0.9.58 优化streamlit组件

czsc/__init__.py

@@ -203,6 +203,7 @@
     remove_beta_effects, vwap, twap,
     cross_sectional_strategy,
     judge_factor_direction,
+    monotonicity,
 )
 
 

czsc/eda.py

@@ -148,3 +148,14 @@ def judge_factor_direction(df: pd.DataFrame, factor, target='n1b', by='symbol',
     dfc = df.groupby(by)[[factor, target]].corr(method=method).unstack().iloc[:, 1].reset_index()
     return "positive" if dfc[factor].mean().iloc[0] >= 0 else "negative"
 
+
+def monotonicity(sequence):
+    """计算序列的单调性
+
+    原理：计算序列与自然数序列的相关系数，系数越接近1，表示单调递增；系数越接近-1，表示单调递减；接近0表示无序
+
+    :param sequence: list, tuple 序列
+    :return: float, 单调性系数
+    """
+    from scipy.stats import spearmanr
+    return spearmanr(sequence, range(len(sequence)))[0]

czsc/utils/st_components.py

@@ -1,3 +1,5 @@
+# 飞书文档：https://s0cqcxuy3p.feishu.cn/wiki/AATuw5vN7iN9XbkVPuwcE186n9f
+
 import czsc
 import hashlib
 import optuna
@@ -195,8 +197,10 @@ def show_sectional_ic(df, x_col, y_col, method="pearson", **kwargs):
     :param y_col: str，收益列名
     :param method: str，计算IC的方法，可选 pearson 和 spearman
     :param kwargs:
+
         - show_cumsum_ic: bool，是否展示累计IC曲线，默认为 True
         - show_factor_histgram: bool，是否展示因子数据分布图，默认为 False
+
     """
     dfc, res = czsc.cross_sectional_ic(df, x_col=x_col, y_col=y_col, dt_col="dt", method=method)
 
@@ -325,7 +329,6 @@ def show_factor_layering(df, factor, target="n1b", **kwargs):
     :param kwargs:
 
         - n: 分层数量，默认为10
-
     """
     n = kwargs.get("n", 10)
     df = czsc.feture_cross_layering(df, factor, n=n)
@@ -335,7 +338,26 @@ def show_factor_layering(df, factor, target="n1b", **kwargs):
     if "第00层" in mrr.columns:
         mrr.drop(columns=["第00层"], inplace=True)
 
-    czsc.show_daily_return(mrr, stat_hold_days=False)
+    # 计算每层的累计收益率
+    dfc = mrr.sum(axis=0).to_frame("绝对收益")
+
+    dfc["text"] = dfc["绝对收益"].apply(lambda x: f"{x:.2%}")
+    fig = px.bar(
+        dfc,
+        y="绝对收益",
+        title="因子分层绝对收益 | 单调性：{:.2%}".format(czsc.monotonicity(dfc["绝对收益"])),
+        color="绝对收益",
+        color_continuous_scale="RdYlGn_r",
+        text="text",
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+    czsc.show_daily_return(
+        mrr,
+        stat_hold_days=False,
+        yearly_days=kwargs.get("yearly_days", 252),
+        show_dailys=kwargs.get("show_dailys", False),
+    )
 
 
 def show_symbol_factor_layering(df, x_col, y_col="n1b", **kwargs):

examples/Streamlit组件库使用案例/因子分析.py

@@ -0,0 +1,13 @@
+import czsc
+import pandas as pd
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+df = pd.read_feather(r"C:\Users\zengb\Downloads\ST组件样例数据\因子数据样例.feather")
+factor = [x for x in df.columns if x.startswith("F#")][0]
+# czsc.show_factor_layering(df, factor=factor, target="n1b", n=10)
+
+# czsc.show_feature_returns(df, factor, target="n1b", fit_intercept=True)
+
+czsc.show_sectional_ic(df, factor, "n1b", show_factor_histgram=True, show_cumsum_ic=True)

requirements.txt

@@ -1,28 +1,35 @@
 requests>=2.24.0
 pyecharts>=1.9.1
-tqdm
+tqdm>=4.66.4
 pandas>=1.1.0
 numpy>=1.16.5
-tushare
+tushare>=1.4.6
 python-docx>=0.8.11
-matplotlib
+matplotlib>=3.9.0
 seaborn
 Deprecated>=1.2.12
 scikit-learn>=0.24.1
-dill
+dill>=0.3.8
 openpyxl
 pyarrow
 loguru>=0.6.0
-click
-pytest
+click>=8.1.7
+pytest>=8.2.1
 tenacity>=8.1.0
 requests-toolbelt>=0.10.1
 plotly>=5.11.0
 parse>=1.19.0
 lightgbm>=4.0.0
-streamlit
-redis
-oss2
-statsmodels
-optuna
-cryptography
+streamlit>=1.37.1
+redis>=5.0.4
+oss2>=2.18.5
+statsmodels>=0.14.2
+optuna>=3.6.1
+cryptography>=42.0.7
+czsc>=0.9.58
+tqsdk>=3.6.0
+pyautogui>=0.9.54
+pytz>=2024.1
+flask>=3.0.3
+setuptools>=68.0.0
+scipy>=1.13.1