事件研究法-python-万矿

    import pandas as pd
    from WindPy import *
    import numpy as np
    import scipy as sp
    import matplotlib.pyplot as plt
    from scipy.optimize import leastsq
    
    w.start()
    data_df_QTJY=w.wsd("300359.SZ", "close", "2018-12-01", "2019-02-28", "Fill=Previous;PriceAdj=F")
    data_df_CYBZ=w.wsd("399006.SZ", "close", "2018-12-01", "2019-02-28", "Fill=Previous;PriceAdj=F")
    
    data_df_QTJY=pd.DataFrame(data_df_QTJY.Data,columns=data_df_QTJY.Times,index=data_df_QTJY.Fields)
    data_df_QTJY=data_df_QTJY.T
    data_df_CYBZ=pd.DataFrame(data_df_CYBZ.Data,columns=data_df_CYBZ.Times,index=data_df_CYBZ.Fields)
    data_df_CYBZ=data_df_CYBZ.T
    
    data_df_QTJY['return']=np.log(data_df_QTJY['CLOSE']/data_df_QTJY['CLOSE'].shift(1))
    data_df_QTJY['index_return']=np.log(data_df_CYBZ['CLOSE']/data_df_CYBZ['CLOSE'].shift(1))
    data_df_QTJY=data_df_QTJY.dropna()
    
    ##样本数据(Xi,Yi)，需要转换成数组(列表)形式
    xi=np.array(data_df_QTJY['index_return'])
    yi=np.array(data_df_QTJY['return'])
    xi
    ##需要拟合的函数func 
    def func(p,x):
    k,b=p
    return k*x+b
     
    ##偏差函数：x,y都是列表:这里的x,y更上面的Xi,Yi中是一一对应的
    def error(p,x,y):
    return func(p,x)-y
     
    #k,b的初始值，可以任意设定,经过几次试验，发现p0的值会影响cost的值：Para[1]
    p0=[0.5,0.03]
     
    #把error函数中除了p0以外的参数打包到args中(使用要求)
    Para=leastsq(error,p0,args=(xi,yi))
    Para
    #读取结果
    k,b=Para[0]
    print("k=",k,"b=",b)
     
    #画样本点
    plt.figure(figsize=(8,6)) ##指定图像比例： 8：6
    plt.scatter(xi,yi,color="green",label="样本数据",linewidth=2) 
     
    #画拟合直线
    x=np.linspace(-0.1,0.1,100) ##在150-190直接画100个连续点
    y=k*x+b ##函数式
    plt.plot(x,y,color="red",label="拟合直线",linewidth=2) 
    plt.legend() #绘制图例
    plt.show()
    def get_ar(k):
    # 计算窗口期 AR
    data_df_QTJY=w.wsd("300359.SZ", "close", "2019-04-01", "2019-04-15", "PriceAdj=F")
    data_df_CYBZ=w.wsd("399006.SZ", "close", "2019-04-01", "2019-04-15", "PriceAdj=F")
    data_df_QTJY=pd.DataFrame(data_df_QTJY.Data,columns=data_df_QTJY.Times,index=data_df_QTJY.Fields)
    data_df_QTJY=data_df_QTJY.T
    data_df_CYBZ=pd.DataFrame(data_df_CYBZ.Data,columns=data_df_CYBZ.Times,index=data_df_CYBZ.Fields)
    data_df_CYBZ=data_df_CYBZ.T
    data_df_QTJY['return']=np.log(data_df_QTJY['CLOSE']/data_df_QTJY['CLOSE'].shift(1))
    data_df_QTJY['index_return']=np.log(data_df_CYBZ['CLOSE']/data_df_CYBZ['CLOSE'].shift(1))
    data_df_QTJY=data_df_QTJY.dropna()
    data_df_QTJY['moni_return']=(data_df_QTJY['index_return']-0.03/365)*k
    data_df_QTJY['ar']=data_df_QTJY['return']-data_df_QTJY['moni_return']
    
    return data_df_QTJY
    
    k=0.9595205659434853
    df=get_ar(k=k)    
    def get_ar(k):
    # 计算窗口期 AR
    data_df_QTJY=w.wsd("300359.SZ", "close", "2019-04-01", "2019-04-15", "PriceAdj=F")
    data_df_CYBZ=w.wsd("399006.SZ", "close", "2019-04-01", "2019-04-15", "PriceAdj=F")
    data_df_QTJY=pd.DataFrame(data_df_QTJY.Data,columns=data_df_QTJY.Times,index=data_df_QTJY.Fields)
    data_df_QTJY=data_df_QTJY.T
    data_df_CYBZ=pd.DataFrame(data_df_CYBZ.Data,columns=data_df_CYBZ.Times,index=data_df_CYBZ.Fields)
    data_df_CYBZ=data_df_CYBZ.T
    data_df_QTJY['return']=np.log(data_df_QTJY['CLOSE']/data_df_QTJY['CLOSE'].shift(1))
    data_df_QTJY['index_return']=np.log(data_df_CYBZ['CLOSE']/data_df_CYBZ['CLOSE'].shift(1))
    data_df_QTJY=data_df_QTJY.dropna()
    data_df_QTJY['moni_return']=(data_df_QTJY['index_return']-0.03/365)*k
    data_df_QTJY['ar']=data_df_QTJY['return']-data_df_QTJY['moni_return']
    
    return data_df_QTJY
    
    k=0.9595205659434853
    df=get_ar(k=k)    
    
    # t检验
    from scipy import stats
    result=stats.ttest_1samp(df['ar'],0)
    car=[0.0986 ,0.0933 ,0.0512 ,0.0344 ,0.1287 ,0.0957 ,0.0837 ,0.1155 ,0.0916]
    print('car显著性检验',stats.ttest_1samp(car,1))
    print('单样本检验结果',result)
    result2=stats.ttest_ind(df['return'],df['moni_return'],equal_var=False)
    print('双样本检验结果',result2)
    result3=stats.levene(df['return'],df['moni_return'])
    print('原假设方差不同',result3)
    x=[1,2,3,4,5,6,7,8,9]
    plt.plot(x,df['return'],color='blue',label='actual return')
    plt.plot(x,df['moni_return'],color='black',label='semulated_return')
    plt.plot(x,np.zeros(9),color='red')
    plt.legend() #绘制图例
    plt.show()
    x=[1,2,3,4,5,6,7,8,9]
    plt.plot(x,df['ar'],color='blue',label='ar')
    car=[0.0986 ,0.0933 ,0.0512 ,0.0344 ,0.1287 ,0.0957 ,0.0837 ,0.1155 ,0.0916]
    plt.plot(x,car,color='black',label='car')
    plt.plot(x,np.zeros(9),color='red')
    plt.legend() #绘制图例
    plt.show()
    
    
    python
    
    
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