Esempio n. 1
0
def find_pairs( i , n , min_price ):
    
    # 開啟matlab引擎
    #eng=matlab.engine.start_matlab()  
    
    # 選擇適合的 VECM model,並且檢定 formation period 是否有結構性斷裂,並刪除該配對,其餘配對則回傳共整合係數。
    #rank = 1
    #t1 = int(len(min_price)*3/4)     # 一天的時間長度(偵測兩天中間是否有結構性斷裂)
    
    local_select_model = []
    local_weight = []
    local_name = []
    local_pval = []
    for j in range(i+1,n+1):
            
        stock1 = min_price.iloc[:,i]
        stock2 = min_price.iloc[:,j]
            
        stock1_name = min_price.columns.values[i]
        stock2_name = min_price.columns.values[j]
        
        z = ( np.vstack( [stock1 , stock2] ).T )
        model = VAR(z)
        p = order_select(z,5)
        #p = int(model.select_order(5).bic)
            
        # VAR 至少需落後1期
        if p < 1:     
                
            continue
            
        # portmanteau test
        if model.fit(p).test_whiteness( nlags = 5 ).pvalue < 0.05:
                
            continue
            
        # Normality test
        if model.fit(p).test_normality().pvalue < 0.05:
                
            continue
        
        #r1 = eng.rank_jci( matlab.double(z.tolist()) , 'H2' , (p-1) ) 
        #r2 = eng.rank_jci( matlab.double(z.tolist()) , 'H1*' , (p-1))
        #r3 = eng.rank_jci( matlab.double(z.tolist()) , 'H1' , (p-1) )
        
        r1 = rank( pd.DataFrame(z) , 'H2' , p ) 
        r2 = rank( pd.DataFrame(z) , 'H1*' , p )
        r3 = rank( pd.DataFrame(z) , 'H1' , p )
        #r4 = rank( pd.DataFrame(z) , 'H*' , p )
            
        if r3 > 0:                      # 在 model 3 上有 rank
                
            if r2 > 0:                  # 在 model 2 上有 rank
                    
                if r1 > 0:              # select model 1 and model 2 and model 3
                        
                    #lambda_model2 = eng.eig_jci( matlab.double(z.tolist()) , 'H1*' , (p-1) , r2 )
                    #lambda_model3 = eng.eig_jci( matlab.double(z.tolist()) , 'H1' , (p-1) , r2 )
                
                    lambda_model2 = eig( pd.DataFrame(z) , 'H1*' , p , r2 )
                    lambda_model3 = eig( pd.DataFrame(z) , 'H1' , p , r2 )

                    test = np.log(lambda_model2/lambda_model3) * (len(min_price)-p)
                    if test <= 0:
                        raise ValueError('test value error')
                    if test > 3.8414:
                    
                        #bp1 = chow_test( z , t1 , p , 'H1' , r3 )
                    
                        #if bp1 == 0:               
                        
                        local_select_model.append('model3')
                    
                        #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H1' , (p-1) , r3 ) )
                        local_weight.append( weigh( pd.DataFrame(z) , 'H1' , p , r3 ) )
                    
                        local_name.append([stock1_name,stock2_name])
                        
                        local_pval.append( vecm_pvalue('model3', vecm( pd.DataFrame(z),'H1',p)[0][0] ) )
                   
                    else:
                            
                        #lambda_model1 = eng.eig_jci( matlab.double(z.tolist()) , 'H2' , (p-1) , r1 )
                    
                        lambda_model1 = eig( pd.DataFrame(z) , 'H2' , p , r1 )
                    
                        test = np.log(lambda_model1/lambda_model2) * (len(min_price)-p)
                                
                        if test > 3.8414:
                        
                            #bp1 = chow_test( z , t1 , p , 'H1*' , r2 )
                        
                            #if bp1 == 0:    
                            
                            local_select_model.append('model2')
                        
                            #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H1*' , (p-1) , r2 ) )
                            local_weight.append( weigh( pd.DataFrame(z) , 'H1*' , p , r2 ) )
                                
                            local_name.append([stock1_name,stock2_name])
                            
                            local_pval.append( vecm_pvalue('model2',vecm(pd.DataFrame(z),'H1*',p)[0][1] ) ) 
                                
                        else:
                            
                            #bp1 = chow_test( z , t1 , p , 'H2' , r1 ) 
                    
                            #if bp1 == 0:      
                        
                            local_select_model.append('model1')
                    
                            #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H2' , (p-1) , r1 ) )
                            local_weight.append( weigh( pd.DataFrame(z) , 'H2' , p , r1 ) )
                    
                            local_name.append([stock1_name,stock2_name])
                            
                            local_pval.append( vecm_pvalue('model1',vecm(pd.DataFrame(z),'H2',p)[0][0] ) )
                    
                else:                   # select model 2 and model 3
                   
                    #lambda_model2 = eng.eig_jci( matlab.double(z.tolist()) , 'H1*' , (p-1) , r2 )
                    #lambda_model3 = eng.eig_jci( matlab.double(z.tolist()) , 'H1' , (p-1) , r2 )
                
                    lambda_model2 = eig( pd.DataFrame(z) , 'H1*' , p , r2 )
                    lambda_model3 = eig( pd.DataFrame(z) , 'H1' , p , r2 )

                    test = np.log(lambda_model2/lambda_model3) * (len(min_price)-p)
                    
                    if test <= 0:
                        raise ValueError('test value error')
                    if test > 3.8414:
                            
                        #bp1 = chow_test( z , t1 , p , 'H1' , r3 )
                    
                        #if bp1 == 0:               
                        
                        local_select_model.append('model3')
                    
                        #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H1' , (p-1) , r3 ) )
                        local_weight.append( weigh( pd.DataFrame(z) , 'H1' , p , r3 ) )
                    
                        local_name.append([stock1_name,stock2_name])
                        
                        local_pval.append( vecm_pvalue('model3',vecm(pd.DataFrame(z),'H1',p)[0][0] ) )
                   
                    else:
                         
                        #bp1 = chow_test( z , t1 , p , 'H1*' , r2 ) 
                    
                        #if bp1 == 0:      
                        
                        local_select_model.append('model2')
                    
                        #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H1*' , (p-1) , r2 ) )
                        local_weight.append( weigh( pd.DataFrame(z) , 'H1*' , p , r2 ) )
                    
                        local_name.append([stock1_name,stock2_name])
                        
                        local_pval.append( vecm_pvalue('model2',vecm(pd.DataFrame(z),'H1*',p)[0][1] ) )
                    
            else :                     # 只在 model 3 上有rank
                    
                
                #bp1 = chow_test( z , t1 , p , 'H1' , r3 ) 
                
                #if bp1 == 0:            
                    
                local_select_model.append('model3')
                
                #weight.append( eng.coin_jci( matlab.double(z.tolist()) , 'H1' , (p-1) , r3 ) )
                local_weight.append( weigh( pd.DataFrame(z) , 'H1' , p , r3 ) )
                
                local_name.append([stock1_name,stock2_name])
                
                local_pval.append( vecm_pvalue('model3',vecm(pd.DataFrame(z),'H1',p)[0][0] ) )
                
                
        else:       # 表示此配對無rank
            
            continue
        
        #local_pval.append(1)
        
       #if VECM residuals 不是常態則搜尋下一個配對
        #if JB_VECM( stock1 , stock2 , local_select_model , p) == 1:
            
            #continue
    # 關閉 matlab 引擎
    #eng.quit()
        
    return local_weight, local_name, local_select_model , local_pval
Esempio n. 2
0
def find_pairs(i, n, min_price):

    local_select_model = []
    local_weight = []
    local_name = []
    local_pval = []
    for j in range(1):
        stock1 = min_price[[i]]
        stock2 = min_price[[n]]
        stock1_name = stock_number1
        stock2_name = stock_number2
        z = (np.hstack([stock1, stock2]))
        model = VAR(z)
        p = order_select(z, 5)
        if p < 1:
            continue
        # portmanteau test
        if model.fit(p).test_whiteness(nlags=5).pvalue < 0.05:
            continue
        # Normality test
        if model.fit(p).test_normality().pvalue < 0.05:
            continue
        r1 = rank(pd.DataFrame(z), 'H2', p)
        r2 = rank(pd.DataFrame(z), 'H1*', p)
        r3 = rank(pd.DataFrame(z), 'H1', p)
        if r3 > 0:  # 在 model 3 上有 rank
            if r2 > 0:  # 在 model 2 上有 rank
                if r1 > 0:  # select model 1 and model 2 and model 3
                    lambda_model2 = eig(pd.DataFrame(z), 'H1*', p, r2)
                    lambda_model3 = eig(pd.DataFrame(z), 'H1', p, r2)
                    test = np.log(
                        lambda_model2 / lambda_model3) * (len(min_price) - p)

                    if test > 3.8414:
                        local_select_model.append('model3')
                        local_weight.append(weigh(pd.DataFrame(z), 'H1', p,
                                                  r3))
                        local_name.append([stock1_name, stock2_name])
                        local_pval.append(
                            vecm_pvalue('model3',
                                        vecm(pd.DataFrame(z), 'H1', p)[0][0]))

                    else:
                        lambda_model1 = eig(pd.DataFrame(z), 'H2', p, r1)
                        test = np.log(lambda_model1 /
                                      lambda_model2) * (len(min_price) - p)

                        if test > 3.8414:

                            local_select_model.append('model2')
                            local_weight.append(
                                weigh(pd.DataFrame(z), 'H1*', p, r2))
                            local_name.append([stock1_name, stock2_name])
                            local_pval.append(
                                vecm_pvalue(
                                    'model2',
                                    vecm(pd.DataFrame(z), 'H1*', p)[0][1]))

                        else:
                            local_select_model.append('model1')
                            local_weight.append(
                                weigh(pd.DataFrame(z), 'H2', p, r1))
                            local_name.append([stock1_name, stock2_name])
                            local_pval.append(
                                vecm_pvalue(
                                    'model1',
                                    vecm(pd.DataFrame(z), 'H2', p)[0][0]))

                else:  # select model 2 and model 3

                    lambda_model2 = eig(pd.DataFrame(z), 'H1*', p, r2)
                    lambda_model3 = eig(pd.DataFrame(z), 'H1', p, r2)
                    test = np.log(
                        lambda_model2 / lambda_model3) * (len(min_price) - p)

                    if test > 3.8414:

                        local_select_model.append('model3')
                        local_weight.append(weigh(pd.DataFrame(z), 'H1', p,
                                                  r3))
                        local_name.append([stock1_name, stock2_name])
                        local_pval.append(
                            vecm_pvalue('model3',
                                        vecm(pd.DataFrame(z), 'H1', p)[0][0]))

                    else:

                        local_select_model.append('model2')
                        local_weight.append(
                            weigh(pd.DataFrame(z), 'H1*', p, r2))
                        local_name.append([stock1_name, stock2_name])
                        local_pval.append(
                            vecm_pvalue('model2',
                                        vecm(pd.DataFrame(z), 'H1*', p)[0][1]))

            else:  # 只在 model 3 上有rank
                local_select_model.append('model3')
                local_weight.append(weigh(pd.DataFrame(z), 'H1', p, r3))
                local_name.append([stock1_name, stock2_name])
                local_pval.append(
                    vecm_pvalue('model3',
                                vecm(pd.DataFrame(z), 'H1', p)[0][0]))
        else:  # 表示此配對無rank
            continue

    return local_weight, local_name, local_select_model, local_pval