aszerW
eb6a07548c
从聚宽平台迁移全球市场ETF轮动策略，复用动量.py核心模块。ETF池: 纳指100/日经225/德国DAX/黄金/有色金属/南方原油/30年国债/红利低波/创业板。回测(2019~2026): CAGR=44.29%, Sharpe=1.50, MaxDD=-16.93%, Calmar=2.62, 盈利年份8/8, 跑赢基准7/8
300 lines
10 KiB
Python
300 lines
10 KiB
Python
"""
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全球市场ETF轮动策略 - 本地回测版本
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原始策略来源：聚宽 https://www.joinquant.com/post/1399
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核心逻辑（与动量策略共用）：
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1. 加权线性回归（权重1→2递增）计算趋势得分
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2. score = 年化收益率 × R²
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3. ATR动态调整回看窗口（20~60天）
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4. 崩盘过滤：连续3天任一天跌>5%则得分归零
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5. 溢价过滤：溢价率≥5%则降权
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6. 全仓单一品种轮动
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ETF池：全球化配置
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纳指100 / 日经225 / 德国DAX / 黄金 / 有色金属 /
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南方原油 / 30年国债 / 红利低波 / 创业板
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"""
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import sys
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import math
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import warnings
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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# 添加项目根目录
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sys.path.insert(0, str(Path(__file__).parent))
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from dotenv import load_dotenv
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load_dotenv()
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# ==================== 策略配置 ====================
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CONFIG = {
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# 全球市场ETF池（聚宽代码 -> Tushare代码映射）
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'etf_pool': {
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'513100.SH': '纳指100ETF',
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'513520.SH': '日经225ETF',
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'513030.SH': '德国DAX ETF',
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'518880.SH': '黄金ETF华安',
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'159980.SZ': '有色金属ETF',
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'501018.SH': '南方原油LOF',
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'511090.SH': '30年国债ETF',
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'512890.SH': '红利低波ETF',
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'159915.SZ': '创业板ETF易方达',
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},
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'target_num': 1, # 持仓数量
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'auto_day': True, # 是否启用动态周期
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'fixed_days': 25, # 固定回看天数
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'min_days': 20, # 动态周期最小值
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'max_days': 60, # 动态周期最大值
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'premium_threshold': 5.0, # 溢价率阈值(%)
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'trade_cost': 0.001, # 单次交易成本(双边)
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'start_date': '2019-01-01',
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'benchmark': '000300.SH', # 基准：沪深300
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}
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# ==================== 复用动量策略核心模块 ====================
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from 动量 import (
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fetch_all_etf_data,
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fetch_etf_nav_data,
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calc_atr,
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calc_weighted_momentum_score,
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apply_crash_filter,
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calc_premium_rate,
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print_performance,
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print_yearly_returns,
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)
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# ==================== 回测引擎 ====================
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def run_backtest(config: dict):
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"""执行回测"""
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end_date = datetime.now().strftime('%Y-%m-%d')
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etf_pool = config['etf_pool']
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etf_codes = list(etf_pool.keys())
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print("=" * 60)
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print(" 全球市场ETF轮动策略 - 本地回测")
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print("=" * 60)
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print(f" 候选ETF: {len(etf_codes)} 只")
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for code, name in etf_pool.items():
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print(f" {code} {name}")
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print(f" 持仓数量: {config['target_num']}")
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print(f" 动态周期: {'开启' if config['auto_day'] else '关闭'}")
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if config['auto_day']:
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print(f" 回看范围: {config['min_days']}~{config['max_days']} 天")
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else:
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print(f" 固定回看: {config['fixed_days']} 天")
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print(f" 回测区间: {config['start_date']} ~ {end_date}")
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# 1. 获取数据
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print(f"\n{'='*60}")
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print("下载ETF价格数据...")
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all_data = fetch_all_etf_data(etf_codes, config['start_date'], end_date, etf_pool)
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print("\n下载ETF净值数据...")
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nav_data = fetch_etf_nav_data(etf_codes, config['start_date'], end_date)
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print(f" 净值数据: {len(nav_data)} 只")
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if not all_data:
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print("无数据，退出")
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return
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# 2. 构建交易日历
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all_dates = set()
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for df in all_data.values():
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all_dates.update(df.index.tolist())
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trade_dates = sorted(all_dates)
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trade_dates = [d for d in trade_dates if d >= pd.Timestamp(config['start_date'])]
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print(f"\n交易日数: {len(trade_dates)}")
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print(f"区间: {trade_dates[0].strftime('%Y-%m-%d')} ~ {trade_dates[-1].strftime('%Y-%m-%d')}")
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# 3. 逐日回测
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print(f"\n{'='*60}")
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print("开始回测...")
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print("=" * 60)
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max_lookback = config['max_days'] + 10
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holding = None
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daily_returns = []
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signals = []
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for i, today in enumerate(trade_dates):
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# 计算每只ETF的得分
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scores = {}
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score_details = {}
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for code in etf_codes:
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if code not in all_data:
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continue
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df = all_data[code]
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hist = df[df.index <= today].tail(max_lookback + 1)
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if len(hist) < config['min_days']:
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continue
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close_arr = hist['close'].values
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if config['auto_day']:
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if len(hist) < max_lookback:
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lookback = config['fixed_days']
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else:
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long_atr = calc_atr(hist['high'], hist['low'], hist['close'],
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config['max_days'])
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short_atr = calc_atr(hist['high'], hist['low'], hist['close'],
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config['min_days'])
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la = long_atr.iloc[-1]
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sa = short_atr.iloc[-1]
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if la > 0 and not np.isnan(la) and not np.isnan(sa):
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ratio = min(0.9, sa / la)
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lookback = int(config['min_days'] +
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(config['max_days'] - config['min_days']) * (1 - ratio))
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else:
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lookback = config['fixed_days']
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prices = close_arr[-lookback:]
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else:
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prices = close_arr[-config['fixed_days']:]
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if len(prices) < 5:
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continue
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result = calc_weighted_momentum_score(prices)
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score = result['score']
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score = apply_crash_filter(close_arr, score)
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# 溢价过滤
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if code in nav_data:
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nav_df = nav_data[code]
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nav_row = nav_df[nav_df.index <= today]
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if not nav_row.empty:
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nav_val = nav_row.iloc[-1]['nav']
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etf_price = close_arr[-1]
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premium = calc_premium_rate(etf_price, nav_val)
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if premium >= config['premium_threshold']:
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score -= 1
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if 0 < score < 6:
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scores[code] = score
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score_details[code] = result
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# 选出排名最高的标的
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if scores:
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ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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target = ranked[0][0]
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else:
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target = None
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# 计算当日收益
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if holding is not None and holding in all_data:
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df_h = all_data[holding]
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if today in df_h.index:
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prev_dates = df_h[df_h.index < today].index
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if len(prev_dates) > 0:
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prev_date = prev_dates[-1]
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prev_price = df_h.loc[prev_date, 'close']
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today_price = df_h.loc[today, 'close']
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daily_ret = today_price / prev_price - 1
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else:
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daily_ret = 0.0
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else:
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daily_ret = 0.0
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else:
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daily_ret = 0.0
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# 调仓成本
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trade_cost = 0.0
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if target != holding:
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trade_cost = config['trade_cost']
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if holding is not None:
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signals.append({
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'date': today, 'action': '调仓',
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'from': holding, 'to': target or '空仓',
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'score': scores.get(target, 0) if target else 0,
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})
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holding = target
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daily_returns.append({
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'date': today,
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'daily_return': daily_ret - trade_cost if trade_cost > 0 else daily_ret,
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'holding': holding or '空仓',
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})
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# 4. 计算绩效
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result_df = pd.DataFrame(daily_returns).set_index('date')
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result_df['nav'] = (1 + result_df['daily_return']).cumprod()
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# 基准数据
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benchmark_code = config['benchmark']
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print(f"\n获取基准数据 {benchmark_code}...")
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import os, tushare as ts
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pro = ts.pro_api(os.getenv("TUSHARE_TOKEN"))
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bench_df = pro.index_daily(
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ts_code=benchmark_code,
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start_date=config['start_date'].replace('-', ''),
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end_date=end_date.replace('-', ''),
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)
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if bench_df is not None and not bench_df.empty:
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bench_df['date'] = pd.to_datetime(bench_df['trade_date'])
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bench_df = bench_df.set_index('date').sort_index()
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bench_close = bench_df['close'].reindex(result_df.index, method='ffill')
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result_df['bench_return'] = bench_close / bench_close.iloc[0]
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else:
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result_df['bench_return'] = 1.0
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# 5. 输出绩效报告
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print_performance(result_df, signals, config)
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# 6. 年度收益统计
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print_yearly_returns(result_df)
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# 7. 生成图表
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save_chart(result_df, config)
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return result_df
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# ==================== 图表生成 ====================
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def save_chart(result_df: pd.DataFrame, config: dict):
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"""生成净值曲线图"""
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try:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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matplotlib.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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matplotlib.rcParams['axes.unicode_minus'] = False
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), height_ratios=[3, 1],
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gridspec_kw={'hspace': 0.3})
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ax1.plot(result_df.index, result_df['nav'], label='全球市场轮动', linewidth=1.5, color='#2ecc71')
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ax1.plot(result_df.index, result_df['bench_return'], label='沪深300', linewidth=1, color='#95a5a6')
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ax1.set_title('全球市场ETF轮动策略 净值曲线', fontsize=14, fontweight='bold')
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ax1.legend(loc='upper left')
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ax1.grid(True, alpha=0.3)
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ax1.set_ylabel('净值')
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peak = result_df['nav'].cummax()
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drawdown = (result_df['nav'] - peak) / peak
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ax2.fill_between(result_df.index, drawdown, 0, alpha=0.4, color='#e74c3c')
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ax2.set_title('回撤', fontsize=12)
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ax2.set_ylabel('回撤')
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ax2.grid(True, alpha=0.3)
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chart_path = Path(__file__).parent / 'results' / 'global_market_chart.png'
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chart_path.parent.mkdir(exist_ok=True)
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fig.savefig(chart_path, dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"\n报告图表已保存: {chart_path}")
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except Exception as e:
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print(f"\n图表生成失败: {e}")
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# ==================== 主入口 ====================
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if __name__ == "__main__":
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run_backtest(CONFIG)
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