feat: 完善 QMT 交易模块文档和配置展示功能

- 优化前端仪表盘界面
- 添加配置文件可视化展示
- 编写 QMT 模块配置文档
- 完善项目规则体系（KiloCode）

main/factor/momentum_factors.py

@@ -122,53 +122,38 @@ class TrendEfficiency(StockWiseFactor):
         
         return efficiency_ratio
 
-# -------------------- 统一计算函数 --------------------
-def calculate_momentum_factors(df: pl.DataFrame) -> pl.DataFrame:
-    """
-    统一计算动量因子的函数
-    
-    Parameters:
-    df (pl.DataFrame): 输入的股票数据表，必须包含以下列：
-                      ts_code, trade_date, close, pct_chg, high, low, vol
-    
-    Returns:
-    pl.DataFrame: 包含所有动量因子的DataFrame
-    """
-    # 初始化结果DataFrame
-    result_df = df.clone()
-    
-    # 定义要计算的因子列表
-    # 先计算股票截面因子（时间序列因子）
-    stock_operators = [
-        ReturnFactor(5),
-        ReturnFactor(20),
-        VolatilityFactor(10),
-        VolatilityFactor(30),
-        MomentumFactor(10),
-        MomentumFactor(30),
-        RSI_Factor(14)
-    ]
-    
-    # 依次应用股票截面因子算子
-    for operator in stock_operators:
-        try:
-            result_df = operator.apply(result_df)
-        except Exception as e:
-            print(f"计算股票截面因子 {operator.factor_id} 时出错: {e}")
-    
-    # 再计算日期截面因子（横截面排序因子）
-    date_operators = [
-        CrossSectionalRanking("return_5d"),
-        CrossSectionalRanking("return_20d"),
-        CrossSectionalRanking("volatility_10d"),
-        CrossSectionalRanking("momentum_10d")
-    ]
-    
-    # 依次应用日期截面因子算子
-    for operator in date_operators:
-        try:
-            result_df = operator.apply(result_df)
-        except Exception as e:
-            print(f"计算日期截面因子 {operator.factor_id} 时出错: {e}")
-    
-    return result_df
+class SimpleVolatilityFactor(StockWiseFactor):
+    factor_id = "simple_volatility"
+    required_factor_ids = ["high", "low", "vol"]
+
+    def __init__(self):
+        super(SimpleVolatilityFactor, self).__init__(
+            name=self.factor_id,
+            parameters={},
+            required_factor_ids=self.required_factor_ids
+        )
+
+    def calc_factor(self, g: pl.DataFrame) -> pl.Series:
+        high = g["high"]
+        low = g["low"]
+        vol = g["vol"]
+
+        # Step 1: 计算 EM_i,t
+        # 注意：shift(1) 得到 t-1 的值
+        em = ((high + low) - (high.shift(1) + low.shift(1))) / 2.0
+        em = em.fill_null(0.0)  # 第一天无前值，设为0
+
+        # Step 2: 计算 BR_i,t
+        # 避免除零：若 High == Low，设 BR = 0
+        range_ = high - low
+        br = vol / range_
+        br = br.fill_null(0.0).replace({float('inf'): 0.0, float('-inf'): 0.0})
+
+        # Step 3: 计算 MM_i,t = EM / BR
+        mm = em / br
+        mm = mm.fill_null(0.0).replace({float('inf'): 0.0, float('-inf'): 0.0})
+
+        # Step 4: 计算 239 日简单移动平均
+        emv = mm.rolling_mean(window_size=239, min_periods=1)
+
+        return emv.alias(self.factor_id)