91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
"""
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行业/横截面因子模块
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包含基于日期截面的行业/横截面因子实现
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"""
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import numpy as np
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import polars as pl
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from main.factor.operator_framework import DateWiseFactor
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class IndustryMomentumFactor(DateWiseFactor):
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"""行业动量因子"""
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def __init__(self, factor_name: str):
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super().__init__(
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name=f"industry_momentum",
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parameters={"factor_name": factor_name},
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required_factor_ids=[factor_name, "cat_l2_code"]
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)
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self.factor_name = factor_name
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算行业动量基准
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# 这里需要先计算每个行业的平均值，然后与个股比较
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if self.factor_name in group_df.columns and "cat_l2_code" in group_df.columns:
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# 按行业计算平均值
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industry_means = group_df.group_by("cat_l2_code").agg([
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pl.col(self.factor_name).mean().alias("industry_mean")
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])
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# 将行业均值合并回原数据
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result_df = group_df.join(industry_means, on="cat_l2_code", how="left")
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# 计算与行业均值的偏差
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deviation = result_df[self.factor_name] - result_df["industry_mean"]
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return deviation.alias(self.factor_id)
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else:
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# 如果缺少必要列，返回全NaN
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return pl.Series([None] * len(group_df)).alias(self.factor_id)
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class MarketBreadthFactor(DateWiseFactor):
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"""市场宽度因子"""
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def __init__(self):
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super().__init__(
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name="market_breadth",
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parameters={},
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required_factor_ids=["pct_chg"]
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)
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算市场宽度：上涨股票数 / 总股票数
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pct_chg = group_df["pct_chg"]
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positive_count = (pct_chg > 0).sum()
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total_count = len(group_df)
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# 避免除零
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breadth = positive_count / (total_count + 1e-8)
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return pl.Series([breadth] * len(group_df)).alias(self.factor_id)
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class SectorRotationFactor(DateWiseFactor):
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"""板块轮动因子"""
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def __init__(self, sector_factor: str):
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super().__init__(
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name=f"sector_rotation_{sector_factor}",
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parameters={"sector_factor": sector_factor},
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required_factor_ids=[sector_factor, "cat_l2_code"]
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)
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self.sector_factor = sector_factor
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算板块轮动因子
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if self.sector_factor in group_df.columns and "cat_l2_code" in group_df.columns:
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# 计算每个板块的因子均值
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sector_means = group_df.group_by("cat_l2_code").agg([
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pl.col(self.sector_factor).mean().alias("sector_mean")
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])
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# 将板块均值合并回原数据
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result_df = group_df.join(sector_means, on="cat_l2_code", how="left")
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# 计算个股与板块均值的偏差
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deviation = result_df[self.sector_factor] - result_df["sector_mean"]
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return deviation.alias(self.factor_id)
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else:
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# 如果缺少必要列，返回全NaN
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return pl.Series([None] * len(group_df)).alias(self.factor_id)
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