0.9.45 update

czsc/__init__.py

@@ -132,16 +132,18 @@
     normalize_feature,
     normalize_ts_feature,
     feture_cross_layering,
-    rolling_rank,
-    rolling_norm,
-    rolling_qcut,
-    rolling_compare,
     find_most_similarity,
 )
 
 from czsc.features.utils import (
     is_event_feature,
     rolling_corr,
+    rolling_rank,
+    rolling_norm,
+    rolling_qcut,
+    rolling_compare,
+    rolling_scale,
+    rolling_slope,
 )
 
 __version__ = "0.9.45"

czsc/utils/features.py

@@ -72,7 +72,7 @@ def normalize_ts_feature(df, x_col, n=10, **kwargs):
     assert df[x_col].nunique() > n, "因子值的取值数量必须大于分层数量"
     assert df[x_col].isna().sum() == 0, "因子有缺失值，缺失数量为：{}".format(df[x_col].isna().sum())
     method = kwargs.get("method", "rolling")
-    window = kwargs.get("window", 1000)
+    window = kwargs.get("window", 2000)
     min_periods = kwargs.get("min_periods", 300)
 
     if f"{x_col}_norm" not in df.columns:
@@ -89,7 +89,7 @@ def normalize_ts_feature(df, x_col, n=10, **kwargs):
 
         # # 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()
+        df.loc[df[f"{x_col}_norm"].isna(), f"{x_col}_norm"] = 0
 
     if f"{x_col}_qcut" not in df.columns:
         if method == "expanding":
@@ -106,11 +106,11 @@ def normalize_ts_feature(df, x_col, n=10, **kwargs):
         # 对于缺失值，获取原始值，然后进行分位数处理分层
         # na_x = df[df[f"{x_col}_qcut"].isna()][x_col].values
         # df.loc[df[f"{x_col}_qcut"].isna(), f"{x_col}_qcut"] = pd.qcut(na_x, q=n, labels=False, duplicates='drop', retbins=False)
-        df[f"{x_col}_qcut"] = df[f"{x_col}_qcut"].fillna(-1)
-        if df[f'{x_col}_qcut'].isna().sum() > 0:
-            logger.warning(f"因子 {x_col} 分层存在 {df[f'{x_col}_qcut'].isna().sum()} 个缺失值，已使用前值填充")
-            df[f'{x_col}_qcut'] = df[f'{x_col}_qcut'].ffill()
+        # if df[f'{x_col}_qcut'].isna().sum() > 0:
+        #     logger.warning(f"因子 {x_col} 分层存在 {df[f'{x_col}_qcut'].isna().sum()} 个缺失值，已使用前值填充")
+        #     df[f'{x_col}_qcut'] = df[f'{x_col}_qcut'].ffill()
 
+        df[f"{x_col}_qcut"] = df[f"{x_col}_qcut"].fillna(-1)
         # 第00层表示缺失值
         df[f'{x_col}分层'] = df[f'{x_col}_qcut'].apply(lambda x: f'第{str(int(x+1)).zfill(2)}层')
 

czsc/utils/st_components.py

@@ -571,22 +571,16 @@ def show_ts_rolling_corr(df, col1, col2, **kwargs):
     if sub_title:
         st.subheader(sub_title, divider="rainbow", anchor=hashlib.md5(sub_title.encode('utf-8')).hexdigest()[:8])
 
-    min_periods = kwargs.get('min_periods', None)
-    window = kwargs.get('window', None)
+    min_periods = kwargs.get('min_periods', 300)
+    window = kwargs.get('window', 2000)
     corr_method = kwargs.get('corr_method', 'pearson')
-
-    if not window or window <= 0:
-        method = 'expanding'
-        corr_result = df[col1].expanding(min_periods=min_periods).corr(df[col2], pairwise=True)
-    else:
-        method = 'rolling'
-        corr_result = df[col1].rolling(window=window, min_periods=min_periods).corr(df[col2], pairwise=True)
+    corr_result = df[col1].rolling(window=window, min_periods=min_periods).corr(df[col2], pairwise=True)
 
     corr_result = corr_result.dropna()
     corr_result = corr_result.rename('corr')
     line = go.Scatter(x=corr_result.index, y=corr_result, mode='lines', name='corr')
     layout = go.Layout(
-        title=f'滑动（{method}）相关系数',
+        title='滑动相关系数',
         xaxis=dict(title=''),
         yaxis=dict(title='corr'),
         annotations=[