前面介紹過針對每一個單獨的指數(shù)沥邻,可以通過市盈率和市凈率的計算獲得指數(shù)的估值百分位危虱,從而進(jìn)行相應(yīng)的投資(《基于Python的指數(shù)基金量化投資 - 通過市盈率和市凈率對指數(shù)估值》)。
這里介紹一個可以把所有指數(shù)估值信息進(jìn)行匯總的方式:指數(shù)估值榜唐全,這樣可以直觀的看出所有指數(shù)的估值點位埃跷,從整體上來進(jìn)行選擇,而不是一個指數(shù)一個指數(shù)的篩選邮利,更加高效和便捷弥雹。
圖中標(biāo)示出了大部分目前的主流指數(shù),縱坐標(biāo)的單位是0%到100%延届,然后通過不同的背景顏色對估值高低進(jìn)行區(qū)分剪勿,從上到下分為7個顏色區(qū)間,從紅色過度綠色分別對應(yīng)嚴(yán)重高估方庭、高估厕吉、正常偏高、正常械念、正常偏低头朱、低估和嚴(yán)重低估,這個區(qū)間是按照前面介紹的估值區(qū)間劃分的(《基于Python的指數(shù)基金量化投資 - 通過市盈率和市凈率對指數(shù)估值》)龄减,如下所示项钮。
分別計算出每個指數(shù)的估值百分位,然后放到對應(yīng)的區(qū)間,就能獲得估值榜的數(shù)據(jù)烁巫。
例如滬深300算出來的估值百分位是75%署隘,然后就把它放在60%-80%正常偏高這個區(qū)間內(nèi)找到75%的位置畫一個白點進(jìn)行標(biāo)識,而中證500算出來的估值百分位是23%亚隙,則在20%-40%正常偏低區(qū)間內(nèi)找到23%的位置畫一個白點進(jìn)行標(biāo)識磁餐,依次找出所有指數(shù)的估值點位后畫出即可。
下面是具體的代碼實現(xiàn)過程恃鞋。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
index_name_csv = ['g_hs300.csv',? #滬深300 - 0
??????????????? 'g_zz500.csv',? #中證500 - 1
??????????????? 'g_zz100.csv',? #中證100 - 2
??????????????? 'g_shz50.csv',? #上證50? - 3
??????????????? 'g_hsyy300.csv',? #滬深醫(yī)藥300 - 4
??????????????? 'g_zzyh.csv',?? #中證銀行? -5
??????????????? 'g_zzxf.csv',?? #中證消費? -6
??????????????? 'g_zzbj.csv',?? #中證白酒? -7
??????????????? 'g_db500.csv',? # 500低波動-8
??????????????? 'g_jz300.csv',? # 300價值?? -9
??????????????? 'g_yy100.csv',? #醫(yī)藥100?? -10
??????????????? 'g_zzyyao.csv',?? #中證醫(yī)藥? -11
??????????????? 'g_jbm50.csv',? #基本面50? -12
??????????????? 'g_shzhl.csv',? #上證紅利? -13
??????????????? 'g_zzhl.csv',?? #中證紅利? -14
??????????????? 'g_zzjg.csv',?? #中證軍工? -15
???????????????'g_spyl.csv',?? #食品飲料? -16
??????????????? 'g_zqgs.csv',?? #證券公司? -17
??????????????? 'g_ylcy.csv',?? #養(yǎng)老產(chǎn)業(yè)? -18
??????????????? 'g_szhl.csv',?? #深證紅利? -19
??????????????? 'g_zzhb.csv',?? #中證環(huán)保? -20
??????????????? 'g_cyb.csv',??? #創(chuàng)業(yè)板??? -21
??????????????? 'g_hszs.csv',?? #恒生指數(shù)? -22
??????????????? 'g_hsgqzs.csv',?? #恒生國企指數(shù)? -23
??????????????? 'g_zghl50.csv',? #中國互聯(lián)50? -24
??????????????? 'g_xgdp.csv',? #香港大盤? -25
??????????????? 'g_xgzx.csv']? #香港中小? -26
# 指數(shù)估值數(shù)據(jù)
index_info =np.zeros([len(index_name_csv),1])
for i in range(len(index_name_csv)):
???index_data = pd.read_csv('./importfile/indexSeries/indexValuation/g/' +index_name_csv[i])
???if index_name_csv[i] == 'g_zzyh.csv' or index_name_csv[i] =='g_zqgs.csv':
???????data_calc = index_data['pb']
???else:
???????data_calc = index_data['pe']
???xx = np.where(data_calc < data_calc.values[-1])
???data_percentage = len(xx[0]) / data_calc.shape[0]
???index_info[i] = data_percentage
# 指數(shù)估值百分位計算
plt.figure(1)
plt.rcParams['font.sans-serif'] =['Microsoft YaHei']
plt.plot(index_info, '--ow', ms=12,linewidth=2)
plt.axhspan(0, 0.1, facecolor='#4D8070')
plt.axhspan(0.1, 0.2, facecolor='#21DB85')
plt.axhspan(0.2, 0.4, facecolor='#B0D91C')
plt.axhspan(0.4, 0.6, facecolor='#E3BD00')
plt.axhspan(0.6, 0.8, facecolor='#FF9421')
plt.axhspan(0.8, 0.9, facecolor='#F56600')
plt.axhspan(0.9, 1.4, facecolor='#B22222')
plt.xlim(-1, len(index_info))
plt.ylim(-0.15, 1.4)
scale_y = 0.07
offset_x = 0.25
offset_y = 0.062
offset_x_num = 0.1
offset_x_alpha = 0.15
font_size = 22
# 估值區(qū)間繪制(背景)
index_name = ['滬深300',
????????????? '中證500',
????????????? '中證100',
????????????? '上證50',
????????????? '滬深醫(yī)藥300',
????????????? '中證銀行',
????????????? '中證消費',
?????????????'中證白酒',
????????????? '500低波動',
????????????? '300價值',
????????????? '醫(yī)藥100',
????????????? '中證醫(yī)藥',
????????????? '基本面50',
????????????? '上證紅利',
????????????? '中證紅利',
????????????? '中證軍工',
????????????? '食品飲料',
????????????? '證券公司',
????????????? '養(yǎng)老產(chǎn)業(yè)',
????????????? '深證紅利',
????????????? '中證環(huán)保',
????????????? '創(chuàng)業(yè)板',
????????????? '恒生指數(shù)',
????????????? 'H股指數(shù)',
????????????? '中國互聯(lián)50',
????????????? '香港大盤',
????????????? '香港中小']
font = {'size': font_size, 'color': 'w','weight': 'black'}
for i in range(len(index_name)):
???index_name_word = index_name[i]
???for j in range(len(index_name_word)):
???????if index_name_word[j].isdigit():
???????????plt.text(i - offset_x + offset_x_num, index_info[i] - j * scale_y +len(index_name_word) * offset_y,
???????????????????? index_name_word[j],fontdict=font)
???????else:
???????????plt.text(i - offset_x, index_info[i] - j * scale_y +len(index_name_word) * offset_y, index_name_word[j],
???????????????????? fontdict=font)
# 根據(jù)每個指數(shù)的估值百分位畫出具體的位置
time_end = '2021/04/09'
plt.figure(1)
plt.plot([-1,len(index_info)],[-0.025,-0.025],color='#CCCCCC',linewidth=2,linestyle='--')
shift_x = 4.23
text_base_x = -0.8
text_base_shift_x = 0.55
plt.gca().add_patch(plt.Rectangle((-1+0*shift_x,-0.15),2.6,0.1,color='#4D8070'))
plt.text(text_base_x+0*shift_x,-0.123,'嚴(yán)重低估',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+1*shift_x,-0.15),2.6,0.1,color='#21DB85'))
plt.text(text_base_x+text_base_shift_x+1*shift_x,-0.123,'低估',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+2*shift_x,-0.15),2.6,0.1,color='#B0D91C'))
plt.text(text_base_x+2*shift_x,-0.123,'正常偏低',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+3*shift_x,-0.15),2.6,0.1,color='#E3BD00'))
plt.text(text_base_x+text_base_shift_x+3*shift_x,-0.123,'正常',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+4*shift_x,-0.15),2.6,0.1,color='#FF9421'))
plt.text(text_base_x+4*shift_x,-0.123,'正常偏高',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+5*shift_x,-0.15),2.6,0.1,color='#F56600'))
plt.text(text_base_x+text_base_shift_x+5*shift_x,-0.123,'高估',fontdict=font)
plt.gca().add_patch(plt.Rectangle((-1+6*shift_x,-0.15),2.6,0.1,color='#B22222'))
plt.text(text_base_x+6*shift_x,-0.123,'嚴(yán)重高估',fontdict=font)
plt.title(time_end,size=15)
# plt.gca().add_patch(plt.Rectangle((-1,-0.15), 2.6, 0.1, color='#4D8070'))
# plt.text(-0.7, -0.123, '嚴(yán)重低估', fontdict=font)
plt.axis('off')
plt.show()
# 最后畫圖
如需代碼里面用到的指數(shù)估值數(shù)據(jù)或者有疑問崖媚,大家可以留言,歡迎拍磚^_^
課程參考:網(wǎng)易云課堂? 基于Python的量化指數(shù)基金投資