diff --git a/app/features/extended_factors.py b/app/features/extended_factors.py new file mode 100644 index 0000000..62f2f25 --- /dev/null +++ b/app/features/extended_factors.py @@ -0,0 +1,195 @@ +"""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 + +from app.core.indicators import momentum, volatility, rolling_mean, normalize + + +@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] = [ + # 增强动量因子 + FactorSpec("mom_10_30", 0), # 10日与30日动量差 + FactorSpec("mom_5_20_rank", 0), # 相对排名动量因子 + FactorSpec("mom_dynamic", 0), # 动态窗口动量因子 + # 波动率相关因子 + FactorSpec("volat_5", 5), # 短期波动率 + FactorSpec("volat_ratio", 0), # 长短期波动率比率 + # 换手率扩展因子 + FactorSpec("turn_60", 60), # 长期换手率 + FactorSpec("turn_rank", 0), # 换手率相对排名 + # 价格均线比率因子 + FactorSpec("price_ma_10_ratio", 0), # 当前价格与10日均线比率 + FactorSpec("price_ma_20_ratio", 0), # 当前价格与20日均线比率 + FactorSpec("price_ma_60_ratio", 0), # 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", 0), # 当前成交量与5日均线比率 + FactorSpec("volume_ma_20_ratio", 0), # 当前成交量与20日均线比率 + # 高级估值因子 + FactorSpec("val_ps_score", 0), # PS估值评分 + FactorSpec("val_multiscore", 0), # 综合估值评分 + FactorSpec("val_dividend_score", 0), # 股息率估值评分 + # 市场状态因子 + FactorSpec("market_regime", 0), # 市场状态因子 + FactorSpec("trend_strength", 0), # 趋势强度因子 +] + + +def compute_extended_factor_values( + close_series: Sequence[float], + volume_series: Sequence[float], + turnover_series: Sequence[float], + latest_fields: Dict[str, float], +) -> Dict[str, float]: + """Compute values for extended factors. + + Args: + close_series: Closing prices series (most recent first) + volume_series: Trading volume series (most recent first) + turnover_series: Turnover rate series (most recent first) + latest_fields: Latest available fields including valuation ratios + + Returns: + Dictionary mapping factor names to computed values + """ + if not close_series: + return {} + + results: Dict[str, float] = {} + + # 增强动量因子 + # 10日与30日动量差 + if len(close_series) >= 30: + mom_10 = momentum(close_series, 10) + mom_30 = momentum(close_series, 30) + if mom_10 is not None and mom_30 is not None: + results["mom_10_30"] = mom_10 - mom_30 + + # 相对排名动量因子 + # 这里需要市场数据来计算相对排名，暂时使用简化版本 + if len(close_series) >= 20: + mom_20 = momentum(close_series, 20) + if mom_20 is not None: + # 简化处理：将动量标准化 + results["mom_5_20_rank"] = min(1.0, max(0.0, (mom_20 + 0.2) / 0.4)) + + # 动态窗口动量因子 + # 根据波动率动态调整窗口 + if len(close_series) >= 20: + volat_20 = volatility(close_series, 20) + mom_20 = momentum(close_series, 20) + if volat_20 is not None and mom_20 is not None and volat_20 > 0: + # 波动率调整后的动量 + results["mom_dynamic"] = mom_20 / volat_20 + + # 波动率相关因子 + # 短期波动率 + if len(close_series) >= 5: + results["volat_5"] = volatility(close_series, 5) + + # 长短期波动率比率 + if len(close_series) >= 20 and len(close_series) >= 5: + volat_5 = volatility(close_series, 5) + volat_20 = volatility(close_series, 20) + if volat_5 is not None and volat_20 is not None and volat_20 > 0: + results["volat_ratio"] = volat_5 / volat_20 + + # 换手率扩展因子 + # 长期换手率 + if len(turnover_series) >= 60: + results["turn_60"] = rolling_mean(turnover_series, 60) + + # 换手率相对排名 + if len(turnover_series) >= 20: + turn_20 = rolling_mean(turnover_series, 20) + if turn_20 is not None: + # 简化处理：将换手率标准化 + results["turn_rank"] = min(1.0, max(0.0, turn_20 / 5.0)) # 假设5%为高换手率 + + # 价格均线比率因子 + if len(close_series) >= 10: + ma_10 = rolling_mean(close_series, 10) + if ma_10 is not None and ma_10 > 0: + results["price_ma_10_ratio"] = close_series[0] / ma_10 + + if len(close_series) >= 20: + ma_20 = rolling_mean(close_series, 20) + if ma_20 is not None and ma_20 > 0: + results["price_ma_20_ratio"] = close_series[0] / ma_20 + + if len(close_series) >= 60: + ma_60 = rolling_mean(close_series, 60) + if ma_60 is not None and ma_60 > 0: + results["price_ma_60_ratio"] = close_series[0] / ma_60 + + # 成交量均线比率因子 + if len(volume_series) >= 5: + vol_ma_5 = rolling_mean(volume_series, 5) + if vol_ma_5 is not None and vol_ma_5 > 0: + results["volume_ma_5_ratio"] = volume_series[0] / vol_ma_5 + + if len(volume_series) >= 20: + vol_ma_20 = rolling_mean(volume_series, 20) + if vol_ma_20 is not None and vol_ma_20 > 0: + results["volume_ma_20_ratio"] = volume_series[0] / vol_ma_20 + + # 高级估值因子 + ps = latest_fields.get("daily_basic.ps") + if ps is not None and ps > 0: + # PS估值评分 + results["val_ps_score"] = 2.5 / (2.5 + ps) # 使用2.5作为scale参数 + + pe = latest_fields.get("daily_basic.pe") + pb = latest_fields.get("daily_basic.pb") + # 综合估值评分 + scores = [] + if pe is not None and pe > 0: + scores.append(12.0 / (12.0 + pe)) # PE评分 + if pb is not None and pb > 0: + scores.append(2.5 / (2.5 + pb)) # PB评分 + if ps is not None and ps > 0: + scores.append(2.5 / (2.5 + ps)) # PS评分 + + if scores: + results["val_multiscore"] = sum(scores) / len(scores) + + dv_ratio = latest_fields.get("daily_basic.dv_ratio") + if dv_ratio is not None: + # 股息率估值评分 + results["val_dividend_score"] = min(1.0, max(0.0, dv_ratio / 5.0)) # 假设5%为高股息率 + + # 市场状态因子 + # 简单的市场状态指标：基于价格位置 + if len(close_series) >= 60: + ma_60 = rolling_mean(close_series, 60) + if ma_60 is not None and ma_60 > 0: + results["market_regime"] = close_series[0] / ma_60 + + # 趋势强度因子 + if len(close_series) >= 20: + mom_20 = momentum(close_series, 20) + volat_20 = volatility(close_series, 20) + if mom_20 is not None and volat_20 is not None and volat_20 > 0: + # 趋势强度：动量与波动率的比率 + results["trend_strength"] = abs(mom_20) / volat_20 + + return results \ No newline at end of file diff --git a/app/features/factors.py b/app/features/factors.py index 81f3e3f..9130c8f 100644 --- a/app/features/factors.py +++ b/app/features/factors.py @@ -11,6 +11,8 @@ from app.data.schema import initialize_database from app.utils.data_access import DataBroker from app.utils.db import db_session from app.utils.logging import get_logger +# 导入扩展因子模块 +from app.features.extended_factors import compute_extended_factor_values LOGGER = get_logger(__name__) @@ -31,16 +33,47 @@ class FactorResult: values: Dict[str, float | None] +# 基础因子和扩展因子的完整列表 DEFAULT_FACTORS: List[FactorSpec] = [ + # 基础动量因子 FactorSpec("mom_5", 5), FactorSpec("mom_20", 20), FactorSpec("mom_60", 60), + # 波动率因子 FactorSpec("volat_20", 20), + # 换手率因子 FactorSpec("turn_20", 20), FactorSpec("turn_5", 5), + # 估值因子 FactorSpec("val_pe_score", 0), FactorSpec("val_pb_score", 0), + # 量比因子 FactorSpec("volume_ratio_score", 0), + # 扩展因子 + # 增强动量因子 + FactorSpec("mom_10_30", 0), # 10日与30日动量差 + FactorSpec("mom_5_20_rank", 0), # 相对排名动量因子 + FactorSpec("mom_dynamic", 0), # 动态窗口动量因子 + # 波动率相关因子 + FactorSpec("volat_5", 5), # 短期波动率 + FactorSpec("volat_ratio", 0), # 长短期波动率比率 + # 换手率扩展因子 + FactorSpec("turn_60", 60), # 长期换手率 + FactorSpec("turn_rank", 0), # 换手率相对排名 + # 价格均线比率因子 + FactorSpec("price_ma_10_ratio", 0), # 当前价格与10日均线比率 + FactorSpec("price_ma_20_ratio", 0), # 当前价格与20日均线比率 + FactorSpec("price_ma_60_ratio", 0), # 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", 0), # 当前成交量与5日均线比率 + FactorSpec("volume_ma_20_ratio", 0), # 当前成交量与20日均线比率 + # 高级估值因子 + FactorSpec("val_ps_score", 0), # PS估值评分 + FactorSpec("val_multiscore", 0), # 综合估值评分 + FactorSpec("val_dividend_score", 0), # 股息率估值评分 + # 市场状态因子 + FactorSpec("market_regime", 0), # 市场状态因子 + FactorSpec("trend_strength", 0), # 趋势强度因子 ] @@ -389,6 +422,16 @@ def _compute_security_factors( trade_date, max_turn_window, ) + + # 获取成交量数据用于扩展因子计算 + volume_series = _fetch_series_values( + broker, + "daily", + "vol", + ts_code, + trade_date, + max_close_window, # 使用与价格相同的窗口 + ) # 获取最新字段值 latest_fields = broker.fetch_latest( @@ -400,6 +443,8 @@ def _compute_security_factors( "daily_basic.ps", "daily_basic.volume_ratio", "daily.amount", + "daily.vol", + "daily_basic.dv_ratio", # 股息率用于扩展因子 ], ) @@ -439,6 +484,12 @@ def _compute_security_factors( extra=LOG_EXTRA, ) + # 计算扩展因子值 + extended_factors = compute_extended_factor_values( + close_series, volume_series, turnover_series, latest_fields + ) + results.update(extended_factors) + # 确保返回结果不为空 if not any(v is not None for v in results.values()): return {} diff --git a/tests/test_factors.py b/tests/test_factors.py index 550ce2a..544c180 100644 --- a/tests/test_factors.py +++ b/tests/test_factors.py @@ -202,3 +202,69 @@ def test_compute_factor_range_filters_universe(isolated_db): repeated = compute_factor_range(start_day, end_day, ts_codes=[code_a]) assert repeated == [] + + +def test_compute_extended_factors(isolated_db): + """Test computation of extended factors.""" + # Use the existing _populate_sample_data function + from app.utils.data_access import DataBroker + broker = DataBroker() + + # Sample data for 5 trading days + dates = ["20240101", "20240102", "20240103", "20240104", "20240105"] + ts_codes = ["000001.SZ", "000002.SZ", "600000.SH"] + + # Populate daily data + for ts_code in ts_codes: + for i, trade_date in enumerate(dates): + broker.insert_or_update_daily( + ts_code, + trade_date, + open_price=10.0 + i * 0.1, + high=10.5 + i * 0.1, + low=9.5 + i * 0.1, + close=10.0 + i * 0.2, # 上涨趋势 + pre_close=10.0 + (i - 1) * 0.2 if i > 0 else 10.0, + vol=100000 + i * 10000, + amount=1000000 + i * 100000, + ) + + broker.insert_or_update_daily_basic( + ts_code, + trade_date, + close=10.0 + i * 0.2, + turnover_rate=1.0 + i * 0.1, + turnover_rate_f=1.0 + i * 0.1, + volume_ratio=1.0 + (i % 3) * 0.2, # 在0.8-1.2之间变化 + pe=15.0 + (i % 3) * 2, # 在15-19之间变化 + pe_ttm=15.0 + (i % 3) * 2, + pb=1.5 + (i % 3) * 0.1, # 在1.5-1.7之间变化 + ps=3.0 + (i % 3) * 0.2, # 在3.0-3.4之间变化 + ps_ttm=3.0 + (i % 3) * 0.2, + dv_ratio=2.0 + (i % 3) * 0.1, # 股息率 + total_mv=1000000 + i * 100000, + circ_mv=800000 + i * 80000, + ) + + # Compute factors with extended factors + from app.features.extended_factors import EXTENDED_FACTORS + all_factors = list(DEFAULT_FACTORS) + EXTENDED_FACTORS + + trade_day = date(2024, 1, 5) + results = compute_factors(trade_day, all_factors) + + # Verify that we got results + assert results + + # Verify that extended factors are computed + result_map = {result.ts_code: result for result in results} + ts_code = "000001.SZ" + assert ts_code in result_map + result = result_map[ts_code] + + # Check that extended factors are present in the results + extended_factor_names = [spec.name for spec in EXTENDED_FACTORS] + for factor_name in extended_factor_names: + assert factor_name in result.values + # Values should not be None + assert result.values[factor_name] is not None