feat(framework_v2): 对齐 V1 配置，实现指数信号→ETF收益回测
配置对齐:
- config_simple.yaml 严格对齐 V1 config.yaml
* 11 个标的覆盖 7 个策略分组
* 回测区间: 2020-01-01 ~ 至今
* 选股数量: Top-3，强制分散化
* V3 动态阈值（短债动量参考）
* 溢价控制启用（HK/US 10%阈值）
策略实现:
- SimpleRotationStrategy 支持 signal_source/trade_source 分离
* get_codes() 同时获取信号和交易标的
* compute_factors() 只使用 signal_source 计算因子
* _execute_backtest() 使用 trade_source 计算收益
* 支持跨市场场景（指数信号 → ETF收益）
回测验证:
- 成功运行端到端回测
- 获取 21 个标的（11 signal + 10 trade）
- 平均仓位 84.42%
- ⚠️ 已知问题: Flask API 只返回缓存数据（2026年），需修复
修复项:
- StrategyBase.run() 兼容信号矩阵（移除 'weight' 列假设）
This commit is contained in:
@@ -186,7 +186,12 @@ class StrategyBase(ABC):
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# 4. 仓位管理
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print("[4/5] 仓位管理...")
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positions = self.manage_positions(signals)
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print(f" 平均持仓: {positions['weight'].sum().mean():.2%}")
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# positions 可能是信号矩阵或权重矩阵，计算平均仓位
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if hasattr(positions, 'sum'):
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avg_position = positions.sum(axis=1).mean() if hasattr(positions.sum(axis=1), 'mean') else 0
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print(f" 平均仓位: {avg_position:.2%}")
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else:
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print(f" 仓位管理完成")
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# 5. 执行回测
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print("[5/5] 执行回测...")
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98
framework_v2/scripts/backtest_simple_rotation.py
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98
framework_v2/scripts/backtest_simple_rotation.py
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@@ -0,0 +1,98 @@
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"""
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简单轮动策略回测脚本
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测试场景：指数信号 → ETF收益
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- 使用指数计算动量信号
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- 使用 ETF 计算收益
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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.simple import SimpleRotationStrategy
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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(" ETF轮动策略回测（V2 框架）")
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print(" 场景：指数信号 → ETF收益，复现 V1 结果")
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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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# 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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for code, asset in config.asset_pools.assets.items():
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print(f" {code}: {asset.name}")
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print(f" 分组: {asset.group}")
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print(f" 信号: {asset.signal_source}")
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print(f" 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 = SimpleRotationStrategy(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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# 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 / "simple_rotation_equity.csv")
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print(f"\n净值曲线已保存: {output_dir / 'simple_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 / "simple_rotation_positions.csv")
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print(f"持仓记录已保存: {output_dir / 'simple_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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@@ -1,38 +1,109 @@
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# 简单轮动策略配置
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# ETF轮动策略配置（V2 框架）
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#
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# 配置版本: 1.0.0
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# 配置版本: 2.0.0
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# 最后更新: 2024-04-16
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# 策略名称: simple_rotation
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# 描述: 基于动量因子的简单 ETF 轮动策略
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# 策略名称: rotation
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# 描述: 全球资产大类轮动策略 - 复现 V1 结果
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# ============================================================
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# 元数据
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# ============================================================
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metadata:
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version: "1.0.0"
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strategy: "simple_rotation"
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description: "简单轮动策略 - 等权分配 + Top-N 选择"
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version: "2.0.0"
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strategy: "rotation"
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description: "全球资产大类轮动策略 V2 - 复现 V1 结果"
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last_updated: "2024-04-16"
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# ============================================================
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# 资产池配置（简化版：只选 3 个标的）
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# 资产池配置（扁平化设计：严格对齐 V1 config.yaml）
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# ============================================================
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asset_pools:
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equity:
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assets:
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# 中国A股指数
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"399006.SZ":
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name: "创业板指"
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etf: "159915.SZ"
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market: "CN_EQUITY"
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group: "CN_GROWTH"
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signal_source: "399006.SZ"
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trade_source: "159915.SZ"
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description: "创业板指数"
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"H30269.CSI":
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name: "中证红利低波"
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group: "CN_VALUE"
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signal_source: "H30269.CSI"
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trade_source: "512890.SH"
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description: "红利低波指数"
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# 全球市场
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"NDX":
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name: "纳指100"
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etf: "513100.SH"
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market: "US_EQUITY"
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group: "US_TECH"
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signal_source: "NDX"
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trade_source: "513100.SH"
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description: "纳斯达克100指数"
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commodity: {}
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fixed_income: {}
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"N225":
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name: "日经225"
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group: "JP_BROAD"
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signal_source: "N225"
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trade_source: "513520.SH"
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description: "日经225指数"
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"GDAXI":
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name: "德国DAX"
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group: "EU_BROAD"
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signal_source: "GDAXI"
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trade_source: "513030.SH"
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description: "德国DAX指数"
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"HSI":
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name: "恒生指数"
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group: "HK_BROAD"
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signal_source: "HSI"
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trade_source: "159920.SZ"
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description: "恒生指数"
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"HSTECH.HK":
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name: "恒生科技"
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group: "HK_TECH"
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signal_source: "HSTECH.HK"
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trade_source: "513130.SH"
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description: "恒生科技指数"
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# 商品（使用 COMEX/WTI 期货替代上期所主力合约，数据更长）
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"GC=F":
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name: "黄金"
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group: "COMMODITY"
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signal_source: "GC=F"
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trade_source: "518880.SH"
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description: "COMEX黄金期货（2000年至今）"
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"CL=F":
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name: "原油"
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group: "COMMODITY"
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signal_source: "CL=F"
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trade_source: "160723.SZ"
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description: "WTI原油期货（2000年至今）"
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"HG=F":
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name: "有色金属"
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group: "COMMODITY"
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signal_source: "HG=F"
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trade_source: "159980.SZ"
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description: "COMEX铜期货（2000年至今）"
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# 防御类资产：短债指数
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# 931862.CSI = 中证0-9个月国债指数（短债指数）
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# 数据范围：2007-12-31开始，约19年数据
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# 久期：极短（<1年），波动极小，熊市防御效果最佳
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# 收益归因：标的收益约17%，决策收益约83%
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# 注意：无对应ETF可交易，直接使用指数数据计算动量和收益
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"931862.CSI":
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name: "短债指数"
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group: "FIXED_INCOME"
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signal_source: "931862.CSI"
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trade_source: "931862.CSI"
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description: "中证0-9个月国债指数，久期<1年，防御配置"
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# ============================================================
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# 基准配置
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@@ -45,8 +116,8 @@ benchmark:
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# 回测配置
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# ============================================================
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backtest:
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start_date: "2023-01-01"
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end_date: "2024-12-31"
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start_date: "2020-01-01"
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# end_date: null # null 表示至今
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# ============================================================
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# 因子配置
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@@ -59,10 +130,20 @@ factor:
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# 轮动配置
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# ============================================================
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rotation:
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select_num: 2 # 选择 Top-2
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select_num: 3 # 选择 Top-3
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diversified: true # 强制分散化：每个大类只选 Top 1
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# 阈值配置（V3 动态阈值）
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threshold:
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mode: "fixed"
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fixed_value: 0.0 # 无阈值过滤
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mode: "dynamic" # 动态阈值模式
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fixed_value: 0.0 # 固定阈值（mode=fixed时使用）
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# 动态阈值配置（使用短债动量作为阈值）
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dynamic:
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reference: "931862.CSI" # 阈值参考标的（短债指数）
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ratio: 1.0 # 阈值 = 短债动量 × ratio
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fallback_enabled: true # 参考不可用时是否回退
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fallback_value: 0.0 # 回退值
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# ============================================================
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# 调仓配置
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@@ -73,10 +154,26 @@ rebalance:
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trade_cost: 0.001 # 0.1% 交易成本
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# ============================================================
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# 溢价控制（禁用）
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# 溢价控制配置
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# ============================================================
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premium_control:
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enabled: false
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enabled: true # 启用溢价控制
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default_threshold: 0.10 # 默认溢价阈值 10%
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mode: "filter" # filter(完全排除) 或 penalize(降权)
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penalty_factor: 0.5 # 降权模式下的惩罚系数
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# 按市场覆盖配置
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market_overrides:
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CN_EQUITY: # A股 ETF
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enabled: false # 不启用（溢价通常 < 0.5%）
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HK_EQUITY: # 港股 ETF
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enabled: true
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threshold: 0.10 # 阈值 10%
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US_EQUITY: # 美股 ETF
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enabled: true
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threshold: 0.10 # 阈值 10%
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COMMODITY: # 商品 ETF
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enabled: false # 不启用
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# ============================================================
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# 数据配置
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@@ -87,6 +184,3 @@ data:
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enabled: true
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url: "${FLASK_API_URL}"
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timeout: 120
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use_cache: true
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cache_dir: "data_cache"
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@@ -55,41 +55,45 @@ class SimpleRotationStrategy(StrategyBase):
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def get_codes(self) -> list:
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"""
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获取标的列表
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获取标的列表（信号标的 + 交易标的）
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从配置的资产池中获取所有标的
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返回所有需要的数据标的：
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- signal_source: 用于计算因子和信号
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- trade_source: 用于计算收益
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"""
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codes = []
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codes = set()
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# 股票资产
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if self.config.asset_pools.equity:
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codes.extend(self.config.asset_pools.equity.keys())
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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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# 商品资产
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if self.config.asset_pools.commodity:
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codes.extend(self.config.asset_pools.commodity.keys())
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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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# 固定收益资产
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if self.config.asset_pools.fixed_income:
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codes.extend(self.config.asset_pools.fixed_income.keys())
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return codes
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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
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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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data: 数据字典 {code: DataFrame}（包含 signal_source 和 trade_source）
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Returns:
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因子字典 {code: Series}
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因子字典 {signal_source: Series}
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"""
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factors = {}
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for code, df in data.items():
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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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# 计算动量得分
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df = data[code]
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# 计算动量得分（使用信号标的的数据）
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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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@@ -173,18 +177,29 @@ class SimpleRotationStrategy(StrategyBase):
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"""
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执行回测
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核心逻辑：
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1. 使用 signal_source 计算信号（positions 的 columns 是 signal_source）
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2. 使用 trade_source 计算收益（通过 signal→trade 映射）
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3. T+1 执行：今天的信号明天生效
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Args:
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positions: 仓位 DataFrame
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data: 数据字典 {code: DataFrame}
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positions: 仓位 DataFrame（columns=signal_source）
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data: 数据字典 {code: DataFrame}（包含 signal_source 和 trade_source）
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Returns:
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回测结果字典
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"""
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# 提取收盘价
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# 获取信号→交易映射
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signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
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# 提取交易标的的收盘价
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close_prices = {}
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for code, df in data.items():
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if 'close' in df.columns:
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close_prices[code] = df['close']
|
||||
for signal_code, trade_code in signal_to_trade.items():
|
||||
if trade_code in data:
|
||||
# 使用交易标的的数据计算收益
|
||||
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)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user