添加 回归分析
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196
回归分析
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196
回归分析
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import pandas as pd
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from pymongo import MongoClient
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import numpy as np
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from datetime import datetime
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from concurrent.futures import ThreadPoolExecutor
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import statsmodels.api as sm
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from ast import literal_eval
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from datetime import timedelta
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import statsmodels.api as sm
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from scipy import stats
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import matplotlib.pyplot as plt
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# 数据库连接配置
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#----------------------获取每一天的沪深500--------------------------
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config = {
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'host': 'www.bldcapital.cn',
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'port': 27217,
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'username': 'ZhangZH',
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'password': 'S$#r)JAHE_2C',
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'authSource': 'index_stocks', # 指定认证数据库
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'authMechanism': 'SCRAM-SHA-1' # 指定认证机制
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}
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client = MongoClient(**config)
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client.admin.command('ping')
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print("成功连接到MongoDB服务器")
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db1= client['index_stocks']
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col1 = db1["000300.XSHG"]
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query = {
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"time": { # 替换为你的日期字段名
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"$gte": datetime(2015, 1, 1), # 大于等于2020年1月1日
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"$lt": datetime(2025 , 1, 1) # 小于2025年1月1日（包含2024全年）
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}
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}
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Projection={
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'_id':0
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}
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cursor1= col1.find(query,Projection)
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data1= list(cursor1)
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stockindex=pd.DataFrame(data1)
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stockindex['Code'] = stockindex['Code'].apply(
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lambda codes: [c.replace('.XSHE', '_XSHE').replace('.XSHG', '_XSHG') for c in codes]
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)
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#----------------------获取股票数据--------------------------
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config = {
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'host': 'www.bldcapital.cn',
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'port': 27217,
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'username': 'ZhangZH',
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'password': 'S$#r)JAHE_2C',
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'authSource': 'CrossSectionData', # 指定认证数据库
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'authMechanism': 'SCRAM-SHA-1' # 指定认证机制
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}
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client = MongoClient(**config)
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client.admin.command('ping')
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print("成功连接到MongoDB服务器")
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db2 = client['CrossSectionData']
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def get_data(fun):
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col2=db2[fun]
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cursor2= col2.find(query,Projection)
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data2= list(cursor2)
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df=pd.DataFrame(data2)
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#----------------------筛选每一天的沪深500--------------------------
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valid_stocks = set(stockindex['Code'].explode())
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df_filtered = df[[c for c in df.columns if c in valid_stocks]]
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result = pd.DataFrame(index=df_filtered.index, columns=df_filtered.columns)
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# 5. 按日期匹配（注意：stockindex['time'] 是日期列）
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for idx in stockindex.index:
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codes = stockindex.loc[idx, 'Code']
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codes_in_df = [c for c in codes if c in df_filtered.columns]
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# 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）
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# 如果顺序不对，需要按日期对齐（见下方说明）
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if idx in df_filtered.index:
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result.loc[idx, codes_in_df] = df_filtered.loc[idx, codes_in_df]
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# 6. 添加时间列（从 stockindex）
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result = result.dropna(axis=1, how='all')
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result.insert(0, 'time', stockindex['time'])
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return result
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#X
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cap=get_data('stock_lcap')
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five=get_data('stock_5mDayRetVola')
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#Y
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closereal=get_data('stock_close_real')
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cap=cap.set_index(cap.columns[0])
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five=five.set_index(five.columns[0])
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closereal=closereal.set_index(closereal.columns[0])
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monthly_close = closereal.resample('M').last()
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# 计算月末收益率并去除第一行缺失值
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monthly_return = monthly_close.pct_change(axis=0)
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##对X做数值处理
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#---------------------去极值-------------------------
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def extreme_3sigma(dt,n=3):
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mean = dt.mean() # 截面数据均值
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std = dt.std() # 截面数据标准差
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dt_up = mean + n*std # 上限
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dt_down = mean - n*std # 下限
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return dt.clip(dt_down, dt_up, axis=1) # 超出上下限的值，赋值为上下限
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df1=extreme_3sigma(five)
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#---------------------标准化-------------------------
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def standardize_z(dt):
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mean = dt.mean() # 截面数据均值
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std = dt.std() # 截面数据标准差
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return (dt - mean)/std # 标准化处理
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df2 = standardize_z(df1)
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#----------------------市值中性化--------------------------
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def neutralization_size(factor, market_cap):
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Y = factor.astype(float)
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M = market_cap.astype(float)
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df = pd.DataFrame(index=Y.index, columns=Y.columns, dtype=float)
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for date in Y.index: # 1. 逐日
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y = Y.loc[date].dropna() # 2. 当天因子
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m = M.loc[date].dropna()
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m = m[m > 0]
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common = y.index.intersection(m.index)
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if len(common) < 2: # 样本太少
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df.loc[date, common] = y.loc[common]
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continue
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y = y.loc[common]
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x = pd.Series(np.log(m.loc[common]), index=common) # 3. 确保是 Series
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X = sm.add_constant(x)
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try:
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res = sm.OLS(y, X).fit().resid
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df.loc[date, res.index] = res
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except Exception as e:
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print(f"{date} 中性化失败: {e}")
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df.loc[date, y.index] = y
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return df
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df3=neutralization_size(df2, cap)
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#----------------筛选月末数据--------------------
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monthly_df = df3.resample('M').last()
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#----------------回归--------------
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def factortest_regression(factor, stock):
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# 1. 防除 0
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stock = stock.where(stock > 0, np.nan)
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# 2. 计算次月收益
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stock_return = stock.shift(-1, axis=1) / stock - 1
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stock_return = stock_return.iloc[:, :-1]
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# 3. 统一把 inf 变 NaN
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stock_return = stock_return.replace([np.inf, -np.inf], np.nan)
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# 4. 准备与原始索引对齐的容器
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tickers = factor.index
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factor_return = pd.Series(index=tickers, dtype='float64')
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tvalue = pd.Series(index=tickers, dtype='float64')
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# 5. 逐只股票循环（或逐列，取决于你的 shape）
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for tic in tickers:
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x = factor.loc[tic] # 该股票所有月份的因子值
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y = stock_return.loc[tic] # 该股票所有月份的次月收益
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mask = x.notna() & y.notna()
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if mask.sum() < 10: # 样本不足，用 NaN 占位
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continue
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res = sm.OLS(y.loc[mask], sm.add_constant(x.loc[mask])).fit()
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factor_return[tic] = res.params[1]
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tvalue[tic] = res.tvalues[1]
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# 6. 拼成 DataFrame，索引与原表一致
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return pd.DataFrame({'factor_return': factor_return,
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'tvalue': tvalue})
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fr = factortest_regression(monthly_df,monthly_return).dropna()
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# t值绝对值序列平均值
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t_ma = fr['tvalue'].abs().mean()
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# t值序列绝对值大于2 的占比
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t_ratio = len(fr[(fr['tvalue'].abs()>2)])/len(fr['tvalue'])
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# 因子收益率序列平均值
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factors_ma = fr['factor_return'].mean()
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# t值序列均值的绝对值除以t值序列的标准差
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t_div= abs(fr['tvalue'].mean())/fr['tvalue'].std()
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# 因子收益率累积曲线
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fig = plt.figure()
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fr['factor_return'].cumsum().plot(kind = 'line',label = 'factor_return')
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plt.legend()
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plt.show()
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