diff --git a/czsc/__init__.py b/czsc/__init__.py index 5c653066a..6035a6d82 100644 --- a/czsc/__init__.py +++ b/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 ( diff --git a/czsc/utils/st_components.py b/czsc/utils/st_components.py index 20217ee44..52a73cd46 100644 --- a/czsc/utils/st_components.py +++ b/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") diff --git "a/examples/develop/ST\346\227\245\346\224\266\347\233\212\347\232\204\346\227\266\351\227\264\346\225\210\345\272\224.py" "b/examples/develop/ST\346\227\245\346\224\266\347\233\212\347\232\204\346\227\266\351\227\264\346\225\210\345\272\224.py" new file mode 100644 index 000000000..5b337a341 --- /dev/null +++ "b/examples/develop/ST\346\227\245\346\224\266\347\233\212\347\232\204\346\227\266\351\227\264\346\225\210\345\272\224.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()