From 574a7c26828967ce38c15621e88181a4dd4941e3 Mon Sep 17 00:00:00 2001 From: sam Date: Wed, 8 Oct 2025 18:52:07 +0800 Subject: [PATCH] add valuation, market regime and risk factors with adjusted factor limits --- app/features/extended_factors.py | 127 +++++++++++++++++++++++++++++++ app/features/factors.py | 2 +- app/features/validation.py | 12 +-- 3 files changed, 134 insertions(+), 7 deletions(-) diff --git a/app/features/extended_factors.py b/app/features/extended_factors.py index b20a29a..71ecf54 100644 --- a/app/features/extended_factors.py +++ b/app/features/extended_factors.py @@ -116,6 +116,18 @@ EXTENDED_FACTORS: List[FactorSpec] = [ # 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 + + # 估值因子 + FactorSpec("val_ps_score", 0), # PS估值分数 + FactorSpec("val_multiscore", 0), # 多维估值分数 + FactorSpec("val_dividend_score", 0), # 股息评分 + + # 市场状态因子 + FactorSpec("market_regime", 20), # 市场状态 + FactorSpec("trend_strength", 20), # 趋势强度 + + # 风险因子 + FactorSpec("risk_penalty", 20), # 风险惩罚因子 ] @@ -337,6 +349,121 @@ class ExtendedFactors: ma = rolling_mean(volume_series, window) return volume_series[0] / ma if ma > 0 else None + # 估值因子 + elif factor_name == "val_ps_score": + # PS估值分数：基于PS比率的估值指标 + # 假设PS比率越低，估值越有吸引力 + if len(close_series) < 10: + return None + + # 简化的PS估值：基于价格与历史均值的比较 + current_price = close_series[0] + avg_price = np.mean(close_series[:10]) + + if avg_price > 0: + # 当前价格相对于历史均值的偏离程度 + ps_ratio = current_price / avg_price + # 标准化到[-1, 1]区间 + return np.clip((1.0 - ps_ratio) / 2.0, -1, 1) + return None + + elif factor_name == "val_multiscore": + # 多维估值分数：综合多个估值维度的评分 + if len(close_series) < 20: + return None + + # 使用价格动量、波动率和相对强度作为估值代理 + momentum_5 = momentum(close_series, 5) + momentum_20 = momentum(close_series, 20) + + # 计算波动率（手动实现，避免依赖外部函数） + 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 + volatility_20 = np.std(returns) if returns else 0 + else: + volatility_20 = 0 + + # 综合评分：动量正向，波动率负向 + if volatility_20 > 0: + score = (momentum_5 + momentum_20) / (2 * volatility_20) + return np.clip(score, -1, 1) + return None + + elif factor_name == "val_dividend_score": + # 股息评分：基于价格稳定性和趋势的股息吸引力评分 + if len(close_series) < 20: + return None + + # 计算价格稳定性（低波动率） + 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 = np.std(returns) if returns else 0 + else: + vol = 0 + + # 计算趋势强度 + trend = momentum(close_series, 20) + + # 股息吸引力：稳定性正向，趋势正向 + stability_score = 1.0 - np.clip(vol, 0, 1) + trend_score = np.clip(trend, -1, 1) + + return (stability_score + trend_score) / 2.0 + + # 市场状态因子 + elif factor_name == "market_regime": + # 市场状态：基于价格和成交量的市场状态判断 + if len(close_series) < 20 or len(volume_series) < 20: + return None + + # 价格趋势 + price_trend = momentum(close_series, 20) + # 成交量趋势 + volume_trend = momentum(volume_series, 20) + + # 市场状态：牛市（价格↑成交量↑）、熊市（价格↓成交量↓）、 + # 震荡市（价格平稳成交量平稳）、背离市（价格成交量反向） + regime_score = price_trend * volume_trend + return np.clip(regime_score, -1, 1) + + elif factor_name == "trend_strength": + # 趋势强度：基于价格变动的趋势强度度量 + if len(close_series) < 20: + return None + + # 计算不同时间窗口的动量 + momentum_5 = momentum(close_series, 5) + momentum_10 = momentum(close_series, 10) + momentum_20 = momentum(close_series, 20) + + # 趋势强度：短期、中期、长期动量的一致性 + 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, diff --git a/app/features/factors.py b/app/features/factors.py index 5017228..2e4443d 100644 --- a/app/features/factors.py +++ b/app/features/factors.py @@ -585,7 +585,7 @@ def _compute_security_factors( volume_ratio = latest_fields.get("daily_basic.volume_ratio") results[spec.name] = _volume_ratio_score(volume_ratio) else: - LOGGER.debug( + LOGGER.info( "忽略未识别的因子 name=%s ts_code=%s", spec.name, ts_code, diff --git a/app/features/validation.py b/app/features/validation.py index 42b55d8..6ffdcc2 100644 --- a/app/features/validation.py +++ b/app/features/validation.py @@ -14,18 +14,18 @@ FACTOR_LIMITS = { "mom_": (-1.0, 1.0), # 波动率类因子限制在 0-50% "volat_": (0, 0.5), - # 换手率类因子限制在 0-500% (实际换手率可能超过100%) - "turn_": (0, 5.0), + # 换手率类因子限制在 0-1000% (实际换手率可能很高) + "turn_": (0, 10.0), # 估值评分类因子限制在 -1到1 "val_": (-1.0, 1.0), # 量价类因子 "volume_": (0, 10.0), # 市场状态类因子 "market_": (-1.0, 1.0), - # 技术指标类因子 - "tech_": (-1.0, 1.0), - # 趋势类因子 - "trend_": (-1.0, 1.0), + # 技术指标类因子（放宽范围，允许原始技术指标值） + "tech_": (-100.0, 100.0), + # 趋势类因子（放宽范围，允许原始趋势指标值） + "trend_": (-10.0, 10.0), # 微观结构类因子 "micro_": (-1.0, 1.0), # 情绪类因子