0.9.61 新增时间效应分析组件

czsc/__init__.py

@@ -162,6 +162,8 @@
     show_factor_value,
     show_code_editor,
     show_classify,
+    show_df_describe,
+    show_date_effect,
 )
 
 from czsc.utils.bi_info import (

czsc/utils/st_components.py

@@ -1721,8 +1721,9 @@ def show_classify(df, col1, col2, n=10, method="cut", **kwargs):
         fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
         st.plotly_chart(fig, use_container_width=True)
 
-    dfg = dfg.style.background_gradient(cmap="RdYlGn_r", axis=None, subset=["count"])
-    dfg = dfg.background_gradient(cmap="RdYlGn_r", axis=None, subset=["mean", "std", "min", "25%", "50%", "75%", "max"])
+    dfg = dfg.style.background_gradient(cmap="RdYlGn_r", axis=None, subset=["mean"])
+    dfg = dfg.background_gradient(cmap="RdYlGn_r", axis=None, subset=["std"])
+    dfg = dfg.background_gradient(cmap="RdYlGn_r", axis=None, subset=["min", "25%", "50%", "75%", "max"])
     dfg = dfg.format(
         {
             "count": "{:.0f}",
@@ -1780,3 +1781,84 @@ def show_corr_graph(df, columns=None, threshold=0.2, **kwargs):
 
         dfr = dfr.style.background_gradient(cmap="RdYlGn_r", axis=None).format("{:.4f}", na_rep="MISS")
         st.dataframe(dfr, use_container_width=True)
+
+
+def show_df_describe(df: pd.DataFrame):
+    """展示 DataFrame 的描述性统计信息
+
+    :param df: pd.DataFrame，必须是 df.describe() 的结果
+    """
+    quantiles = [x for x in df.columns if "%" in x]
+    df = df.style.background_gradient(cmap="RdYlGn_r", axis=None, subset=["mean"])
+    df = df.background_gradient(cmap="RdYlGn_r", axis=None, subset=["std"])
+    df = df.background_gradient(cmap="RdYlGn_r", axis=None, subset=["max", "min"] + quantiles)
+
+    format_dict = {
+        "count": "{:.0f}",
+        "mean": "{:.4f}",
+        "std": "{:.4f}",
+        "min": "{:.4f}",
+        "max": "{:.4f}",
+    }
+    for q in quantiles:
+        format_dict[q] = "{:.4f}"
+
+    df = df.format(format_dict)
+    st.dataframe(df, use_container_width=True)
+
+
+def show_date_effect(df: pd.DataFrame, ret_col: str, **kwargs):
+    """分析日收益数据的日历效应
+
+    :param df: pd.DataFrame, 包含日期的日收益数据
+    :param ret_col: str, 收益列名称
+    :param kwargs: dict, 其他参数
+
+        - show_weekday: bool, 是否展示星期效应，默认为 True
+        - show_month: bool, 是否展示月份效应，默认为 True
+        - percentiles: list, 分位数，默认为 [0.1, 0.25, 0.5, 0.75, 0.9]
+
+    """
+    show_weekday = kwargs.get("show_weekday", True)
+    show_month = kwargs.get("show_month", True)
+    percentiles = kwargs.get("percentiles", [0.1, 0.25, 0.5, 0.75, 0.9])
+
+    assert ret_col in df.columns, f"ret_col 必须是 {df.columns} 中的一个"
+    assert show_month or show_weekday, "show_month 和 show_weekday 不能同时为 False"
+
+    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"
+    df = df.copy()
+
+    st.write(
+        f"交易区间 {df.index.min().strftime('%Y-%m-%d')} ~ {df.index.max().strftime('%Y-%m-%d')}；总天数：{len(df)}"
+    )
+
+    if show_weekday:
+        st.write("##### 星期效应")
+        df["weekday"] = df.index.weekday
+        sorted_weekday = sorted(df["weekday"].unique().tolist())
+        weekday_map = {0: "周一", 1: "周二", 2: "周三", 3: "周四", 4: "周五", 5: "周六", 6: "周日"}
+        df["weekday"] = df["weekday"].map(weekday_map)
+        sorted_rows = [weekday_map[i] for i in sorted_weekday]
+
+        weekday_effect = df.groupby("weekday")[ret_col].describe(percentiles=percentiles)
+        weekday_effect = weekday_effect.loc[sorted_rows]
+        show_df_describe(weekday_effect)
+
+    if show_month:
+        st.write("##### 月份效应")
+        df["month"] = df.index.month
+        month_map = {i: f"{i}月" for i in range(1, 13)}
+        sorted_month = sorted(df["month"].unique().tolist())
+        sorted_rows = [month_map[i] for i in sorted_month]
+
+        df["month"] = df["month"].map(month_map)
+        month_effect = df.groupby("month")[ret_col].describe(percentiles=percentiles)
+        month_effect = month_effect.loc[sorted_rows]
+        show_df_describe(month_effect)
+
+    st.caption("数据说明：count 为样本数量，mean 为均值，std 为标准差，min 为最小值，n% 为分位数，max 为最大值")

examples/develop/ST日收益的时间效应.py

@@ -0,0 +1,99 @@
+import pandas as pd
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+
+def show_df_describe(df: pd.DataFrame):
+    """展示 DataFrame 的描述性统计信息
+
+    :param df: pd.DataFrame，必须是 df.describe() 的结果
+    """
+    quantiles = [x for x in df.columns if "%" in x]
+    df = df.style.background_gradient(cmap="RdYlGn_r", axis=None, subset=["mean"])
+    df = df.background_gradient(cmap="RdYlGn_r", axis=None, subset=["std"])
+    df = df.background_gradient(cmap="RdYlGn_r", axis=None, subset=["max", "min"] + quantiles)
+
+    format_dict = {
+        "count": "{:.0f}",
+        "mean": "{:.4f}",
+        "std": "{:.4f}",
+        "min": "{:.4f}",
+        "max": "{:.4f}",
+    }
+    for q in quantiles:
+        format_dict[q] = "{:.4f}"
+
+    df = df.format(format_dict)
+    st.dataframe(df, use_container_width=True)
+
+
+def show_date_effect(df: pd.DataFrame, ret_col: str, **kwargs):
+    """分析日收益数据的日历效应
+
+    :param df: pd.DataFrame, 包含日期的日收益数据
+    :param ret_col: str, 收益列名称
+    :param kwargs: dict, 其他参数
+
+        - show_weekday: bool, 是否展示星期效应，默认为 True
+        - show_month: bool, 是否展示月份效应，默认为 True
+        - percentiles: list, 分位数，默认为 [0.1, 0.25, 0.5, 0.75, 0.9]
+
+    """
+    show_weekday = kwargs.get("show_weekday", True)
+    show_month = kwargs.get("show_month", True)
+    percentiles = kwargs.get("percentiles", [0.1, 0.25, 0.5, 0.75, 0.9])
+
+    assert ret_col in df.columns, f"ret_col 必须是 {df.columns} 中的一个"
+    assert show_month or show_weekday, "show_month 和 show_weekday 不能同时为 False"
+
+    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"
+    df = df.copy()
+
+    st.write(
+        f"交易区间 {df.index.min().strftime('%Y-%m-%d')} ~ {df.index.max().strftime('%Y-%m-%d')}；总天数：{len(df)}"
+    )
+
+    if show_weekday:
+        st.write("##### 星期效应")
+        df["weekday"] = df.index.weekday
+        sorted_weekday = sorted(df["weekday"].unique().tolist())
+        weekday_map = {0: "周一", 1: "周二", 2: "周三", 3: "周四", 4: "周五", 5: "周六", 6: "周日"}
+        df["weekday"] = df["weekday"].map(weekday_map)
+        sorted_rows = [weekday_map[i] for i in sorted_weekday]
+
+        weekday_effect = df.groupby("weekday")[ret_col].describe(percentiles=percentiles)
+        weekday_effect = weekday_effect.loc[sorted_rows]
+        show_df_describe(weekday_effect)
+
+    if show_month:
+        st.write("##### 月份效应")
+        df["month"] = df.index.month
+        month_map = {i: f"{i}月" for i in range(1, 13)}
+        sorted_month = sorted(df["month"].unique().tolist())
+        sorted_rows = [month_map[i] for i in sorted_month]
+
+        df["month"] = df["month"].map(month_map)
+        month_effect = df.groupby("month")[ret_col].describe(percentiles=percentiles)
+        month_effect = month_effect.loc[sorted_rows]
+        show_df_describe(month_effect)
+
+    st.caption("数据说明：count 为样本数量，mean 为均值，std 为标准差，min 为最小值，n% 为分位数，max 为最大值")
+
+
+def main():
+    df = pd.read_feather(r"A:\量化研究\BTC策略1H持仓权重和日收益241201\BTC_2H_001-daily_return.feather")
+    df["date"] = pd.to_datetime(df["date"])
+    df = df[df["date"] >= pd.to_datetime("2021-01-01")].copy()
+    df.set_index("date", inplace=True)
+    df["total"] = df.mean(axis=1) * 10000
+
+    show_date_effect(df, ret_col="total")
+
+
+if __name__ == "__main__":
+    main()