0.9.54 增加超额分析

czsc/traders/weight_backtest.py

@@ -15,6 +15,8 @@
 from typing import Union, AnyStr, Callable
 from multiprocessing import cpu_count
 from concurrent.futures import ProcessPoolExecutor
+
+import czsc
 from czsc.traders.base import CzscTrader
 from czsc.utils.io import save_json
 from czsc.utils.stats import daily_performance, evaluate_pairs
@@ -226,9 +228,13 @@ class WeightBacktest:
     """持仓权重回测
 
     飞书文档：https://s0cqcxuy3p.feishu.cn/wiki/Pf1fw1woQi4iJikbKJmcYToznxb
+
+    更新日志：
+
+    - V240627: 增加dailys属性，品种每日的交易信息
     """
 
-    version = "V231126"
+    version = "V240627"
 
     def __init__(self, dfw, digits=2, **kwargs) -> None:
         """持仓权重回测
@@ -278,6 +284,7 @@ def __init__(self, dfw, digits=2, **kwargs) -> None:
         self.dfw["weight"] = self.dfw["weight"].astype("float").round(digits)
         self.symbols = list(self.dfw["symbol"].unique().tolist())
         default_n_jobs = min(cpu_count() // 2, len(self.symbols))
+        self._dailys = None
         self.results = self.backtest(n_jobs=kwargs.get("n_jobs", default_n_jobs))
 
     @property
@@ -290,6 +297,46 @@ def daily_return(self) -> pd.DataFrame:
         """品种等权费后日收益率"""
         return self.results.get("品种等权日收益", pd.DataFrame())
 
+    @property
+    def dailys(self) -> pd.DataFrame:
+        """品种每日的交易信息
+
+        columns = ['date', 'symbol', 'edge', 'return', 'cost', 'n1b', 'turnover']
+
+        其中:
+            date        交易日，
+            symbol      合约代码，
+            n1b         品种每日收益率，
+            edge        策略每日收益率，
+            return      策略每日收益率减去交易成本后的真实收益，
+            cost        交易成本
+            turnover    当日的单边换手率
+        """
+        return self._dailys.copy() if self._dailys is not None else pd.DataFrame()
+
+    @property
+    def alpha(self) -> pd.DataFrame:
+        """策略超额收益
+
+        columns = ['date', '策略', '基准', '超额']
+        """
+        if self._dailys is None:
+            return pd.DataFrame()
+        df1 = self._dailys.groupby("date").agg({"return": "mean", "n1b": "mean"})
+        df1["alpha"] = df1["return"] - df1["n1b"]
+        df1.rename(columns={"return": "策略", "n1b": "基准", "alpha": "超额"}, inplace=True)
+        df1 = df1.reset_index()
+        return df1
+
+    @property
+    def alpha_stats(self):
+        """策略超额收益统计"""
+        df = self.alpha.copy()
+        stats = czsc.daily_performance(df["超额"].to_list())
+        stats["开始日期"] = df["date"].min().strftime("%Y-%m-%d")
+        stats["结束日期"] = df["date"].max().strftime("%Y-%m-%d")
+        return stats
+
     def get_symbol_daily(self, symbol):
         """获取某个合约的每日收益率
 
@@ -301,19 +348,21 @@ def get_symbol_daily(self, symbol):
         4. 计算每条数据扣除手续费后的收益（edge_post_fee）：收益减去手续费。
         5. 根据日期进行分组，并对每组进行求和操作，得到每日的总收益、总扣除手续费后的收益和总手续费。
         6. 重置索引，并将交易标的符号添加到DataFrame中。
-        7. 重命名列名，将'edge_post_fee'列改为'return'，将'dt'列改为'date'。
+        7. 重命名列名，将'edge_post_fee'列改为 return，将'dt'列改为 date。
         8. 选择需要的列，并返回包含日期、交易标的、收益、扣除手续费后的收益和手续费的DataFrame。
 
         :param symbol: str，合约代码
         :return: pd.DataFrame，品种每日收益率，
 
-            columns = ['date', 'symbol', 'edge', 'return', 'cost']
+            columns = ['date', 'symbol', 'edge', 'return', 'cost', 'n1b']
             其中
-                date    为交易日，
-                symbol  为合约代码，
-                edge    为每日收益率，
-                return  为每日收益率减去交易成本后的真实收益，
-                cost    为交易成本
+                date        交易日，
+                symbol      合约代码，
+                n1b         品种每日收益率，
+                edge        策略每日收益率，
+                return      策略每日收益率减去交易成本后的真实收益，
+                cost        交易成本
+                turnover    当日的单边换手率
 
             数据样例如下：
 
@@ -328,13 +377,19 @@ def get_symbol_daily(self, symbol):
                 ==========  ========  ============  ============  =======
         """
         dfs = self.dfw[self.dfw["symbol"] == symbol].copy()
-        dfs["edge"] = dfs["weight"] * (dfs["price"].shift(-1) / dfs["price"] - 1)
-        dfs["cost"] = abs(dfs["weight"].shift(1) - dfs["weight"]) * self.fee_rate
+        dfs["n1b"] = dfs["price"].shift(-1) / dfs["price"] - 1
+        dfs["edge"] = dfs["weight"] * dfs["n1b"]
+        dfs["turnover"] = abs(dfs["weight"].shift(1) - dfs["weight"])
+        dfs["cost"] = dfs["turnover"] * self.fee_rate
         dfs["edge_post_fee"] = dfs["edge"] - dfs["cost"]
-        daily = dfs.groupby(dfs["dt"].dt.date).agg({"edge": "sum", "edge_post_fee": "sum", "cost": "sum"}).reset_index()
+        daily = (
+            dfs.groupby(dfs["dt"].dt.date)
+            .agg({"edge": "sum", "edge_post_fee": "sum", "cost": "sum", "n1b": "sum", "turnover": "sum"})
+            .reset_index()
+        )
         daily["symbol"] = symbol
         daily.rename(columns={"edge_post_fee": "return", "dt": "date"}, inplace=True)
-        daily = daily[["date", "symbol", "edge", "return", "cost"]]
+        daily = daily[["date", "symbol", "n1b", "edge", "return", "cost", "turnover"]].copy()
         return daily
 
     def get_symbol_pairs(self, symbol):
@@ -485,6 +540,8 @@ def backtest(self, n_jobs=1):
                 ):
                     res[symbol] = res_symbol
 
+        self._dailys = pd.concat([v["daily"] for k, v in res.items() if k in symbols], ignore_index=True)
+
         dret = pd.concat([v["daily"] for k, v in res.items() if k in symbols], ignore_index=True)
         dret = pd.pivot_table(dret, index="date", columns="symbol", values="return").fillna(0)
         dret["total"] = dret[list(res.keys())].mean(axis=1)

examples/test_offline/test_weight_backtest.py

@@ -1,22 +1,28 @@
 import sys
+
 sys.path.insert(0, ".")
 sys.path.insert(0, "..")
 import czsc
 import pandas as pd
 
-assert czsc.WeightBacktest.version == "V231126"
+assert czsc.WeightBacktest.version == "V240627"
 
 
 def run_by_weights():
     """从持仓权重样例数据中回测"""
     dfw = pd.read_feather(r"C:\Users\zengb\Downloads\weight_example.feather")
     wb = czsc.WeightBacktest(dfw, digits=1, fee_rate=0.0002, n_jobs=1)
     # wb = czsc.WeightBacktest(dfw, digits=1, fee_rate=0.0002)
+    dailys = wb.dailys
+
+    # 计算等权组合的超额
+    df1 = dailys.groupby("date").agg({"return": "mean", "n1b": "mean"})
+    df1["alpha"] = df1["return"] - df1["n1b"]
 
     # ------------------------------------------------------------------------------------
     # 查看绩效评价
     # ------------------------------------------------------------------------------------
-    print(wb.results['绩效评价'])
+    print(wb.results["绩效评价"])
     # {'开始日期': '20170103',
     #  '结束日期': '20230731',
     #  '年化': 0.093,                       # 品种等权之后的年化收益率
@@ -41,5 +47,5 @@ def run_by_weights():
     wb.report(res_path=r"C:\Users\zengb\Desktop\231005\weight_example")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run_by_weights()