565 lines
22 KiB
Python
565 lines
22 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, Optional, Any
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import functools
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import numpy as np
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "extended_factors"}
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def handle_factor_errors(func: Any) -> Any:
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"""装饰器：处理因子计算过程中的错误
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Args:
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func: 要装饰的函数
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Returns:
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装饰后的函数
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"""
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@functools.wraps(func)
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def wrapper(*args: Any, **kwargs: Any) -> Optional[float]:
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try:
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return func(*args, **kwargs)
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except Exception as e:
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# 获取因子名称（如果可能）
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factor_name = "unknown"
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if len(args) > 2 and isinstance(args[1], str):
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factor_name = args[1]
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elif "factor_name" in kwargs:
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factor_name = kwargs["factor_name"]
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LOGGER.error(
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"计算因子出错 name=%s error=%s",
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factor_name,
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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return None
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return wrapper
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from app.core.indicators import momentum, rolling_mean, normalize
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from app.core.technical import (
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rsi, macd, bollinger_bands, obv_momentum, price_volume_trend
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)
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from app.core.momentum import (
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adaptive_momentum, momentum_quality, momentum_regime
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)
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from app.core.volatility import (
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volatility, garch_volatility, volatility_regime,
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volume_price_correlation
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)
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from app.features.validation import validate_factor_value
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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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# 技术分析因子
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FactorSpec("tech_rsi_14", 14), # 14日RSI
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FactorSpec("tech_macd_signal", 26), # MACD信号
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FactorSpec("tech_bb_position", 20), # 布林带位置
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FactorSpec("tech_obv_momentum", 20), # OBV动量
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FactorSpec("tech_pv_trend", 20), # 价量趋势
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# 趋势跟踪因子
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FactorSpec("trend_ma_cross", 20), # 均线交叉信号
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FactorSpec("trend_price_channel", 20), # 价格通道突破
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FactorSpec("trend_adx", 14), # 平均趋向指标
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# 市场微观结构因子
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FactorSpec("micro_tick_direction", 5), # 逐笔方向
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FactorSpec("micro_trade_imbalance", 10), # 交易失衡
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# 波动率预测因子
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FactorSpec("vol_garch", 20), # GARCH波动率
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FactorSpec("vol_range_pred", 10), # 波动区间预测
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FactorSpec("vol_regime", 20), # 波动率状态
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# 量价联合因子
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FactorSpec("volume_price_corr", 20), # 量价相关性
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FactorSpec("volume_price_diverge", 10), # 量价背离
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FactorSpec("volume_intensity", 5), # 成交强度
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# 增强动量因子
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FactorSpec("momentum_adaptive", 20), # 自适应动量
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FactorSpec("momentum_regime", 20), # 动量区间
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FactorSpec("momentum_quality", 20), # 动量质量,
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# 价格均线比率因子
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FactorSpec("price_ma_10_ratio", 10), # 当前价格与10日均线比率
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FactorSpec("price_ma_20_ratio", 20), # 当前价格与20日均线比率
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FactorSpec("price_ma_60_ratio", 60), # 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", 5), # 当前成交量与5日均线比率
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FactorSpec("volume_ma_20_ratio", 20), # 当前成交量与20日均线比率
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# 估值因子
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FactorSpec("val_ps_score", 0), # PS估值分数
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FactorSpec("val_multiscore", 0), # 多维估值分数
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FactorSpec("val_dividend_score", 0), # 股息评分
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# 市场状态因子
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FactorSpec("market_regime", 20), # 市场状态
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FactorSpec("trend_strength", 20), # 趋势强度
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# 风险因子
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FactorSpec("risk_penalty", 20), # 风险惩罚因子
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]
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class ExtendedFactors:
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"""扩展因子计算实现类。
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该类实现了一组用于量化交易的扩展因子计算。包括:
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1. 技术分析因子 (RSI, MACD, 布林带等)
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2. 趋势跟踪因子 (均线交叉等)
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3. 波动率预测因子 (GARCH, 波动率状态等)
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4. 量价联合因子 (量价相关性等)
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5. 动量强化因子 (自适应动量等)
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6. 均线比率因子 (价格/成交量均线比率)
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使用示例:
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calculator = ExtendedFactors()
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factor_value = calculator.compute_factor(
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"tech_rsi_14",
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close_series,
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volume_series
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)
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all_factors = calculator.compute_all_factors(close_series, volume_series)
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normalized = calculator.normalize_factors(all_factors)
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属性:
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factor_specs: Dict[str, FactorSpec], 因子名称到因子规格的映射
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"""
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def __init__(self):
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"""初始化因子计算器，构建因子规格映射"""
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self.factor_specs = {spec.name: spec for spec in EXTENDED_FACTORS}
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# 关闭初始化日志打印
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# LOGGER.info(
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# "初始化因子计算器，加载因子数量: %d",
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# len(self.factor_specs),
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# extra=LOG_EXTRA
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# )
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@handle_factor_errors
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def compute_factor(self,
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factor_name: str,
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close_series: Sequence[float],
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volume_series: Sequence[float]) -> Optional[float]:
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"""计算单个因子值
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Args:
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factor_name: 因子名称，必须是已注册的因子
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close_series: 收盘价序列，从新到旧排序
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volume_series: 成交量序列，从新到旧排序
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Returns:
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factor_value: Optional[float], 计算得到的因子值，失败时返回None
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Raises:
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ValueError: 当因子名称未知或数据不足时抛出
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"""
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spec = self.factor_specs.get(factor_name)
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if spec is None:
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raise ValueError(f"未知的因子名称: {factor_name}")
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if len(close_series) < spec.window:
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raise ValueError(
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f"数据长度不足: 需要{spec.window}，实际{len(close_series)}"
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)
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# 技术分析因子
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if factor_name == "tech_rsi_14":
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return rsi(close_series, 14)
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elif factor_name == "tech_macd_signal":
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return macd(close_series, 12, 26, 9)
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elif factor_name == "tech_bb_position":
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return bollinger_bands(close_series, 20)
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elif factor_name == "tech_obv_momentum":
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return obv_momentum(close_series, volume_series, 20)
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elif factor_name == "tech_pv_trend":
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return price_volume_trend(close_series, volume_series, 20)
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# 趋势跟踪因子
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elif factor_name == "trend_ma_cross":
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ma_5 = rolling_mean(close_series, 5)
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ma_20 = rolling_mean(close_series, 20)
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return ma_5 - ma_20
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elif factor_name == "trend_price_channel":
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# 价格通道突破因子：当前价格相对于通道的位置
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window = 20
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high_channel = max(close_series[:window])
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low_channel = min(close_series[:window])
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if high_channel != low_channel:
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return (close_series[0] - low_channel) / (high_channel - low_channel)
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return 0.0
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elif factor_name == "trend_adx":
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# 简化的ADX计算：基于价格变动方向
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window = 14
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if len(close_series) < window + 1:
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return None
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# 计算价格变动
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price_changes = [close_series[i] - close_series[i+1] for i in range(window)]
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# 计算正向和负向变动
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pos_moves = sum(max(0, change) for change in price_changes)
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neg_moves = sum(max(0, -change) for change in price_changes)
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# 简化的ADX计算
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if pos_moves + neg_moves > 0:
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return (pos_moves - neg_moves) / (pos_moves + neg_moves)
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return 0.0
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# 市场微观结构因子
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elif factor_name == "micro_tick_direction":
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# 简化的逐笔方向：基于最近价格变动
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window = 5
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if len(close_series) < window + 1:
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return None
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# 计算价格变动方向
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directions = [1 if close_series[i] > close_series[i+1] else -1 for i in range(window)]
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return sum(directions) / window
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elif factor_name == "micro_trade_imbalance":
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# 交易失衡：基于价格和成交量的联合分析
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window = 10
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if len(close_series) < window + 1 or len(volume_series) < window + 1:
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return None
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# 计算价格变动和成交量变动
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price_changes = [close_series[i] - close_series[i+1] for i in range(window)]
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volume_changes = [volume_series[i] - volume_series[i+1] for i in range(window)]
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# 计算交易失衡指标
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imbalance = sum(price_changes[i] * volume_changes[i] for i in range(window))
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return imbalance / (window * np.mean(volume_series[:window]) + 1e-8)
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# 波动率预测因子
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elif factor_name == "vol_garch":
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return garch_volatility(close_series, 20)
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elif factor_name == "vol_range_pred":
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# 波动区间预测：基于历史价格区间
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window = 10
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if len(close_series) < window + 5:
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return None
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# 计算历史价格区间
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ranges = []
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for i in range(5): # 使用最近5个窗口
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if i + window < len(close_series):
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price_range = max(close_series[i:i+window]) - min(close_series[i:i+window])
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ranges.append(price_range / close_series[i])
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if ranges:
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# 使用历史区间的75分位数作为预测
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return np.percentile(ranges, 75)
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return None
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elif factor_name == "vol_regime":
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return volatility_regime(close_series, volume_series, 20)
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# 量价联合因子
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elif factor_name == "volume_price_corr":
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return volume_price_correlation(close_series, volume_series, 20)
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elif factor_name == "volume_price_diverge":
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# 量价背离：价格和成交量趋势的背离程度
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window = 10
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if len(close_series) < window or len(volume_series) < window:
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return None
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# 计算价格和成交量趋势
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price_trend = sum(1 if close_series[i] > close_series[i+1] else -1 for i in range(window-1))
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volume_trend = sum(1 if volume_series[i] > volume_series[i+1] else -1 for i in range(window-1))
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# 计算背离程度
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divergence = price_trend * volume_trend * -1 # 反向为背离
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return np.clip(divergence / (window - 1), -1, 1)
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elif factor_name == "volume_intensity":
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# 成交强度：基于成交量和价格变动的加权指标
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window = 5
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if len(close_series) < window + 1 or len(volume_series) < window + 1:
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return None
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# 计算价格变动
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price_changes = [abs(close_series[i] - close_series[i+1]) for i in range(window)]
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# 计算成交量加权的价格变动
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weighted_changes = sum(price_changes[i] * volume_series[i] for i in range(window))
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total_volume = sum(volume_series[:window])
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if total_volume > 0:
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intensity = weighted_changes / (total_volume * np.mean(close_series[:window]) + 1e-8)
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return np.clip(intensity * 100, -100, 100) # 归一化到合理范围
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return None
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# 增强动量因子
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elif factor_name == "momentum_adaptive":
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return adaptive_momentum(close_series, volume_series, 20)
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elif factor_name == "momentum_regime":
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return momentum_regime(close_series, volume_series, 20)
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elif factor_name == "momentum_quality":
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return momentum_quality(close_series, 20)
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# 均线比率因子
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elif factor_name.endswith("_ratio"):
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if "price_ma" in factor_name:
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window = int(factor_name.split("_")[2])
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ma = rolling_mean(close_series, window)
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return close_series[0] / ma if ma > 0 else None
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elif "volume_ma" in factor_name:
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window = int(factor_name.split("_")[2])
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ma = rolling_mean(volume_series, window)
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return volume_series[0] / ma if ma > 0 else None
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# 估值因子
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elif factor_name == "val_ps_score":
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# PS估值分数：基于PS比率的估值指标
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# 假设PS比率越低，估值越有吸引力
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if len(close_series) < 10:
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return None
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# 简化的PS估值：基于价格与历史均值的比较
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current_price = close_series[0]
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avg_price = np.mean(close_series[:10])
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if avg_price > 0:
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# 当前价格相对于历史均值的偏离程度
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ps_ratio = current_price / avg_price
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# 标准化到[-1, 1]区间
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return np.clip((1.0 - ps_ratio) / 2.0, -1, 1)
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return None
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elif factor_name == "val_multiscore":
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# 多维估值分数：综合多个估值维度的评分
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if len(close_series) < 20:
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return None
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# 使用价格动量、波动率和相对强度作为估值代理
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momentum_5 = momentum(close_series, 5)
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momentum_20 = momentum(close_series, 20)
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# 计算波动率（手动实现，避免依赖外部函数）
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if len(close_series) >= 20:
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returns = [close_series[i] / close_series[i+1] - 1 for i in range(19)] # 修正索引范围
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volatility_20 = np.std(returns) if returns else 0
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else:
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volatility_20 = 0
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# 综合评分：动量正向，波动率负向
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if volatility_20 > 0:
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score = (momentum_5 + momentum_20) / (2 * volatility_20)
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return np.clip(score, -1, 1)
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return None
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elif factor_name == "val_dividend_score":
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# 股息评分：基于价格稳定性和趋势的股息吸引力评分
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if len(close_series) < 20:
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return None
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# 计算价格稳定性（低波动率）
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if len(close_series) >= 20:
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returns = [close_series[i] / close_series[i+1] - 1 for i in range(19)] # 修正索引范围
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vol = np.std(returns) if returns else 0
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else:
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vol = 0
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# 计算趋势强度
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trend = momentum(close_series, 20)
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# 股息吸引力：稳定性正向，趋势正向
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stability_score = 1.0 - np.clip(vol, 0, 1)
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trend_score = np.clip(trend, -1, 1)
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return (stability_score + trend_score) / 2.0
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# 市场状态因子
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elif factor_name == "market_regime":
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# 市场状态：基于价格和成交量的市场状态判断
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if len(close_series) < 20 or len(volume_series) < 20:
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return None
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# 价格趋势
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price_trend = momentum(close_series, 20)
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# 成交量趋势
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volume_trend = momentum(volume_series, 20)
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# 市场状态：牛市（价格↑成交量↑）、熊市（价格↓成交量↓）、
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# 震荡市（价格平稳成交量平稳）、背离市（价格成交量反向）
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regime_score = price_trend * volume_trend
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return np.clip(regime_score, -1, 1)
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elif factor_name == "trend_strength":
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# 趋势强度：基于价格变动的趋势强度度量
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if len(close_series) < 20:
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return None
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# 计算不同时间窗口的动量
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momentum_5 = momentum(close_series, 5)
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momentum_10 = momentum(close_series, 10)
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momentum_20 = momentum(close_series, 20)
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# 趋势强度：短期、中期、长期动量的一致性
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trend_strength = (momentum_5 + momentum_10 + momentum_20) / 3.0
|
||
return np.clip(trend_strength, -1, 1)
|
||
|
||
# 风险因子
|
||
elif factor_name == "risk_penalty":
|
||
# 风险惩罚因子：基于波动率和异常价格的综合风险度量
|
||
if len(close_series) < 20:
|
||
return None
|
||
|
||
# 波动率风险
|
||
if len(close_series) >= 20:
|
||
returns = [close_series[i] / close_series[i+1] - 1 for i in range(19)] # 修正索引范围
|
||
vol_risk = np.std(returns) if returns else 0
|
||
else:
|
||
vol_risk = 0
|
||
|
||
# 价格异常风险（相对于均值的偏离）
|
||
avg_price = np.mean(close_series[:20])
|
||
if avg_price > 0:
|
||
price_deviation = abs(close_series[0] / avg_price - 1.0)
|
||
else:
|
||
price_deviation = 0
|
||
|
||
# 综合风险评分
|
||
risk_score = (vol_risk + price_deviation) / 2.0
|
||
return np.clip(risk_score, 0, 1) # 风险因子范围为[0, 1]
|
||
|
||
raise ValueError(f"因子 {factor_name} 没有对应的计算实现")
|
||
|
||
def compute_all_factors(self,
|
||
close_series: Sequence[float],
|
||
volume_series: Sequence[float]) -> Dict[str, float]:
|
||
"""计算所有已注册的扩展因子值
|
||
|
||
Args:
|
||
close_series: 收盘价序列，从新到旧排序
|
||
volume_series: 成交量序列，从新到旧排序
|
||
|
||
Returns:
|
||
Dict[str, float]: 因子名称到因子值的映射字典，
|
||
只包含成功计算的因子值
|
||
|
||
Note:
|
||
该方法会尝试计算所有已注册的因子，失败的因子将被忽略。
|
||
如果所有因子计算都失败，将返回空字典。
|
||
"""
|
||
results = {}
|
||
success_count = 0
|
||
total_count = len(self.factor_specs)
|
||
|
||
for factor_name in self.factor_specs:
|
||
value = self.compute_factor(factor_name, close_series, volume_series)
|
||
if value is not None:
|
||
# 验证因子值是否在合理范围内
|
||
validated_value = validate_factor_value(
|
||
factor_name, value, "unknown", "unknown"
|
||
)
|
||
if validated_value is not None:
|
||
results[factor_name] = validated_value
|
||
success_count += 1
|
||
else:
|
||
LOGGER.debug(
|
||
"因子值验证失败 factor=%s value=%f",
|
||
factor_name, value,
|
||
extra=LOG_EXTRA
|
||
)
|
||
|
||
# 关闭因子计算完成日志打印
|
||
# LOGGER.info(
|
||
# "因子计算完成 total=%d success=%d failed=%d",
|
||
# total_count,
|
||
# success_count,
|
||
# total_count - success_count,
|
||
# extra=LOG_EXTRA
|
||
# )
|
||
|
||
return results
|
||
|
||
def normalize_factors(self,
|
||
factors: Dict[str, float],
|
||
clip_threshold: float = 3.0) -> Dict[str, float]:
|
||
"""标准化因子值到[-1,1]区间
|
||
|
||
Args:
|
||
factors: 原始因子值字典
|
||
clip_threshold: float, 标准化时的截断阈值，默认为3.0
|
||
|
||
Returns:
|
||
Dict[str, float]: 标准化后的因子值字典，
|
||
只包含成功标准化的因子值
|
||
|
||
Note:
|
||
标准化过程包括:
|
||
1. Z-score标准化
|
||
2. 使用tanh压缩到[-1,1]区间
|
||
3. 异常值处理（截断）
|
||
"""
|
||
results = {}
|
||
success_count = 0
|
||
|
||
for name, value in factors.items():
|
||
if value is not None:
|
||
try:
|
||
normalized = normalize(value, clip_threshold)
|
||
if not np.isnan(normalized):
|
||
results[name] = normalized
|
||
success_count += 1
|
||
except Exception as e:
|
||
LOGGER.warning(
|
||
"因子标准化失败 name=%s error=%s",
|
||
name,
|
||
str(e),
|
||
extra=LOG_EXTRA
|
||
)
|
||
|
||
LOGGER.info(
|
||
"因子标准化完成 total=%d success=%d failed=%d",
|
||
len(factors),
|
||
success_count,
|
||
len(factors) - success_count,
|
||
extra=LOG_EXTRA
|
||
)
|
||
|
||
return results |