0.9.28 update

waditu · Sep 2, 2023 · 6c7dfb5 · 6c7dfb5
1 parent fb5d6d8
commit 6c7dfb5
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Showing 3 changed files with 16 additions and 13 deletions.
diff --git a/czsc/data/ts.py b/czsc/data/ts.py
@@ -93,10 +93,10 @@ def format_kline(kline: pd.DataFrame, freq: Freq) -> List[RawBar]:
 
     for i, record in enumerate(records):
         if freq == Freq.D:
-            vol = int(record['vol']*100)
-            amount = int(record.get('amount', 0)*1000)
+            vol = int(record['vol'] * 100) if record['vol'] > 0 else 0
+            amount = int(record.get('amount', 0) * 1000)
         else:
-            vol = int(record['vol'])
+            vol = int(record['vol']) if record['vol'] > 0 else 0
             amount = int(record.get('amount', 0))
 
         # 将每一根K线转换成 RawBar 对象

diff --git a/czsc/utils/corr.py b/czsc/utils/corr.py
@@ -135,5 +135,3 @@ def cross_sectional_ic(df, x_col='open', y_col='n1b', method='spearman', **kwarg
     res['IC胜率'] = round(len(df[df['ic'] > 0]) / len(df), 4)
     res['IC绝对值>2%占比'] = round(len(df[df['ic'].abs() > 0.02]) / len(df), 4)
     return df, res
-
-
diff --git a/czsc/utils/stats.py b/czsc/utils/stats.py
@@ -3,7 +3,7 @@
 author: zengbin93
 email: zeng_bin8888@163.com
 create_dt: 2023/4/19 23:27
-describe: 
+describe: 绩效表现统计
 """
 import numpy as np
 import pandas as pd
@@ -19,7 +19,7 @@ def subtract_fee(df, fee=1):
     if 'n1b' not in df.columns:
         assert 'price' in df.columns, '当n1b列不存在时，price 列必须存在'
         df['n1b'] = (df['price'].shift(-1) / df['price'] - 1) * 10000
-    
+
     df['date'] = df['dt'].dt.date
     df['edge_pre_fee'] = df['pos'] * df['n1b']
     df['edge_post_fee'] = df['pos'] * df['n1b']
@@ -41,25 +41,30 @@ def daily_performance(daily_returns):
         [0.01, 0.02, -0.01, 0.03, 0.02, -0.02, 0.01, -0.01, 0.02, 0.01]
     :return: dict
     """
-    if isinstance(daily_returns, list):
-        daily_returns = np.array(daily_returns)
-
+    daily_returns = np.array(daily_returns, dtype=np.float64)
+
     if len(daily_returns) == 0 or np.std(daily_returns) == 0 or all(x == 0 for x in daily_returns):
-        return {"年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0}
-    
+        return {"年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0, "年化波动率": 0, "非零覆盖": 0}
+
     annual_returns = np.sum(daily_returns) / len(daily_returns) * 252
     sharpe_ratio = np.mean(daily_returns) / np.std(daily_returns) * np.sqrt(252)
     cum_returns = np.cumsum(daily_returns)
     max_drawdown = np.max(np.maximum.accumulate(cum_returns) - cum_returns)
     kama = annual_returns / max_drawdown if max_drawdown != 0 else 10
     win_pct = len(daily_returns[daily_returns > 0]) / len(daily_returns)
-    return {
+    annual_volatility = np.std(daily_returns) * np.sqrt(252)
+    none_zero_cover = len(daily_returns[daily_returns != 0]) / len(daily_returns)
+
+    sta = {
         "年化": round(annual_returns, 4),
         "夏普": round(sharpe_ratio, 2),
         "最大回撤": round(max_drawdown, 4),
         "卡玛": round(kama, 2),
         "日胜率": round(win_pct, 4),
+        "年化波动率": round(annual_volatility, 4),
+        "非零覆盖": round(none_zero_cover, 4),
     }
+    return sta
 
 
 def net_value_stats(nv: pd.DataFrame, exclude_zero: bool = False, sub_cost=True) -> dict: