feat(全球市场): 迁移聚宽策略为Tushare独立回测版本

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

全球市场.py

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