From c5ffe5964a91566697a0f1045aa704ceb4895820 Mon Sep 17 00:00:00 2001 From: zengbin93 Date: Mon, 15 Jan 2024 21:21:31 +0800 Subject: [PATCH] =?UTF-8?q?0.9.41=20=E4=BC=98=E5=8C=96=20streamlit=20?= =?UTF-8?q?=E7=BB=84=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- czsc/__init__.py | 1 + czsc/utils/st_components.py | 80 ++++++++++++++++++++++++++++++++++++- 2 files changed, 79 insertions(+), 2 deletions(-) diff --git a/czsc/__init__.py b/czsc/__init__.py index 1b409b347..2152505c3 100644 --- a/czsc/__init__.py +++ b/czsc/__init__.py @@ -97,6 +97,7 @@ # streamlit 量化分析组件 from czsc.utils.st_components import ( show_daily_return, + show_splited_daily, show_monthly_return, show_correlation, show_sectional_ic, diff --git a/czsc/utils/st_components.py b/czsc/utils/st_components.py index 7bf4d8cdc..f40c36a98 100644 --- a/czsc/utils/st_components.py +++ b/czsc/utils/st_components.py @@ -17,8 +17,13 @@ def show_daily_return(df, **kwargs): - legend_only_cols: list，仅在图例中展示的列名 """ + 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().fillna(0) + df.sort_index(inplace=True, ascending=True) def _stats(df_, type_='持有日'): df_ = df_.copy() @@ -69,7 +74,14 @@ def _stats(df_, type_='持有日'): def show_monthly_return(df, ret_col='total', title="月度累计收益", **kwargs): """展示指定列的月度累计收益""" - assert df.index.dtype == 'datetime64[ns]', "index 必须是 datetime 类型" + 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().fillna(0) + df.sort_index(inplace=True, ascending=True) + st.subheader(title, divider="rainbow") monthly = df[[ret_col]].resample('M').sum() monthly['year'] = monthly.index.year @@ -78,7 +90,11 @@ def show_monthly_return(df, ret_col='total', title="月度累计收益", **kwarg month_cols = [f"{x}月" for x in monthly.columns] monthly.columns = month_cols monthly['年收益'] = monthly.sum(axis=1) - monthly = monthly.style.background_gradient(cmap='RdYlGn_r', axis=None, subset=month_cols).format('{:.2%}', na_rep='-') + + monthly = monthly.style.background_gradient(cmap='RdYlGn_r', axis=None, subset=month_cols) + monthly = monthly.background_gradient(cmap='RdYlGn_r', axis=None, subset=['年收益']) + monthly = monthly.format('{:.2%}', na_rep='-') + st.dataframe(monthly, use_container_width=True) @@ -316,3 +332,63 @@ def show_weight_backtest(dfw, **kwargs): st.dataframe(df_.style.background_gradient(cmap='RdYlGn_r').format("{:.2%}"), use_container_width=True) return wb + + +def show_splited_daily(df, ret_col, **kwargs): + """展示分段日收益表现 + + :param df: pd.DataFrame + :param ret_col: str, df 中的列名，指定收益列 + :param kwargs: + + sub_title: str, 子标题 + + """ + 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().fillna(0) + df.sort_index(inplace=True, ascending=True) + + sub_title = kwargs.get("sub_title", "") + if sub_title: + st.subheader(sub_title, divider="rainbow") + + last_dt = df.index[-1] + sdt_map = { + "过去1周": last_dt - pd.Timedelta(days=7), + "过去2周": last_dt - pd.Timedelta(days=14), + "过去1月": last_dt - pd.Timedelta(days=30), + "过去3月": last_dt - pd.Timedelta(days=90), + "过去6月": last_dt - pd.Timedelta(days=180), + "过去1年": last_dt - pd.Timedelta(days=365), + "今年以来": pd.to_datetime(f"{last_dt.year}-01-01"), + } + + rows = [] + for name, sdt in sdt_map.items(): + df1 = df.loc[sdt:last_dt].copy() + row = czsc.daily_performance(df1[ret_col]) + row['开始日期'] = sdt.strftime('%Y-%m-%d') + row['结束日期'] = last_dt.strftime('%Y-%m-%d') + row['收益名称'] = name + row['绝对收益'] = df1[ret_col].sum() + rows.append(row) + dfv = pd.DataFrame(rows).set_index('收益名称') + cols = ['开始日期', '结束日期', '绝对收益', '年化', '夏普', '最大回撤', '卡玛', '年化波动率', '非零覆盖', '日胜率', '盈亏平衡点'] + dfv = dfv[cols].copy() + + dfv = dfv.style.background_gradient(cmap='RdYlGn_r', subset=['绝对收益']) + dfv = dfv.background_gradient(cmap='RdYlGn_r', subset=['年化']) + dfv = dfv.background_gradient(cmap='RdYlGn_r', subset=['夏普']) + dfv = dfv.background_gradient(cmap='RdYlGn', subset=['最大回撤']) + dfv = dfv.background_gradient(cmap='RdYlGn_r', subset=['卡玛']) + dfv = dfv.background_gradient(cmap='RdYlGn', subset=['年化波动率']) + dfv = dfv.background_gradient(cmap='RdYlGn', subset=['盈亏平衡点']) + dfv = dfv.background_gradient(cmap='RdYlGn_r', subset=['日胜率']) + dfv = dfv.background_gradient(cmap='RdYlGn_r', subset=['非零覆盖']) + dfv = dfv.format({'盈亏平衡点': '{:.2f}', '年化波动率': '{:.2%}', '最大回撤': '{:.2%}', '卡玛': '{:.2f}', '年化': '{:.2%}', + '夏普': '{:.2f}', '非零覆盖': '{:.2%}', '日胜率': '{:.2%}', '绝对收益': '{:.2%}'}) + st.dataframe(dfv, use_container_width=True)