feat: 新增slope_r2因子并切换为默认因子（年化19.84%, 夏普1.14）
- simple_rotation.py: 新增3种score函数（vol_adjusted_momentum, slope_r2, momentum） - config_loader.py: FactorType枚举新增VOL_ADJUSTED_MOMENTUM - config_simple.yaml: factor.type 切换为 slope_r2 - experiments/factor_comparison.py: 4种因子对比实验脚本 - experiments/output: 实验结果（slope_r2全面胜出）
This commit is contained in:
@@ -33,6 +33,7 @@ class FactorType(str, Enum):
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MOMENTUM = "momentum"
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SLOPE_R2 = "slope_r2"
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WEIGHTED_MOMENTUM = "weighted_momentum"
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VOL_ADJUSTED_MOMENTUM = "vol_adjusted_momentum"
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class PremiumMode(str, Enum):
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@@ -1,186 +1,118 @@
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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: 2.0.0
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# 最后更新: 2024-04-16
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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: "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（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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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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group: "A"
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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: "A"
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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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group: "US"
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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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"N225":
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name: "日经225"
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group: "JP"
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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"
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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"
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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"
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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: "BOND"
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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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# ============================================================
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benchmark:
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code: "000300.SH"
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name: "沪深300"
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# ============================================================
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# 回测配置
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# ============================================================
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399006.SZ:
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description: 创业板指数
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group: A
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name: 创业板指
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signal_source: 399006.SZ
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trade_source: 159915.SZ
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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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group: BOND
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name: 短债指数
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signal_source: 931862.CSI
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trade_source: 931862.CSI
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CL=F:
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description: WTI原油期货（2000年至今）
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group: COMMODITY
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name: 原油
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signal_source: CL=F
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trade_source: 160723.SZ
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GC=F:
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description: COMEX黄金期货（2000年至今）
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group: COMMODITY
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name: 黄金
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signal_source: GC=F
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trade_source: 518880.SH
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GDAXI:
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description: 德国DAX指数
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group: EU
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name: 德国DAX
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signal_source: GDAXI
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trade_source: 513030.SH
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H30269.CSI:
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description: 红利低波指数
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group: A
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name: 中证红利低波
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signal_source: H30269.CSI
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trade_source: 512890.SH
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HG=F:
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description: COMEX铜期货（2000年至今）
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group: COMMODITY
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name: 有色金属
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signal_source: HG=F
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trade_source: 159980.SZ
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HSI:
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description: 恒生指数
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group: HK
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name: 恒生指数
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signal_source: HSI
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trade_source: 159920.SZ
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HSTECH.HK:
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description: 恒生科技指数
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group: HK
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name: 恒生科技
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signal_source: HSTECH.HK
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trade_source: 513130.SH
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N225:
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description: 日经225指数
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group: JP
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name: 日经225
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signal_source: N225
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trade_source: 513520.SH
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NDX:
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description: 纳斯达克100指数
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group: US
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name: 纳指100
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signal_source: NDX
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trade_source: 513100.SH
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backtest:
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start_date: "2020-01-10" # 与 V1 保持一致（第一个完整交易日）
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# end_date: "2026-05-22" # 与 V1 保持一致
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# ============================================================
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# 因子配置
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# ============================================================
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start_date: '2020-01-10'
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benchmark:
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code: 000300.SH
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name: 沪深300
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data:
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sources:
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- enabled: true
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timeout: 120
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type: flask_api
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url: ${FLASK_API_URL}
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factor:
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type: "weighted_momentum" # 加权动量
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n_days: 25 # 25 天窗口
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# ============================================================
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# 轮动配置
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# ============================================================
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rotation:
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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: "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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# ============================================================
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n_days: 25
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type: slope_r2
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metadata:
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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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strategy: rotation
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version: 2.0.0
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premium_control:
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default_threshold: 0.1
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enabled: false
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market_overrides:
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A:
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enabled: false
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COMMODITY:
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enabled: false
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HK:
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enabled: true
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threshold: 0.1
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US:
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enabled: true
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threshold: 0.1
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mode: filter
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penalty_factor: 0.5
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rebalance:
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min_hold_days: 1
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score_threshold: 0.0
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trade_cost: 0.001 # 万1 交易成本（场内ETF万0.5免5）
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# ============================================================
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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 # 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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A: # A股 ETF
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enabled: false # 不启用（溢价通常 < 0.5%）
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HK: # 港股 ETF
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enabled: true
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threshold: 0.10 # 阈值 10%
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US: # 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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# ============================================================
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data:
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sources:
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- type: "flask_api"
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enabled: true
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url: "${FLASK_API_URL}"
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timeout: 120
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trade_cost: 0.001
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rotation:
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diversified: true
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select_num: 3
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threshold:
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dynamic:
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fallback_enabled: true
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fallback_value: 0.0
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ratio: 1.0
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reference: 931862.CSI
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fixed_value: 0.0
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mode: dynamic
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144
rotation/experiments/factor_comparison.py
Normal file
144
rotation/experiments/factor_comparison.py
Normal file
@@ -0,0 +1,144 @@
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"""
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因子类型对比实验
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测试 4 种动量因子的回测表现：
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1. weighted_momentum: 加权线性回归动量 (当前默认)
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2. vol_adjusted_momentum: 波动率调整动量 (Moskowitz TSMOM)
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3. slope_r2: 斜率 × R² (未加权)
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4. momentum: 简单收益率
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运行方式:
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cd /Users/aszer/code/etf
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python3 rotation/experiments/factor_comparison.py
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"""
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import os
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import sys
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import json
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import yaml
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import copy
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from pathlib import Path
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from datetime import datetime
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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 rotation.simple_rotation import SimpleRotationStrategy
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from rotation.config_loader import FactorType
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FACTOR_TYPES = [
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("weighted_momentum", "加权线性回归动量 (年化收益×R²)"),
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("vol_adjusted_momentum", "波动率调整动量 (Sharpe-like×R²)"),
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("slope_r2", "斜率×R² (未加权归一化)"),
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("momentum", "简单收益率 (last/first-1)"),
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]
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def load_config():
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"""Load base config"""
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config_path = PROJECT_ROOT / "rotation" / "config_simple.yaml"
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def run_factor_experiment(factor_type: str):
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"""Run backtest with a specific factor type"""
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print(f"\n{'='*60}")
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print(f" Testing: {factor_type}")
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print(f"{'='*60}")
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# Modify config to use this factor type
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config_path = PROJECT_ROOT / "rotation" / "config_simple.yaml"
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with open(config_path, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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original_type = config['factor']['type']
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config['factor']['type'] = factor_type
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# Write temporary config
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with open(config_path, 'w', encoding='utf-8') as f:
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yaml.dump(config, f, allow_unicode=True, default_flow_style=False)
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try:
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# Run strategy
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strategy = SimpleRotationStrategy()
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result = strategy.run()
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if result:
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metrics = result.get('metrics', {})
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return {
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'factor_type': factor_type,
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'annual_return': metrics.get('annual_return', 0),
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'total_return': metrics.get('total_return', 0),
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'sharpe_ratio': metrics.get('sharpe_ratio', 0),
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'max_drawdown': metrics.get('max_drawdown', 0),
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'win_rate': metrics.get('win_rate', 0),
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'rebalance_count': metrics.get('rebalance_count', 0),
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'calmar_ratio': metrics.get('calmar_ratio', 0),
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}
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finally:
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# Restore original config
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config['factor']['type'] = original_type
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with open(config_path, 'w', encoding='utf-8') as f:
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yaml.dump(config, f, allow_unicode=True, default_flow_style=False)
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return None
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def main():
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if 'FLASK_API_URL' not in os.environ:
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os.environ['FLASK_API_URL'] = 'https://k3s.tokenpluse.xyz'
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print("="*60)
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print(" ETF轮动策略 - 因子类型对比实验")
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print("="*60)
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results = []
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for factor_type, description in FACTOR_TYPES:
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print(f"\n>>> {description}")
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result = run_factor_experiment(factor_type)
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if result:
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results.append(result)
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print(f" ✓ {factor_type}: 年化={result['annual_return']:.2%}, "
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f"夏普={result['sharpe_ratio']:.2f}, 回撤={result['max_drawdown']:.2%}")
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else:
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print(f" ✗ {factor_type}: 运行失败")
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# Summary table
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print(f"\n{'='*60}")
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print(" 对比结果汇总")
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print(f"{'='*60}")
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print(f"{'因子类型':<25} {'年化收益':>10} {'夏普比率':>8} {'最大回撤':>10} {'调仓次数':>8} {'胜率':>6}")
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print("-"*60)
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for r in results:
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print(f"{r['factor_type']:<25} "
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f"{r['annual_return']:>9.2%} "
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f"{r['sharpe_ratio']:>8.2f} "
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f"{r['max_drawdown']:>9.2%} "
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f"{r['rebalance_count']:>8d} "
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f"{r['win_rate']:>5.1%}")
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# Find best
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if results:
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best = max(results, key=lambda x: x['sharpe_ratio'])
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print(f"\n★ 最优因子 (按夏普): {best['factor_type']}")
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print(f" 年化收益: {best['annual_return']:.2%}")
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print(f" 夏普比率: {best['sharpe_ratio']:.2f}")
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print(f" 最大回撤: {best['max_drawdown']:.2%}")
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# Save results
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output_dir = PROJECT_ROOT / "rotation" / "experiments" / "output"
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output_dir.mkdir(exist_ok=True)
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output_path = output_dir / "factor_comparison_results.json"
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump({
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'timestamp': datetime.now().isoformat(),
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'results': results
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
print(f"\n结果已保存: {output_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
45
rotation/experiments/output/factor_comparison_results.json
Normal file
45
rotation/experiments/output/factor_comparison_results.json
Normal file
@@ -0,0 +1,45 @@
|
||||
{
|
||||
"timestamp": "2026-06-06T15:41:51.261231",
|
||||
"results": [
|
||||
{
|
||||
"factor_type": "weighted_momentum",
|
||||
"annual_return": 0.1836425584586039,
|
||||
"total_return": 1.8188636662013202,
|
||||
"sharpe_ratio": 1.0221559991160554,
|
||||
"max_drawdown": -0.16355913241141637,
|
||||
"win_rate": 0.539754363283775,
|
||||
"rebalance_count": 405,
|
||||
"calmar_ratio": 1.1227900010906742
|
||||
},
|
||||
{
|
||||
"factor_type": "vol_adjusted_momentum",
|
||||
"annual_return": 0.1315526234967641,
|
||||
"total_return": 1.1376143317727325,
|
||||
"sharpe_ratio": 0.8543126134924596,
|
||||
"max_drawdown": -0.18613474523686568,
|
||||
"win_rate": 0.5585003232062056,
|
||||
"rebalance_count": 393,
|
||||
"calmar_ratio": 0.7067601662943525
|
||||
},
|
||||
{
|
||||
"factor_type": "slope_r2",
|
||||
"annual_return": 0.198416094188119,
|
||||
"total_return": 2.0421974188211456,
|
||||
"sharpe_ratio": 1.1350010914615083,
|
||||
"max_drawdown": -0.15352659557851117,
|
||||
"win_rate": 0.541343669250646,
|
||||
"rebalance_count": 394,
|
||||
"calmar_ratio": 1.2923890707043786
|
||||
},
|
||||
{
|
||||
"factor_type": "momentum",
|
||||
"annual_return": 0.09270306870862322,
|
||||
"total_return": 0.7245114082990154,
|
||||
"sharpe_ratio": 0.5741296434738409,
|
||||
"max_drawdown": -0.17419361103962644,
|
||||
"win_rate": 0.5326438267614738,
|
||||
"rebalance_count": 729,
|
||||
"calmar_ratio": 0.5321840919156022
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -24,7 +24,7 @@ from typing import Dict, List, Optional, Tuple
|
||||
PROJECT_ROOT = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
from rotation.config_loader import load_rotation_config, RotationStrategyConfig
|
||||
from rotation.config_loader import load_rotation_config, RotationStrategyConfig, FactorType
|
||||
|
||||
# ============================================================
|
||||
# HTTP client (requests + trust_env=False，绕过系统代理避免 SSL EOF)
|
||||
@@ -79,6 +79,58 @@ def weighted_momentum_score(prices: np.ndarray) -> float:
|
||||
return annualized_return * r2
|
||||
|
||||
|
||||
def vol_adjusted_momentum_score(prices: np.ndarray) -> float:
|
||||
"""Volatility-adjusted momentum score = (annualized_return / annualized_vol) * R^2
|
||||
|
||||
Moskowitz, Ooi & Pedersen (2012) TSMOM approach:
|
||||
divide momentum by realized volatility to make cross-asset comparison fair.
|
||||
"""
|
||||
if len(prices) < 5:
|
||||
return 0.0
|
||||
prices = np.clip(prices, 0.01, None)
|
||||
y = np.log(prices)
|
||||
if np.any(np.isnan(y)) or np.any(np.isinf(y)):
|
||||
return 0.0
|
||||
x = np.arange(len(y))
|
||||
weights = np.linspace(1, 2, len(y))
|
||||
slope, intercept = np.polyfit(x, y, 1, w=weights)
|
||||
annualized_return = math.exp(slope * 250) - 1
|
||||
# realized volatility (annualized)
|
||||
daily_returns = np.diff(y)
|
||||
realized_vol = float(np.std(daily_returns)) * math.sqrt(250)
|
||||
if realized_vol < 0.01: # guard against near-zero vol
|
||||
realized_vol = 0.01
|
||||
# trend quality (R^2)
|
||||
y_pred = slope * x + intercept
|
||||
ss_res = np.sum(weights * (y - y_pred) ** 2)
|
||||
ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
|
||||
r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
|
||||
return (annualized_return / realized_vol) * r2
|
||||
|
||||
|
||||
def slope_r2_score(prices: np.ndarray) -> float:
|
||||
"""Slope * R^2 score (unweighted, normalized prices)"""
|
||||
if len(prices) < 5:
|
||||
return 0.0
|
||||
prices = np.clip(prices, 0.01, None)
|
||||
y = prices / prices[0] # normalize
|
||||
x = np.arange(len(y))
|
||||
slope, intercept = np.polyfit(x, y, 1)
|
||||
y_pred = slope * x + intercept
|
||||
ss_res = np.sum((y - y_pred) ** 2)
|
||||
ss_tot = np.sum((y - np.mean(y)) ** 2)
|
||||
r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
|
||||
return 10000 * slope * r2
|
||||
|
||||
|
||||
def momentum_score(prices: np.ndarray) -> float:
|
||||
"""Simple price return: (last / first) - 1"""
|
||||
if len(prices) < 5:
|
||||
return 0.0
|
||||
prices = np.clip(prices, 0.01, None)
|
||||
return prices[-1] / prices[0] - 1
|
||||
|
||||
|
||||
def is_crash(prices: np.ndarray) -> bool:
|
||||
"""Crash filter: 3 consecutive days drop > 5%"""
|
||||
if len(prices) < 4:
|
||||
@@ -340,7 +392,29 @@ class SimpleRotationStrategy:
|
||||
print(f" Benchmark: {len(bm_df)} rows")
|
||||
|
||||
def _compute_momentum(self, signal_code: str, date: pd.Timestamp) -> Optional[float]:
|
||||
"""Compute momentum for a single code on a given date"""
|
||||
"""Compute momentum for a single code on a given date (uses configured score function)"""
|
||||
if signal_code not in self.index_data:
|
||||
return None
|
||||
df = self.index_data[signal_code]
|
||||
mask = df.index <= date
|
||||
recent = df.loc[mask]
|
||||
if len(recent) < self.n_days:
|
||||
return None
|
||||
prices = recent['close'].values[-self.n_days:]
|
||||
if len(prices) >= 4 and is_crash(prices):
|
||||
return 0.0
|
||||
# Dispatch based on factor type
|
||||
ft = self.config.factor.type
|
||||
if ft == FactorType.VOL_ADJUSTED_MOMENTUM:
|
||||
return vol_adjusted_momentum_score(prices)
|
||||
elif ft == FactorType.SLOPE_R2:
|
||||
return slope_r2_score(prices)
|
||||
elif ft == FactorType.MOMENTUM:
|
||||
return momentum_score(prices)
|
||||
return weighted_momentum_score(prices)
|
||||
|
||||
def _compute_raw_momentum(self, signal_code: str, date: pd.Timestamp) -> Optional[float]:
|
||||
"""Always compute weighted momentum (for threshold comparison)"""
|
||||
if signal_code not in self.index_data:
|
||||
return None
|
||||
df = self.index_data[signal_code]
|
||||
@@ -374,23 +448,37 @@ class SimpleRotationStrategy:
|
||||
if not factors:
|
||||
return [], {}, None
|
||||
|
||||
# Bond threshold always uses raw (weighted) momentum
|
||||
bond_momentum = None
|
||||
if self.use_dynamic_threshold and self.bond_code:
|
||||
bond_momentum = self._compute_momentum(self.bond_code, date)
|
||||
bond_momentum = self._compute_raw_momentum(self.bond_code, date)
|
||||
if bond_momentum is None:
|
||||
bond_momentum = self.fallback_value
|
||||
|
||||
# Raw factors for threshold comparison
|
||||
raw_factors: Dict[str, float] = {}
|
||||
if self.config.factor.type == FactorType.VOL_ADJUSTED_MOMENTUM:
|
||||
for code in self.signal_codes:
|
||||
score = self._compute_raw_momentum(code, date)
|
||||
if score is not None:
|
||||
raw_factors[code] = score
|
||||
else:
|
||||
raw_factors = factors
|
||||
|
||||
groups = self.config.asset_pools.by_group
|
||||
selected_by_group: Dict[str, Tuple[str, float]] = {}
|
||||
|
||||
for group_name, assets in groups.items():
|
||||
group_codes = [a.signal_source for a in assets.values()]
|
||||
group_factors = {c: factors[c] for c in group_codes if c in factors}
|
||||
group_raw = {c: raw_factors[c] for c in group_codes if c in raw_factors}
|
||||
if not group_factors:
|
||||
continue
|
||||
# Threshold comparison uses raw (weighted) momentum
|
||||
if group_name != 'BOND' and bond_momentum is not None:
|
||||
thresh = bond_momentum * self.bond_ratio
|
||||
group_factors = {c: s for c, s in group_factors.items() if s >= thresh}
|
||||
group_factors = {c: s for c, s in group_factors.items()
|
||||
if group_raw.get(c, 0) >= thresh}
|
||||
if not group_factors:
|
||||
continue
|
||||
top_code = max(group_factors, key=group_factors.get)
|
||||
|
||||
Reference in New Issue
Block a user