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
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