rosemarycox5334-debug
5c798a5d5c
Add 4 new features (WILLR_14, CCI_14, ROC_12, TYPICAL_DEV) and 10 new operators from WorldQuant Alpha 101 + web research. Alpha 101 core (4): DELTA, TS_ARG_MAX_5, TS_ARG_MIN_5, DECAY_LINEAR_5 Web research supplements (6): SCALE, COVARIANCE_10, PRODUCT_5, SIGNED_POWER_2, TS_DECAY_EXP_5, DELTA_5 Verified: 44/44 operators pass smoke test, 1-step training on forex group succeeds (1.4s). Old strategies remain compatible via VOCAB_VERSION pinning.
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
from dataclasses import dataclass
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from .ops import OPS_CONFIG
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# 词表版本：特征顺序变化时递增，旧 checkpoint / best_strategy.json 与新版本不兼容
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VOCAB_VERSION = "3.0" # v1.0=10特征, v2.0=20特征(2026-07), v3.0=30特征+44算子(2026-07-03)
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FEATURE_NAMES = (
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# 趋势类（0-4）
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"RET", # 0 单期对数收益率
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"RET5", # 1 5期动量
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"RET20", # 2 20期动量
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"MA_DIFF", # 3 MA10/MA30-1 短长均线差
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"SLOPE20", # 4 20期线性回归斜率
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# 波动类（5-8）
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"ATR", # 5 平均真实波幅
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"RVOL", # 6 已实现波动率
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"HL_RANGE", # 7 (high-low)/close
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"VOL_REGIME", # 8 ATR/MA(ATR)-1 波动率状态
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# 反转类（9-13）
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"DEV", # 9 (close-MA20)/MA20
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"DEV60", # 10 (close-MA60)/MA60
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"RSI14", # 11 RSI归一化[-1,1]
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"PRESSURE", # 12 (close-open)/(high-low)
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"AC1", # 13 一阶自相关
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# 成交量类（14-16）
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"VOL_RATIO", # 14 volume/MA20(volume)
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"VOL_Z", # 15 量能z分数
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"PV_CORR", # 16 价量10期相关系数
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# 跨资产相对强弱（17-19）
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"REL_RET5", # 17 相对5期收益（截面去均值）
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"REL_RET20", # 18 相对20期收益（截面去均值）
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"REL_VOL", # 19 相对波动率（截面去均值）
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# ── v3.0 新增特征（20-25）──────────────────────────────────────────
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"VWAP_DEV", # 20 (close-VWAP)/VWAP 成交量加权均价偏离
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"BOLL_POS", # 21 布林带位置[0,1] 标准化超买超卖
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"BOLL_WIDTH", # 22 布林带宽度 波动率收缩/扩张
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"MACD_HIST", # 23 MACD柱 (快线-慢线-信号线)
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"OBV_SLOPE", # 24 能量潮斜率 累积量能趋势
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"MFI14", # 25 资金流量指标 带量版RSI[-1,1]
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# ── v3.0 Alpha 101 + 互补特征（26-29）──────────────────────────────
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"WILLR_14", # 26 威廉指标[-1,0] 超买超卖（比RSI更敏感）
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"CCI_14", # 27 商品通道指标 均值回归信号
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"ROC_12", # 28 12期变化率 与RET5/RET20互补
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"TYPICAL_DEV", # 29 典型价格偏离MA 与VWAP_DEV互补
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)
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@dataclass(frozen=True)
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class FormulaVocab:
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feature_names: tuple[str, ...]
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operator_names: tuple[str, ...]
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@property
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def feature_count(self) -> int:
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return len(self.feature_names)
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@property
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def operator_offset(self) -> int:
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return self.feature_count
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@property
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def token_names(self) -> tuple[str, ...]:
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return self.feature_names + self.operator_names
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@property
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def size(self) -> int:
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return len(self.token_names)
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FORMULA_VOCAB = FormulaVocab(
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feature_names=FEATURE_NAMES,
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operator_names=tuple(cfg[0] for cfg in OPS_CONFIG),
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)
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