0.9.45 update

waditu · Mar 16, 2024 · e3a60de · e3a60de
1 parent 63724e2
commit e3a60de
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Showing 3 changed files with 11 additions and 5 deletions.
diff --git a/czsc/traders/weight_backtest.py b/czsc/traders/weight_backtest.py
@@ -471,6 +471,10 @@ def backtest(self, n_jobs=1):
         pairs_stats = {k: v for k, v in pairs_stats.items() if k in ['单笔收益', '持仓K线数', '交易胜率', '持仓天数']}
         stats.update(pairs_stats)
 
+        dfw = self.dfw.copy()
+        long_rate = dfw[dfw['weight'] > 0].shape[0] / dfw.shape[0]
+        stats.update({"多头占比": long_rate})
+
         res['绩效评价'] = stats
         return res
 

diff --git a/czsc/utils/features.py b/czsc/utils/features.py
@@ -87,9 +87,9 @@ def normalize_ts_feature(df, x_col, n=10, **kwargs):
         else:
             raise ValueError("method 必须为 expanding 或 rolling")
 
-        # 对于缺失值，获取原始值，然后进行标准化
-        na_x = df[df[f"{x_col}_norm"].isna()][x_col].values
-        df.loc[df[f"{x_col}_norm"].isna(), f"{x_col}_norm"] = na_x - na_x.mean() / na_x.std()
+        # # 对于缺失值，获取原始值，然后进行标准化
+        # na_x = df[df[f"{x_col}_norm"].isna()][x_col].values
+        # df.loc[df[f"{x_col}_norm"].isna(), f"{x_col}_norm"] = na_x - na_x.mean() / na_x.std()
 
     if f"{x_col}_qcut" not in df.columns:
         if method == "expanding":

diff --git a/czsc/utils/st_components.py b/czsc/utils/st_components.py
@@ -366,15 +366,17 @@ def show_weight_backtest(dfw, **kwargs):
     stat = wb.results['绩效评价']
 
     st.divider()
-    c1, c2, c3, c4, c5, c6, c7, c8 = st.columns([1, 1, 1, 1, 1, 1, 1, 1])
+    c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = st.columns([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
     c1.metric("盈亏平衡点", f"{stat['盈亏平衡点']:.2%}")
-    c2.metric("单笔收益", f"{stat['单笔收益']} BP")
+    c2.metric("单笔收益（BP）", f"{stat['单笔收益']}")
     c3.metric("交易胜率", f"{stat['交易胜率']:.2%}")
     c4.metric("持仓K线数", f"{stat['持仓K线数']}")
     c5.metric("最大回撤", f"{stat['最大回撤']:.2%}")
     c6.metric("年化收益率", f"{stat['年化']:.2%}")
     c7.metric("夏普比率", f"{stat['夏普']:.2f}")
     c8.metric("卡玛比率", f"{stat['卡玛']:.2f}")
+    c9.metric("年化波动率", f"{stat['年化波动率']:.2%}")
+    c10.metric("多头占比", f"{stat['多头占比']:.2%}")
     st.divider()
 
     dret = wb.results['品种等权日收益']