195 lines
7.2 KiB
Python
195 lines
7.2 KiB
Python
"""Extended factor implementations for the quant system.
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This module contains additional high-quality factors that extend the default factor set.
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All factors are designed to be lightweight and programmatically generated to meet
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end-to-end automated decision-making requirements.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Sequence
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from app.core.indicators import momentum, volatility, rolling_mean, normalize
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@dataclass
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class FactorSpec:
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"""Specification for a factor computation.
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Attributes:
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name: Factor name identifier
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window: Required lookback window (0 for snapshot-only factors)
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"""
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name: str
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window: int
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# Extended factors focusing on momentum, value, and liquidity signals
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EXTENDED_FACTORS: List[FactorSpec] = [
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# 增强动量因子
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FactorSpec("mom_10_30", 0), # 10日与30日动量差
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FactorSpec("mom_5_20_rank", 0), # 相对排名动量因子
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FactorSpec("mom_dynamic", 0), # 动态窗口动量因子
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# 波动率相关因子
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FactorSpec("volat_5", 5), # 短期波动率
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FactorSpec("volat_ratio", 0), # 长短期波动率比率
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# 换手率扩展因子
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FactorSpec("turn_60", 60), # 长期换手率
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FactorSpec("turn_rank", 0), # 换手率相对排名
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# 价格均线比率因子
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FactorSpec("price_ma_10_ratio", 0), # 当前价格与10日均线比率
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FactorSpec("price_ma_20_ratio", 0), # 当前价格与20日均线比率
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FactorSpec("price_ma_60_ratio", 0), # 当前价格与60日均线比率
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# 成交量均线比率因子
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FactorSpec("volume_ma_5_ratio", 0), # 当前成交量与5日均线比率
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FactorSpec("volume_ma_20_ratio", 0), # 当前成交量与20日均线比率
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# 高级估值因子
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FactorSpec("val_ps_score", 0), # PS估值评分
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FactorSpec("val_multiscore", 0), # 综合估值评分
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FactorSpec("val_dividend_score", 0), # 股息率估值评分
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# 市场状态因子
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FactorSpec("market_regime", 0), # 市场状态因子
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FactorSpec("trend_strength", 0), # 趋势强度因子
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]
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def compute_extended_factor_values(
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close_series: Sequence[float],
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volume_series: Sequence[float],
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turnover_series: Sequence[float],
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latest_fields: Dict[str, float],
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) -> Dict[str, float]:
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"""Compute values for extended factors.
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Args:
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close_series: Closing prices series (most recent first)
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volume_series: Trading volume series (most recent first)
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turnover_series: Turnover rate series (most recent first)
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latest_fields: Latest available fields including valuation ratios
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Returns:
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Dictionary mapping factor names to computed values
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"""
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if not close_series:
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return {}
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results: Dict[str, float] = {}
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# 增强动量因子
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# 10日与30日动量差
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if len(close_series) >= 30:
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mom_10 = momentum(close_series, 10)
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mom_30 = momentum(close_series, 30)
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if mom_10 is not None and mom_30 is not None:
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results["mom_10_30"] = mom_10 - mom_30
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# 相对排名动量因子
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# 这里需要市场数据来计算相对排名，暂时使用简化版本
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if len(close_series) >= 20:
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mom_20 = momentum(close_series, 20)
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if mom_20 is not None:
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# 简化处理：将动量标准化
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results["mom_5_20_rank"] = min(1.0, max(0.0, (mom_20 + 0.2) / 0.4))
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# 动态窗口动量因子
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# 根据波动率动态调整窗口
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if len(close_series) >= 20:
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volat_20 = volatility(close_series, 20)
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mom_20 = momentum(close_series, 20)
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if volat_20 is not None and mom_20 is not None and volat_20 > 0:
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# 波动率调整后的动量
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results["mom_dynamic"] = mom_20 / volat_20
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# 波动率相关因子
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# 短期波动率
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if len(close_series) >= 5:
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results["volat_5"] = volatility(close_series, 5)
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# 长短期波动率比率
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if len(close_series) >= 20 and len(close_series) >= 5:
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volat_5 = volatility(close_series, 5)
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volat_20 = volatility(close_series, 20)
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if volat_5 is not None and volat_20 is not None and volat_20 > 0:
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results["volat_ratio"] = volat_5 / volat_20
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# 换手率扩展因子
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# 长期换手率
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if len(turnover_series) >= 60:
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results["turn_60"] = rolling_mean(turnover_series, 60)
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# 换手率相对排名
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if len(turnover_series) >= 20:
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turn_20 = rolling_mean(turnover_series, 20)
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if turn_20 is not None:
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# 简化处理：将换手率标准化
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results["turn_rank"] = min(1.0, max(0.0, turn_20 / 5.0)) # 假设5%为高换手率
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# 价格均线比率因子
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if len(close_series) >= 10:
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ma_10 = rolling_mean(close_series, 10)
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if ma_10 is not None and ma_10 > 0:
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results["price_ma_10_ratio"] = close_series[0] / ma_10
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if len(close_series) >= 20:
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ma_20 = rolling_mean(close_series, 20)
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if ma_20 is not None and ma_20 > 0:
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results["price_ma_20_ratio"] = close_series[0] / ma_20
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if len(close_series) >= 60:
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ma_60 = rolling_mean(close_series, 60)
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if ma_60 is not None and ma_60 > 0:
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results["price_ma_60_ratio"] = close_series[0] / ma_60
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# 成交量均线比率因子
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if len(volume_series) >= 5:
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vol_ma_5 = rolling_mean(volume_series, 5)
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if vol_ma_5 is not None and vol_ma_5 > 0:
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results["volume_ma_5_ratio"] = volume_series[0] / vol_ma_5
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if len(volume_series) >= 20:
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vol_ma_20 = rolling_mean(volume_series, 20)
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if vol_ma_20 is not None and vol_ma_20 > 0:
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results["volume_ma_20_ratio"] = volume_series[0] / vol_ma_20
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# 高级估值因子
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ps = latest_fields.get("daily_basic.ps")
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if ps is not None and ps > 0:
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# PS估值评分
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results["val_ps_score"] = 2.5 / (2.5 + ps) # 使用2.5作为scale参数
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pe = latest_fields.get("daily_basic.pe")
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pb = latest_fields.get("daily_basic.pb")
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# 综合估值评分
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scores = []
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if pe is not None and pe > 0:
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scores.append(12.0 / (12.0 + pe)) # PE评分
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if pb is not None and pb > 0:
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scores.append(2.5 / (2.5 + pb)) # PB评分
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if ps is not None and ps > 0:
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scores.append(2.5 / (2.5 + ps)) # PS评分
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if scores:
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results["val_multiscore"] = sum(scores) / len(scores)
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dv_ratio = latest_fields.get("daily_basic.dv_ratio")
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if dv_ratio is not None:
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# 股息率估值评分
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results["val_dividend_score"] = min(1.0, max(0.0, dv_ratio / 5.0)) # 假设5%为高股息率
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# 市场状态因子
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# 简单的市场状态指标：基于价格位置
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if len(close_series) >= 60:
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ma_60 = rolling_mean(close_series, 60)
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if ma_60 is not None and ma_60 > 0:
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results["market_regime"] = close_series[0] / ma_60
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# 趋势强度因子
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if len(close_series) >= 20:
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mom_20 = momentum(close_series, 20)
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volat_20 = volatility(close_series, 20)
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if mom_20 is not None and volat_20 is not None and volat_20 > 0:
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# 趋势强度：动量与波动率的比率
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results["trend_strength"] = abs(mom_20) / volat_20
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return results |