259 lines
8.7 KiB
Python
259 lines
8.7 KiB
Python
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"""
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市场情绪因子模块
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包含基于股票截面的市场情绪因子实现
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"""
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import numpy as np
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import polars as pl
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import talib
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from main.factor.operator_framework import DateWiseFactor, StockWiseFactor
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class SentimentPanicGreedFactor(StockWiseFactor):
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"""恐慌/贪婪指数因子"""
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def __init__(self, window_atr: int = 14, window_smooth: int = 5):
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super().__init__(
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name="sentiment_panic_greed",
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parameters={"window_atr": window_atr, "window_smooth": window_smooth},
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required_factor_ids=["open", "high", "low", "close", "vol", "pct_chg"]
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)
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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# 使用talib计算ATR
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close_array = group_df["close"].to_numpy()
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high_array = group_df["high"].to_numpy()
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low_array = group_df["low"].to_numpy()
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prev_close = group_df["close"].shift(1).to_numpy()
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window_atr = self.parameters["window_atr"]
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window_smooth = self.parameters["window_smooth"]
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# 计算ATR
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atr_values = talib.ATR(high_array, low_array, close_array, timeperiod=window_atr)
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# 计算真实波幅和波动性意外
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tr = np.maximum(high_array - low_array,
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np.abs(high_array - prev_close),
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np.abs(low_array - prev_close))
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volatility_surprise = (tr / (atr_values + 1e-8) - 1) * group_df["pct_chg"].to_numpy()
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# 计算情绪指数
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sentiment = volatility_surprise * 2 # 放大跳空影响
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# 平滑处理
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smoothed_sentiment = talib.SMA(sentiment, timeperiod=window_smooth)
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return pl.Series(smoothed_sentiment).alias(self.factor_id)
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class SentimentBreadthFactor(StockWiseFactor):
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"""市场宽度情绪代理因子"""
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def __init__(self, window_vol: int = 20, window_smooth: int = 3):
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super().__init__(
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name="sentiment_breadth",
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parameters={"window_vol": window_vol, "window_smooth": window_smooth},
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required_factor_ids=["pct_chg", "vol"]
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)
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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window_vol = self.parameters["window_vol"]
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window_smooth = self.parameters["window_smooth"]
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# 计算滚动平均成交量
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vol = group_df["vol"].to_numpy()
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rolling_avg_vol = talib.SMA(vol, timeperiod=window_vol)
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# 计算价量配合度
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pct_chg = group_df["pct_chg"].to_numpy()
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breadth = pct_chg * (vol / (rolling_avg_vol + 1e-8))
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# 平滑处理
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smoothed_breadth = talib.SMA(breadth, timeperiod=window_smooth)
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return pl.Series(smoothed_breadth).alias(self.factor_id)
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class SentimentReversalFactor(StockWiseFactor):
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"""情绪反转因子"""
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def __init__(self, window_ret: int = 5, window_vol: int = 5):
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super().__init__(
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name="sentiment_reversal",
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parameters={"window_ret": window_ret, "window_vol": window_vol},
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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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window_ret = self.parameters["window_ret"]
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window_vol = self.parameters["window_vol"]
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# 计算累积收益率
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pct_chg = group_df["pct_chg"].to_numpy()
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return_period = window_ret
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cum_return = np.array([np.prod(1 + pct_chg[i:i+return_period]) - 1
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for i in range(len(pct_chg) - return_period + 1)])
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cum_return = np.pad(cum_return, (return_period - 1, 0), constant_values=np.nan)
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# 计算波动率
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volatility = talib.STDDEV(pct_chg, timeperiod=window_vol)
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# 计算反转因子
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reversal = -cum_return * volatility
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return pl.Series(reversal).alias(self.factor_id)
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class PriceDeductionFactor(StockWiseFactor):
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"""价格抵扣因子"""
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def __init__(self, n: int = 10):
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super().__init__(
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name="price_deduction",
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parameters={"n": n},
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required_factor_ids=["close"]
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)
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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n = self.parameters["n"]
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# 计算抵扣价（n-1周期前的价格）
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deduction_price = group_df["close"].shift(n - 1)
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price_diff = group_df["close"] - deduction_price
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return price_diff.alias(self.factor_id)
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class PriceDeductionRatioFactor(StockWiseFactor):
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"""价格抵扣比例因子"""
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def __init__(self, n: int = 10):
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super().__init__(
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name="price_deduction_ratio",
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parameters={"n": n},
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required_factor_ids=["close"]
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)
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def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
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n = self.parameters["n"]
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# 计算N周期SMA
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sma = group_df["close"].rolling_mean(n)
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# 计算抵扣价
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deduction_price = group_df["close"].shift(n - 1)
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# 计算比例
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diff = group_df["close"] - deduction_price
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ratio = diff / (sma + 1e-8) # 避免除零
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return ratio.alias(self.factor_id)
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class IndustryMomentumLeadership(StockWiseFactor):
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factor_id = "industry_momentum_leadership"
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required_factor_ids = [
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"industry_return_5_percentile",
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"industry_return_20_percentile",
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"roe"
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]
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def __init__(self):
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super(IndustryMomentumLeadership, self).__init__(
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name=self.factor_id,
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parameters={},
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required_factor_ids=self.required_factor_ids
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)
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def calc_factor(self, g: pl.DataFrame) -> pl.Series:
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pct5 = g["industry_return_5_percentile"]
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pct20 = g["industry_return_20_percentile"]
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roe = g["roe"]
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# 动量综合：5日权重更高（短期龙头）
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momentum_score = 0.7 * pct5 + 0.3 * pct20
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# 基本面质量：ROE 越高越好，取 log1p 防极端值
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quality_score = pl.when(roe > 0).then(roe.log1p()).otherwise(0.0)
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# 龙头得分 = 动量 × 基本面
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leadership = momentum_score * (quality_score + 1.0)
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return leadership.alias(self.factor_id)
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class LeadershipPersistenceScore(StockWiseFactor):
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factor_id = "leadership_persistence_score"
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required_factor_ids = [
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"industry_return_5_percentile",
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"industry_return_20_percentile",
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"undist_profit_ps",
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"roe",
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"bps"
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]
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def __init__(self):
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super(LeadershipPersistenceScore, self).__init__(
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name=self.factor_id,
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parameters={},
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required_factor_ids=self.required_factor_ids
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)
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def calc_factor(self, g: pl.DataFrame) -> pl.Series:
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pct5 = g["industry_return_5_percentile"]
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pct20 = g["industry_return_20_percentile"]
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undist = g["undist_profit_ps"]
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roe = g["roe"]
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bps = g["bps"]
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momentum = 0.6 * pct5 + 0.4 * pct20
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# 基本面质量（全部取 log1p 处理）
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quality = (
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pl.when(undist > 0).then(undist.log1p()).otherwise(0.0) +
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pl.when(roe > 0).then(roe.log1p()).otherwise(0.0) +
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pl.when(bps > 0).then(bps.log1p()).otherwise(0.0)
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)
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score = momentum * (quality + 1.0)
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return score.alias(self.factor_id)
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class DynamicIndustryLeadership(DateWiseFactor):
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factor_id = "dynamic_industry_leadership"
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required_factor_ids = ["l2_code", "return_5", "lg_flow", "turnover_rate"]
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def __init__(self):
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super(DynamicIndustryLeadership, self).__init__(
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name=self.factor_id,
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parameters={},
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required_factor_ids=self.required_factor_ids
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)
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def calc_factor(self, g: pl.DataFrame) -> pl.Series:
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# 使用窗口函数：按 industry 分组计算 z-score
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mom = pl.col("return_5")
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flow = pl.col("lg_flow")
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turn = pl.col("turnover_rate").log1p()
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# 行业内均值和标准差
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mom_mean = mom.mean().over("l2_code")
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mom_std = mom.std().over("l2_code")
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flow_mean = flow.mean().over("l2_code")
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flow_std = flow.std().over("l2_code")
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turn_mean = turn.mean().over("l2_code")
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turn_std = turn.std().over("l2_code")
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# 安全 z-score：避免 std=0
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mom_z = pl.when(mom_std > 1e-8).then((mom - mom_mean) / mom_std).otherwise(0.0)
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flow_z = pl.when(flow_std > 1e-8).then((flow - flow_mean) / flow_std).otherwise(0.0)
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turn_z = pl.when(turn_std > 1e-8).then((turn - turn_mean) / turn_std).otherwise(0.0)
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# 合成因子
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leadership = mom_z + flow_z + turn_z
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# 执行表达式并返回 Series
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result = g.select(leadership.alias(self.factor_id))
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return result.to_series()
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