前面介紹過《基于Python的指數(shù)基金量化投資-通過市盈率和市凈率對(duì)指數(shù)估值》饰抒，通過估值來進(jìn)行指數(shù)基金的投資箫措。

當(dāng)估值百分位為90%-100%的時(shí)候佩迟，指數(shù)處于嚴(yán)重高估狀態(tài)，可以考慮全倉賣出遏乔；

當(dāng)估值百分位為80%-90%的時(shí)候点寥，指數(shù)處于高估狀態(tài)赊级，可以考慮大份額賣出；

當(dāng)估值百分位為60%-80%的時(shí)候俐银，指數(shù)處于正常偏高狀態(tài)尿背，可以考慮小份額賣出；

當(dāng)估值百分位為40%-60%的時(shí)候捶惜，指數(shù)處于正常狀態(tài)田藐，可以考慮持有；

當(dāng)估值百分位為20%-40%的時(shí)候吱七，指數(shù)處于正常偏低狀態(tài)汽久，可以考慮小份額定投；

當(dāng)估值百分位為10%-20%的時(shí)候踊餐，指數(shù)處于低估狀態(tài)景醇，可以考慮大份額定投；

當(dāng)估值百分位為0%-10%的時(shí)候市袖，指數(shù)處于嚴(yán)重低估狀態(tài)啡直，可以考慮大份額定投+增量買入烁涌；

有了這個(gè)策略，就需要觀察每個(gè)指數(shù)的估值百分位情況酒觅，如果一個(gè)一個(gè)查看會(huì)很麻煩撮执，最簡單的辦法就是把各個(gè)指數(shù)直觀的通過圖形的方式表現(xiàn)出來，如下圖所示：

圖中按照嚴(yán)重低估舷丹、低估抒钱、正常偏低、正常颜凯、正常偏高谋币、高估和嚴(yán)重高估劃分了對(duì)應(yīng)的區(qū)域，并用不同的顏色進(jìn)行了區(qū)分症概，然后分別計(jì)算每一個(gè)指數(shù)的估值百分位蕾额，然后把計(jì)算出來的結(jié)果畫在相應(yīng)的區(qū)域，這樣就可以非常直觀的看到所有指數(shù)的一個(gè)估值情況彼城。

源碼

# 源碼中用到的估值數(shù)據(jù)文件g_*.csv可以聯(lián)系小將獲取诅蝶。

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

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(fèi)? -6

??????????????? 'g_zzbj.csv',?? #中證白酒? -7

??????????????? 'g_db500.csv',? # 500低波動(dòng)-8

??????????????? 'g_jz300.csv',? # 300價(jià)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

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]

???if index_name_csv[i] == 'g_zzyh.csv':

???????data_calc = index_data['pe']

???????xx = np.where(data_calc < data_calc.values[-1])

???????data_percentage_add = len(xx[0]) / data_calc.shape[0]

???????index_info[i] = 0.8*data_percentage + 0.2*data_percentage_add

???else:

???????index_info[i] = data_percentage

plt.figure(3)

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

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(fèi)',

????????????? '中證白酒',

????????????? '500低波動(dòng)',

????????????? '300價(jià)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)

time_end = '2020/12/21'

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=28)

plt.axis('off')

g_globalMarket =pd.read_csv('./importfile/indexSeries/indexValuation/g_globalMarket/g_globalMarket.csv')

index_data_pe =g_globalMarket['pe'].values[1:len(g_globalMarket['pe']):1]

index_data_pb = g_globalMarket['pb'].values[1:len(g_globalMarket['pb']):1]

size_title = 28

size_label = 23

size_text = 35

size_line = 3

size_rotation = 20

plt_gap = 10

plt.show()

程序中用到的數(shù)據(jù)如果有問題，大家可以留言獲取募壕，歡迎大家一起交流溝通^_^

課程參考：網(wǎng)易云課堂? 基于Python的量化指數(shù)基金投資