"""Extended factor implementations for the quant system. This module contains additional high-quality factors that extend the default factor set. All factors are designed to be lightweight and programmatically generated to meet end-to-end automated decision-making requirements. """ from __future__ import annotations from dataclasses import dataclass from typing import Dict, List, Sequence, Optional import numpy as np from app.core.indicators import momentum, rolling_mean, normalize from app.core.technical import ( rsi, macd, bollinger_bands, obv_momentum, price_volume_trend ) from app.core.momentum import ( adaptive_momentum, momentum_quality, momentum_regime ) from app.core.volatility import ( volatility, garch_volatility, volatility_regime, volume_price_correlation ) @dataclass class FactorSpec: """Specification for a factor computation. Attributes: name: Factor name identifier window: Required lookback window (0 for snapshot-only factors) """ name: str window: int # Extended factors focusing on momentum, value, and liquidity signals EXTENDED_FACTORS: List[FactorSpec] = [ # 技术分析因子 FactorSpec("tech_rsi_14", 14), # 14日RSI FactorSpec("tech_macd_signal", 26), # MACD信号 FactorSpec("tech_bb_position", 20), # 布林带位置 FactorSpec("tech_obv_momentum", 20), # OBV动量 FactorSpec("tech_pv_trend", 20), # 价量趋势 # 趋势跟踪因子 FactorSpec("trend_ma_cross", 20), # 均线交叉信号 FactorSpec("trend_price_channel", 20), # 价格通道突破 FactorSpec("trend_adx", 14), # 平均趋向指标 # 市场微观结构因子 FactorSpec("micro_tick_direction", 5), # 逐笔方向 FactorSpec("micro_trade_imbalance", 10), # 交易失衡 # 波动率预测因子 FactorSpec("vol_garch", 20), # GARCH波动率 FactorSpec("vol_range_pred", 10), # 波动区间预测 FactorSpec("vol_regime", 20), # 波动率状态 # 量价联合因子 FactorSpec("volume_price_corr", 20), # 量价相关性 FactorSpec("volume_price_diverge", 10), # 量价背离 FactorSpec("volume_intensity", 5), # 成交强度 # 增强动量因子 FactorSpec("momentum_adaptive", 20), # 自适应动量 FactorSpec("momentum_regime", 20), # 动量区间 FactorSpec("momentum_quality", 20), # 动量质量, # 价格均线比率因子 FactorSpec("price_ma_10_ratio", 10), # 当前价格与10日均线比率 FactorSpec("price_ma_20_ratio", 20), # 当前价格与20日均线比率 FactorSpec("price_ma_60_ratio", 60), # 当前价格与60日均线比率 # 成交量均线比率因子 FactorSpec("volume_ma_5_ratio", 5), # 当前成交量与5日均线比率 FactorSpec("volume_ma_20_ratio", 20), # 当前成交量与20日均线比率 ] class ExtendedFactors: """扩展因子计算实现类。 该类实现了一组用于量化交易的扩展因子计算。包括: 1. 技术分析因子 (RSI, MACD, 布林带等) 2. 趋势跟踪因子 (均线交叉等) 3. 波动率预测因子 (GARCH, 波动率状态等) 4. 量价联合因子 (量价相关性等) 5. 动量强化因子 (自适应动量等) 6. 均线比率因子 (价格/成交量均线比率) 使用示例: calculator = ExtendedFactors() factor_value = calculator.compute_factor( "tech_rsi_14", close_series, volume_series ) all_factors = calculator.compute_all_factors(close_series, volume_series) normalized = calculator.normalize_factors(all_factors) """ def __init__(self): """初始化因子计算器""" self.factor_specs = {spec.name: spec for spec in EXTENDED_FACTORS} def compute_factor(self, factor_name: str, close_series: Sequence[float], volume_series: Sequence[float]) -> Optional[float]: """计算单个因子值 Args: factor_name: 因子名称 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: 因子值，如果计算失败则返回None """ try: spec = self.factor_specs.get(factor_name) if spec is None: print(f"Unknown factor: {factor_name}") return None if len(close_series) < spec.window: return None # 技术分析因子 if factor_name == "tech_rsi_14": return rsi(close_series, 14) elif factor_name == "tech_macd_signal": _, signal = macd(close_series) return signal elif factor_name == "tech_bb_position": upper, lower = bollinger_bands(close_series, 20) pos = (close_series[0] - lower) / (upper - lower + 1e-8) return pos elif factor_name == "tech_obv_momentum": return obv_momentum(close_series, volume_series, 20) elif factor_name == "tech_pv_trend": return price_volume_trend(close_series, volume_series, 20) # 趋势跟踪因子 elif factor_name == "trend_ma_cross": ma_5 = rolling_mean(close_series, 5) ma_20 = rolling_mean(close_series, 20) return ma_5 - ma_20 # 波动率预测因子 elif factor_name == "vol_garch": return garch_volatility(close_series, 20) elif factor_name == "vol_regime": regime, _ = volatility_regime(close_series, volume_series, 20) return regime # 量价联合因子 elif factor_name == "volume_price_corr": return volume_price_correlation(close_series, volume_series, 20) # 增强动量因子 elif factor_name == "momentum_adaptive": return adaptive_momentum(close_series, volume_series, 20) elif factor_name == "momentum_regime": return momentum_regime(close_series, volume_series, 20) elif factor_name == "momentum_quality": return momentum_quality(close_series, 20) # 均线比率因子 elif factor_name.endswith("_ratio"): if "price_ma" in factor_name: window = int(factor_name.split("_")[2]) ma = rolling_mean(close_series, window) return close_series[0] / ma if ma > 0 else None elif "volume_ma" in factor_name: window = int(factor_name.split("_")[2]) ma = rolling_mean(volume_series, window) return volume_series[0] / ma if ma > 0 else None return None except Exception as e: print(f"Error computing factor {factor_name}: {str(e)}") return None def compute_all_factors(self, close_series: Sequence[float], volume_series: Sequence[float]) -> Dict[str, float]: """计算所有扩展因子值 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: 因子名称到因子值的映射字典 """ results = {} for factor_name in self.factor_specs: value = self.compute_factor(factor_name, close_series, volume_series) if value is not None: results[factor_name] = value return results def normalize_factors(self, factors: Dict[str, float]) -> Dict[str, float]: """标准化因子值到[-1,1]区间 Args: factors: 原始因子值字典 Returns: 标准化后的因子值字典 """ results = {} for name, value in factors.items(): if value is not None: results[name] = normalize(value) return results