAV_CSV = RESULTS_DIR / 'simple_rotation_nav.csv'
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SIGNALS_CSV = RESULTS_DIR / 'simple_rotation_signals.csv'
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METRICS_JSON = RESULTS_DIR / 'simple_rotation_metrics.json'
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# ============================================================
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# 数据加载
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# ============================================================
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def load_nav() -> pd.DataFrame:
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"""加载 NAV 曲线，返回 DataFrame(date, nav, daily_return)"""
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df = pd.read_csv(NAV_CSV, parse_dates=['date'])
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return df
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def load_signals() -> pd.DataFrame:
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"""加载信号 CSV，返回 DataFrame(date, holdings, is_rebalance, added, removed)"""
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df = pd.read_csv(SIGNALS_CSV, parse_dates=['date'])
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return df
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def load_detail() -> dict:
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"""加载 detail JSON（约 338K 行），返回 dict{meta, days}"""
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with open(DETAIL_JSON, 'r') as f:
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return json.load(f)
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def load_detail_days() -> List[dict]:
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"""仅加载 detail JSON 的 days 部分"""
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data = load_detail()
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return data['days']
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def load_detail_meta() -> dict:
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"""仅加载 detail JSON 的 meta 部分"""
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data = load_detail()
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return data['meta']
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def load_metrics() -> dict:
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"""加载绩效指标 JSON"""
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with open(METRICS_JSON, 'r') as f:
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return json.load(f)
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def days_to_dataframe(days: List[dict]) -> pd.DataFrame:
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"""将 detail JSON 的 days 列表转换为宽表 DataFrame。
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列: date, nav, daily_return, is_rebalance, holdings, added, removed
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以及每个资产 code 的 momentum_{code}, rank_{code}, threshold_{code}, ...
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"""
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rows = []
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for day in days:
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row = {
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'date': pd.Timestamp(day['date']),
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'nav': day['nav'],
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'daily_return': day['daily_return'],
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'is_rebalance': day['is_rebalance'],
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'holdings': day['holdings'],
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'added': day.get('added', []),
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'removed': day.get('removed', []),
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}
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for code, asset in day.get('assets', {}).items():
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safe_code = code.replace('.', '_').replace('=', '_')
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row[f'momentum_{safe_code}'] = asset.get('momentum')
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row[f'rank_{safe_code}'] = asset.get('rank')
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row[f'threshold_{safe_code}'] = asset.get('threshold')
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row[f'above_threshold_{safe_code}'] = asset.get('above_threshold')
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row[f'premium_{safe_code}'] = asset.get('premium')
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row[f'is_held_{safe_code}'] = asset.get('is_held')
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row[f'index_return_{safe_code}'] = asset.get('index_return')
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row[f'etf_return_ctc_{safe_code}'] = asset.get('etf_return_ctc')
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row[f'holding_days_{safe_code}'] = asset.get('holding_days')
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row[f'cum_return_etf_{safe_code}'] = asset.get('cum_return_etf')
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rows.append(row)
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return pd.DataFrame(rows)
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def days_to_assets_long(days: List[dict]) -> pd.DataFrame:
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"""将 detail JSON 转换为长表（每日每资产一行）。
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列: date, code, momentum, rank, threshold, above_threshold, premium,
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is_held, index_return, etf_return_ctc, holding_days, cum_return_etf
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"""
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rows = []
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for day in days:
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date = pd.Timestamp(day['date'])
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for code, asset in day.get('assets', {}).items():
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rows.append({
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'date': date,
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'code': code,
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'momentum': asset.get('momentum'),
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'rank': asset.get('rank'),
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'threshold': asset.get('threshold'),
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'above_threshold': asset.get('above_threshold'),
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'premium': asset.get('premium'),
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'is_held': asset.get('is_held'),
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'index_return': asset.get('index_return'),
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'etf_return_ctc': asset.get('etf_return_ctc'),
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'holding_days': asset.get('holding_days'),
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'cum_return_etf': asset.get('cum_return_etf'),
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'cum_return_idx': asset.get('cum_return_idx'),
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})
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return pd.DataFrame(rows)
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# ============================================================
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# 统计工具
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# ============================================================
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def compute_drawdown(nav: pd.Series) -> pd.Series:
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"""计算回撤序列"""
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peak = nav.cummax()
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return (nav - peak) / peak
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def compute_sharpe(returns: pd.Series, rf: float = 0.0) -> float:
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"""计算年化夏普比率"""
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excess = returns - rf / 252
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if excess.std() == 0:
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return 0.0
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return float(excess.mean() / excess.std() * np.sqrt(252))
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def compute_calmar(returns: pd.Series, nav: pd.Series) -> float:
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"""计算 Calmar 比率"""
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n = len(returns)
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total = nav.iloc[-1] / nav.iloc[0] - 1
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annual = (1 + total) ** (252 / n) - 1
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dd = compute_drawdown(nav).min()
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if dd == 0:
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return 0.0
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return float(annual / abs(dd))
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def compute_annual_return(nav: pd.Series) -> float:
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"""计算年化收益率"""
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n = len(nav)
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total = nav.iloc[-1] / nav.iloc[0] - 1
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return (1 + total) ** (252 / n) - 1
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def yearly_stats(nav_df: pd.DataFrame) -> pd.DataFrame:
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"""按年份计算收益、回撤、夏普等统计"""
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nav_df = nav_df.copy()
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nav_df['year'] = nav_df['date'].dt.year
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rows = []
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for year, grp in nav_df.groupby('year'):
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n = len(grp)
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total_ret = grp['nav'].iloc[-1] / grp['nav'].iloc[0] - 1
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dd = compute_drawdown(grp['nav']).min()
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sharpe = compute_sharpe(grp['daily_return'])
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rows.append({
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'year': year,
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'total_return': total_ret,
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'max_drawdown': dd,
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'sharpe': sharpe,
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'n_days': n,
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})
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return pd.DataFrame(rows)
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# ============================================================
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# 输出工具
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# ============================================================
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def ensure_output_dir():
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"""确保输出目录存在"""
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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def print_section(title: str):
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"""打印分隔符 + 标题"""
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print(f"\n{'='*60}")
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print(f" {title}")
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print(f"{'='*60}")
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def save_figure(fig, name: str):
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"""保存 matplotlib 图表到 output 目录"""
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ensure_output_dir()
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path = OUTPUT_DIR / name
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fig.savefig(path, dpi=150, bbox_inches='tight')
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print(f" + 图表已保存: {path}")
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return path
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rotation/experiments/output/diagnosis_report.md
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rotation/experiments/output/diagnosis_report.md
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# ETF 轮动策略深度诊断报告
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生成时间: 2026-06-06 13:51:49
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回测期间: 2020-01-10 ~ 2026-06-05
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## 策略表现快照
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| 指标 | 数值 |
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|---|---|
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| 累计收益 | +181.89% |
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| 年化收益 | +18.36% |
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| 最大回撤 | -16.36% |
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| 夏普比率 | 1.02 |
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| Calmar 比率 | 1.12 |
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| 日胜率 | 54.0% |
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| 调仓次数 | 405 |
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## Task 1: 信号产生问题诊断
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状态: OK | 耗时: 0.2s
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### 诊断结论
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- 调仓频率: 每 3.8 天一次，无效调仓率 43.0%
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- 短期抖动事件: 141 次
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- 债券持有占比: 32.9%
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## Task 2: 收益计算问题诊断
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状态: OK | 耗时: 0.1s
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### 诊断结论
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- 首日 NAV = 0.997823，存在轻微逻辑瑕疵
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- 极端日共 7 次
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## Task 3: 调仓逻辑问题诊断
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状态: OK | 耗时: 0.1s
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### 诊断结论
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- 最小持仓期模拟结果:
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- 1天: 年化=+19.93%, 回撤=-16.02%, 夏普=1.10
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- 3天: 年化=+21.92%, 回撤=-15.68%, 夏普=1.19
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- 5天: 年化=+22.85%, 回撤=-15.68%, 夏普=1.23
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- 10天: 年化=+24.14%, 回撤=-15.42%, 夏普=1.29
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## Task 4: 资金管理问题诊断
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状态: OK | 耗时: 0.1s
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### 诊断结论
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- 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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- 高波动期减仓有助于控制回撤
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## Task 5: 整体收益归因分析
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状态: OK | 耗时: 0.1s
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### 诊断结论
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- 收益依赖度: 最好5天贡献了 25.7% 的最终净值
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## Task 6: 回撤诊断
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状态: OK | 耗时: 0.1s
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### 诊断结论
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- 最大回撤 -16.36% 发生在 2022-05-10
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- CVaR(5%): -2.6196%
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- 最大连续亏损: 6 天
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## 综合优化建议
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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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## 执行统计
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| Task | 状态 | 耗时 |
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|---|---|---|
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| Task 1: 信号产生问题诊断 | OK | 0.2s |
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| Task 2: 收益计算问题诊断 | OK | 0.1s |
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| Task 3: 调仓逻辑问题诊断 | OK | 0.1s |
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| Task 4: 资金管理问题诊断 | OK | 0.1s |
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| Task 5: 整体收益归因分析 | OK | 0.1s |
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| Task 6: 回撤诊断 | OK | 0.1s |
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| **总计** | | **0.6s** |
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# ETF动量轮动策略调仓频率优化实验报告
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**实验时间**: 2026-06-06
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**实验目标**: 降低调仓频率，减少无效调仓，提升策略整体收益
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**实验状态**: 已完成（代码已还原）
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---
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## 1. 问题诊断
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### 1.1 现象描述
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原始策略存在以下问题：
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- **调仓过于频繁**: 1549个交易日内调仓405次，平均每3.8天一次
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- **无效调仓占比高**: 43%的调仓T+1收益为负
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- **交易成本拖累**: 累计交易成本约占总收益的22%
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### 1.2 根本原因分析
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通过深度数据诊断，发现三个层面的问题：
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#### 信号生命周期与调仓频率不匹配
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```
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动量窗口 = 25天（约1个月）
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信号自相关系数:
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Lag-1: 0.88~0.99 ← 今天和昨天的信号几乎一样
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Lag-5: 0.33~0.69 ← 信号开始变化
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Lag-10: 0.04~0.30 ← 信号真正开始失效
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Lag-25: -0.07~0.03 ← 信号完全失效
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理论最优持有期 ≈ 10天（信号半衰期）
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实际持有期 ≈ 3.8天（远低于理论值）
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```
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#### 信号预测力验证
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```
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换入 vs 换出资产后续收益差（信号alpha）:
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T+1: +0.47%
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T+3: +0.65%
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T+5: +0.85% ← 峰值
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T+10: +0.62% ← 开始衰减
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T+20: +1.05%
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结论: 信号有效，alpha在T+5达到峰值，需要给信号足够时间释放
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```
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#### 短期调仓质量分析
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```
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距上次调仓间隔 vs 本次调仓质量:
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间隔1天: T+1均值=+0.31%, 正收益52%
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间隔2天: T+1均值=+0.08%, 正收益64%
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间隔3天: T+1均值=+0.03%, 正收益51% ← 几乎无效
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间隔4天: T+1均值=-0.09%, 正收益50% ← 负alpha
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间隔7天: T+1均值=+0.43%, 正收益63% ← 质量回升
|
||||
间隔8天: T+1均值=+0.93%, 正收益73% ← 最佳
|
||||
|
||||
结论: 间隔3-4天的调仓是对噪声的反应，无正alpha
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. 解决方案设计
|
||||
|
||||
### 2.1 方案A: 信号变化幅度阈值 (Signal Turnover Threshold)
|
||||
|
||||
**核心思想**: 只有当信号排名变化足够大时才调仓
|
||||
|
||||
**实现逻辑**:
|
||||
```python
|
||||
# 新持仓至少变化N只才触发调仓
|
||||
is_rebalance = len(set(new_holdings) - set(current_holdings)) >= min_changes
|
||||
```
|
||||
|
||||
**参数扫描**:
|
||||
| min_changes | 年化收益 | 夏普比率 | 最大回撤 | 调仓次数 |
|
||||
|-------------|---------|---------|---------|---------|
|
||||
| 1 (基线) | 19.33% | 1.071 | -16.19% | 355 |
|
||||
| 2 | 24.04% | 1.333 | -15.42% | 117 |
|
||||
| 3 | 25.57% | 1.418 | -15.33% | 42 |
|
||||
|
||||
**理论基础**:
|
||||
- No-Trade Region理论 (Magill & Constantinides 1990)
|
||||
- 在比例交易成本下，最优再平衡策略是建立"无交易区域"
|
||||
- 只有当资产权重偏离目标超过某个边界时才调仓
|
||||
|
||||
### 2.2 方案B: 信号置信度加权 (Confidence-Weighted Selection)
|
||||
|
||||
**核心思想**: 新候选者的动量得分必须显著超过当前持有者才替换
|
||||
|
||||
**实现逻辑**:
|
||||
```python
|
||||
# 新候选者必须比当前持有者强buffer%才替换
|
||||
avg_added_momentum > avg_removed_momentum * (1 + buffer)
|
||||
```
|
||||
|
||||
**参数扫描**:
|
||||
| buffer | 年化收益 | 夏普比率 | 最大回撤 | 调仓次数 |
|
||||
|--------|---------|---------|---------|---------|
|
||||
| 2% | 19.56% | 1.084 | -16.10% | 343 |
|
||||
| 5% | 19.89% | 1.102 | -16.10% | 326 |
|
||||
| 10% | 20.24% | 1.121 | -16.10% | 308 |
|
||||
| 15% | 20.54% | 1.137 | -16.10% | 293 |
|
||||
|
||||
**理论基础**:
|
||||
- 滞后性(Hysteresis)在投资决策中的应用 (Dixit 1989)
|
||||
- 在不确定性下，"等待"有期权价值（real options theory）
|
||||
|
||||
### 2.3 方案C: 信号成熟度检查 (Signal Maturity Check)
|
||||
|
||||
**核心思想**: 新资产必须连续N天动量优于当前持有者才被换入
|
||||
|
||||
**实现逻辑**:
|
||||
```python
|
||||
# 跟踪每个资产的"连续优于"天数
|
||||
if superiority_count[new_asset] >= confirm_days:
|
||||
execute_rebalance()
|
||||
```
|
||||
|
||||
**参数扫描**:
|
||||
| confirm_days | 年化收益 | 夏普比率 | 最大回撤 | 调仓次数 |
|
||||
|--------------|---------|---------|---------|---------|
|
||||
| 1 | 18.36% | 1.017 | -16.36% | 331 |
|
||||
| 3 | 18.65% | 1.033 | -16.36% | 316 |
|
||||
| 5 | 18.77% | 1.039 | -16.36% | 308 |
|
||||
| 10 | 18.94% | 1.049 | -16.36% | 301 |
|
||||
|
||||
**理论基础**:
|
||||
- 趋势确认需要时间，单次穿越可能是噪声
|
||||
- 动量信号的自相关性决定最优持仓 (Moskowitz, Ooi & Pedersen 2012)
|
||||
|
||||
---
|
||||
|
||||
## 3. 最终方案: 动量散度驱动的自适应 No-Trade Region
|
||||
|
||||
### 3.1 设计思路
|
||||
|
||||
结合方案A的效果和自适应需求，设计最终方案：
|
||||
|
||||
**核心公式**:
|
||||
```
|
||||
调仓条件: divergence > k × noise_baseline
|
||||
|
||||
其中:
|
||||
divergence = mean(新持仓动量) - mean(被换出持仓动量)
|
||||
noise_baseline = rolling_mean(20日, margin_gap)
|
||||
margin_gap = |最强非持仓动量 - 最弱持仓动量|
|
||||
```
|
||||
|
||||
**自适应性**:
|
||||
- 市场平静（资产动量趋同）→ σ小 → 阈值窄 → 允许更精细的调仓
|
||||
- 市场动荡（资产动量分散）→ σ大 → 阈值宽 → 过滤噪声
|
||||
|
||||
**黑天鹅应对**:
|
||||
- 黑天鹅 → σ飙升，但divergence飙升更猛 → 自动触发调仓
|
||||
- 崩盘过滤器触发（momentum=0）→ 无条件执行卖出（安全退出通道）
|
||||
|
||||
### 3.2 理论支撑
|
||||
|
||||
1. **No-Trade Region理论** (Magill & Constantinides 1990, Davis & Norman 1990)
|
||||
- 在比例交易成本下，最优再平衡策略是建立"无交易区域"
|
||||
- 只有当资产权重偏离目标超过某个边界时才调仓
|
||||
|
||||
2. **Information Horizon框架** (Qian, Sorensen & Hua, JPM 2007)
|
||||
- 信号的信息系数(IC)随时间衰减
|
||||
- 最优调仓频率应与信号的有效信息生命周期匹配
|
||||
|
||||
3. **Fundamental Law of Active Management** (Grinold & Kahn 1999)
|
||||
- `IR ≈ IC × √(Breadth)`
|
||||
- 当信号衰减慢于调仓频率时，增加调仓次数不增加Breadth，反而增加交易成本
|
||||
|
||||
4. **Momentum's Magic Number** (Newfound Research 2018)
|
||||
- 动量策略的最优持有期与形成期之和约为12-18个月
|
||||
- 对于25天形成窗口，最优持有期理论上应为11-17个月
|
||||
|
||||
### 3.3 实现细节
|
||||
|
||||
**代码结构**:
|
||||
```python
|
||||
class SimpleRotationStrategy:
|
||||
def __init__(self, config_path: str = None):
|
||||
# 新增参数
|
||||
self.divergence_k = self.config.rebalance.divergence_k # 0.5
|
||||
self.noise_window = self.config.rebalance.noise_window # 20
|
||||
self._noise_history: List[float] = []
|
||||
|
||||
def _update_noise_history(self, current_holdings, factors):
|
||||
"""每天更新噪声基准（margin gap）"""
|
||||
current_set = set(current_holdings)
|
||||
held_moms = [factors[c] for c in current_set if c in factors]
|
||||
non_held_moms = [v for k, v in factors.items()
|
||||
if k not in current_set and k != self.bond_code]
|
||||
if held_moms and non_held_moms:
|
||||
margin_gap = abs(max(non_held_moms) - min(held_moms))
|
||||
self._noise_history.append(margin_gap)
|
||||
|
||||
def _should_rebalance(self, current_holdings, new_holdings, factors) -> bool:
|
||||
"""No-Trade Region判断"""
|
||||
# 1. 初始建仓或无变化
|
||||
if not current_holdings:
|
||||
return True
|
||||
if sorted(current_holdings) == sorted(new_holdings):
|
||||
return False
|
||||
|
||||
added = set(new_holdings) - set(current_holdings)
|
||||
removed = set(current_holdings) - set(new_holdings)
|
||||
if not added or not removed:
|
||||
return True # 纯增/纯减直接执行
|
||||
|
||||
# 2. 计算动量散度
|
||||
added_mom = [factors[c] for c in added if c in factors]
|
||||
removed_mom = [factors[c] for c in removed if c in factors]
|
||||
divergence = np.mean(added_mom) - np.mean(removed_mom)
|
||||
|
||||
# 3. 安全退出通道（崩盘过滤器触发）
|
||||
if any(m == 0.0 for m in removed_mom):
|
||||
return True
|
||||
|
||||
# 4. 读取噪声基准
|
||||
recent = self._noise_history[-self.noise_window:]
|
||||
noise_baseline = np.mean(recent) if len(recent) >= 5 else 0.3
|
||||
|
||||
# 5. 判断
|
||||
return divergence > self.divergence_k * noise_baseline
|
||||
|
||||
def run(self):
|
||||
for i, date in enumerate(self.trading_calendar):
|
||||
new_holdings, factors, bond_momentum = self._generate_signals(signal_date)
|
||||
|
||||
# 每天更新噪声基准
|
||||
if current_holdings:
|
||||
self._update_noise_history(current_holdings, factors)
|
||||
|
||||
# No-Trade Region判断
|
||||
is_rebalance = self._should_rebalance(current_holdings, new_holdings, factors)
|
||||
|
||||
# 不调仓时使用旧持仓
|
||||
effective_holdings = new_holdings if is_rebalance else current_holdings
|
||||
|
||||
# 计算收益
|
||||
daily_return = self._calculate_daily_return(
|
||||
current_holdings, effective_holdings, date, is_rebalance
|
||||
)
|
||||
nav *= (1 + daily_return)
|
||||
|
||||
current_holdings = effective_holdings
|
||||
```
|
||||
|
||||
**配置参数**:
|
||||
```yaml
|
||||
rebalance:
|
||||
min_hold_days: 1
|
||||
score_threshold: 0.0
|
||||
trade_cost: 0.001
|
||||
# No-Trade Region
|
||||
divergence_k: 0.5
|
||||
noise_window: 20
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. 实证结果
|
||||
|
||||
### 4.1 回测对比
|
||||
|
||||
| 指标 | 原始策略 | No-Trade Region (k=0.5) | 变化 |
|
||||
|------|---------|------------------------|------|
|
||||
| 年化收益 | 18.32% | 21.33% | **+3.01%** |
|
||||
| 总收益 | ~170% | 228.24% | +58% |
|
||||
| 夏普比率 | 1.02 | 1.13 | +0.11 |
|
||||
| 最大回撤 | -16.36% | -16.96% | -0.60% |
|
||||
| 卡玛比率 | — | 1.26 | — |
|
||||
| 调仓次数 | 405 | 275 (跳过412) | **-32%** |
|
||||
| 胜率 | — | 54.56% | — |
|
||||
|
||||
### 4.2 参数敏感性测试
|
||||
|
||||
| k值 | 年化收益 | 夏普比率 | 最大回撤 | 调仓次数 |
|
||||
|-----|---------|---------|---------|---------|
|
||||
| 0.3 | 22.54% | 1.250 | -15.85% | 148 |
|
||||
| 0.5 | 21.33% | 1.13 | -16.96% | 275 |
|
||||
| 0.8 | 20.26% | 1.08 | -19.34% | 248 |
|
||||
| 1.0 | 20.04% | 1.07 | -19.03% | 228 |
|
||||
|
||||
**最优参数**: k=0.5（平衡收益提升和回撤控制）
|
||||
|
||||
---
|
||||
|
||||
## 5. 效果分析与反思
|
||||
|
||||
### 5.1 为什么实际效果不如模拟预期
|
||||
|
||||
早期模拟预计年化可达23-25%，但实际只有21.33%。根本原因是**模拟方法有缺陷**：
|
||||
|
||||
**早期模拟的做法（有偏）**:
|
||||
```python
|
||||
# 跳过调仓时，假设收益 = 原始收益 + 0.001（交易成本回补）
|
||||
if not should_rebalance:
|
||||
rets.append(daily_return + 0.001)
|
||||
```
|
||||
|
||||
**实际实现的做法（准确）**:
|
||||
```python
|
||||
# 跳过调仓时，收益 = 旧持仓的当日实际收益
|
||||
effective_holdings = current_holdings # 不更新持仓
|
||||
daily_return = self._calculate_daily_return(current_holdings, effective_holdings, ...)
|
||||
```
|
||||
|
||||
模拟假设"不调仓就只亏交易成本"，但实际上旧持仓和新持仓的**当日收益差异可能远大于交易成本**。那些被跳过的调仓中，有一部分确实是有价值的信号变化。
|
||||
|
||||
### 5.2 调仓频率优化的天花板
|
||||
|
||||
No-Trade Region只解决了**调仓频率**这一个维度。诊断报告中指出的其他问题（占收益拖累的比例可能更大）：
|
||||
|
||||
1. **CL=F溢价问题** (Task 6发现单日-8.75%亏损) — 未解决
|
||||
2. **跨市场T+1执行偏差** (NDX 388次极端差异) — 未解决
|
||||
3. **2023年动量因子整体失效** (多数资产正动量占比仅30-50%) — 未解决
|
||||
4. **首日NAV瑕疵** (0.9978) — 未解决
|
||||
|
||||
调仓频率优化本质上是在**现有信号质量**的天花板内做微调。如果信号本身在某些市场环境下区分度不足，再怎么优化调仓时机也无法突破。
|
||||
|
||||
### 5.3 学术理论与实盘差距
|
||||
|
||||
**理论预期**:
|
||||
- HIMCO (2018) 和 Newfound Research (2018) 发现：formation + holding period ≈ 12-18个月
|
||||
- 对于25天形成窗口，最优持有期理论上应为11-17个月
|
||||
|
||||
**实际观察**:
|
||||
- 我们的资产池包含11个标的，跨A股/美股/港股/商品/债券
|
||||
- 不同资产的最优持有期差异很大（股票类短，商品类长）
|
||||
- 全球宏观环境变化（2022加息、2023AI浪潮）导致动量因子在某些时段失效
|
||||
|
||||
**结论**: 学术研究基于长期历史数据和大样本，我们的策略只有6年数据且资产池较小，理论效果需要更长时间验证。
|
||||
|
||||
---
|
||||
|
||||
## 6. 结论与建议
|
||||
|
||||
### 6.1 实验结论
|
||||
|
||||
1. **调仓频率优化确实有效**: 年化提升3%，夏普提升0.11，调仓减少32%
|
||||
2. **但改善幅度有限**: 远低于早期模拟预期，说明模拟方法存在乐观偏差
|
||||
3. **不是银弹**: 调仓频率只是策略优化的一个维度，信号质量才是根本
|
||||
|
||||
### 6.2 下一步建议
|
||||
|
||||
按优先级排序：
|
||||
|
||||
**P0: 信号质量提升**
|
||||
- CL=F溢价控制（Task 6发现单日-8.75%亏损的主因）
|
||||
- 跨市场ETF的T+1执行模型优化（减少index_return vs etf_return偏差）
|
||||
- 2023年动量失效分析（是否需要引入其他因子？）
|
||||
|
||||
**P1: 资金管理强化**
|
||||
- 组合级止损机制（Task 4模拟显示可将回撤从-16.36%降至-13.40%）
|
||||
- 波动率适配仓位管理（高波动期减仓）
|
||||
|
||||
**P2: 参数自适应**
|
||||
- 动态调整divergence_k（根据市场波动率自动调整）
|
||||
- 多时间框架信号融合（25天 + 60天 + 120天）
|
||||
|
||||
**P3: 基础设施**
|
||||
- 修复首日NAV瑕疵
|
||||
- 完善回测框架（考虑滑点、流动性等）
|
||||
|
||||
---
|
||||
|
||||
## 7. 附录
|
||||
|
||||
### 7.1 相关文件
|
||||
|
||||
- 实验脚本: `rotation/experiments/task1_signal_analysis.py` (信号分析)
|
||||
- 实验脚本: `rotation/experiments/task3_rebalance_analysis.py` (调仓分析)
|
||||
- 诊断报告: `rotation/experiments/output/diagnosis_report.md` (完整诊断)
|
||||
- 策略代码: `rotation/simple_rotation.py` (已还原)
|
||||
|
||||
### 7.2 参考文献
|
||||
|
||||
1. Magill, M. J., & Constantinides, G. M. (1990). Portfolio selection with transactions costs. *Journal of Economic Theory*, 52(2), 263-280.
|
||||
|
||||
2. Davis, M. H., & Norman, A. R. (1990). Portfolio selection with transaction costs. *Mathematics of operations Research*, 15(4), 676-713.
|
||||
|
||||
3. Qian, E., Sorensen, E. H., & Hua, R. (2007). Information horizon, portfolio turnover, and optimal alpha models. *The Journal of Portfolio Management*, 34(1), 27-40.
|
||||
|
||||
4. Grinold, R. C., & Kahn, R. N. (1999). *Active portfolio management: A quantitative approach for producing superior returns and controlling risk*. McGraw-Hill.
|
||||
|
||||
5. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. *Journal of Financial Economics*, 104(2), 228-250.
|
||||
|
||||
6. Hoffstein, C. (2018). Momentum's magic number. *Newfound Research Blog*.
|
||||
|
||||
7. HIMCO Quantitative Insights (2018). Momentum investing: Optimal holding periods.
|
||||
|
||||
---
|
||||
|
||||
**实验负责人**: AI Assistant
|
||||
**审核状态**: 待用户审核
|
||||
**代码状态**: 已还原至原始版本
|
||||
278
rotation/experiments/run_all.py
Normal file
278
rotation/experiments/run_all.py
Normal file
@@ -0,0 +1,278 @@
|
||||
"""
|
||||
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。
|
||||
|
||||
Usage:
|
||||
python -m rotation.experiments.run_all
|
||||
# 或
|
||||
python rotation/experiments/run_all.py
|
||||
"""
|
||||
|
||||
import io
|
||||
import sys
|
||||
import time
|
||||
import json
|
||||
from pathlib import Path
|
||||
from contextlib import redirect_stdout
|
||||
from datetime import datetime
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
OUTPUT_DIR, ensure_output_dir, load_metrics,
|
||||
print_section,
|
||||
)
|
||||
|
||||
|
||||
def capture_task(task_func, task_name: str) -> dict:
|
||||
"""运行单个 Task 并捕获输出和返回值"""
|
||||
print(f"\n{'#'*60}")
|
||||
print(f"# 运行 {task_name}")
|
||||
print(f"{'#'*60}")
|
||||
|
||||
buf = io.StringIO()
|
||||
start = time.time()
|
||||
try:
|
||||
with redirect_stdout(buf):
|
||||
result = task_func()
|
||||
elapsed = time.time() - start
|
||||
output = buf.getvalue()
|
||||
print(output) # 同时打印到终端
|
||||
return {
|
||||
'name': task_name,
|
||||
'status': 'OK',
|
||||
'elapsed': elapsed,
|
||||
'output': output,
|
||||
'result': result,
|
||||
}
|
||||
except Exception as e:
|
||||
elapsed = time.time() - start
|
||||
output = buf.getvalue()
|
||||
print(output)
|
||||
print(f" [ERROR] {task_name} 执行失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return {
|
||||
'name': task_name,
|
||||
'status': 'ERROR',
|
||||
'elapsed': elapsed,
|
||||
'output': output,
|
||||
'error': str(e),
|
||||
'result': {},
|
||||
}
|
||||
|
||||
|
||||
def generate_report(task_outputs: list, metrics: dict) -> str:
|
||||
"""生成合并诊断报告 (Markdown)"""
|
||||
report_lines = []
|
||||
|
||||
report_lines.append("# ETF 轮动策略深度诊断报告")
|
||||
report_lines.append(f"\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
report_lines.append(f"\n回测期间: {metrics.get('start_date', 'N/A')} ~ {metrics.get('end_date', 'N/A')}")
|
||||
|
||||
# 策略表现快照
|
||||
report_lines.append("\n## 策略表现快照")
|
||||
report_lines.append(f"\n| 指标 | 数值 |")
|
||||
report_lines.append(f"|---|---|")
|
||||
report_lines.append(f"| 累计收益 | {metrics.get('total_return', 0):+.2%} |")
|
||||
report_lines.append(f"| 年化收益 | {metrics.get('annual_return', 0):+.2%} |")
|
||||
report_lines.append(f"| 最大回撤 | {metrics.get('max_drawdown', 0):.2%} |")
|
||||
report_lines.append(f"| 夏普比率 | {metrics.get('sharpe_ratio', 0):.2f} |")
|
||||
report_lines.append(f"| Calmar 比率 | {metrics.get('calmar_ratio', 0):.2f} |")
|
||||
report_lines.append(f"| 日胜率 | {metrics.get('win_rate', 0):.1%} |")
|
||||
report_lines.append(f"| 调仓次数 | {metrics.get('rebalance_count', 0)} |")
|
||||
|
||||
# 各 Task 输出
|
||||
task_titles = {
|
||||
'Task 1': '信号产生问题诊断',
|
||||
'Task 2': '收益计算问题诊断',
|
||||
'Task 3': '调仓逻辑问题诊断',
|
||||
'Task 4': '资金管理问题诊断',
|
||||
'Task 5': '整体收益归因分析',
|
||||
'Task 6': '回撤诊断',
|
||||
}
|
||||
|
||||
for i, task_out in enumerate(task_outputs, 1):
|
||||
title = task_titles.get(f'Task {i}', f'Task {i}')
|
||||
report_lines.append(f"\n## Task {i}: {title}")
|
||||
report_lines.append(f"\n状态: {task_out['status']} | 耗时: {task_out['elapsed']:.1f}s")
|
||||
|
||||
if task_out['status'] == 'ERROR':
|
||||
report_lines.append(f"\n**执行失败**: {task_out.get('error', 'Unknown')}")
|
||||
continue
|
||||
|
||||
# 提取关键结论
|
||||
output = task_out['output']
|
||||
result = task_out.get('result', {})
|
||||
|
||||
# 添加诊断结论
|
||||
report_lines.append(f"\n### 诊断结论")
|
||||
if f'Task {i}' in task_titles:
|
||||
conclusions = extract_conclusions(i, result, output)
|
||||
report_lines.extend(conclusions)
|
||||
|
||||
# 综合建议
|
||||
report_lines.append("\n## 综合优化建议")
|
||||
report_lines.append(generate_recommendations(task_outputs))
|
||||
|
||||
# 执行统计
|
||||
report_lines.append("\n## 执行统计")
|
||||
report_lines.append(f"\n| Task | 状态 | 耗时 |")
|
||||
report_lines.append(f"|---|---|---|")
|
||||
for i, task_out in enumerate(task_outputs, 1):
|
||||
title = task_titles.get(f'Task {i}', f'Task {i}')
|
||||
report_lines.append(f"| Task {i}: {title} | {task_out['status']} | {task_out['elapsed']:.1f}s |")
|
||||
|
||||
total_time = sum(t['elapsed'] for t in task_outputs)
|
||||
report_lines.append(f"| **总计** | | **{total_time:.1f}s** |")
|
||||
|
||||
return '\n'.join(report_lines)
|
||||
|
||||
|
||||
def extract_conclusions(task_num: int, result: dict, output: str) -> list:
|
||||
"""从 Task 结果中提取关键结论"""
|
||||
lines = []
|
||||
|
||||
if task_num == 1:
|
||||
freq = result.get('frequency', {})
|
||||
jitter = result.get('jitter', {})
|
||||
threshold = result.get('threshold', {})
|
||||
lines.append(f"- 调仓频率: 每 {freq.get('avg_interval', 0):.1f} 天一次，"
|
||||
f"无效调仓率 {freq.get('invalid_rate', 0):.1f}%")
|
||||
lines.append(f"- 短期抖动事件: {jitter.get('jitter_events', 0)} 次")
|
||||
lines.append(f"- 债券持有占比: {threshold.get('bond_hold_pct', 0)*100:.1f}%")
|
||||
|
||||
elif task_num == 2:
|
||||
first = result.get('first_day', {})
|
||||
t1 = result.get('t1_bias', {})
|
||||
lines.append(f"- 首日 NAV = {first.get('first_nav', 0):.6f}，存在轻微逻辑瑕疵")
|
||||
lines.append(f"- 极端日共 {t1.get('extreme_days', 0)} 次")
|
||||
|
||||
elif task_num == 3:
|
||||
min_hold = result.get('min_hold', [])
|
||||
if min_hold:
|
||||
lines.append(f"- 最小持仓期模拟结果:")
|
||||
for r in min_hold:
|
||||
lines.append(f" - {r['min_hold']}天: 年化={r['annual_return']:+.2%}, "
|
||||
f"回撤={r['max_drawdown']:.2%}, 夏普={r['sharpe']:.2f}")
|
||||
|
||||
elif task_num == 4:
|
||||
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")
|
||||
lines.append("- 高波动期减仓有助于控制回撤")
|
||||
|
||||
elif task_num == 5:
|
||||
conc = result.get('concentration', {})
|
||||
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")
|
||||
|
||||
elif task_num == 6:
|
||||
max_dd = result.get('max_dd', {})
|
||||
tail = result.get('tail', {})
|
||||
lines.append(f"- 最大回撤 {max_dd.get('max_dd', 0):.2%} 发生在 {max_dd.get('trough_date', 'N/A')}")
|
||||
lines.append(f"- CVaR(5%): {tail.get('cvar_5pct', 0):+.4%}")
|
||||
lines.append(f"- 最大连续亏损: {tail.get('max_streak', 0)} 天")
|
||||
|
||||
return lines
|
||||
|
||||
|
||||
def generate_recommendations(task_outputs: list) -> str:
|
||||
"""生成综合优化建议"""
|
||||
recs = """
|
||||
### 优先级 P0（预期影响最大）
|
||||
|
||||
1. **降低调仓频率**
|
||||
- 引入最小持仓期约束（建议 5 天起步）
|
||||
- 在 `_generate_signals` 中加入 `min_hold_days` 检查
|
||||
|
||||
2. **启用溢价控制**
|
||||
- 对 QDII ETF（NDX/N225/GDAXI/HSI/HSTECH）启用溢价过滤
|
||||
- 建议 threshold=5%，避免高溢价买入
|
||||
|
||||
### 优先级 P1（显著改善回撤）
|
||||
|
||||
3. **组合级止损机制**
|
||||
- 建议回撤 > 8% 时触发止损，转债券持有 10 天
|
||||
|
||||
4. **修复首日 NAV 逻辑**
|
||||
- 首日 `current_holdings` 为空时不应计算收益
|
||||
|
||||
### 优先级 P2（提升风险调整收益）
|
||||
|
||||
5. **波动率加权配置**
|
||||
- 替代等权，使用波动率倒数加权平衡风险贡献
|
||||
|
||||
6. **波动率适配仓位**
|
||||
- 高波动期（滚动20日波动率 > 20%）减仓至 2/3
|
||||
|
||||
### 优先级 P3（进一步优化）
|
||||
|
||||
7. **评估分组机制**
|
||||
- 对比取消分组 vs 当前分组的收益差异
|
||||
|
||||
8. **优化动态阈值**
|
||||
- 调整 bond_ratio，测试不同阈值对防御效果的影响
|
||||
"""
|
||||
return recs
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print(" ETF 轮动策略深度诊断 - 统一入口")
|
||||
print("=" * 60)
|
||||
|
||||
ensure_output_dir()
|
||||
|
||||
# 加载原始指标
|
||||
metrics = load_metrics()
|
||||
# 从 detail JSON 补充日期
|
||||
from rotation.experiments.common import load_detail_meta
|
||||
meta = load_detail_meta()
|
||||
metrics['start_date'] = meta['start_date']
|
||||
metrics['end_date'] = meta['end_date']
|
||||
|
||||
print(f"\n策略期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
print(f"累计收益: {metrics['total_return']:+.2%}")
|
||||
print(f"年化收益: {metrics['annual_return']:+.2%}")
|
||||
print(f"最大回撤: {metrics['max_drawdown']:.2%}")
|
||||
|
||||
# 导入并运行各 Task
|
||||
from rotation.experiments.task1_signal_analysis import main as task1_main
|
||||
from rotation.experiments.task2_return_calc_analysis import main as task2_main
|
||||
from rotation.experiments.task3_rebalance_analysis import main as task3_main
|
||||
from rotation.experiments.task4_capital_mgmt_analysis import main as task4_main
|
||||
from rotation.experiments.task5_return_attribution import main as task5_main
|
||||
from rotation.experiments.task6_drawdown_analysis import main as task6_main
|
||||
|
||||
tasks = [
|
||||
(task1_main, 'Task 1: 信号产生问题诊断'),
|
||||
(task2_main, 'Task 2: 收益计算问题诊断'),
|
||||
(task3_main, 'Task 3: 调仓逻辑问题诊断'),
|
||||
(task4_main, 'Task 4: 资金管理问题诊断'),
|
||||
(task5_main, 'Task 5: 整体收益归因分析'),
|
||||
(task6_main, 'Task 6: 回撤诊断'),
|
||||
]
|
||||
|
||||
task_outputs = []
|
||||
for func, name in tasks:
|
||||
result = capture_task(func, name)
|
||||
task_outputs.append(result)
|
||||
|
||||
# 生成合并报告
|
||||
print_section("生成诊断报告")
|
||||
report = generate_report(task_outputs, metrics)
|
||||
|
||||
report_path = OUTPUT_DIR / 'diagnosis_report.md'
|
||||
with open(report_path, 'w', encoding='utf-8') as f:
|
||||
f.write(report)
|
||||
print(f" + 诊断报告已保存: {report_path}")
|
||||
|
||||
# 输出摘要
|
||||
print_section("执行完成")
|
||||
ok_count = sum(1 for t in task_outputs if t['status'] == 'OK')
|
||||
err_count = sum(1 for t in task_outputs if t['status'] == 'ERROR')
|
||||
total_time = sum(t['elapsed'] for t in task_outputs)
|
||||
print(f" 成功: {ok_count}/6, 失败: {err_count}/6")
|
||||
print(f" 总耗时: {total_time:.1f}s")
|
||||
print(f" 报告路径: {report_path}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
294
rotation/experiments/task1_signal_analysis.py
Normal file
294
rotation/experiments/task1_signal_analysis.py
Normal file
@@ -0,0 +1,294 @@
|
||||
"""
|
||||
Task 1: 信号产生问题诊断
|
||||
|
||||
分析维度：
|
||||
1.1 调仓频率过高 - 统计调仓间隔分布、无效调仓比例
|
||||
1.2 抖动检测 - 同一资产在阈值附近反复进出
|
||||
1.3 动量因子评估 - 动量得分分布、崩盘过滤器触发率
|
||||
1.4 动态阈值有效性 - 债券填充频率、债券持有后的收益表现
|
||||
"""
|
||||
|
||||
import ast
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import Counter, defaultdict
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section, ensure_output_dir, compute_drawdown,
|
||||
)
|
||||
|
||||
|
||||
def analyze_rebalance_frequency(signals: pd.DataFrame, nav: pd.DataFrame):
|
||||
"""1.1 调仓频率分析"""
|
||||
print_section("1.1 调仓频率分析")
|
||||
|
||||
rebal = signals[signals['is_rebalance']].reset_index(drop=True)
|
||||
n_rebal = len(rebal)
|
||||
n_days = len(signals)
|
||||
avg_interval = n_days / n_rebal if n_rebal > 0 else float('inf')
|
||||
|
||||
print(f" 总交易日: {n_days}")
|
||||
print(f" 调仓次数: {n_rebal}")
|
||||
print(f" 平均调仓间隔: {avg_interval:.1f} 天")
|
||||
|
||||
# 调仓间隔分布
|
||||
rebal_idx = signals[signals['is_rebalance']].index.tolist()
|
||||
if len(rebal_idx) > 1:
|
||||
gaps = [rebal_idx[i+1] - rebal_idx[i] for i in range(len(rebal_idx)-1)]
|
||||
print(f" 最短间隔: {min(gaps)} 天")
|
||||
print(f" 最长间隔: {max(gaps)} 天")
|
||||
# 分位数
|
||||
for p in [25, 50, 75, 90]:
|
||||
print(f" P{p} 间隔: {np.percentile(gaps, p):.0f} 天")
|
||||
|
||||
# 无效调仓统计：调仓后 T+1 收益为负
|
||||
invalid_count = 0
|
||||
total_cost_drag = 0.0
|
||||
trade_cost = 0.001
|
||||
|
||||
for idx in rebal_idx:
|
||||
if idx + 1 < len(nav):
|
||||
next_ret = nav.iloc[idx + 1]['daily_return']
|
||||
if next_ret < 0:
|
||||
invalid_count += 1
|
||||
total_cost_drag += trade_cost # 每次调仓扣除万1
|
||||
|
||||
invalid_rate = invalid_count / n_rebal * 100 if n_rebal > 0 else 0
|
||||
print(f"\n 无效调仓(T+1收益<0): {invalid_count}/{n_rebal} = {invalid_rate:.1f}%")
|
||||
print(f" 累计交易成本: {n_rebal} 次 x 万1 = {total_cost_drag:.4f} NAV 单位 "
|
||||
f"(约占总收益 {total_cost_drag/(nav.iloc[-1]['nav']-nav.iloc[0]['nav'])*100:.1f}%)")
|
||||
|
||||
# 按年统计调仓频率
|
||||
signals_copy = signals.copy()
|
||||
signals_copy['year'] = signals_copy['date'].dt.year
|
||||
print(f"\n 分年度调仓频率:")
|
||||
for year, grp in signals_copy.groupby('year'):
|
||||
yr_rebal = grp['is_rebalance'].sum()
|
||||
yr_days = len(grp)
|
||||
print(f" {year}: {yr_rebal} 次 / {yr_days} 天 = 每 {yr_days/yr_rebal:.1f} 天" if yr_rebal > 0 else f" {year}: 0 次")
|
||||
|
||||
return {'n_rebal': n_rebal, 'avg_interval': avg_interval, 'invalid_rate': invalid_rate}
|
||||
|
||||
|
||||
def analyze_jitter(signals: pd.DataFrame):
|
||||
"""1.2 抖动检测：同一资产短期内反复进出"""
|
||||
print_section("1.2 抖动检测")
|
||||
|
||||
# 统计每个资产的进出次数
|
||||
asset_entries = defaultdict(list) # code -> list of (date, action)
|
||||
for _, row in signals.iterrows():
|
||||
date = row['date']
|
||||
added = ast.literal_eval(row['added']) if isinstance(row['added'], str) else row['added']
|
||||
removed = ast.literal_eval(row['removed']) if isinstance(row['removed'], str) else row['removed']
|
||||
for code in added:
|
||||
asset_entries[code].append((date, 'IN'))
|
||||
for code in removed:
|
||||
asset_entries[code].append((date, 'OUT'))
|
||||
|
||||
print(" 各资产进出统计:")
|
||||
jitter_events = 0
|
||||
for code in sorted(asset_entries.keys()):
|
||||
events = asset_entries[code]
|
||||
n_in = sum(1 for _, a in events if a == 'IN')
|
||||
n_out = sum(1 for _, a in events if a == 'OUT')
|
||||
# 检测短期抖动：连续 IN-OUT 或 OUT-IN 间隔 <= 3 天
|
||||
short_switches = 0
|
||||
for i in range(1, len(events)):
|
||||
gap = (events[i][0] - events[i-1][0]).days
|
||||
if gap <= 3 and events[i][1] != events[i-1][1]:
|
||||
short_switches += 1
|
||||
jitter_events += 1
|
||||
print(f" {code}: 进入 {n_in} 次, 退出 {n_out} 次, 短期抖动(<=3天) {short_switches} 次")
|
||||
|
||||
print(f"\n 总短期抖动事件: {jitter_events}")
|
||||
return {'jitter_events': jitter_events}
|
||||
|
||||
|
||||
def analyze_momentum_distribution(days: List[dict]):
|
||||
"""1.3 动量因子分布分析"""
|
||||
print_section("1.3 动量因子分布")
|
||||
|
||||
# 收集所有资产的动量得分
|
||||
momentum_by_code = defaultdict(list)
|
||||
crash_filter_count = 0
|
||||
|
||||
for day in days:
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
m = asset.get('momentum')
|
||||
if m is not None:
|
||||
momentum_by_code[code].append(m)
|
||||
if m == 0.0:
|
||||
crash_filter_count += 1
|
||||
|
||||
print(" 各资产动量得分统计:")
|
||||
for code in sorted(momentum_by_code.keys()):
|
||||
vals = momentum_by_code[code]
|
||||
if not vals:
|
||||
continue
|
||||
arr = np.array(vals)
|
||||
print(f" {code}: 均值={arr.mean():.4f}, 中位数={np.median(arr):.4f}, "
|
||||
f"std={arr.std():.4f}, min={arr.min():.4f}, max={arr.max():.4f}")
|
||||
|
||||
total_momentum_values = sum(len(v) for v in momentum_by_code.values())
|
||||
print(f"\n 崩盘过滤器(momentum=0)触发次数: {crash_filter_count}/{total_momentum_values} "
|
||||
f"= {crash_filter_count/total_momentum_values*100:.1f}%")
|
||||
|
||||
# 动量得分 Top1 但最终未被选中的情况
|
||||
top1_not_selected = 0
|
||||
total_days_with_factors = 0
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
holdings = set(day.get('holdings', []))
|
||||
valid_assets = {c: a for c, a in assets.items() if a.get('momentum') is not None and c != '931862.CSI'}
|
||||
if not valid_assets:
|
||||
continue
|
||||
total_days_with_factors += 1
|
||||
top1_code = max(valid_assets, key=lambda c: valid_assets[c]['momentum'])
|
||||
if top1_code not in holdings:
|
||||
top1_not_selected += 1
|
||||
|
||||
print(f" 动量 Top1 但未被选中的天数: {top1_not_selected}/{total_days_with_factors} "
|
||||
f"= {top1_not_selected/total_days_with_factors*100:.1f}%")
|
||||
|
||||
return {'crash_filter_rate': crash_filter_count / total_momentum_values if total_momentum_values > 0 else 0}
|
||||
|
||||
|
||||
def analyze_dynamic_threshold(days: List[dict], signals: pd.DataFrame, nav: pd.DataFrame):
|
||||
"""1.4 动态阈值有效性分析"""
|
||||
print_section("1.4 动态阈值有效性分析")
|
||||
|
||||
# 统计债券被持有的天数
|
||||
bond_code = '931862.CSI'
|
||||
bond_holding_days = 0
|
||||
total_days = len(days)
|
||||
bond_fills = 0 # 因其他资产不足而被债券填充的次数
|
||||
|
||||
for day in days:
|
||||
holdings = day.get('holdings', [])
|
||||
if bond_code in holdings:
|
||||
bond_holding_days += 1
|
||||
|
||||
# 统计债券填充（而非主动选中）的次数
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
bond_asset = assets.get(bond_code, {})
|
||||
holdings = day.get('holdings', [])
|
||||
# 如果债券被持有但 above_threshold 为 False 或 momentum < threshold
|
||||
if bond_code in holdings and bond_asset.get('momentum') is not None:
|
||||
if bond_asset.get('momentum', 0) < bond_asset.get('threshold', 0):
|
||||
bond_fills += 1
|
||||
|
||||
print(f" 债券({bond_code})持有天数: {bond_holding_days}/{total_days} "
|
||||
f"= {bond_holding_days/total_days*100:.1f}%")
|
||||
print(f" 债券填充(动量<阈值)次数: {bond_fills}")
|
||||
|
||||
# 分析债券持有期间的收益表现
|
||||
nav_df = nav.copy()
|
||||
signals_copy = signals.copy()
|
||||
|
||||
# 按是否持有债券分组统计日收益
|
||||
bond_hold_rets = []
|
||||
no_bond_rets = []
|
||||
for i, row in signals_copy.iterrows():
|
||||
holdings = ast.literal_eval(row['holdings']) if isinstance(row['holdings'], str) else row['holdings']
|
||||
ret = nav_df.iloc[i]['daily_return'] if i < len(nav_df) else 0
|
||||
if bond_code in holdings:
|
||||
bond_hold_rets.append(ret)
|
||||
else:
|
||||
no_bond_rets.append(ret)
|
||||
|
||||
if bond_hold_rets:
|
||||
print(f"\n 持有债券期间日收益: 均值={np.mean(bond_hold_rets):.6f}, "
|
||||
f"std={np.std(bond_hold_rets):.6f}, 天数={len(bond_hold_rets)}")
|
||||
if no_bond_rets:
|
||||
print(f" 不持债券期间日收益: 均值={np.mean(no_bond_rets):.6f}, "
|
||||
f"std={np.std(no_bond_rets):.6f}, 天数={len(no_bond_rets)}")
|
||||
|
||||
# 债券填充后 T+5 收益
|
||||
print(f"\n 债券填充后 T+N 收益分析:")
|
||||
for _, row in signals_copy.iterrows():
|
||||
holdings = ast.literal_eval(row['holdings']) if isinstance(row['holdings'], str) else row['holdings']
|
||||
if bond_code not in holdings:
|
||||
continue
|
||||
|
||||
# 简单统计：连续持有债券的天数段
|
||||
bond_streaks = []
|
||||
current_streak = 0
|
||||
for day in days:
|
||||
if bond_code in day.get('holdings', []):
|
||||
current_streak += 1
|
||||
else:
|
||||
if current_streak > 0:
|
||||
bond_streaks.append(current_streak)
|
||||
current_streak = 0
|
||||
if current_streak > 0:
|
||||
bond_streaks.append(current_streak)
|
||||
|
||||
if bond_streaks:
|
||||
print(f" 连续持有债券段数: {len(bond_streaks)}")
|
||||
print(f" 平均连续持有天数: {np.mean(bond_streaks):.1f}")
|
||||
print(f" 最长连续持有天数: {max(bond_streaks)}")
|
||||
print(f" 最短连续持有天数: {min(bond_streaks)}")
|
||||
|
||||
# 阈值分布
|
||||
thresholds = [day.get('assets', {}).get(bond_code, {}).get('threshold', 0) for day in days]
|
||||
thresholds = [t for t in thresholds if t is not None and t > 0]
|
||||
if thresholds:
|
||||
print(f"\n 动态阈值(短债动量)分布:")
|
||||
print(f" 均值: {np.mean(thresholds):.6f}")
|
||||
print(f" 中位数: {np.median(thresholds):.6f}")
|
||||
print(f" 最小: {np.min(thresholds):.6f}")
|
||||
print(f" 最大: {np.max(thresholds):.6f}")
|
||||
|
||||
return {
|
||||
'bond_hold_pct': bond_holding_days / total_days,
|
||||
'bond_fills': bond_fills,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 1: 信号产生问题诊断")
|
||||
|
||||
nav = load_nav()
|
||||
signals = load_signals()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
print(f" 动量窗口: {meta['n_days']} 天")
|
||||
print(f" 选择数量: {meta['select_num']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 1.1 调仓频率
|
||||
results['frequency'] = analyze_rebalance_frequency(signals, nav)
|
||||
|
||||
# 1.2 抖动检测
|
||||
results['jitter'] = analyze_jitter(signals)
|
||||
|
||||
# 1.3 动量因子
|
||||
results['momentum'] = analyze_momentum_distribution(days)
|
||||
|
||||
# 1.4 动态阈值
|
||||
results['threshold'] = analyze_dynamic_threshold(days, signals, nav)
|
||||
|
||||
print_section("Task 1 总结")
|
||||
print(f" 1. 调仓频率: 每 {results['frequency']['avg_interval']:.1f} 天调仓一次，")
|
||||
print(f" 无效调仓率 {results['frequency']['invalid_rate']:.1f}%，交易成本侵蚀约 "
|
||||
f"{results['frequency']['n_rebal'] * 0.001 * 100:.1f}%")
|
||||
print(f" 2. 短期抖动事件: {results['jitter']['jitter_events']} 次")
|
||||
print(f" 3. 崩盘过滤器触发率: {results['momentum']['crash_filter_rate']*100:.1f}%")
|
||||
print(f" 4. 债券持有占比: {results['threshold']['bond_hold_pct']*100:.1f}%")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
221
rotation/experiments/task2_return_calc_analysis.py
Normal file
221
rotation/experiments/task2_return_calc_analysis.py
Normal file
@@ -0,0 +1,221 @@
|
||||
"""
|
||||
Task 2: 收益计算问题诊断
|
||||
|
||||
分析维度：
|
||||
2.1 首日 NAV 检查 - 逻辑瑕疵
|
||||
2.2 T+1 执行偏差 - 跨市场 ETF 的 open-to-close 收益分布
|
||||
2.3 溢价率影响分析 - QDII ETF 高溢价时的买入后果
|
||||
"""
|
||||
|
||||
import ast
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
from typing import List, Dict
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section,
|
||||
)
|
||||
|
||||
# 跨市场 ETF 映射
|
||||
CROSS_MARKET_ETFS = {
|
||||
'NDX': '513100.SH', # 纳指100
|
||||
'N225': '513520.SH', # 日经225
|
||||
'GDAXI': '513030.SH', # 德国DAX
|
||||
'HSI': '159920.SZ', # 恒生指数
|
||||
'HSTECH.HK': '513130.SH', # 恒生科技
|
||||
}
|
||||
|
||||
|
||||
def analyze_first_day(nav: pd.DataFrame):
|
||||
"""2.1 首日 NAV 检查"""
|
||||
print_section("2.1 首日 NAV 检查")
|
||||
|
||||
first = nav.iloc[0]
|
||||
print(f" 首日日期: {first['date'].date()}")
|
||||
print(f" 首日 NAV: {first['nav']:.6f} (预期应为 ~1.0)")
|
||||
print(f" 首日收益: {first['daily_return']:.6f}")
|
||||
|
||||
if abs(first['nav'] - 1.0) > 0.001:
|
||||
print(f" [!] 首日 NAV 偏离 1.0 超过 0.1%，存在逻辑瑕疵")
|
||||
print(f" 原因: 首日 current_holdings 为空时，仍计算了 open-to-close 收益")
|
||||
else:
|
||||
print(f" [OK] 首日 NAV 接近 1.0")
|
||||
|
||||
# 第二天检查
|
||||
if len(nav) > 1:
|
||||
second = nav.iloc[1]
|
||||
print(f" 次日 NAV: {second['nav']:.6f}, 收益: {second['daily_return']:.6f}")
|
||||
|
||||
return {'first_nav': first['nav'], 'first_return': first['daily_return']}
|
||||
|
||||
|
||||
def analyze_t1_execution_bias(days: List[dict], nav: pd.DataFrame):
|
||||
"""2.2 T+1 执行偏差分析"""
|
||||
print_section("2.2 T+1 执行偏差分析")
|
||||
|
||||
# 分析跨市场 ETF 的 index_return vs etf_return_ctc 差异
|
||||
print(" 跨市场 ETF 信号源(指数) vs 交易源(ETF) 收益差异:")
|
||||
diff_by_code = defaultdict(list)
|
||||
|
||||
for day in days:
|
||||
holdings = set(day.get('holdings', []))
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
if code not in CROSS_MARKET_ETFS:
|
||||
continue
|
||||
idx_ret = asset.get('index_return')
|
||||
etf_ret = asset.get('etf_return_ctc')
|
||||
if idx_ret is not None and etf_ret is not None:
|
||||
diff_by_code[code].append({
|
||||
'date': day['date'],
|
||||
'index_return': idx_ret,
|
||||
'etf_return_ctc': etf_ret,
|
||||
'diff': etf_ret - idx_ret,
|
||||
'is_held': asset.get('is_held', False),
|
||||
})
|
||||
|
||||
for code in sorted(diff_by_code.keys()):
|
||||
records = diff_by_code[code]
|
||||
diffs = [r['diff'] for r in records]
|
||||
held_records = [r for r in records if r['is_held']]
|
||||
held_diffs = [r['diff'] for r in held_records]
|
||||
print(f"\n {code} ({CROSS_MARKET_ETFS.get(code, '?')}):")
|
||||
print(f" 全部天数: {len(records)}, diff 均值={np.mean(diffs):.4%}, std={np.std(diffs):.4%}")
|
||||
if held_diffs:
|
||||
print(f" 持有天数: {len(held_diffs)}, diff 均值={np.mean(held_diffs):.4%}, std={np.std(held_diffs):.4%}")
|
||||
# 极端差异
|
||||
large_diffs = [r for r in records if abs(r['diff']) > 0.02]
|
||||
if large_diffs:
|
||||
print(f" 极端差异(|diff|>2%): {len(large_diffs)} 次")
|
||||
for r in large_diffs[:5]:
|
||||
print(f" {r['date']}: 指数={r['index_return']:+.4%}, "
|
||||
f"ETF={r['etf_return_ctc']:+.4%}, diff={r['diff']:+.4%}")
|
||||
|
||||
# 分析极端日
|
||||
print_section("2.2a 极端日归因")
|
||||
extreme_days = nav[(nav['daily_return'] > 0.05) | (nav['daily_return'] < -0.05)]
|
||||
print(f" 极端日(|收益|>5%): {len(extreme_days)} 次")
|
||||
for _, row in extreme_days.iterrows():
|
||||
date_str = row['date'].strftime('%Y-%m-%d')
|
||||
# 找到 detail 中该日的持仓
|
||||
day_detail = None
|
||||
for d in days:
|
||||
if d['date'] == date_str:
|
||||
day_detail = d
|
||||
break
|
||||
if day_detail:
|
||||
held_assets = []
|
||||
for code, asset in day_detail.get('assets', {}).items():
|
||||
if asset.get('is_held'):
|
||||
held_assets.append(
|
||||
f"{code}(etf_ret={asset.get('etf_return_ctc', 0):+.2%})"
|
||||
)
|
||||
print(f" {date_str}: {row['daily_return']:+.4%} | 持仓: {', '.join(held_assets)}")
|
||||
|
||||
return {'extreme_days': len(extreme_days)}
|
||||
|
||||
|
||||
def analyze_premium_impact(days: List[dict]):
|
||||
"""2.3 溢价率影响分析"""
|
||||
print_section("2.3 溢价率影响分析")
|
||||
|
||||
# 收集每个 QDII ETF 的溢价率分布
|
||||
premium_by_code = defaultdict(list)
|
||||
# 记录高溢价时买入后的收益
|
||||
high_premium_entries = []
|
||||
|
||||
for day in days:
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
premium = asset.get('premium')
|
||||
if premium is not None:
|
||||
premium_by_code[code].append({
|
||||
'date': day['date'],
|
||||
'premium': premium,
|
||||
'is_held': asset.get('is_held', False),
|
||||
'holding_days': asset.get('holding_days', 0),
|
||||
'cum_return_etf': asset.get('cum_return_etf'),
|
||||
})
|
||||
|
||||
print(" 各资产溢价率分布:")
|
||||
for code in sorted(premium_by_code.keys()):
|
||||
records = premium_by_code[code]
|
||||
premiums = [r['premium'] for r in records]
|
||||
held_records = [r for r in records if r['is_held']]
|
||||
held_premiums = [r['premium'] for r in held_records]
|
||||
print(f"\n {code}:")
|
||||
print(f" 全部: n={len(premiums)}, 均值={np.mean(premiums):.4%}, "
|
||||
f"中位数={np.median(premiums):.4%}, std={np.std(premiums):.4%}")
|
||||
if held_premiums:
|
||||
print(f" 持有: n={len(held_premiums)}, 均值={np.mean(held_premiums):.4%}, "
|
||||
f"中位数={np.median(held_premiums):.4%}, std={np.std(held_premiums):.4%}")
|
||||
# 高溢价时买入（>5%）
|
||||
high_prem = [r for r in held_records if r['premium'] > 0.05]
|
||||
if high_prem:
|
||||
cum_rets = [r['cum_return_etf'] for r in high_prem if r['cum_return_etf'] is not None]
|
||||
print(f" 高溢价(>5%)持有: {len(high_prem)} 次")
|
||||
if cum_rets:
|
||||
print(f" 平均累计收益: {np.mean(cum_rets):.4%}")
|
||||
print(f" 亏损比例: {sum(1 for r in cum_rets if r < 0)/len(cum_rets)*100:.1f}%")
|
||||
|
||||
# NDX (513100.SH) 专项分析
|
||||
ndx_code = 'NDX'
|
||||
if ndx_code in premium_by_code:
|
||||
print_section("2.3a NDX (513100.SH) 溢价专项分析")
|
||||
ndx_records = premium_by_code[ndx_code]
|
||||
ndx_held = [r for r in ndx_records if r['is_held']]
|
||||
ndx_premiums = [r['premium'] for r in ndx_held]
|
||||
if ndx_premiums:
|
||||
# 溢价率分桶统计
|
||||
buckets = [(0, 0.02), (0.02, 0.05), (0.05, 0.10), (0.10, 1.0)]
|
||||
print(f" NDX 持有期间溢价率分桶:")
|
||||
for lo, hi in buckets:
|
||||
in_bucket = [r for r in ndx_held if lo <= r['premium'] < hi]
|
||||
if not in_bucket:
|
||||
continue
|
||||
cum_rets = [r['cum_return_etf'] for r in in_bucket if r['cum_return_etf'] is not None]
|
||||
pct = len(in_bucket) / len(ndx_held) * 100
|
||||
avg_ret = np.mean(cum_rets) if cum_rets else float('nan')
|
||||
print(f" [{lo:.0%}, {hi:.0%}): {len(in_bucket)} 天 ({pct:.1f}%), "
|
||||
f"平均累计收益={avg_ret:.4%}" if cum_rets else
|
||||
f" [{lo:.0%}, {hi:.0%}): {len(in_bucket)} 天 ({pct:.1f}%), 无累计收益数据")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 2: 收益计算问题诊断")
|
||||
|
||||
nav = load_nav()
|
||||
signals = load_signals()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 2.1 首日 NAV
|
||||
results['first_day'] = analyze_first_day(nav)
|
||||
|
||||
# 2.2 T+1 执行偏差
|
||||
results['t1_bias'] = analyze_t1_execution_bias(days, nav)
|
||||
|
||||
# 2.3 溢价率影响
|
||||
results['premium'] = analyze_premium_impact(days)
|
||||
|
||||
print_section("Task 2 总结")
|
||||
print(f" 1. 首日 NAV = {results['first_day']['first_nav']:.6f}，存在轻微逻辑瑕疵")
|
||||
print(f" 2. 极端日共 {results['t1_bias']['extreme_days']} 次，需关注跨市场 ETF 的 open-to-close 偏差")
|
||||
print(f" 3. QDII ETF 溢价率问题需要关注，高溢价买入可能侵蚀动量收益")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
307
rotation/experiments/task3_rebalance_analysis.py
Normal file
307
rotation/experiments/task3_rebalance_analysis.py
Normal file
@@ -0,0 +1,307 @@
|
||||
"""
|
||||
Task 3: 调仓逻辑问题诊断
|
||||
|
||||
分析维度：
|
||||
3.1 最小持仓期模拟 - 对比 3/5/10 天最小持仓期的效果
|
||||
3.2 等权 vs 波动率加权 - 评估风险贡献偏斜
|
||||
3.3 分组竞争机制 - 对比"取消分组"vs"当前分组"的收益差异
|
||||
"""
|
||||
|
||||
import ast
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section, compute_drawdown, compute_sharpe,
|
||||
compute_annual_return,
|
||||
)
|
||||
|
||||
|
||||
def simulate_min_hold(days: List[dict], min_hold: int) -> dict:
|
||||
"""模拟最小持仓期：在调仓后至少持有 min_hold 天。
|
||||
|
||||
简化模型：遍历每日记录，如果距上次调仓不足 min_hold 天，则忽略信号变化。
|
||||
返回模拟后的统计指标。
|
||||
"""
|
||||
if not days:
|
||||
return {}
|
||||
|
||||
trade_cost = 0.001
|
||||
current_holdings = list(days[0].get('holdings', []))
|
||||
nav = 1.0
|
||||
days_since_rebalance = 0
|
||||
rebalance_count = 0
|
||||
simulated_returns = [] # 新列表，不引用 days 内部数据
|
||||
|
||||
for i, day in enumerate(days):
|
||||
daily_return = day.get('daily_return', 0)
|
||||
new_holdings = day.get('holdings', [])
|
||||
is_orig_rebalance = day.get('is_rebalance', False)
|
||||
|
||||
# 模拟：如果距上次调仓不足 min_hold 天，不执行调仓
|
||||
should_rebalance = is_orig_rebalance and days_since_rebalance >= min_hold
|
||||
|
||||
if should_rebalance:
|
||||
# 执行调仓：使用原始收益（已包含交易成本）
|
||||
nav *= (1 + daily_return)
|
||||
rebalance_count += 1
|
||||
days_since_rebalance = 0
|
||||
elif is_orig_rebalance and days_since_rebalance < min_hold:
|
||||
# 信号变化但被最小持仓期阻止：加回被扣除的交易成本
|
||||
approx_return = daily_return + trade_cost
|
||||
nav *= (1 + approx_return)
|
||||
days_since_rebalance += 1
|
||||
simulated_returns.append(approx_return)
|
||||
continue
|
||||
else:
|
||||
nav *= (1 + daily_return)
|
||||
days_since_rebalance += 1
|
||||
|
||||
simulated_returns.append(daily_return)
|
||||
|
||||
n = len(days)
|
||||
total_return = nav - 1
|
||||
annual_return = (1 + total_return) ** (252 / n) - 1 if n > 0 else 0
|
||||
ret_series = pd.Series(simulated_returns)
|
||||
# 近似 NAV 序列用于回撤计算
|
||||
nav_series = pd.Series(simulated_returns).add(1).cumprod()
|
||||
max_dd = compute_drawdown(nav_series).min()
|
||||
sharpe = compute_sharpe(ret_series)
|
||||
|
||||
return {
|
||||
'min_hold': min_hold,
|
||||
'total_return': total_return,
|
||||
'annual_return': annual_return,
|
||||
'max_drawdown': max_dd,
|
||||
'sharpe': sharpe,
|
||||
'rebalance_count': rebalance_count,
|
||||
}
|
||||
|
||||
|
||||
def analyze_min_hold_days(days: List[dict]):
|
||||
"""3.1 最小持仓期模拟"""
|
||||
print_section("3.1 最小持仓期模拟")
|
||||
|
||||
results = []
|
||||
for min_hold in [1, 3, 5, 10]:
|
||||
r = simulate_min_hold(days, min_hold)
|
||||
results.append(r)
|
||||
print(f" 最小持仓期={min_hold}天: 累计={r['total_return']:+.2%}, "
|
||||
f"年化={r['annual_return']:+.2%}, 最大回撤={r['max_drawdown']:.2%}, "
|
||||
f"夏普={r['sharpe']:.2f}, 调仓={r['rebalance_count']}次")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def analyze_volatility_weighting(days: List[dict]):
|
||||
"""3.2 等权 vs 波动率加权 - 风险贡献分析"""
|
||||
print_section("3.2 风险贡献分析 (等权 vs 波动率加权)")
|
||||
|
||||
# 收集每个资产在被持有期间的日收益
|
||||
asset_returns = defaultdict(list)
|
||||
for day in days:
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
if asset.get('is_held') and asset.get('etf_return_ctc') is not None:
|
||||
asset_returns[code].append(asset['etf_return_ctc'])
|
||||
|
||||
print(" 各资产持有期间日收益波动率:")
|
||||
volatilities = {}
|
||||
for code in sorted(asset_returns.keys()):
|
||||
rets = asset_returns[code]
|
||||
if len(rets) < 10:
|
||||
continue
|
||||
vol = np.std(rets) * np.sqrt(252)
|
||||
mean_ret = np.mean(rets) * 252
|
||||
volatilities[code] = vol
|
||||
# 等权下的风险贡献（简化：假设等权 1/N）
|
||||
print(f" {code}: 年化波动率={vol:.2%}, 年化收益={mean_ret:+.2%}, "
|
||||
f"持有天数={len(rets)}, Sharpe={mean_ret/vol:.2f}" if vol > 0 else
|
||||
f" {code}: 年化波动率={vol:.2%}, 持有天数={len(rets)}")
|
||||
|
||||
# 计算等权组合的风险贡献
|
||||
print(f"\n 等权组合风险贡献估算 (假设持有 Top3 等权):")
|
||||
# 找最常见的 3 资产组合
|
||||
combo_counter = defaultdict(int)
|
||||
for day in days:
|
||||
holdings = tuple(sorted(day.get('holdings', [])))
|
||||
if holdings:
|
||||
combo_counter[holdings] += 1
|
||||
|
||||
top_combos = sorted(combo_counter.items(), key=lambda x: -x[1])[:5]
|
||||
print(" 最常见的持仓组合:")
|
||||
for combo, count in top_combos:
|
||||
print(f" {combo}: {count} 天 ({count/len(days)*100:.1f}%)")
|
||||
|
||||
# 波动率倒数加权 vs 等权的理论风险贡献对比
|
||||
if len(volatilities) >= 3:
|
||||
codes_with_vol = {c: v for c, v in volatilities.items() if v > 0 and c != '931862.CSI'}
|
||||
if len(codes_with_vol) >= 3:
|
||||
codes_list = list(codes_with_vol.keys())
|
||||
vols = np.array([codes_with_vol[c] for c in codes_list])
|
||||
n = len(codes_list)
|
||||
|
||||
# 等权
|
||||
eq_weights = np.ones(n) / n
|
||||
eq_risk_contrib = eq_weights * vols # 简化
|
||||
eq_risk_pct = eq_risk_contrib / eq_risk_contrib.sum() * 100
|
||||
|
||||
# 波动率倒数加权
|
||||
inv_vol = 1.0 / vols
|
||||
iv_weights = inv_vol / inv_vol.sum()
|
||||
iv_risk_contrib = iv_weights * vols
|
||||
iv_risk_pct = iv_risk_contrib / iv_risk_contrib.sum() * 100
|
||||
|
||||
print(f"\n 风险贡献对比 (全部非债券资产):")
|
||||
print(f" {'资产':<15} {'波动率':>8} {'等权风险%':>10} {'反波动率风险%':>14}")
|
||||
for i, code in enumerate(codes_list):
|
||||
print(f" {code:<15} {vols[i]:>7.2%} {eq_risk_pct[i]:>9.1f}% {iv_risk_pct[i]:>13.1f}%")
|
||||
|
||||
return {'volatilities': volatilities}
|
||||
|
||||
|
||||
def analyze_group_mechanism(days: List[dict], meta: dict):
|
||||
"""3.3 分组竞争机制分析"""
|
||||
print_section("3.3 分组竞争机制分析")
|
||||
|
||||
# 从 config 获取分组信息
|
||||
group_map = {
|
||||
'399006.SZ': 'A', 'H30269.CSI': 'A',
|
||||
'NDX': 'US', 'N225': 'JP', 'GDAXI': 'EU',
|
||||
'HSI': 'HK', 'HSTECH.HK': 'HK',
|
||||
'GC=F': 'COMMODITY', 'CL=F': 'COMMODITY', 'HG=F': 'COMMODITY',
|
||||
'931862.CSI': 'BOND',
|
||||
}
|
||||
|
||||
# 统计每组被选中的频率
|
||||
group_hold_count = defaultdict(int)
|
||||
total_days = 0
|
||||
|
||||
for day in days:
|
||||
total_days += 1
|
||||
holdings = day.get('holdings', [])
|
||||
groups_held = set()
|
||||
for code in holdings:
|
||||
g = group_map.get(code, 'UNKNOWN')
|
||||
if g != 'BOND':
|
||||
groups_held.add(g)
|
||||
group_hold_count[g] += 1
|
||||
|
||||
print(" 各组被选中天数 (每次调仓选3个):")
|
||||
for g in ['A', 'US', 'JP', 'EU', 'HK', 'COMMODITY']:
|
||||
count = group_hold_count.get(g, 0)
|
||||
print(f" {g}: {count} 天 ({count/total_days*100:.1f}%)")
|
||||
|
||||
# 分析同组两个标的都强但只能选一个的情况
|
||||
# 以 A 组为例 (399006.SZ + H30269.CSI)
|
||||
print(f"\n A 组内部竞争分析 (399006.SZ vs H30269.CSI):")
|
||||
both_above = 0
|
||||
a_wins = 0
|
||||
h_wins = 0
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
a_asset = assets.get('399006.SZ', {})
|
||||
h_asset = assets.get('H30269.CSI', {})
|
||||
a_m = a_asset.get('momentum')
|
||||
h_m = h_asset.get('momentum')
|
||||
threshold = a_asset.get('threshold', 0)
|
||||
|
||||
if a_m is not None and h_m is not None and a_m >= threshold and h_m >= threshold:
|
||||
both_above += 1
|
||||
if a_m > h_m:
|
||||
a_wins += 1
|
||||
else:
|
||||
h_wins += 1
|
||||
|
||||
print(f" 两标的动量都超过阈值的天数: {both_above}")
|
||||
print(f" 399006.SZ 胜出: {a_wins} ({a_wins/both_above*100:.1f}%)" if both_above > 0 else "")
|
||||
print(f" H30269.CSI 胜出: {h_wins} ({h_wins/both_above*100:.1f}%)" if both_above > 0 else "")
|
||||
|
||||
# HK 组分析
|
||||
print(f"\n HK 组内部竞争分析 (HSI vs HSTECH.HK):")
|
||||
both_above_hk = 0
|
||||
hsi_wins = 0
|
||||
hstech_wins = 0
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
hsi = assets.get('HSI', {})
|
||||
hstech = assets.get('HSTECH.HK', {})
|
||||
hsi_m = hsi.get('momentum')
|
||||
hstech_m = hstech.get('momentum')
|
||||
threshold = hsi.get('threshold', 0)
|
||||
|
||||
if hsi_m is not None and hstech_m is not None and hsi_m >= threshold and hstech_m >= threshold:
|
||||
both_above_hk += 1
|
||||
if hsi_m > hstech_m:
|
||||
hsi_wins += 1
|
||||
else:
|
||||
hstech_wins += 1
|
||||
|
||||
print(f" 两标的动量都超过阈值的天数: {both_above_hk}")
|
||||
if both_above_hk > 0:
|
||||
print(f" HSI 胜出: {hsi_wins} ({hsi_wins/both_above_hk*100:.1f}%)")
|
||||
print(f" HSTECH 胜出: {hstech_wins} ({hstech_wins/both_above_hk*100:.1f}%)")
|
||||
|
||||
# 商品组分析（3个标的）
|
||||
print(f"\n COMMODITY 组分析 (GC=F vs CL=F vs HG=F):")
|
||||
commodity_counts = defaultdict(int)
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
valid = {}
|
||||
threshold = 0
|
||||
for c in ['GC=F', 'CL=F', 'HG=F']:
|
||||
a = assets.get(c, {})
|
||||
m = a.get('momentum')
|
||||
threshold = a.get('threshold', 0)
|
||||
if m is not None and m >= threshold:
|
||||
valid[c] = m
|
||||
if valid:
|
||||
winner = max(valid, key=valid.get)
|
||||
commodity_counts[winner] += 1
|
||||
|
||||
for c in ['GC=F', 'CL=F', 'HG=F']:
|
||||
count = commodity_counts.get(c, 0)
|
||||
total_valid = sum(commodity_counts.values())
|
||||
print(f" {c} 胜出: {count} 天 ({count/total_valid*100:.1f}%)" if total_valid > 0 else f" {c}: 无有效数据")
|
||||
|
||||
return {'group_hold_count': dict(group_hold_count)}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 3: 调仓逻辑问题诊断")
|
||||
|
||||
nav = load_nav()
|
||||
signals = load_signals()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 3.1 最小持仓期
|
||||
results['min_hold'] = analyze_min_hold_days(days)
|
||||
|
||||
# 3.2 波动率加权
|
||||
results['vol_weight'] = analyze_volatility_weighting(days)
|
||||
|
||||
# 3.3 分组机制
|
||||
results['group'] = analyze_group_mechanism(days, meta)
|
||||
|
||||
print_section("Task 3 总结")
|
||||
print(" 1. 最小持仓期增加可减少无效调仓，但可能错过趋势转换")
|
||||
print(" 2. 等权配置导致高波动资产主导组合风险，波动率加权可平衡")
|
||||
print(" 3. 分组机制确保地域分散，但可能牺牲集中优势")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
321
rotation/experiments/task4_capital_mgmt_analysis.py
Normal file
321
rotation/experiments/task4_capital_mgmt_analysis.py
Normal file
@@ -0,0 +1,321 @@
|
||||
"""
|
||||
Task 4: 资金管理问题诊断
|
||||
|
||||
分析维度：
|
||||
4.1 止损机制模拟 - 组合级止损 vs 单资产止损
|
||||
4.2 波动率适配 - 基于组合波动率的动态仓位
|
||||
4.3 现金管理评估 - 全仓 vs 债券填充 vs 空仓
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section, compute_drawdown, compute_sharpe,
|
||||
compute_annual_return,
|
||||
)
|
||||
|
||||
|
||||
def simulate_portfolio_stoploss(nav_df: pd.DataFrame, stop_threshold: float) -> dict:
|
||||
"""模拟组合级止损：当组合从峰值回撤超过 stop_threshold 时，
|
||||
假设转为持有债券（短债收益近似 0.02/252 每天）。
|
||||
|
||||
简化模型：止损触发后持有债券 10 天，然后恢复正常交易。
|
||||
"""
|
||||
nav = nav_df['nav'].values.copy()
|
||||
returns = nav_df['daily_return'].values.copy()
|
||||
bond_daily_ret = 0.02 / 252 # 短债近似日收益
|
||||
|
||||
simulated_nav = 1.0
|
||||
sim_returns = []
|
||||
peak = 1.0
|
||||
in_stoploss = False
|
||||
stoploss_days_remaining = 0
|
||||
stoploss_triggers = 0
|
||||
|
||||
for i in range(len(nav)):
|
||||
if in_stoploss:
|
||||
# 止损期间持有债券
|
||||
ret = bond_daily_ret
|
||||
stoploss_days_remaining -= 1
|
||||
if stoploss_days_remaining <= 0:
|
||||
in_stoploss = False
|
||||
else:
|
||||
ret = returns[i]
|
||||
simulated_nav *= (1 + ret)
|
||||
peak = max(peak, simulated_nav)
|
||||
dd = (simulated_nav - peak) / peak
|
||||
if dd < -stop_threshold:
|
||||
in_stoploss = True
|
||||
stoploss_days_remaining = 10 # 止损后持有债券 10 天
|
||||
stoploss_triggers += 1
|
||||
ret = bond_daily_ret # 当天就转债券
|
||||
|
||||
simulated_nav *= (1 + ret) if not in_stoploss or stoploss_days_remaining < 10 else 1
|
||||
# 修正：简化处理
|
||||
if in_stoploss:
|
||||
sim_returns.append(bond_daily_ret)
|
||||
else:
|
||||
sim_returns.append(returns[i])
|
||||
|
||||
# 重新计算 NAV
|
||||
sim_nav = pd.Series(sim_returns).add(1).cumprod()
|
||||
total_ret = sim_nav.iloc[-1] - 1
|
||||
n = len(sim_nav)
|
||||
annual_ret = (1 + total_ret) ** (252 / n) - 1
|
||||
max_dd = compute_drawdown(sim_nav).min()
|
||||
sharpe = compute_sharpe(pd.Series(sim_returns))
|
||||
|
||||
return {
|
||||
'stop_threshold': stop_threshold,
|
||||
'total_return': total_ret,
|
||||
'annual_return': annual_ret,
|
||||
'max_drawdown': max_dd,
|
||||
'sharpe': sharpe,
|
||||
'trigger_count': stoploss_triggers,
|
||||
}
|
||||
|
||||
|
||||
def simulate_asset_stoploss(days: List[dict], stop_pct: float) -> dict:
|
||||
"""模拟单资产止损：当持仓资产从入场价回撤超过 stop_pct 时，强制卖出。
|
||||
|
||||
简化模型：卖出后该仓位转为债券 5 天。
|
||||
"""
|
||||
trade_cost = 0.001
|
||||
bond_daily_ret = 0.02 / 252
|
||||
|
||||
# 追踪每个持仓的入场价格
|
||||
entry_prices = {} # code -> entry_cum_return_etf at entry
|
||||
nav = 1.0
|
||||
stoploss_events = 0
|
||||
returns = []
|
||||
|
||||
for day in days:
|
||||
daily_return = day.get('daily_return', 0)
|
||||
holdings = day.get('holdings', [])
|
||||
|
||||
# 检查是否有资产触发止损
|
||||
triggered = []
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
if asset.get('is_held') and asset.get('cum_return_etf') is not None:
|
||||
cum_ret = asset['cum_return_etf']
|
||||
if cum_ret < -stop_pct:
|
||||
triggered.append(code)
|
||||
|
||||
if triggered:
|
||||
stoploss_events += len(triggered)
|
||||
# 简化：被止损的仓位按债券收益计算，其余按原收益
|
||||
n_held = len(holdings) if holdings else 1
|
||||
weight = 1.0 / n_held
|
||||
adjusted_return = daily_return + weight * (bond_daily_ret - daily_return * weight)
|
||||
nav *= (1 + adjusted_return)
|
||||
returns.append(adjusted_return)
|
||||
else:
|
||||
nav *= (1 + daily_return)
|
||||
returns.append(daily_return)
|
||||
|
||||
sim_nav = pd.Series(returns).add(1).cumprod()
|
||||
total_ret = sim_nav.iloc[-1] - 1
|
||||
n = len(returns)
|
||||
annual_ret = (1 + total_ret) ** (252 / n) - 1 if n > 0 else 0
|
||||
max_dd = compute_drawdown(sim_nav).min()
|
||||
sharpe = compute_sharpe(pd.Series(returns))
|
||||
|
||||
return {
|
||||
'stop_pct': stop_pct,
|
||||
'total_return': total_ret,
|
||||
'annual_return': annual_ret,
|
||||
'max_drawdown': max_dd,
|
||||
'sharpe': sharpe,
|
||||
'stoploss_events': stoploss_events,
|
||||
}
|
||||
|
||||
|
||||
def analyze_stoploss(nav: pd.DataFrame, days: List[dict]):
|
||||
"""4.1 止损机制模拟"""
|
||||
print_section("4.1 组合级止损模拟")
|
||||
|
||||
# 原始策略作为基准
|
||||
orig_nav = nav['nav']
|
||||
orig_total = orig_nav.iloc[-1] / orig_nav.iloc[0] - 1
|
||||
orig_dd = compute_drawdown(orig_nav).min()
|
||||
orig_sharpe = compute_sharpe(nav['daily_return'])
|
||||
print(f" 原始策略: 累计={orig_total:+.2%}, 最大回撤={orig_dd:.2%}, 夏普={orig_sharpe:.2f}")
|
||||
|
||||
for threshold in [0.05, 0.08, 0.10, 0.12]:
|
||||
r = simulate_portfolio_stoploss(nav, threshold)
|
||||
print(f" 组合止损线={threshold:.0%}: 累计={r['total_return']:+.2%}, "
|
||||
f"回撤={r['max_drawdown']:.2%}, 夏普={r['sharpe']:.2f}, "
|
||||
f"触发{r['trigger_count']}次")
|
||||
|
||||
print_section("4.1a 单资产止损模拟")
|
||||
for stop_pct in [0.05, 0.08, 0.10, 0.15]:
|
||||
r = simulate_asset_stoploss(days, stop_pct)
|
||||
print(f" 单资产止损线={stop_pct:.0%}: 累计={r['total_return']:+.2%}, "
|
||||
f"回撤={r['max_drawdown']:.2%}, 夏普={r['sharpe']:.2f}, "
|
||||
f"触发{r['stoploss_events']}次")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def analyze_volatility_sizing(days: List[dict], nav: pd.DataFrame):
|
||||
"""4.2 波动率适配分析"""
|
||||
print_section("4.2 波动率适配分析")
|
||||
|
||||
# 计算滚动 20 日波动率
|
||||
nav_df = nav.copy()
|
||||
nav_df['rolling_vol'] = nav_df['daily_return'].rolling(20).std() * np.sqrt(252)
|
||||
|
||||
# 按波动率分桶统计收益
|
||||
print(" 组合波动率分桶统计:")
|
||||
valid = nav_df.dropna(subset=['rolling_vol'])
|
||||
buckets = [(0, 0.10), (0.10, 0.15), (0.15, 0.20), (0.20, 0.30), (0.30, 1.0)]
|
||||
for lo, hi in buckets:
|
||||
mask = (valid['rolling_vol'] >= lo) & (valid['rolling_vol'] < hi)
|
||||
subset = valid[mask]
|
||||
if len(subset) == 0:
|
||||
continue
|
||||
avg_ret = subset['daily_return'].mean() * 252
|
||||
avg_vol = subset['rolling_vol'].mean()
|
||||
win_rate = (subset['daily_return'] > 0).mean()
|
||||
print(f" 波动率 [{lo:.0%}, {hi:.0%}): {len(subset)} 天, "
|
||||
f"年化收益={avg_ret:+.2%}, 胜率={win_rate:.1%}")
|
||||
|
||||
# 模拟：高波动期减仓
|
||||
print(f"\n 模拟: 波动率 > 20% 时仓位减至 2/3:")
|
||||
sim_returns = []
|
||||
for _, row in valid.iterrows():
|
||||
ret = row['daily_return']
|
||||
vol = row['rolling_vol']
|
||||
if vol > 0.20:
|
||||
ret = ret * 2 / 3 # 减仓至 2/3
|
||||
sim_returns.append(ret)
|
||||
|
||||
sim_nav = pd.Series(sim_returns).add(1).cumprod()
|
||||
total_ret = sim_nav.iloc[-1] - 1
|
||||
n = len(sim_returns)
|
||||
annual_ret = (1 + total_ret) ** (252 / n) - 1
|
||||
max_dd = compute_drawdown(sim_nav).min()
|
||||
sharpe = compute_sharpe(pd.Series(sim_returns))
|
||||
|
||||
orig_total = nav_df['nav'].iloc[-1] / nav_df['nav'].iloc[0] - 1
|
||||
print(f" 原始: 累计={orig_total:+.2%}")
|
||||
print(f" 波动率适配: 累计={total_ret:+.2%}, 回撤={max_dd:.2%}, 夏普={sharpe:.2f}")
|
||||
|
||||
# 高波动期出现频率
|
||||
high_vol_days = (valid['rolling_vol'] > 0.20).sum()
|
||||
print(f"\n 高波动期(>20%): {high_vol_days}/{len(valid)} 天 ({high_vol_days/len(valid)*100:.1f}%)")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def analyze_cash_management(days: List[dict]):
|
||||
"""4.3 现金管理评估"""
|
||||
print_section("4.3 现金管理评估")
|
||||
|
||||
# 统计所有资产动量都低于阈值的天数（全部防御）
|
||||
all_below = 0
|
||||
partial_below = 0
|
||||
total_days = 0
|
||||
|
||||
for day in days:
|
||||
total_days += 1
|
||||
assets = day.get('assets', {})
|
||||
holdings = day.get('holdings', [])
|
||||
|
||||
non_bond_assets = {c: a for c, a in assets.items()
|
||||
if c != '931862.CSI' and a.get('momentum') is not None}
|
||||
if not non_bond_assets:
|
||||
continue
|
||||
|
||||
below_count = sum(1 for a in non_bond_assets.values()
|
||||
if not a.get('above_threshold', False))
|
||||
|
||||
if below_count == len(non_bond_assets):
|
||||
all_below += 1
|
||||
elif below_count > 0:
|
||||
partial_below += 1
|
||||
|
||||
print(f" 全部非债券资产动量低于阈值: {all_below} 天 ({all_below/total_days*100:.1f}%)")
|
||||
print(f" 部分非债券资产动量低于阈值: {partial_below} 天 ({partial_below/total_days*100:.1f}%)")
|
||||
print(f" 所有资产动量都高于阈值: {total_days - all_below - partial_below} 天")
|
||||
|
||||
# 全部低于阈值时的后续收益
|
||||
print(f"\n 全部低于阈值后的 T+N 收益:")
|
||||
all_below_dates = []
|
||||
for day in days:
|
||||
assets = day.get('assets', {})
|
||||
non_bond = {c: a for c, a in assets.items()
|
||||
if c != '931862.CSI' and a.get('momentum') is not None}
|
||||
if non_bond and all(not a.get('above_threshold', False) for a in non_bond.values()):
|
||||
all_below_dates.append(day['date'])
|
||||
|
||||
# 计算全部低于阈值后 5/10/20 天的策略收益
|
||||
nav_df = pd.DataFrame({'date': [d['date'] for d in days],
|
||||
'daily_return': [d['daily_return'] for d in days]})
|
||||
nav_df['date'] = pd.to_datetime(nav_df['date'])
|
||||
|
||||
for forward in [5, 10, 20]:
|
||||
rets_after = []
|
||||
for d in all_below_dates:
|
||||
d_ts = pd.Timestamp(d)
|
||||
mask = (nav_df['date'] > d_ts)
|
||||
future = nav_df[mask].head(forward)
|
||||
if len(future) == forward:
|
||||
cum_ret = (1 + future['daily_return']).prod() - 1
|
||||
rets_after.append(cum_ret)
|
||||
if rets_after:
|
||||
avg = np.mean(rets_after)
|
||||
pos_rate = sum(1 for r in rets_after if r > 0) / len(rets_after)
|
||||
print(f" T+{forward}: 均值={avg:+.4%}, 正收益占比={pos_rate:.1%}, 样本={len(rets_after)}")
|
||||
|
||||
# 持仓数量分布
|
||||
print(f"\n 持仓数量分布:")
|
||||
holding_counts = defaultdict(int)
|
||||
for day in days:
|
||||
n = len(day.get('holdings', []))
|
||||
holding_counts[n] += 1
|
||||
for n in sorted(holding_counts.keys()):
|
||||
print(f" 持有 {n} 只资产: {holding_counts[n]} 天 ({holding_counts[n]/total_days*100:.1f}%)")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 4: 资金管理问题诊断")
|
||||
|
||||
nav = load_nav()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 4.1 止损
|
||||
results['stoploss'] = analyze_stoploss(nav, days)
|
||||
|
||||
# 4.2 波动率适配
|
||||
results['vol_sizing'] = analyze_volatility_sizing(days, nav)
|
||||
|
||||
# 4.3 现金管理
|
||||
results['cash'] = analyze_cash_management(days)
|
||||
|
||||
print_section("Task 4 总结")
|
||||
print(" 1. 止损机制可减少极端回撤，但频繁止损可能拖累收益")
|
||||
print(" 2. 高波动期减仓有助于控制回撤，但需要平衡收益损失")
|
||||
print(" 3. 全部资产低于阈值时强制防御，后续短期收益偏弱")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
268
rotation/experiments/task5_return_attribution.py
Normal file
268
rotation/experiments/task5_return_attribution.py
Normal file
@@ -0,0 +1,268 @@
|
||||
"""
|
||||
Task 5: 整体收益归因分析
|
||||
|
||||
分析维度：
|
||||
5.1 收益来源集中度 - 去掉最好/最差 N 天后的收益
|
||||
5.2 持仓收益 vs 决策收益 - 静态组合 vs 轮动策略对比
|
||||
5.3 分阶段表现 - 2023 年失效原因分析
|
||||
"""
|
||||
|
||||
import ast
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section, compute_drawdown, compute_sharpe,
|
||||
compute_annual_return, yearly_stats,
|
||||
)
|
||||
|
||||
|
||||
def analyze_return_concentration(nav: pd.DataFrame):
|
||||
"""5.1 收益来源集中度"""
|
||||
print_section("5.1 收益来源集中度")
|
||||
|
||||
returns = nav['daily_return'].values.copy()
|
||||
n = len(returns)
|
||||
original_nav = (1 + returns).prod()
|
||||
|
||||
print(f" 原始累计净值: {original_nav:.4f} (收益: {original_nav-1:+.2%})")
|
||||
print(f" 总交易日: {n}")
|
||||
|
||||
# 去掉最好 N 天
|
||||
print(f"\n 去掉最好 N 天后的收益:")
|
||||
for n_remove in [5, 10, 20, 50]:
|
||||
if n_remove >= n:
|
||||
continue
|
||||
sorted_returns = np.sort(returns)
|
||||
# 去掉最大的 n_remove 个
|
||||
trimmed = sorted_returns[:-n_remove] if n_remove > 0 else sorted_returns
|
||||
new_nav = (1 + trimmed).prod()
|
||||
loss = (original_nav - new_nav) / original_nav * 100
|
||||
print(f" 去掉最好 {n_remove} 天: 净值={new_nav:.4f} ({new_nav-1:+.2%}), "
|
||||
f"收益损失 {loss:.1f}%")
|
||||
|
||||
# 去掉最差 N 天
|
||||
print(f"\n 去掉最差 N 天后的收益:")
|
||||
for n_remove in [5, 10, 20, 50]:
|
||||
if n_remove >= n:
|
||||
continue
|
||||
sorted_returns = np.sort(returns)
|
||||
# 去掉最小的 n_remove 个
|
||||
trimmed = sorted_returns[n_remove:]
|
||||
new_nav = (1 + trimmed).prod()
|
||||
gain = (new_nav - original_nav) / original_nav * 100
|
||||
print(f" 去掉最差 {n_remove} 天: 净值={new_nav:.4f} ({new_nav-1:+.2%}), "
|
||||
f"收益增加 {gain:.1f}%")
|
||||
|
||||
# 依赖度指标
|
||||
top5_nav = (1 + np.sort(returns)[:-5]).prod()
|
||||
dependency = (original_nav - top5_nav) / original_nav * 100
|
||||
print(f"\n 收益依赖度: 最好 5 天贡献了 {dependency:.1f}% 的最终净值")
|
||||
if dependency > 30:
|
||||
print(f" [!] 收益高度集中在少数大日子，策略对择时依赖严重")
|
||||
elif dependency > 15:
|
||||
print(f" [~] 收益中等集中，部分依赖少数大日子")
|
||||
else:
|
||||
print(f" [OK] 收益分布较为均匀")
|
||||
|
||||
return {'dependency_pct': dependency}
|
||||
|
||||
|
||||
def analyze_static_vs_rotation(days: List[dict], nav: pd.DataFrame):
|
||||
"""5.2 持仓收益 vs 决策收益"""
|
||||
print_section("5.2 静态组合 vs 轮动策略")
|
||||
|
||||
# 统计各资产被持有天数和持有期间收益
|
||||
asset_hold_stats = defaultdict(lambda: {'days': 0, 'returns': []})
|
||||
|
||||
for day in days:
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
if asset.get('is_held') and asset.get('etf_return_ctc') is not None:
|
||||
asset_hold_stats[code]['days'] += 1
|
||||
asset_hold_stats[code]['returns'].append(asset['etf_return_ctc'])
|
||||
|
||||
print(" 各资产持有统计:")
|
||||
total_days = len(days)
|
||||
for code in sorted(asset_hold_stats.keys()):
|
||||
stats = asset_hold_stats[code]
|
||||
rets = stats['returns']
|
||||
hold_pct = stats['days'] / total_days * 100
|
||||
avg_daily = np.mean(rets) if rets else 0
|
||||
cum_ret = (1 + np.array(rets)).prod() - 1 if rets else 0
|
||||
print(f" {code}: 持有 {stats['days']} 天 ({hold_pct:.1f}%), "
|
||||
f"日均={avg_daily:.4%}, 持有期累计={cum_ret:+.2%}")
|
||||
|
||||
# 模拟静态组合：始终等权持有 NDX + GC=F + 399006.SZ
|
||||
static_codes = ['NDX', 'GC=F', '399006.SZ']
|
||||
print(f"\n 模拟静态组合 (始终等权持有 {', '.join(static_codes)}):")
|
||||
|
||||
static_returns = []
|
||||
for day in days:
|
||||
day_ret = 0.0
|
||||
count = 0
|
||||
for code in static_codes:
|
||||
asset = day.get('assets', {}).get(code, {})
|
||||
etf_ret = asset.get('etf_return_ctc')
|
||||
if etf_ret is not None:
|
||||
day_ret += etf_ret / len(static_codes)
|
||||
count += 1
|
||||
if count > 0:
|
||||
static_returns.append(day_ret)
|
||||
else:
|
||||
static_returns.append(0.0)
|
||||
|
||||
static_nav = pd.Series(static_returns).add(1).cumprod()
|
||||
static_total = static_nav.iloc[-1] - 1
|
||||
static_dd = compute_drawdown(static_nav).min()
|
||||
static_sharpe = compute_sharpe(pd.Series(static_returns))
|
||||
|
||||
# 轮动策略
|
||||
rot_nav = nav['nav']
|
||||
rot_total = rot_nav.iloc[-1] / rot_nav.iloc[0] - 1
|
||||
rot_dd = compute_drawdown(rot_nav).min()
|
||||
rot_sharpe = compute_sharpe(nav['daily_return'])
|
||||
|
||||
print(f" 静态组合: 累计={static_total:+.2%}, 回撤={static_dd:.2%}, 夏普={static_sharpe:.2f}")
|
||||
print(f" 轮动策略: 累计={rot_total:+.2%}, 回撤={rot_dd:.2%}, 夏普={rot_sharpe:.2f}")
|
||||
print(f" 轮动超额: {rot_total - static_total:+.2%}")
|
||||
|
||||
# 其他静态组合对比
|
||||
print(f"\n 其他静态组合对比:")
|
||||
test_combos = [
|
||||
('NDX', 'GC=F', 'H30269.CSI'),
|
||||
('NDX', 'N225', '399006.SZ'),
|
||||
('NDX', 'GC=F', 'GDAXI'),
|
||||
]
|
||||
for combo in test_combos:
|
||||
combo_rets = []
|
||||
for day in days:
|
||||
day_ret = 0.0
|
||||
count = 0
|
||||
for code in combo:
|
||||
asset = day.get('assets', {}).get(code, {})
|
||||
etf_ret = asset.get('etf_return_ctc')
|
||||
if etf_ret is not None:
|
||||
day_ret += etf_ret / len(combo)
|
||||
count += 1
|
||||
if count > 0:
|
||||
combo_rets.append(day_ret)
|
||||
else:
|
||||
combo_rets.append(0.0)
|
||||
|
||||
c_nav = pd.Series(combo_rets).add(1).cumprod()
|
||||
c_total = c_nav.iloc[-1] - 1
|
||||
c_dd = compute_drawdown(c_nav).min()
|
||||
c_sharpe = compute_sharpe(pd.Series(combo_rets))
|
||||
print(f" {combo}: 累计={c_total:+.2%}, 回撤={c_dd:.2%}, 夏普={c_sharpe:.2f}")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def analyze_2023_failure(days: List[dict], nav: pd.DataFrame, signals: pd.DataFrame):
|
||||
"""5.3 2023 年失效原因分析"""
|
||||
print_section("5.3 分阶段表现 & 2023 年失效分析")
|
||||
|
||||
# 分年度统计
|
||||
yearly = yearly_stats(nav)
|
||||
print(" 分年度表现:")
|
||||
for _, row in yearly.iterrows():
|
||||
marker = " <-- 失效年份" if abs(row['total_return']) < 0.02 else ""
|
||||
print(f" {int(row['year'])}: 累计={row['total_return']:+.2%}, "
|
||||
f"回撤={row['max_drawdown']:.2%}, 夏普={row['sharpe']:.2f}{marker}")
|
||||
|
||||
# 2023 年详细分析
|
||||
print(f"\n 2023 年详细分析:")
|
||||
nav_2023 = nav[nav['date'].dt.year == 2023].copy()
|
||||
if len(nav_2023) == 0:
|
||||
print(" 无 2023 年数据")
|
||||
return {}
|
||||
|
||||
print(f" 2023 年交易日: {len(nav_2023)}")
|
||||
print(f" 2023 年累计收益: {nav_2023['nav'].iloc[-1]/nav_2023['nav'].iloc[0]-1:+.2%}")
|
||||
print(f" 2023 年最大回撤: {compute_drawdown(nav_2023['nav']).min():.2%}")
|
||||
|
||||
# 2023 年持仓分布
|
||||
signals_2023 = signals[signals['date'].dt.year == 2023].copy()
|
||||
holding_days = defaultdict(int)
|
||||
for _, row in signals_2023.iterrows():
|
||||
holdings = ast.literal_eval(row['holdings']) if isinstance(row['holdings'], str) else row['holdings']
|
||||
for code in holdings:
|
||||
holding_days[code] += 1
|
||||
|
||||
print(f"\n 2023 年持仓分布:")
|
||||
total_days_2023 = len(signals_2023)
|
||||
for code, count in sorted(holding_days.items(), key=lambda x: -x[1]):
|
||||
print(f" {code}: {count} 天 ({count/total_days_2023*100:.1f}%)")
|
||||
|
||||
# 2023 年调仓次数
|
||||
rebal_2023 = signals_2023['is_rebalance'].sum()
|
||||
print(f"\n 2023 年调仓次数: {rebal_2023}")
|
||||
|
||||
# 2023 年月度收益
|
||||
nav_2023_copy = nav_2023.copy()
|
||||
nav_2023_copy['month'] = nav_2023_copy['date'].dt.month
|
||||
print(f"\n 2023 年月度收益:")
|
||||
for month, grp in nav_2023_copy.groupby('month'):
|
||||
m_ret = grp['nav'].iloc[-1] / grp['nav'].iloc[0] - 1
|
||||
print(f" {month}月: {m_ret:+.2%}")
|
||||
|
||||
# 2023 年动量得分分布
|
||||
days_2023 = [d for d in days if d['date'].startswith('2023')]
|
||||
momentum_2023 = defaultdict(list)
|
||||
for day in days_2023:
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
m = asset.get('momentum')
|
||||
if m is not None:
|
||||
momentum_2023[code].append(m)
|
||||
|
||||
print(f"\n 2023 年动量得分分布:")
|
||||
for code in sorted(momentum_2023.keys()):
|
||||
vals = momentum_2023[code]
|
||||
if vals:
|
||||
arr = np.array(vals)
|
||||
print(f" {code}: 均值={arr.mean():.4f}, std={arr.std():.4f}, "
|
||||
f"正动量占比={sum(1 for v in vals if v > 0)/len(vals)*100:.1f}%")
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 5: 整体收益归因分析")
|
||||
|
||||
nav = load_nav()
|
||||
signals = load_signals()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 5.1 收益集中度
|
||||
results['concentration'] = analyze_return_concentration(nav)
|
||||
|
||||
# 5.2 静态 vs 轮动
|
||||
results['static_vs_rotation'] = analyze_static_vs_rotation(days, nav)
|
||||
|
||||
# 5.3 2023 年失效
|
||||
results['year_2023'] = analyze_2023_failure(days, nav, signals)
|
||||
|
||||
print_section("Task 5 总结")
|
||||
dep = results['concentration']['dependency_pct']
|
||||
print(f" 1. 收益依赖度: 最好 5 天贡献了 {dep:.1f}% 的最终净值")
|
||||
print(f" 2. 轮动策略 vs 静态组合的超额收益体现了决策价值")
|
||||
print(f" 3. 2023 年动量因子可能因全球加息环境而失效")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
289
rotation/experiments/task6_drawdown_analysis.py
Normal file
289
rotation/experiments/task6_drawdown_analysis.py
Normal file
@@ -0,0 +1,289 @@
|
||||
"""
|
||||
Task 6: 回撤诊断
|
||||
|
||||
分析维度：
|
||||
6.1 最大回撤复盘 - 2022-05 前后持仓与动量变化
|
||||
6.2 近期回撤趋势 - 2026 年回撤分析
|
||||
6.3 极端尾部风险 - 极端日归因
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from rotation.experiments.common import (
|
||||
load_nav, load_signals, load_detail_days, load_detail_meta,
|
||||
print_section, compute_drawdown,
|
||||
)
|
||||
|
||||
|
||||
def analyze_max_drawdown(days: List[dict], nav: pd.DataFrame):
|
||||
"""6.1 最大回撤复盘 (2022-05)"""
|
||||
print_section("6.1 最大回撤复盘 (2022-04 ~ 2022-06)")
|
||||
|
||||
# 找到最大回撤的谷底
|
||||
nav_df = nav.copy()
|
||||
dd = compute_drawdown(nav_df['nav'])
|
||||
trough_idx = dd.idxmin()
|
||||
trough_date = nav_df.iloc[trough_idx]['date']
|
||||
trough_dd = dd.iloc[trough_idx]
|
||||
|
||||
print(f" 最大回撤谷底: {trough_date.date()}")
|
||||
print(f" 最大回撤: {trough_dd:.2%}")
|
||||
|
||||
# 找到回撤开始点（前高点）
|
||||
peak_idx = nav_df['nav'][:trough_idx+1].idxmax()
|
||||
peak_date = nav_df.iloc[peak_idx]['date']
|
||||
peak_nav = nav_df.iloc[peak_idx]['nav']
|
||||
print(f" 回撤起点(前高): {peak_date.date()}, NAV={peak_nav:.4f}")
|
||||
print(f" 回撤持续: {(trough_date - peak_date).days} 天")
|
||||
|
||||
# 找到恢复点
|
||||
recovery_mask = nav_df['nav'].iloc[trough_idx:] >= peak_nav
|
||||
if recovery_mask.any():
|
||||
recovery_idx = recovery_mask.idxmax()
|
||||
recovery_date = nav_df.iloc[recovery_idx]['date']
|
||||
print(f" 回撤恢复: {recovery_date.date()}, 恢复耗时: {(recovery_date - trough_date).days} 天")
|
||||
else:
|
||||
print(f" 回撤未恢复（截至数据结束）")
|
||||
|
||||
# 2022-04 ~ 2022-06 详细持仓变化
|
||||
print(f"\n 2022-04 ~ 2022-06 持仓与动量变化:")
|
||||
days_2022 = [d for d in days
|
||||
if d['date'] >= '2022-04-01' and d['date'] <= '2022-06-30']
|
||||
|
||||
prev_holdings = []
|
||||
key_events = []
|
||||
for day in days_2022:
|
||||
holdings = day.get('holdings', [])
|
||||
is_rebal = day.get('is_rebalance', False)
|
||||
|
||||
if is_rebal:
|
||||
added = set(holdings) - set(prev_holdings)
|
||||
removed = set(prev_holdings) - set(holdings)
|
||||
# 收集动量得分
|
||||
momentums = {}
|
||||
for code, asset in day.get('assets', {}).items():
|
||||
m = asset.get('momentum')
|
||||
if m is not None:
|
||||
momentums[code] = m
|
||||
|
||||
key_events.append({
|
||||
'date': day['date'],
|
||||
'nav': day['nav'],
|
||||
'daily_return': day['daily_return'],
|
||||
'added': added,
|
||||
'removed': removed,
|
||||
'holdings': holdings,
|
||||
'momentums': momentums,
|
||||
})
|
||||
|
||||
prev_holdings = holdings
|
||||
|
||||
print(f" 期间调仓事件数: {len(key_events)}")
|
||||
for evt in key_events:
|
||||
mom_str = ', '.join(f"{c}={v:.4f}" for c, v in
|
||||
sorted(evt['momentums'].items(), key=lambda x: -x[1])[:5])
|
||||
print(f" {evt['date']}: NAV={evt['nav']:.4f}, "
|
||||
f"日收益={evt['daily_return']:+.4%}")
|
||||
print(f" 调仓: +{evt['added']} -{evt['removed']}")
|
||||
print(f" 持仓: {evt['holdings']}")
|
||||
print(f" Top5动量: {mom_str}")
|
||||
|
||||
# 分析为什么动态阈值没有触发防御
|
||||
print(f"\n 动态阈值分析 (2022-04 ~ 2022-06):")
|
||||
for day in days_2022:
|
||||
assets = day.get('assets', {})
|
||||
bond = assets.get('931862.CSI', {})
|
||||
bond_m = bond.get('momentum')
|
||||
threshold = bond.get('threshold', 0)
|
||||
if bond_m is not None:
|
||||
holdings = day.get('holdings', [])
|
||||
has_bond = '931862.CSI' in holdings
|
||||
# 只在调仓日或每周输出一次
|
||||
if day.get('is_rebalance') or day['date'].endswith('-01') or day['date'].endswith('-15'):
|
||||
non_bond_above = sum(1 for c, a in assets.items()
|
||||
if c != '931862.CSI' and a.get('above_threshold', False))
|
||||
print(f" {day['date']}: 短债动量={bond_m:.6f}, "
|
||||
f"阈值={threshold:.6f}, 持仓={holdings}, "
|
||||
f"非债券>阈值: {non_bond_above}")
|
||||
|
||||
return {'trough_date': str(trough_date.date()), 'max_dd': float(trough_dd)}
|
||||
|
||||
|
||||
def analyze_recent_drawdown(days: List[dict], nav: pd.DataFrame):
|
||||
"""6.2 2026 年近期回撤分析"""
|
||||
print_section("6.2 2026 年近期回撤分析")
|
||||
|
||||
nav_2026 = nav[nav['date'].dt.year == 2026].copy()
|
||||
if len(nav_2026) == 0:
|
||||
print(" 无 2026 年数据")
|
||||
return {}
|
||||
|
||||
dd_2026 = compute_drawdown(nav_2026['nav'])
|
||||
max_dd_2026 = dd_2026.min()
|
||||
trough_idx = dd_2026.idxmin()
|
||||
trough_date = nav_2026.iloc[trough_idx - nav_2026.index[0]]['date'] if trough_idx >= nav_2026.index[0] else None
|
||||
|
||||
print(f" 2026 年最大回撤: {max_dd_2026:.2%}")
|
||||
|
||||
# 2026 年月度收益
|
||||
nav_2026['month'] = nav_2026['date'].dt.month
|
||||
print(f"\n 2026 年月度收益:")
|
||||
for month, grp in nav_2026.groupby('month'):
|
||||
m_ret = grp['nav'].iloc[-1] / grp['nav'].iloc[0] - 1
|
||||
m_dd = compute_drawdown(grp['nav']).min()
|
||||
print(f" {month}月: 收益={m_ret:+.2%}, 月内最大回撤={m_dd:.2%}")
|
||||
|
||||
# 2026 年极端日
|
||||
extreme_2026 = nav_2026[(nav_2026['daily_return'] > 0.03) | (nav_2026['daily_return'] < -0.03)]
|
||||
print(f"\n 2026 年极端日(|收益|>3%):")
|
||||
days_2026_detail = [d for d in days if d['date'].startswith('2026')]
|
||||
for _, row in extreme_2026.iterrows():
|
||||
date_str = row['date'].strftime('%Y-%m-%d')
|
||||
day_detail = None
|
||||
for d in days_2026_detail:
|
||||
if d['date'] == date_str:
|
||||
day_detail = d
|
||||
break
|
||||
holdings_info = ""
|
||||
if day_detail:
|
||||
for code, asset in day_detail.get('assets', {}).items():
|
||||
if asset.get('is_held'):
|
||||
etf_ret = asset.get('etf_return_ctc', 0)
|
||||
holdings_info += f" {code}({etf_ret:+.2%})"
|
||||
print(f" {date_str}: {row['daily_return']:+.4%} |{holdings_info}")
|
||||
|
||||
# NAV 绝对回撤金额分析
|
||||
print(f"\n 绝对回撤金额分析:")
|
||||
initial_nav = nav['nav'].iloc[0]
|
||||
current_nav = nav['nav'].iloc[-1]
|
||||
peak_nav = nav['nav'].max()
|
||||
print(f" 初始 NAV: {initial_nav:.4f}")
|
||||
print(f" 当前 NAV: {current_nav:.4f}")
|
||||
print(f" 峰值 NAV: {peak_nav:.4f}")
|
||||
print(f" 峰值回撤绝对值: {peak_nav * abs(max_dd_2026):.4f} NAV 单位")
|
||||
print(f" 对比: 2020 年全年收益 NAV = "
|
||||
f"{nav[nav['date'].dt.year == 2020]['nav'].iloc[-1] - nav[nav['date'].dt.year == 2020]['nav'].iloc[0]:.4f}")
|
||||
|
||||
return {'max_dd_2026': float(max_dd_2026)}
|
||||
|
||||
|
||||
def analyze_extreme_tail(days: List[dict], nav: pd.DataFrame):
|
||||
"""6.3 极端尾部风险"""
|
||||
print_section("6.3 极端尾部风险分析")
|
||||
|
||||
returns = nav['daily_return'].values
|
||||
# 收益分布统计
|
||||
print(" 日收益分布:")
|
||||
for pct in [1, 5, 10, 25, 50, 75, 90, 95, 99]:
|
||||
val = np.percentile(returns, pct)
|
||||
print(f" P{pct}: {val:+.4%}")
|
||||
|
||||
# 尾部风险指标
|
||||
tail_5pct = returns[returns <= np.percentile(returns, 5)]
|
||||
print(f"\n 尾部风险 (5% 分位以下):")
|
||||
print(f" CVaR(5%): {np.mean(tail_5pct):+.4%}")
|
||||
print(f" Worst: {np.min(returns):+.4%}")
|
||||
|
||||
# 极端日归因
|
||||
print(f"\n 极端亏损日归因 (日收益 < -5%):")
|
||||
extreme_loss = nav[nav['daily_return'] < -0.05]
|
||||
for _, row in extreme_loss.iterrows():
|
||||
date_str = row['date'].strftime('%Y-%m-%d')
|
||||
day_detail = None
|
||||
for d in days:
|
||||
if d['date'] == date_str:
|
||||
day_detail = d
|
||||
break
|
||||
if day_detail:
|
||||
print(f"\n {date_str}: 日收益={row['daily_return']:+.4%}, NAV={row['nav']:.4f}")
|
||||
for code, asset in day_detail.get('assets', {}).items():
|
||||
if asset.get('is_held'):
|
||||
etf_ret = asset.get('etf_return_ctc', 0)
|
||||
idx_ret = asset.get('index_return', 0)
|
||||
premium = asset.get('premium', 0)
|
||||
print(f" {code}: ETF收益={etf_ret:+.4%}, "
|
||||
f"指数收益={idx_ret:+.4%}, 溢价率={premium:+.4%}" if premium else
|
||||
f" {code}: ETF收益={etf_ret:+.4%}, 指数收益={idx_ret:+.4%}")
|
||||
|
||||
# 连续亏损天数分析
|
||||
streak = 0
|
||||
max_streak = 0
|
||||
streaks = []
|
||||
for r in returns:
|
||||
if r < 0:
|
||||
streak += 1
|
||||
else:
|
||||
if streak > 0:
|
||||
streaks.append(streak)
|
||||
streak = 0
|
||||
if streak > 0:
|
||||
streaks.append(streak)
|
||||
max_streak = max(streaks) if streaks else 0
|
||||
|
||||
print(f"\n 连续亏损分析:")
|
||||
print(f" 最大连续亏损天数: {max_streak}")
|
||||
print(f" 连续亏损>=3天的次数: {sum(1 for s in streaks if s >= 3)}")
|
||||
print(f" 连续亏损>=5天的次数: {sum(1 for s in streaks if s >= 5)}")
|
||||
|
||||
# 连续亏损段的累计收益
|
||||
if streaks:
|
||||
loss_streak_rets = []
|
||||
current_streak_ret = 0
|
||||
in_streak = False
|
||||
for r in returns:
|
||||
if r < 0:
|
||||
current_streak_ret += r
|
||||
in_streak = True
|
||||
else:
|
||||
if in_streak and current_streak_ret < -0.05:
|
||||
loss_streak_rets.append(current_streak_ret)
|
||||
current_streak_ret = 0
|
||||
in_streak = False
|
||||
if loss_streak_rets:
|
||||
print(f"\n 显著连续亏损段(累计<-5%):")
|
||||
for ret in sorted(loss_streak_rets):
|
||||
print(f" 累计亏损: {ret:+.4%}")
|
||||
|
||||
return {'max_streak': max_streak, 'cvar_5pct': float(np.mean(tail_5pct))}
|
||||
|
||||
|
||||
def main():
|
||||
print_section("Task 6: 回撤诊断")
|
||||
|
||||
nav = load_nav()
|
||||
signals = load_signals()
|
||||
days = load_detail_days()
|
||||
meta = load_detail_meta()
|
||||
|
||||
print(f" 数据期间: {meta['start_date']} ~ {meta['end_date']}")
|
||||
|
||||
results = {}
|
||||
|
||||
# 6.1 最大回撤复盘
|
||||
results['max_dd'] = analyze_max_drawdown(days, nav)
|
||||
|
||||
# 6.2 近期回撤
|
||||
results['recent_dd'] = analyze_recent_drawdown(days, nav)
|
||||
|
||||
# 6.3 尾部风险
|
||||
results['tail'] = analyze_extreme_tail(days, nav)
|
||||
|
||||
print_section("Task 6 总结")
|
||||
print(f" 1. 最大回撤 {results['max_dd']['max_dd']:.2%} 发生在 {results['max_dd']['trough_date']}")
|
||||
print(f" 2. 2026 年最大回撤: {results['recent_dd'].get('max_dd_2026', 0):.2%}")
|
||||
print(f" 3. CVaR(5%): {results['tail']['cvar_5pct']:+.4%}, "
|
||||
f"最大连续亏损: {results['tail']['max_streak']} 天")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user