feat(v2): 实现全球轮动策略正式版（GlobalRotationStrategy）
核心功能： - 交易成本计算：每次调仓扣除 0.1%（829 次调仓） - 动态短债阈值：标的动量 < 短债动量 × 1.0 → 不持有 - 强制分散化：每个 group 内竞争，只选 Top 1 - 溢价过滤：预留接口（阈值 10%） - 调仓控制：rebalance_days + rebalance_threshold（预留接口） - A 股交易日过滤：只保留 SSE 交易日（1539 天） 策略逻辑： 1. 计算各指数标的动量得分（加权线性回归） 2. 使用动态短债阈值过滤负动量标的 3. 每个 group 内竞争，只选 Top 1（强制分散化） 4. 溢价过滤：排除溢价率 > 阈值的 ETF 5. 调仓控制：最低持仓天数 + 调仓阈值 6. 等权分配仓位 7. 扣除交易成本（0.1%） 回测验证（2020-01-10 ~ 2026-05-22）： - 总收益：135.63%（vs V1 的 103.29%，+32.34%） - 年化收益：15.07%（vs V1 的 12.32%，+2.75%） - 最大回撤：-17.57%（vs V1 的 -17.72%，略好） - 夏普比率：1.15（vs V1 的 0.78，+47%） - 调仓次数：829 次（vs V1 的 404 次） 新增文件： - rotation.py: GlobalRotationStrategy 正式版实现（456 行） - __init__.py: 导出 SimpleRotationStrategy 和 GlobalRotationStrategy - backtest_global_rotation.py: 正式版回测脚本（117 行）
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116
framework_v2/scripts/backtest_global_rotation.py
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116
framework_v2/scripts/backtest_global_rotation.py
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"""
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全球资产大类轮动策略回测脚本（V2 正式版）
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支持功能：
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- 信号-交易分离（指数信号 → ETF收益）
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- 强制分散化选股（每个 group 只选 1 个）
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- 动态短债阈值（标的动量 < 短债动量 → 不持有）
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- 溢价过滤（避免买入高溢价 ETF）
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- 调仓控制（rebalance_days + rebalance_threshold）
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- 交易成本计算（trade_cost: 0.1%）
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用法:
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python framework_v2/scripts/backtest_global_rotation.py
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"""
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import sys
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from pathlib import Path
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# 添加项目根目录到 Python 路径
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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from framework_v2.config import load_config
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from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
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def run_backtest():
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"""运行回测"""
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print("=" * 70)
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print(" 全球资产大类轮动策略回测（V2 正式版）")
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print(" 场景：指数信号 → ETF收益，完整功能")
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print("=" * 70)
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# 加载配置
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config_file = project_root / "framework_v2" / "strategies" / "rotation" / "config_simple.yaml"
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print(f"\n配置文件: {config_file}")
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config = load_config(str(config_file))
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# 打印配置摘要
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print("\n" + "=" * 70)
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print(" 配置摘要")
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print("=" * 70)
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print(f"策略名称: {config.metadata.strategy}")
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print(f"回测区间: {config.backtest.start_date} ~ {config.backtest.end_date or '至今'}")
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print(f"因子类型: {config.factor.type.value}")
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print(f"动量窗口: {config.factor.n_days} 天")
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print(f"选股数量: {config.rotation.select_num}")
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print(f"强制分散: {config.rotation.diversified}")
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# 打印策略参数
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rotation_config = config.rotation
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print(f"\n策略参数:")
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print(f" 动态阈值: {'启用' if rotation_config and rotation_config.threshold and rotation_config.threshold.mode == 'dynamic' else '禁用'}")
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print(f" 调仓控制: rebalance_days={getattr(rotation_config, 'rebalance_days', 1)}, threshold={getattr(rotation_config, 'rebalance_threshold', 0.0)}")
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print(f" 交易成本: {getattr(config.backtest, 'trade_cost', 0.001):.2%}")
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print(f" 溢价控制: {'启用' if hasattr(config, 'premium_control') and config.premium_control.enabled else '禁用'}")
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# 打印资产池
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print(f"\n资产池 ({config.asset_pools.count()} 个标的):")
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groups = config.asset_pools.by_group
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for group_name, assets in groups.items():
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print(f" [{group_name}] {len(assets)} 个标的:")
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for code, asset in assets.items():
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print(f" {code}: {asset.name}")
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print(f" 信号: {asset.signal_source}, 交易: {asset.trade_source}")
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print(f" 跨市场: {'是' if asset.is_cross_market else '否'}")
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# 创建策略
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print("\n" + "=" * 70)
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print(" 运行回测...")
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print("=" * 70)
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strategy = GlobalRotationStrategy(config)
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result = strategy.run()
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# 打印结果
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print("\n" + "=" * 70)
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print(" 回测结果")
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print("=" * 70)
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metrics = result['metrics']
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print(f"总收益: {metrics['total_return']:.2%}")
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print(f"年化收益: {metrics['annual_return']:.2%}")
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print(f"最大回撤: {metrics['max_drawdown']:.2%}")
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print(f"夏普比率: {metrics['sharpe_ratio']:.2f}")
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print(f"交易天数: {metrics['n_days']}")
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print(f"调仓次数: {metrics['rebalance_count']}")
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# 打印净值曲线
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equity_curve = result['equity_curve']
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print(f"\n净值曲线:")
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print(f" 起始净值: {equity_curve.iloc[0]:.4f}")
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print(f" 结束净值: {equity_curve.iloc[-1]:.4f}")
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print(f" 数据点数: {len(equity_curve)}")
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# 保存结果
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output_dir = project_root / "framework_v2" / "results"
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output_dir.mkdir(exist_ok=True)
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# 保存净值曲线
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equity_curve.to_csv(output_dir / "global_rotation_equity.csv")
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print(f"\n净值曲线已保存: {output_dir / 'global_rotation_equity.csv'}")
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# 保存持仓记录
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positions = result['positions']
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positions.to_csv(output_dir / "global_rotation_positions.csv")
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print(f"持仓记录已保存: {output_dir / 'global_rotation_positions.csv'}")
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print("\n" + "=" * 70)
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print(" 回测完成！")
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print("=" * 70)
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if __name__ == "__main__":
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run_backtest()
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8
framework_v2/strategies/rotation/__init__.py
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8
framework_v2/strategies/rotation/__init__.py
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"""
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轮动策略模块
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"""
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from framework_v2.strategies.rotation.simple import SimpleRotationStrategy
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from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
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__all__ = ['SimpleRotationStrategy', 'GlobalRotationStrategy']
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455
framework_v2/strategies/rotation/rotation.py
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framework_v2/strategies/rotation/rotation.py
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"""
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全球资产大类轮动策略（V2 正式版）
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基于动量因子的全球资产轮动策略
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- 支持信号-交易分离（指数信号 → ETF收益）
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- 强制分散化选股（每个 group 只选 1 个）
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- 动态短债阈值（标的动量 < 短债动量 → 不持有）
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- 溢价过滤（避免买入高溢价 ETF）
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- 调仓控制（rebalance_days + rebalance_threshold）
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- 交易成本计算（trade_cost: 0.1%）
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, Optional, Tuple
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from datetime import datetime, timedelta
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from framework_v2.core.strategy import StrategyBase
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from framework_v2.config.schemas import StrategyConfig
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from framework_v2.shared.factors import MomentumFactor
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class GlobalRotationStrategy(StrategyBase):
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"""
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全球资产大类轮动策略（V2 正式版）
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策略逻辑：
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1. 计算各指数标的动量得分（加权线性回归）
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2. 使用动态短债阈值过滤负动量标的
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3. 每个 group 内竞争，只选 Top 1（强制分散化）
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4. 溢价过滤：排除溢价率 > 阈值的 ETF
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5. 调仓控制：最低持仓天数 + 调仓阈值
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6. 等权分配仓位
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7. 扣除交易成本（0.1%）
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示例:
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from framework_v2.config import load_config
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from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
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config = load_config('config_simple.yaml')
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strategy = GlobalRotationStrategy(config)
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result = strategy.run()
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"""
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def __init__(self, config: StrategyConfig):
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"""
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初始化策略
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Args:
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config: 策略配置
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"""
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super().__init__(config)
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# 初始化动量因子
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self.momentum = MomentumFactor(
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n_days=config.factor.n_days,
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weighted=(config.factor.type.value == 'weighted_momentum')
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)
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# 策略参数（从 config 中读取）
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rotation_config = config.rotation
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self.select_num = rotation_config.select_num if rotation_config else 3
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self.diversified = rotation_config.diversified if rotation_config else True
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# 动态阈值配置
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self.use_dynamic_threshold = False
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self.bond_code = None
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self.bond_ratio = 1.0
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self.fill_bond = True
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if rotation_config and rotation_config.threshold:
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threshold_config = rotation_config.threshold
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if hasattr(threshold_config, 'mode') and threshold_config.mode == 'dynamic':
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self.use_dynamic_threshold = True
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dynamic_config = threshold_config.dynamic
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self.bond_code = dynamic_config.reference
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self.bond_ratio = dynamic_config.ratio
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# 调仓控制
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self.rebalance_days = getattr(rotation_config, 'rebalance_days', 1) if rotation_config else 1
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self.rebalance_threshold = getattr(rotation_config, 'rebalance_threshold', 0.0) if rotation_config else 0.0
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# 交易成本
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self.trade_cost = getattr(config.backtest, 'trade_cost', 0.001) if config.backtest else 0.001
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# 溢价控制
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self.use_premium_control = False
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self.premium_threshold = 0.10 # 默认 10%
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if hasattr(config, 'premium_control'):
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premium_config = config.premium_control
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self.use_premium_control = getattr(premium_config, 'enabled', False)
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if self.use_premium_control:
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self.premium_threshold = getattr(premium_config, 'default_threshold', 0.10)
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def get_codes(self) -> list:
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"""
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获取标的列表（信号标的 + 交易标的 + 短债）
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Returns:
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标的代码列表
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"""
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codes = set()
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# 添加所有信号标的
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codes.update(self.config.asset_pools.get_signal_codes())
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# 添加所有交易标的
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codes.update(self.config.asset_pools.get_trade_codes())
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# 如果使用动态阈值，添加短债标的
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if self.use_dynamic_threshold and self.bond_code:
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codes.add(self.bond_code)
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return list(codes)
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def compute_factors(self, data: Dict[str, pd.DataFrame]) -> Dict[str, pd.Series]:
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"""
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计算动量因子（只使用信号标的的数据）
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Args:
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data: 数据字典 {code: DataFrame}
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Returns:
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因子字典 {signal_source: Series}
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"""
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factors = {}
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# 只使用信号标的计算因子
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signal_codes = self.config.asset_pools.get_signal_codes()
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for code in signal_codes:
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if code not in data:
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print(f" 警告: {code} 数据不存在，跳过")
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continue
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try:
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df = data[code]
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factor_values = self.momentum.compute(df)
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factors[code] = factor_values
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except Exception as e:
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print(f" 警告: {code} 因子计算失败 - {e}")
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continue
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# 如果使用动态阈值，计算短债因子
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if self.use_dynamic_threshold and self.bond_code and self.bond_code in data:
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try:
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df = data[self.bond_code]
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bond_factor = self.momentum.compute(df)
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factors[self.bond_code] = bond_factor
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print(f" [阈值] 短债动量因子已计算: {self.bond_code}")
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except Exception as e:
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print(f" 警告: 短债因子计算失败 - {e}")
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return factors
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def generate_signals(self, factors: Dict[str, pd.Series]) -> pd.DataFrame:
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"""
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生成轮动信号（支持动态阈值和强制分散化）
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逻辑：
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1. 计算动态短债阈值（如果使用）
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2. 每个 group 内竞争，选 Top 1
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3. 溢价过滤（如果启用）
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4. 组合所有 group 的选股结果
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Args:
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factors: 因子字典 {code: Series}
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Returns:
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信号 DataFrame（index=日期, columns=signal_source, values=1或0）
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"""
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if not factors:
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return pd.DataFrame()
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# 对齐所有因子的日期
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factor_df = pd.DataFrame(factors)
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# 获取动态短债阈值（如果使用）
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bond_threshold = None
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if self.use_dynamic_threshold and self.bond_code and self.bond_code in factors:
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bond_threshold = factors[self.bond_code]
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print(f" [阈值] 使用动态短债阈值: {self.bond_code}")
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# 获取溢价率数据（如果启用溢价控制）
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premium_data = None
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if self.use_premium_control:
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premium_data = self._get_premium_data()
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print(f" [溢价] 启用溢价过滤，阈值: {self.premium_threshold:.1%}")
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# 按 group 分组选股
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signals = pd.DataFrame(index=factor_df.index, columns=factor_df.columns, data=0)
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groups = self.config.asset_pools.by_group
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for date in factor_df.index:
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selected_codes = []
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# 对每个 group 独立选股
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for group_name, assets in groups.items():
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# 获取该 group 的信号标的
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group_signal_codes = [asset.signal_source for asset in assets.values()]
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# 获取当日因子值
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date_factors = factor_df.loc[date][group_signal_codes].dropna()
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if date_factors.empty:
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continue
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# 应用动态阈值过滤
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if bond_threshold is not None and date in bond_threshold.index:
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threshold_value = bond_threshold.loc[date] * self.bond_ratio
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date_factors = date_factors[date_factors >= threshold_value]
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if date_factors.empty:
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continue
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# 应用溢价过滤
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if premium_data is not None:
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date_factors = self._filter_by_premium(
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date_factors, date, premium_data
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)
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if date_factors.empty:
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continue
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# 选择 Top 1（强制分散化）
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top_code = date_factors.idxmax()
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selected_codes.append(top_code)
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# 标记信号
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if selected_codes:
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signals.loc[date, selected_codes] = 1
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return signals.astype(int)
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def manage_positions(self, signals: pd.DataFrame) -> pd.DataFrame:
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"""
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仓位管理（等权分配 + 调仓控制）
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Args:
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signals: 信号 DataFrame
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Returns:
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仓位 DataFrame
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"""
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positions = signals.astype(float).copy()
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# 跟踪上次调仓日期
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last_rebalance_date = None
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for date in positions.index:
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signal_row = positions.loc[date].copy()
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n_selected = signal_row.sum()
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if n_selected == 0:
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# 空仓
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positions.loc[date] = 0
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continue
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# 检查是否需要调仓
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if last_rebalance_date is not None:
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# 检查持仓天数
|
||||
holding_days = (date - last_rebalance_date).days
|
||||
if holding_days < self.rebalance_days:
|
||||
# 未达到最低持仓天数，保持上次仓位
|
||||
positions.loc[date] = positions.loc[last_rebalance_date]
|
||||
continue
|
||||
|
||||
# 等权分配
|
||||
positions.loc[date] = signal_row / n_selected
|
||||
last_rebalance_date = date
|
||||
|
||||
return positions
|
||||
|
||||
def _execute_backtest(self, positions: pd.DataFrame, data: Dict[str, pd.DataFrame]) -> Dict[str, any]:
|
||||
"""
|
||||
执行回测（包含交易成本和调仓控制）
|
||||
|
||||
Args:
|
||||
positions: 仓位 DataFrame
|
||||
data: 数据字典
|
||||
|
||||
Returns:
|
||||
回测结果字典
|
||||
"""
|
||||
# 获取信号→交易映射
|
||||
signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
|
||||
|
||||
# 提取交易标的的收盘价
|
||||
close_prices = {}
|
||||
for signal_code, trade_code in signal_to_trade.items():
|
||||
if trade_code in data:
|
||||
close_prices[signal_code] = data[trade_code]['close']
|
||||
else:
|
||||
print(f" 警告: {trade_code} 数据不存在，跳过")
|
||||
|
||||
close_df = pd.DataFrame(close_prices)
|
||||
|
||||
# 计算收益率
|
||||
returns = close_df.pct_change()
|
||||
|
||||
# 获取 A 股交易日历并过滤
|
||||
print("\n [过滤] 获取 A 股交易日历...")
|
||||
trading_calendar = self._get_trading_calendar()
|
||||
|
||||
# 过滤到 A 股交易日
|
||||
original_days = len(returns)
|
||||
returns = returns[returns.index.isin(trading_calendar)]
|
||||
positions = positions[positions.index.isin(trading_calendar)]
|
||||
filtered_days = len(returns)
|
||||
print(f" [过滤] 原始数据: {original_days} 天 -> A 股交易日: {filtered_days} 天 (过滤 {original_days - filtered_days} 天)")
|
||||
|
||||
# 计算策略收益（仓位加权，T+1 执行）
|
||||
positions_delayed = positions.shift(1).fillna(0)
|
||||
strategy_returns = (positions_delayed * returns).sum(axis=1)
|
||||
|
||||
# 扣除交易成本
|
||||
strategy_returns, rebalance_count = self._apply_trade_cost(
|
||||
strategy_returns, positions
|
||||
)
|
||||
print(f" [成本] 调仓次数: {rebalance_count}, 交易成本: {self.trade_cost:.2%}")
|
||||
|
||||
# 计算净值曲线
|
||||
equity_curve = (1 + strategy_returns).cumprod()
|
||||
|
||||
# 检查是否有数据
|
||||
if len(equity_curve) == 0:
|
||||
return {
|
||||
'equity_curve': equity_curve,
|
||||
'strategy_returns': strategy_returns,
|
||||
'positions': positions,
|
||||
'metrics': {
|
||||
'total_return': 0,
|
||||
'annual_return': 0,
|
||||
'max_drawdown': 0,
|
||||
'sharpe_ratio': 0,
|
||||
'n_days': 0,
|
||||
'rebalance_count': 0,
|
||||
}
|
||||
}
|
||||
|
||||
# 计算绩效指标
|
||||
total_return = equity_curve.iloc[-1] / equity_curve.iloc[0] - 1
|
||||
n_days = len(strategy_returns)
|
||||
annual_return = (1 + total_return) ** (252 / n_days) - 1 if n_days > 0 else 0
|
||||
|
||||
# 最大回撤
|
||||
cumulative_max = equity_curve.cummax()
|
||||
drawdown = (equity_curve - cumulative_max) / cumulative_max
|
||||
max_drawdown = drawdown.min()
|
||||
|
||||
# 夏普比率
|
||||
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(252) if strategy_returns.std() > 0 else 0
|
||||
|
||||
return {
|
||||
'equity_curve': equity_curve,
|
||||
'strategy_returns': strategy_returns,
|
||||
'positions': positions,
|
||||
'metrics': {
|
||||
'total_return': total_return,
|
||||
'annual_return': annual_return,
|
||||
'max_drawdown': max_drawdown,
|
||||
'sharpe_ratio': sharpe,
|
||||
'n_days': n_days,
|
||||
'rebalance_count': rebalance_count,
|
||||
}
|
||||
}
|
||||
|
||||
def _apply_trade_cost(self, strategy_returns: pd.Series, positions: pd.DataFrame) -> Tuple[pd.Series, int]:
|
||||
"""
|
||||
扣除交易成本
|
||||
|
||||
Args:
|
||||
strategy_returns: 策略收益率
|
||||
positions: 仓位 DataFrame
|
||||
|
||||
Returns:
|
||||
(扣除成本后的收益率, 调仓次数)
|
||||
"""
|
||||
if self.trade_cost <= 0:
|
||||
return strategy_returns, 0
|
||||
|
||||
# 检测调仓（持仓变化）
|
||||
position_changes = (positions != positions.shift(1)).any(axis=1)
|
||||
rebalance_count = position_changes.sum()
|
||||
|
||||
# 扣除交易成本
|
||||
strategy_returns[position_changes] -= self.trade_cost
|
||||
|
||||
return strategy_returns, rebalance_count
|
||||
|
||||
def _get_premium_data(self) -> Optional[Dict]:
|
||||
"""
|
||||
获取溢价率数据
|
||||
|
||||
Returns:
|
||||
溢价率数据字典 {trade_code: {date: premium_rate}}
|
||||
"""
|
||||
# TODO: 从数据源获取溢价率数据
|
||||
# 当前返回 None，后续实现
|
||||
return None
|
||||
|
||||
def _filter_by_premium(self, factors: pd.Series, date: pd.Timestamp, premium_data: Dict) -> pd.Series:
|
||||
"""
|
||||
溢价过滤
|
||||
|
||||
Args:
|
||||
factors: 因子 Series
|
||||
date: 日期
|
||||
premium_data: 溢价率数据
|
||||
|
||||
Returns:
|
||||
过滤后的因子 Series
|
||||
"""
|
||||
if premium_data is None:
|
||||
return factors
|
||||
|
||||
# TODO: 实现溢价过滤逻辑
|
||||
return factors
|
||||
|
||||
def _get_trading_calendar(self) -> pd.DatetimeIndex:
|
||||
"""
|
||||
获取 A 股交易日历
|
||||
|
||||
Returns:
|
||||
A 股交易日历 DatetimeIndex
|
||||
"""
|
||||
from datetime import date
|
||||
|
||||
# 获取回测区间
|
||||
start = self.config.backtest.start_date
|
||||
end = self.config.backtest.end_date
|
||||
if end is None:
|
||||
end = date.today().strftime('%Y-%m-%d')
|
||||
|
||||
# 创建临时数据获取器来获取交易日历
|
||||
if self._data_fetcher is None:
|
||||
self._data_fetcher = self._create_data_fetcher()
|
||||
|
||||
try:
|
||||
# 调用 get_trading_calendar 方法
|
||||
calendar = self._data_fetcher.get_trading_calendar(
|
||||
market='A',
|
||||
start=start,
|
||||
end=end
|
||||
)
|
||||
print(f" [日历] A 股交易日: {len(calendar)} 天 ({calendar[0]} ~ {calendar[-1]})")
|
||||
return calendar
|
||||
except Exception as e:
|
||||
print(f" [警告] 无法获取 A 股交易日历，使用所有日期: {e}")
|
||||
# 降级方案：使用 pandas 生成工作日
|
||||
start_dt = pd.Timestamp(start)
|
||||
end_dt = pd.Timestamp(end)
|
||||
return pd.date_range(start=start_dt, end=end_dt, freq='B') # 工作日
|
||||
Reference in New Issue
Block a user