update

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

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
 ]
 
 
@@ -390,6 +423,16 @@ def _compute_security_factors(
         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(
         ts_code,
@@ -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 {}

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