添加 回归分析

回归分析

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+import pandas as pd
+from pymongo import MongoClient
+import numpy as np
+from datetime import datetime
+from concurrent.futures import ThreadPoolExecutor
+import statsmodels.api as sm
+from ast import literal_eval
+from datetime import timedelta
+import statsmodels.api as sm
+from scipy import stats
+import matplotlib.pyplot as plt
+# 数据库连接配置
+#----------------------获取每一天的沪深500--------------------------
+config = {
+    'host': 'www.bldcapital.cn',
+    'port': 27217,
+    'username': 'ZhangZH',
+    'password': 'S$#r)JAHE_2C',
+    'authSource': 'index_stocks',  # 指定认证数据库
+    'authMechanism': 'SCRAM-SHA-1'  # 指定认证机制
+}
+client = MongoClient(**config)
+client.admin.command('ping')
+print("成功连接到MongoDB服务器")  
+db1= client['index_stocks']
+col1  = db1["000300.XSHG"]
+query = {
+    "time": {  # 替换为你的日期字段名
+        "$gte": datetime(2015, 1, 1),  # 大于等于2020年1月1日
+        "$lt": datetime(2025  , 1, 1)    # 小于2025年1月1日（包含2024全年）
+    }
+}
+Projection={
+    '_id':0
+    }
+cursor1= col1.find(query,Projection)
+data1= list(cursor1)
+stockindex=pd.DataFrame(data1)
+stockindex['Code'] = stockindex['Code'].apply(
+    lambda codes: [c.replace('.XSHE', '_XSHE').replace('.XSHG', '_XSHG') for c in codes]
+)
+#----------------------获取股票数据--------------------------
+config = {
+    'host': 'www.bldcapital.cn',
+    'port': 27217,
+    'username': 'ZhangZH',
+    'password': 'S$#r)JAHE_2C',
+    'authSource': 'CrossSectionData',  # 指定认证数据库
+    'authMechanism': 'SCRAM-SHA-1'  # 指定认证机制
+}
+client = MongoClient(**config)
+client.admin.command('ping')
+print("成功连接到MongoDB服务器")  
+db2 = client['CrossSectionData']
+def get_data(fun):
+    col2=db2[fun]
+    cursor2= col2.find(query,Projection)
+    data2= list(cursor2)
+    df=pd.DataFrame(data2)
+
+    #----------------------筛选每一天的沪深500--------------------------
+    valid_stocks = set(stockindex['Code'].explode())
+    df_filtered = df[[c for c in df.columns if c in valid_stocks]]
+
+    result = pd.DataFrame(index=df_filtered.index, columns=df_filtered.columns)
+
+    # 5. 按日期匹配（注意：stockindex['time'] 是日期列）
+    for idx in stockindex.index:
+        codes = stockindex.loc[idx, 'Code']
+        codes_in_df = [c for c in codes if c in df_filtered.columns]
+
+        # 找到 df 中对应日期的行（这里假设 df 和 stockindex 是按顺序一一对应）
+        # 如果顺序不对，需要按日期对齐（见下方说明）
+        if idx in df_filtered.index:
+            result.loc[idx, codes_in_df] = df_filtered.loc[idx, codes_in_df]
+
+    # 6. 添加时间列（从 stockindex）
+    result = result.dropna(axis=1, how='all')
+    result.insert(0, 'time', stockindex['time'])
+    return result
+#X
+cap=get_data('stock_lcap')
+five=get_data('stock_5mDayRetVola')
+
+
+#Y
+closereal=get_data('stock_close_real')
+cap=cap.set_index(cap.columns[0])
+five=five.set_index(five.columns[0])
+closereal=closereal.set_index(closereal.columns[0])
+
+monthly_close = closereal.resample('M').last()
+
+# 计算月末收益率并去除第一行缺失值
+monthly_return = monthly_close.pct_change(axis=0)
+
+##对X做数值处理
+
+#---------------------去极值-------------------------
+def extreme_3sigma(dt,n=3):
+
+    mean = dt.mean()           # 截面数据均值
+    std = dt.std()             # 截面数据标准差
+    dt_up = mean + n*std       # 上限
+    dt_down = mean - n*std     # 下限
+    return dt.clip(dt_down, dt_up, axis=1)   # 超出上下限的值，赋值为上下限
+df1=extreme_3sigma(five)
+#---------------------标准化-------------------------
+def standardize_z(dt):
+    mean = dt.mean()     #  截面数据均值
+    std = dt.std()       #  截面数据标准差
+    return (dt - mean)/std     # 标准化处理
+
+df2 = standardize_z(df1)
+#----------------------市值中性化--------------------------
+def neutralization_size(factor, market_cap):
+    Y = factor.astype(float)
+    M = market_cap.astype(float)
+
+    df = pd.DataFrame(index=Y.index, columns=Y.columns, dtype=float)
+
+    for date in Y.index:                      # 1. 逐日
+        y = Y.loc[date].dropna()              # 2. 当天因子
+        m = M.loc[date].dropna()
+        m = m[m > 0]
+
+        common = y.index.intersection(m.index)
+        if len(common) < 2:                   # 样本太少
+            df.loc[date, common] = y.loc[common]
+            continue
+
+        y = y.loc[common]
+        x = pd.Series(np.log(m.loc[common]), index=common)  # 3. 确保是 Series
+        X = sm.add_constant(x)
+
+        try:
+            res = sm.OLS(y, X).fit().resid
+            df.loc[date, res.index] = res
+        except Exception as e:
+            print(f"{date} 中性化失败: {e}")
+            df.loc[date, y.index] = y
+
+    return df
+df3=neutralization_size(df2, cap)
+
+
+#----------------筛选月末数据--------------------
+monthly_df = df3.resample('M').last()
+#----------------回归--------------
+
+def factortest_regression(factor, stock):
+    # 1. 防除 0
+    stock = stock.where(stock > 0, np.nan)
+
+    # 2. 计算次月收益
+    stock_return = stock.shift(-1, axis=1) / stock - 1
+    stock_return = stock_return.iloc[:, :-1]
+
+    # 3. 统一把 inf 变 NaN
+    stock_return = stock_return.replace([np.inf, -np.inf], np.nan)
+
+    # 4. 准备与原始索引对齐的容器
+    tickers = factor.index
+    factor_return = pd.Series(index=tickers, dtype='float64')
+    tvalue = pd.Series(index=tickers, dtype='float64')
+
+    # 5. 逐只股票循环（或逐列，取决于你的 shape）
+    for tic in tickers:
+        x = factor.loc[tic]          # 该股票所有月份的因子值
+        y = stock_return.loc[tic]    # 该股票所有月份的次月收益
+
+        mask = x.notna() & y.notna()
+        if mask.sum() < 10:          # 样本不足，用 NaN 占位
+            continue
+
+        res = sm.OLS(y.loc[mask], sm.add_constant(x.loc[mask])).fit()
+        factor_return[tic] = res.params[1]
+        tvalue[tic] = res.tvalues[1]
+
+    # 6. 拼成 DataFrame，索引与原表一致
+    return pd.DataFrame({'factor_return': factor_return,
+                         'tvalue': tvalue})
+fr = factortest_regression(monthly_df,monthly_return).dropna()
+# t值绝对值序列平均值
+t_ma = fr['tvalue'].abs().mean()
+# t值序列绝对值大于2 的占比
+t_ratio = len(fr[(fr['tvalue'].abs()>2)])/len(fr['tvalue'])
+# 因子收益率序列平均值
+factors_ma = fr['factor_return'].mean()
+# t值序列均值的绝对值除以t值序列的标准差
+t_div= abs(fr['tvalue'].mean())/fr['tvalue'].std()
+# 因子收益率累积曲线
+fig = plt.figure()
+fr['factor_return'].cumsum().plot(kind = 'line',label = 'factor_return')
+plt.legend()
+plt.show()