本文分享代码用于实时计算各个行业及个股的市盈率，以便及时发现价值洼地，用于选股决策。
计算完后发现有的行业确实是投资的绝佳机会。
市盈率的概念对于接触量化的小伙伴来说再熟悉不过了。下面借用百度百科的解释：
市盈率（Price earnings ratio，即P/E ratio）也称“本益比”、“股价收益比率”或“市价盈利比率（简称市盈率）”。
市盈率是最常用来评估股价水平是否合理的指标之一，由股价除以年度每股盈余（EPS）得出（以公司市值除以年度股东应占溢利亦可得出相同结果）。计算时，股价通常取最新收盘价，而EPS方面，若按已公布的上年度EPS计算，称为历史市盈率（historical P/E）；计算预估市盈率所用的EPS预估值，一般采用市场平均预估（consensus estimates），即追踪公司业绩的机构收集多位分析师的预测所得到的预估平均值或中值。何谓合理的市盈率没有一定的准则。
市盈率是某种股票每股市价与每股盈利的比率。市场广泛谈及市盈率通常指的是静态市盈率，通常用来作为比较不同价格的股票是否被高估或者低估的指标。用市盈率衡量一家公司股票的质地时，并非总是准确的。一般认为，如果一家公司股票的市盈率过高，那么该股票的价格具有泡沫，价值被高估。当一家公司增长迅速以及未来的业绩增长非常看好时，利用市盈率比较不同股票的投资价值时，这些股票必须属于同一个行业，因为此时公司的每股收益比较接近，相互比较才有效。
在对企业进行估值时，常用的办法有两种，分别是绝对估值法和相对估值法。绝对估值法是依据企业盈利增速和折现率计算，企业增速这个指标不好预测，我感觉绝对估值法难度较大。而相对估值法则计算起来相对容易，这就需要用到市盈率了。
市盈率简单来讲就是回本要用的时间，假设你花78元买了五粮液的股票，2017年，每一股分配2块8毛5的利润，那么这个利润水平下，回本时间是27.6，也就是说要差不多27.6年能够回本。
市盈率越低，回本时间越短。作为投资来讲，这个就越划算。
关于市盈率的更详细介绍可以看下面这篇文章：
市盈率指标详解及相关文献概述
import pandas as pd
import numpy as np
#证监会行业
HY_ZJH = ['A01','A02','A03','A04','A05','B06','B07','B08','B09','B11','C13','C14','C15',
'C17','C18','C19','C20','C21','C22','C23','C24','C25','C26','C27','C28','C29',
'C30','C31','C32','C33','C34','C35','C36','C37','C38','C39','C40','C41','C42',
'D44','D45','D46','E47','E48','E50','F51','F52','G53','G54','G55','G56','G58',
'G59','H61','H62','I63','I64','I65','J66','J67','J68','J69','K70','L71','L72',
'M73','M74','N77','N78','P82','Q83','R85','R86','R87','S90']
#聚宽一级行业
HY_JK1 = ['HY001','HY002','HY003','HY004','HY005','HY006','HY007','HY008','HY009','HY010','HY011']
#聚宽二级行业
HY_JK2 = ['HY401','HY402','HY403','HY404','HY405','HY406','HY407','HY408','HY409','HY410',
'HY411','HY412','HY413','HY414','HY415','HY416','HY417','HY418','HY419','HY420',
'HY421','HY422','HY423','HY424','HY425','HY426','HY427','HY428','HY429','HY432',
'HY433','HY434','HY435','HY436','HY437','HY438','HY439','HY440','HY441','HY442',
'HY443','HY444','HY445','HY446','HY447','HY448','HY449','HY450','HY451','HY452',
'HY453','HY454','HY455','HY457','HY458','HY459','HY460','HY461','HY462','HY463',
'HY464','HY465','HY466','HY467','HY468','HY469','HY470','HY471','HY472','HY473',
'HY474','HY476','HY477','HY478','HY479','HY480','HY481','HY483','HY484','HY485',
'HY486','HY487','HY488','HY489','HY491','HY492','HY493','HY494','HY496','HY497',
'HY500','HY501','HY504','HY505','HY506','HY509','HY510','HY511','HY512','HY513',
'HY514','HY515','HY516','HY517','HY518','HY519','HY520','HY521','HY523','HY524',
'HY525','HY526','HY527','HY528','HY529','HY530','HY531','HY570','HY571','HY572',
'HY573','HY574','HY576','HY578','HY579','HY587','HY588','HY591','HY593','HY595',
'HY596','HY597','HY598','HY601']
#申万一级行业
HY_SW1 = ['801010','801020','801030','801040','801050','801080','801110','801120','801130',
'801140','801150','801160','801170','801180','801200','801210','801230','801710',
'801720','801730','801740','801750','801760','801770','801780','801790','801880',
'801890']
#申万二级行业
HY_SW2 = ['801011','801012','801013','801014','801015','801016','801017','801018','801021',
'801022','801023','801024','801032','801033','801034','801035','801036','801037',
'801041','801051','801053','801054','801055','801072','801073','801074','801075',
'801076','801081','801082','801083','801084','801085','801092','801093','801094',
'801101','801102','801111','801112','801123','801131','801132','801141','801142',
'801143','801144','801151','801152','801153','801154','801155','801156','801161',
'801162','801163','801164','801171','801172','801173','801174','801175','801176',
'801177','801178','801181','801182','801191','801192','801193','801194','801202',
'801203','801204','801205','801211','801212','801213','801214','801215','801222',
'801223','801231','801711','801712','801713','801721','801722','801723','801724',
'801725','801731','801732','801733','801734','801741','801742','801743','801744',
'801751','801752','801761','801881']
#申万三级行业
HY_SW3 = ['850111','850112','850113','850121','850122','850131','850141','850151','850152','850154','850161',
'850171','850181','850211','850221','850222','850231','850241','850242','850311','850313','850321',
'850322','850323','850324','850331','850332','850333','850334','850335','850336','850337','850338',
'850339','850341','850342','850343','850344','850345','850351','850352','850353','850361','850362',
'850363','850372','850373','850381','850382','850383','850411','850412','850521','850522','850523',
'850531','850541','850542','850543','850544','850551','850552','850553','850611','850612','850614',
'850615','850616','850623','850711','850712','850713','850714','850715','850716','850721','850722',
'850723','850724','850725','850726','850727','850728','850729','850731','850741','850751','850811',
'850812','850813','850822','850823','850831','850832','850833','850841','850851','850852','850911',
'850912','850913','850921','850935','850936','850941','851012','851013','851014','851021','851111',
'851112','851113','851114','851115','851121','851122','851231','851232','851233','851234','851235',
'851236','851241','851242','851243','851244','851311','851312','851313','851314','851315','851316',
'851322','851323','851324','851325','851326','851327','851411','851421','851432','851433','851434',
'851435','851441','851511','851512','851521','851531','851541','851551','851561','851611','851612',
'851613','851614','851615','851621','851631','851641','851711','851721','851731','851741','851751',
'851761','851771','851781','851811','851821','851911','851921','851931','851941','852021','852031',
'852032','852033','852041','852051','852052','852111','852112','852121','852131','852141','852151',
'852211','852221','852222','852223','852224','852225','852226','852241','852242','852243','852244',
'852311','857221','857231','857232','857233','857234','857235','857241','857242','857243','857244',
'857251','857321','857322','857323','857331','857332','857333','857334','857335','857336','857341',
'857342','857343','857344','857411','857421','857431','858811']
#概念
HY_GN = ['GN001','GN028','GN030','GN031','GN032','GN034','GN035','GN036','GN039','GN040','GN041','GN045','GN046',
'GN050','GN057','GN062','GN069','GN074','GN075','GN076','GN077','GN079','GN080','GN081','GN086','GN087',
'GN088','GN089','GN090','GN091','GN092','GN093','GN096','GN098','GN099','GN100','GN101','GN103','GN104',
'GN106','GN107','GN109','GN110','GN111','GN112','GN113','GN114','GN115','GN116','GN119','GN121','GN123',
'GN124','GN125','GN126','GN127','GN128','GN129','GN130','GN131','GN132','GN133','GN134','GN135','GN136',
'GN137','GN138','GN139','GN140','GN141','GN142','GN144','GN145','GN146','GN148','GN149','GN151','GN152',
'GN153','GN154','GN155','GN156','GN157','GN158','GN159','GN160','GN161','GN162','GN163','GN164','GN165',
'GN166','GN167','GN168','GN169','GN170','GN171','GN172','GN173','GN174','GN175','GN176','GN177','GN178',
'GN179','GN180','GN181','GN182','GN183','GN184','GN185','GN186','GN187','GN188','GN189','GN190','GN191',
'GN192','GN193','GN194','GN195','GN196','GN197','GN198','GN199','GN200','GN201','GN202','GN203','GN204',
'GN205','GN206','GN207','GN208','GN209','GN210','GN211','GN212','GN213','GN214','GN215','GN216','GN217',
'GN218','GN219','GN220','GN221','GN222','GN223','GN224','GN225','GN226','GN227','GN228','GN229','GN230']
qj_count = 10 #从当天向前取几天的数据平均
past_q = ['2018q1','2017q4','2017q3','2017q2']
stocks_for_pe = get_index_stocks('000300.XSHG')
def get_industry_pe(industry_codes):
list_industry_code = []
list_pe_mean = []
list_pe1_mean = []
for i in industry_codes:
stocks = get_industry_stocks(i)
q = query( valuation.code,valuation.day, valuation.market_cap, valuation.pe_ratio, valuation.capitalization,
income.np_parent_company_owners,indicator.eps,income.net_profit
).filter(valuation.code.in_(stocks))
df = get_fundamentals(q)
df.index = df['code']
close_price = history(count = qj_count, field='close',security_list=stocks).mean()
close_price = pd.DataFrame(close_price,columns = ['close_price'])
#计算过期四个季度的财务数据
for sd in past_q:
df_past = get_fundamentals(q,statDate=sd)
df_past.index = df_past['code']
df[sd] = df_past['np_parent_company_owners']
df = pd.merge(close_price,df, left_index=True,right_index=True)
df['new_market_cap'] = (df['close_price']*df['capitalization'])/(10000)
df['pe1'] = df['close_price']/(df['eps']*4)
df = df.dropna(axis=0)
df_profit = df.loc[:,past_q]
df_profit.fillna(method='ffill',axis=1)
df['profit_sum'] = df_profit.sum(axis=1)
df['new_pe'] = (df['new_market_cap']*(1e8))/df['profit_sum']
#df['pe_change'] = df['pe_ratio']- df['new_pe']
#df = df.sort_values(by = 'new_pe', ascending = True)
index = df.index
#去除PE为负和过大的股票,负值会影响平均PE的结果
for j in index:
if ((df['new_pe'].loc[j] < 0)|(df['new_pe'].loc[j] > 200)|(df['pe1'].loc[j] > 200)):
df.drop(j,axis=0,inplace = True)
#查看单个行业
if i == '801192':
print('行业代码：%s'%i)
print('行业股票代码:%s'%stocks)
pe = df['new_pe'].mean()
print('行业PE：%.2f'%pe)
print(df)
print(df_profit)
pe_mean = df['new_pe'].mean()
pe1_mean = df['pe1'].mean()
list_industry_code.append(i)
list_pe_mean.append(pe_mean)
list_pe1_mean.append(pe1_mean)
#print('%s pe mean: %.2f' %(i, pe_mean))
#print(df)
#print(list_industry_code)
industry_pe = pd.DataFrame(index = list_industry_code, data=list_pe_mean,columns=['pe'])
industry_pe_sort = industry_pe.sort_values(by='pe',ascending=True)
industry_pe1 = pd.DataFrame(index = list_industry_code, data=list_pe1_mean,columns=['pe1'])
industry_pe1_sort = industry_pe1.sort_values(by='pe1',ascending=True)
industry_pe = pd.concat([industry_pe,industry_pe1],axis = 1)
print(industry_pe.sort_values(by='pe',ascending=True))
'''
个股市盈率计算
'''
def get_stock_pe(stocks):
q = query( valuation.code,valuation.day, valuation.market_cap, valuation.pe_ratio, valuation.capitalization,
income.np_parent_company_owners,indicator.eps,income.net_profit
).filter(valuation.code.in_(stocks))
df = get_fundamentals(q)
df.index = df['code']
close_price = history(count = qj_count, field='close',security_list=stocks).mean()
close_price = pd.DataFrame(close_price,columns = ['close_price'])
#计算过期四个季度的财务数据
for sd in past_q:
df_past = get_fundamentals(q,statDate=sd)
df_past.index = df_past['code']
df[sd] = df_past['np_parent_company_owners']
df = pd.merge(close_price,df, left_index=True,right_index=True)
df['new_market_cap'] = (df['close_price']*df['capitalization'])/(10000)
df['pe1'] = df['close_price']/(df['eps']*4)
df = df.dropna(axis=0)
df_profit = df.loc[:,past_q]
df_profit.fillna(method='ffill',axis=1)
df['profit_sum'] = df_profit.sum(axis=1)
df['new_pe'] = (df['new_market_cap']*(1e8))/df['profit_sum']
index = df.index
#去除PE为负和过大的股票,负值会影响平均PE的结果
for j in index:
if ((df['new_pe'].loc[j] < 0)|(df['new_pe'].loc[j] > 200)|(df['pe1'].loc[j] > 200)):
df.drop(j,axis=0,inplace = True)
df_pe = df.loc[:,['new_pe','pe1','pe_ratio']].sort_values(by='new_pe')
print(df_pe)
get_stock_pe(stocks_for_pe)
#get_industry_pe(HY_JK2)
new_pe pe1 pe_ratio 600015.XSHG 4.719528 5.152903 4.7692 600016.XSHG 4.929376 3.474969 5.0662 601166.XSHG 5.073355 4.168726 5.1765 600000.XSHG 5.104780 4.826118 5.2084 002146.XSHE 5.794492 14.241492 5.6126 600919.XSHG 5.835985 5.393170 6.0833 601818.XSHG 5.906820 5.224768 6.0331 000069.XSHE 5.965094 11.985725 5.7838 601328.XSHG 5.995122 5.296673 6.2242 601988.XSHG 6.044115 5.387387 6.3170 601288.XSHG 6.060792 4.693031 6.2858 600383.XSHG 6.232594 10.057732 6.1393 601009.XSHG 6.375755 5.579071 6.4749 000898.XSHE 6.428185 6.190855 6.4601 601668.XSHG 6.454049 4.813076 6.3590 000001.XSHE 6.457299 5.769331 6.5782 601398.XSHG 6.495245 5.956581 6.8183 601939.XSHG 6.584878 5.488652 6.9192 601998.XSHG 6.587789 5.867860 6.6725 601169.XSHG 6.605728 5.444993 6.6733 000625.XSHE 6.623577 7.290373 6.5758 600068.XSHG 6.797976 10.533435 6.6401 600376.XSHG 6.813262 13.101489 6.6533 601186.XSHG 6.855928 8.314484 6.8142 600153.XSHG 7.079494 10.626829 6.9625 601225.XSHG 7.097087 6.630588 7.4597 000402.XSHE 7.520015 10.349154 7.1553 601229.XSHG 7.573691 6.823869 7.6274 600516.XSHG 7.726706 5.548107 7.9193 600340.XSHG 7.791812 7.932872 7.9006 ... ... ... ... 601857.XSHG 50.571940 33.909910 51.1228 600038.XSHG 51.111688 82.264387 52.2983 600893.XSHG 51.636881 -222.451172 54.1780 002500.XSHE 52.557552 53.387097 53.5103 603288.XSHG 52.759486 41.200314 55.2139 002797.XSHE 54.021650 82.842640 52.9624 600118.XSHG 54.043207 92.554134 55.4024 600867.XSHG 54.178246 37.103125 56.1723 600339.XSHG 56.719236 -202.264151 55.5552 300024.XSHE 60.776645 108.698718 61.2892 300003.XSHE 64.494262 51.592244 64.6338 600570.XSHG 65.281073 138.437500 63.2510 002153.XSHE 72.168671 114.142743 73.6384 000839.XSHE 73.487588 102.500000 73.7392 600436.XSHG 74.077287 51.496762 75.7766 600583.XSHG 75.675894 -15.806748 76.0724 300059.XSHE 79.051281 45.616097 81.4026 600276.XSHG 80.477397 54.672584 79.3631 002065.XSHE 84.068263 51.857477 88.5378 600489.XSHG 90.690284 102.068452 91.4968 600111.XSHG 92.566003 92.550000 90.0156 300015.XSHE 93.206089 57.617388 94.2654 600588.XSHG 100.178719 -80.172999 100.8781 600050.XSHG 102.690357 28.636905 103.5228 601989.XSHG 109.509694 83.250000 110.1674 002044.XSHE 111.583998 -146.878307 109.2275 300122.XSHE 115.609112 70.295749 123.2951 601958.XSHG 117.811361 76.620603 117.8307 002230.XSHE 148.191654 133.483051 150.2991 600100.XSHG 173.800728 -28.849206 178.3031 [279 rows x 3 columns]
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