0.9.61 新增 pyd 打包支持

czsc/traders/weight_backtest.py

@@ -231,10 +231,13 @@ class WeightBacktest:
 
     更新日志：
 
-    - V240627: 增加dailys属性，品种每日的交易信息
+    #### 20241125
+
+    1. 新增 yearly_days 参数，用于指定每年的交易日天数，默认为 252。
+
     """
 
-    version = "V240627"
+    version = "V241125"
 
     def __init__(self, dfw, digits=2, **kwargs) -> None:
         """持仓权重回测
@@ -285,7 +288,8 @@ 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())
         self._dailys = None
-        self.results = self.backtest(n_jobs=kwargs.get("n_jobs", 1))
+        self.yearly_days = kwargs.pop("yearly_days", 252)
+        self.results = self.backtest(n_jobs=kwargs.pop("n_jobs", 1))
 
     @property
     def stats(self):
@@ -332,7 +336,7 @@ def alpha(self) -> pd.DataFrame:
     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 = czsc.daily_performance(df["超额"].to_list(), yearly_days=self.yearly_days)
         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
@@ -341,7 +345,7 @@ def alpha_stats(self):
     def bench_stats(self):
         """基准收益统计"""
         df = self.alpha.copy()
-        stats = czsc.daily_performance(df["基准"].to_list())
+        stats = czsc.daily_performance(df["基准"].to_list(), yearly_days=self.yearly_days)
         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
@@ -559,7 +563,7 @@ def backtest(self, n_jobs=1):
         res["品种等权日收益"] = dret
 
         stats = {"开始日期": dret["date"].min().strftime("%Y%m%d"), "结束日期": dret["date"].max().strftime("%Y%m%d")}
-        stats.update(daily_performance(dret["total"]))
+        stats.update(daily_performance(dret["total"], yearly_days=self.yearly_days))
         dfp = pd.concat([v["pairs"] for k, v in res.items() if k in symbols], ignore_index=True)
         pairs_stats = evaluate_pairs(dfp)
         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"]}

czsc/utils/st_components.py

@@ -486,8 +486,15 @@ def show_weight_backtest(dfw, **kwargs):
         - n_jobs: int, 并行计算的进程数，默认为 1
 
     """
+    from czsc.eda import cal_yearly_days
+
     fee = kwargs.get("fee", 2)
     digits = kwargs.get("digits", 2)
+    yearly_days = kwargs.pop("yearly_days", None)
+
+    if not yearly_days:
+        yearly_days = cal_yearly_days(dts=dfw["dt"].unique())
+
     if (dfw.isnull().sum().sum() > 0) or (dfw.isna().sum().sum() > 0):
         st.warning("show_weight_backtest :: 持仓权重数据中存在空值，请检查数据后再试；空值数据如下：")
         st.dataframe(dfw[dfw.isnull().sum(axis=1) > 0], use_container_width=True)
@@ -509,13 +516,14 @@ def show_weight_backtest(dfw, **kwargs):
     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%}")
     c11.metric("空头占比", f"{stat['空头占比']:.2%}")
+    st.caption(f"回测参数：单边手续费 {fee} BP，权重小数位数 {digits} ，年交易天数 {yearly_days}")
     st.divider()
 
     dret = wb.results["品种等权日收益"].copy()
     dret["dt"] = pd.to_datetime(dret["date"])
     dret = dret.set_index("dt").drop(columns=["date"])
     # dret.index = pd.to_datetime(dret.index)
-    show_daily_return(dret, legend_only_cols=dfw["symbol"].unique().tolist(), **kwargs)
+    show_daily_return(dret, legend_only_cols=dfw["symbol"].unique().tolist(), yearly_days=yearly_days, **kwargs)
 
     if kwargs.get("show_drawdowns", False):
         show_drawdowns(dret, ret_col="total", sub_title="")
@@ -532,7 +540,7 @@ def show_weight_backtest(dfw, **kwargs):
 
     if kwargs.get("show_splited_daily", False):
         with st.expander("品种等权日收益分段表现", expanded=False):
-            show_splited_daily(dret[["total"]].copy(), ret_col="total")
+            show_splited_daily(dret[["total"]].copy(), ret_col="total", yearly_days=yearly_days)
 
     if kwargs.get("show_yearly_stats", False):
         with st.expander("年度绩效指标", expanded=False):

czsc/utils/stats.py

@@ -163,7 +163,7 @@ def __min_max(x, min_val, max_val, digits=4):
 
 
 def rolling_daily_performance(df: pd.DataFrame, ret_col, window=252, min_periods=100, **kwargs):
-    """计算滚动日收益
+    """计算滚动日收益的各项指标
 
     :param df: pd.DataFrame, 日收益数据，columns=['dt', ret_col]
     :param ret_col: str, 收益列名
@@ -173,19 +173,23 @@ def rolling_daily_performance(df: pd.DataFrame, ret_col, window=252, min_periods
 
         - yearly_days: int, 252, 一年的交易日数
     """
+    from czsc.eda import cal_yearly_days
+
     if not df.index.dtype == "datetime64[ns]":
         df["dt"] = pd.to_datetime(df["dt"])
         df.set_index("dt", inplace=True)
     assert df.index.dtype == "datetime64[ns]", "index必须是datetime64[ns]类型, 请先使用 pd.to_datetime 进行转换"
 
+    yearly_days = kwargs.get("yearly_days", cal_yearly_days(df.index))
+
     df = df[[ret_col]].copy().fillna(0)
     df.sort_index(inplace=True, ascending=True)
     dts = sorted(df.index.to_list())
     res = []
     for edt in dts[min_periods:]:
         sdt = edt - pd.Timedelta(days=window)
         dfg = df[(df.index >= sdt) & (df.index <= edt)].copy()
-        s = daily_performance(dfg[ret_col].to_list(), yearly_days=kwargs.get("yearly_days", 252))
+        s = daily_performance(dfg[ret_col].to_list(), yearly_days=yearly_days)
         s["sdt"] = sdt
         s["edt"] = edt
         res.append(s)

examples/develop/weight_backtest.py

@@ -13,7 +13,7 @@ def test_ensemble_weight():
     from czsc import WeightBacktest
 
     dfw = pd.read_feather(r"C:\Users\zengb\Downloads\weight_example.feather")
-    wb = WeightBacktest(dfw, digits=1, fee_rate=0.0002)
+    wb = WeightBacktest(dfw, digits=2, fee_rate=0.0002, n_jobs=1)
     ss = sorted(wb.stats.items())
     print(ss)
 
@@ -24,7 +24,7 @@ def test_rust_weight_backtest():
 
     dfw = pd.read_feather(r"C:\Users\zengb\Downloads\weight_example.feather")
 
-    wb = WeightBacktest(czsc.to_arrow(dfw), digits=1, fee_rate=0.0002, n_jobs=1)
+    wb = WeightBacktest(czsc.to_arrow(dfw), digits=2, fee_rate=0.0002, n_jobs=1)
 
-    ss = sorted(wb.stats.items())
-    print(ss)
+    # ss = sorted(wb.stats.items())
+    # print(ss)

examples/期货套利因子样例.py

@@ -67,6 +67,7 @@ def main():
     import czsc
     from czsc.connectors import cooperation as coo
 
+    # 构建策略
     df1 = coo.get_raw_bars(symbol="DLy9001", freq="日线", sdt="20170101", edt="20221231", raw_bars=False, fq="后复权")
     df2 = coo.get_raw_bars(symbol="DLp9001", freq="日线", sdt="20170101", edt="20221231", raw_bars=False, fq="后复权")
     df = pd.concat([df1, df2], axis=0)
@@ -76,6 +77,8 @@ def main():
     df["price"] = df["close"]
 
     dfw = df[["dt", "symbol", "price", "weight"]].copy()
+
+    # 执行回测
     st.title("期货套利研究")
     czsc.show_weight_backtest(
         dfw, fee_rate=0.0002, show_drawdowns=True, show_yearly_stats=True, show_monthly_return=True