175 lines
5.9 KiB
Python
175 lines
5.9 KiB
Python
"""
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动量因子模块
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包含基于股票截面和日期截面的动量因子实现
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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 StockWiseFactor, DateWiseFactor
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# -------------------- 股票截面因子：基于时间序列的动量因子 --------------------
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class ReturnFactor(StockWiseFactor):
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"""N日收益率因子"""
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def __init__(self, period: int = 20):
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super().__init__(
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name="return",
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parameters={"period": period},
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required_factor_ids=["close"]
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)
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self.period = period
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算N日收益率（时间序列操作）
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return group_df["close"].pct_change(self.period).alias(self.factor_id)
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class VolatilityFactor(StockWiseFactor):
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"""N日波动率因子"""
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def __init__(self, period: int = 20):
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super().__init__(
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name="volatility",
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parameters={"period": period},
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required_factor_ids=["pct_chg"]
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)
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self.period = period
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算N日波动率（时间序列操作）
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return group_df["pct_chg"].rolling_std(self.period).alias(self.factor_id)
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class MomentumFactor(StockWiseFactor):
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"""动量因子：过去N日累计收益率"""
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def __init__(self, period: int = 20):
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super().__init__(
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name="momentum",
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parameters={"period": period},
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required_factor_ids=["pct_chg"]
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)
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self.period = period
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 计算N日累计动量（时间序列操作）
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return group_df["pct_chg"].rolling_sum(self.period).alias(self.factor_id)
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class MomentumAcceleration(StockWiseFactor):
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"""
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动量加速因子：
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(短期波动率调整后动量 - 长期波动率调整后动量)
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用于捕捉趋势正在形成或加强的股票
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"""
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def __init__(self, short_period: int = 20, long_period: int = 60):
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super().__init__(
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name="momentum_acceleration",
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parameters={"short_period": short_period, "long_period": long_period},
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required_factor_ids=["pct_chg"]
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)
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self.short_period = short_period
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self.long_period = long_period
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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epsilon = 1e-9
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# 计算短期波动率调整后动量
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short_momentum = group_df["pct_chg"].rolling_sum(self.short_period)
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short_vol = group_df["pct_chg"].rolling_std(self.short_period)
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short_adj_momentum = short_momentum / (short_vol + epsilon)
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# 计算长期波动率调整后动量
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long_momentum = group_df["pct_chg"].rolling_sum(self.long_period)
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long_vol = group_df["pct_chg"].rolling_std(self.long_period)
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long_adj_momentum = long_momentum / (long_vol + epsilon)
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# 计算加速因子
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acceleration = (short_adj_momentum - long_adj_momentum).alias(self.factor_id)
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return acceleration
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class TrendEfficiency(StockWiseFactor):
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"""
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趋势效率因子：
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过去N日价格净变化 / 过去N日每日价格变化的绝对值之和
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衡量趋势的信噪比，值越接近1，趋势越清晰、噪声越小
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"""
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def __init__(self, period: int = 20):
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super().__init__(
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name="trend_efficiency",
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parameters={"period": period},
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# 此因子需要收盘价来计算
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required_factor_ids=["close"]
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)
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self.period = period
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 1. 计算N日内的净价格变动（信号）
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# 使用 diff(n) 计算当前价格与n天前价格的差值
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net_change = group_df["close"].diff(self.period).abs()
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# 2. 计算N日内每日价格变动的绝对值之和（总路径/噪声）
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# 先计算每日变动 diff(1)，取绝对值，再滚动求和
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total_path = group_df["close"].diff(1).abs().rolling_sum(self.period)
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# 3. 计算效率比率
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epsilon = 1e-9
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efficiency_ratio = (net_change / (total_path + epsilon)).alias(self.factor_id)
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return efficiency_ratio
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# -------------------- 统一计算函数 --------------------
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def calculate_momentum_factors(df: pl.DataFrame) -> pl.DataFrame:
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"""
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统一计算动量因子的函数
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Parameters:
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df (pl.DataFrame): 输入的股票数据表，必须包含以下列：
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ts_code, trade_date, close, pct_chg, high, low, vol
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Returns:
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pl.DataFrame: 包含所有动量因子的DataFrame
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"""
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# 初始化结果DataFrame
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result_df = df.clone()
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# 定义要计算的因子列表
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# 先计算股票截面因子（时间序列因子）
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stock_operators = [
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ReturnFactor(5),
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ReturnFactor(20),
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VolatilityFactor(10),
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VolatilityFactor(30),
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MomentumFactor(10),
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MomentumFactor(30),
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RSI_Factor(14)
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]
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# 依次应用股票截面因子算子
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for operator in stock_operators:
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try:
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result_df = operator.apply(result_df)
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except Exception as e:
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print(f"计算股票截面因子 {operator.factor_id} 时出错: {e}")
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# 再计算日期截面因子（横截面排序因子）
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date_operators = [
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CrossSectionalRanking("return_5d"),
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CrossSectionalRanking("return_20d"),
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CrossSectionalRanking("volatility_10d"),
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CrossSectionalRanking("momentum_10d")
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]
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# 依次应用日期截面因子算子
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for operator in date_operators:
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try:
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result_df = operator.apply(result_df)
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except Exception as e:
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print(f"计算日期截面因子 {operator.factor_id} 时出错: {e}")
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return result_df
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