def crawl_DIV_type(self, year, stocktype):
url = 'https://mops.twse.com.tw/server-java/t05st09sub'
form_data = {
'encodeURIComponent': 1,
'step': 1,
'TYPEK': stocktype,
'YEAR': common.year_CE2RC(year),
'first': '',
'qryType': 2,
}
# 拆解內容
table_array = common.crawl_data2text(url, form_data,
'big5').split('<table')
dfDIV = pd.DataFrame()
for table in table_array:
if '公司代號' in table:
tr_array = table.split('<tr')
for tr in tr_array:
td_array = tr.split('<td')
if len(td_array) > 15:
# 公司代號
ticker = common.col_clear(
td_array[1]).split('-')[0].strip()
# 所屬年度
intyr, vaild = common.TryParse(
'int',
common.col_clear(td_array[3]).split('年')[0])
yr = common.year_RC2CE(intyr)
# 現金股利
CD, vaild = common.TryParse(
'float', common.col_clear(td_array[12]))
# 股票股利
SD, vaild = common.TryParse(
'float', common.col_clear(td_array[15]))
# 判斷是否有該公司當年度資料，更新/新增
index = (ticker, yr)
if len(dfDIV.index) > 0 and index in dfDIV.index:
data = dfDIV.loc[index]
data[0] = data[0] + CD
data[1] = data[1] + SD
else:
df = pd.DataFrame(data=[[CD, SD]],
index=pd.MultiIndex.from_tuples(
[index]),
columns=['CD', 'SD'])
df.index.set_names(['Ticker', 'yr'], inplace=True)
dfDIV = dfDIV.append(df)
return dfDIV
def crawl_FSA_type(self, year, stocktype):
url = 'https://mops.twse.com.tw/mops/web/ajax_t51sb02'
form_data = {
'encodeURIComponent': 1,
'run': 'Y',
'step': 1,
'TYPEK': stocktype,
'year': common.year_CE2RC(year),
'isnew': '',
'firstin': 1,
'off': 1,
'ifrs': 'Y',
}
# 拆解內容
table_array = common.crawl_data2text(url, form_data).split('<table')
dfFSA = pd.DataFrame()
if len(table_array) < 3:
return dfFSA
tr_array = table_array[3].split('<tr')
for tr in tr_array:
td_array = tr.split('<td')
if len(td_array) > 15:
# 公司代號
ticker = common.col_clear(td_array[1]).split('-')[0].strip()
# 負債占資產比率
DR, vaild = common.TryParse('float',
common.col_clear(td_array[3]))
# 長期資金佔不動產廠房及設備比率
LER, vaild = common.TryParse('float',
common.col_clear(td_array[4]))
# 流動比率
CR, vaild = common.TryParse('float',
common.col_clear(td_array[5]))
# 速動比率
UR, vaild = common.TryParse('float',
common.col_clear(td_array[6]))
# 利息保障倍數
IPM, vaild = common.TryParse('float',
common.col_clear(td_array[7]))
# 應收款項周轉率
ARTR, vaild = common.TryParse('float',
common.col_clear(td_array[8]))
# 平均收現日數
ACCD, vaild = common.TryParse('float',
common.col_clear(td_array[9]))
# 存貨週轉率(次)
ITR, vaild = common.TryParse('float',
common.col_clear(td_array[10]))
# 平均銷貨日數
ASD, vaild = common.TryParse('float',
common.col_clear(td_array[11]))
# 不動產廠房及設備週轉率(次)
PETR, vaild = common.TryParse('float',
common.col_clear(td_array[12]))
# 總資產週轉率(次)
TATR, vaild = common.TryParse('float',
common.col_clear(td_array[13]))
# 資產報酬率(%)
ROA, vaild = common.TryParse('float',
common.col_clear(td_array[14]))
# 權益報酬率(%)
ROE, vaild = common.TryParse('float',
common.col_clear(td_array[15]))
# 稅前純益佔實收資本比率(%)
NPBT2PCR, vaild = common.TryParse(
'float', common.col_clear(td_array[16]))
# 純益率(%)
NPR, vaild = common.TryParse('float',
common.col_clear(td_array[17]))
# 每股盈餘(元)
EPS, vaild = common.TryParse('float',
common.col_clear(td_array[18]))
# 現金流量比率(%)
CFR, vaild = common.TryParse('float',
common.col_clear(td_array[19]))
# 現金流量允當比率(%)
CFAR, vaild = common.TryParse('float',
common.col_clear(td_array[20]))
# 現金再投資比率(%)
CRR, vaild = common.TryParse('float',
common.col_clear(td_array[21]))
# 判斷是否有該公司當年度資料，更新/新增
index = (ticker, common.year_RC2CE(year))
data = [
DR, LER, CR, UR, IPM, ARTR, ACCD, ITR, ASD, PETR, TATR,
ROA, ROE, NPBT2PCR, NPR, EPS, CFR, CFAR, CRR
]
df = pd.DataFrame(data=[data],
index=pd.MultiIndex.from_tuples([index]),
columns=[
'DR', 'LER', 'CR', 'UR', 'IPM', 'ARTR',
'ACCD', 'ITR', 'ASD', 'PETR', 'TATR',
'ROA', 'ROE', 'NPBT2PCR', 'NPR', 'EPS',
'CFR', 'CFAR', 'CRR'
])
df.index.set_names(['Ticker', 'yr'], inplace=True)
dfFSA = dfFSA.append(df)
return dfFSA
def crawl_BS_type(self, year, season, stocktype):
url = 'https://mops.twse.com.tw/mops/web/ajax_t163sb05'
form_data = {
'encodeURIComponent': 1,
'step': 1,
'firstin': 1,
'off': 1,
'isQuery': 'Y',
'TYPEK': stocktype,
'year': common.year_CE2RC(year),
'season': season
}
# 拆解內容
table_array = common.crawl_data2text(url, form_data).split('<table')
dfBS = pd.DataFrame()
dtTitle = {
'TA': ['資產總額', '資產總計'],
'TL': ['負債總計', '負債總額'],
'TE': ['權益總計', '權益總額'],
'RNP': ['每股參考淨值'],
'CA': ['流動資產'],
'NCA': ['非流動資產'],
'CL': ['流動負債'],
'NCL': ['非流動負債']
}
for table in table_array:
if '代號</th>' in table:
tr_array = table.split('<tr')
dtIndex = {
'TA': -1,
'TL': -1,
'TE': -1,
'RNP': -1,
'CA': -1,
'NCA': -1,
'CL': -1,
'NCL': -1,
}
for tr in tr_array:
if '<th' in tr:
th_array = tr.split('<th')
for thIndex in range(1, len(th_array)):
title = common.col_clear(th_array[thIndex]).strip()
for key in dtTitle.keys():
if title in dtTitle[key]:
dtIndex[key] = thIndex
continue
td_array = tr.split('<td')
if len(td_array) > 1:
#公司代號, 年, 季
ticker = common.col_clear(td_array[1])
index = (ticker, common.year_RC2CE(year), season)
dtData = {
'TA': 0,
'TL': 0,
'TE': 0,
'RNP': 0,
'CA': 0,
'NCA': 0,
'CL': 0,
'NCL': 0,
}
for key in dtIndex.keys():
if dtIndex[key] >= 0:
val, vaild = common.TryParse(
'float',
common.col_clear(td_array[dtIndex[key]]))
dtData[key] = val
data = [
dtData['TA'], dtData['TL'], dtData['TE'],
dtData['RNP'], dtData['CA'], dtData['NCA'],
dtData['CL'], dtData['NCL']
]
df = pd.DataFrame(
data=[data],
index=pd.MultiIndex.from_tuples([index]),
columns=[
'TA', 'TL', 'TE', 'RNper', 'CA', 'NCA', 'CL',
'NCL'
])
df.index.set_names(['Ticker', 'yr', 'qtr'],
inplace=True)
dfBS = dfBS.append(df)
return dfBS
def crawl_SCI_type(self, year, season, stocktype):
url = 'https://mops.twse.com.tw/mops/web/ajax_t163sb04'
form_data = {
'encodeURIComponent': 1,
'step': 1,
'firstin': 1,
'TYPEK': stocktype,
'code': '',
'year': common.year_CE2RC(year),
'season': season
}
# 拆解內容
table_array = common.crawl_data2text(url, form_data).split('<table')
dfcomprehensiveIncome = pd.DataFrame()
dtTitle = {
'Rev1': ['利息淨收益', '營業收入', '淨收益', '收益', '收入'],
'Rev2': ['利息以外淨損益'],
'GP': ['營業毛利（毛損）'],
'OP': ['營業利益（損失）', '營業利益'],
'NPBT':
['繼續營業單位稅前淨利（淨損）', '稅前淨利（淨損）', '繼續營業單位稅前損益', '繼續營業單位稅前純益（純損）'],
'NPAT': ['本期稅後淨利（淨損）', '本期淨利（淨損）'],
'NPPC': ['淨利（損）歸屬於母公司業主', '淨利（淨損）歸屬於母公司業主'],
'EPS': ['基本每股盈餘（元）']
}
for table in table_array:
if '代號</th>' in table:
tr_array = table.split('<tr')
dtIndex = {
'Rev1': -1,
'Rev2': -1,
'GP': -1,
'OP': -1,
'NPBT': -1,
'NPAT': -1,
'NPPC': -1,
'EPS': -1
}
for tr in tr_array:
if '<th' in tr:
th_array = tr.split('<th')
for thIndex in range(1, len(th_array)):
title = common.col_clear(th_array[thIndex]).strip()
for key in dtTitle.keys():
if title in dtTitle[key]:
dtIndex[key] = thIndex
continue
td_array = tr.split('<td')
if len(td_array) > 1:
#公司代號, 年, 季
ticker = common.col_clear(td_array[1])
index = (ticker, common.year_RC2CE(year), season)
if index not in dfcomprehensiveIncome.index:
dtData = {
'Rev1': 0,
'Rev2': 0,
'GP': 0,
'OP': 0,
'NPBT': 0,
'NPAT': 0,
'NPPC': 0,
'EPS': 0
}
for key in dtIndex.keys():
if dtIndex[key] >= 0:
val, vaild = common.TryParse(
'float',
common.col_clear(
td_array[dtIndex[key]]))
dtData[key] = val
data = [
dtData['Rev1'] + dtData['Rev2'], dtData['GP'],
dtData['OP'] if dtData['OP'] > 0 else
dtData['NPBT'], dtData['NPBT'], dtData['NPAT'],
dtData['NPPC'], dtData['EPS']
]
df = pd.DataFrame(
data=[data],
index=pd.MultiIndex.from_tuples([index]),
columns=[
'Rev', 'GP', 'OP', 'NPBT', 'NPAT', 'NPPC',
'EPS'
])
df['GM'] = df['GP'] / df['Rev']
df.index.set_names(['Ticker', 'yr', 'qtr'],
inplace=True)
dfcomprehensiveIncome = dfcomprehensiveIncome.append(
df)
return dfcomprehensiveIncome
def asFloat(x):
val = x.replace(',', '')
fval, vaild = common.TryParse('float', val)
return fval if vaild else vaild
def get_TWValueScore(self):
dfValueStockScore = pd.DataFrame()
data = []
index = []
for code in self.ComInfo.index:
try:
if code not in self.FSA.index.get_level_values(0):
continue
#今年, 去年
thisYYYY = self.FSA.loc[(code,)].index.max()
lastYYYY = thisYYYY-1
#當月, 前月, 去年同月
thisYYMM = self.REV.loc[(code, )].index.max()
thisYY = int(thisYYMM/100)
thisMM = int(thisYYMM%100)
strlastYYMM = arrow.get(thisYY, thisMM, 1).shift(months=-1).format('YYYYMM')
lastYYMM, vaild = common.TryParse('int',strlastYYMM)
lastYAYYMM = (thisYY-1)*100+thisMM
#當季, 上季, 去年同季
arrthisSSNYYYY = self.SCI.loc[(code,)].index.max()
thisSSNYYYY, thisSSN = arrthisSSNYYYY
lastSSNYYYY = thisSSNYYYY if thisSSN>1 else thisSSNYYYY-1
lastSSN = thisSSN-1 if thisSSN>1 else 4
lastYASSNYYYY = thisSSNYYYY-1
lastYASSNMM = thisSSN
#前日
currDate = self.DQ.loc[(code,)].index.max()
#1.月營收月增率>上月=5
MonthMoM = self.REV.loc[(code, thisYYMM), 'RevMcLM'].values[0]
score1 = 5 if MonthMoM>0 else 0
#2.月營收年增率>去年同期=5
MonthYAYoY = self.REV.loc[(code, thisYYMM), 'RevMcLYM'].values[0]
score2 = 5 if MonthYAYoY>0 else 0
#3.累計營收年增率>去年同期=10
CumYAYoY = self.REV.loc[(code, thisYYMM), 'RevYCml2LYCml'].values[0]
score3 = 10 if CumYAYoY>0 else 0
#4.毛利率季增率>上季=5
thisSSNGM = self.SCI.loc[(code, thisSSNYYYY, thisSSN)]['GM']
lastSSNGM = self.SCI.loc[(code, lastSSNYYYY, lastSSN)]['GM']
GrossMarginQoQ = 0 if lastSSNGM == 0 else (thisSSNGM/lastSSNGM)-1
score4 = 5 if GrossMarginQoQ>0 else 0
#5.毛利率年增率>去年同季=5
thisYASSNGP = self.SCI.loc[(code, thisSSNYYYY, list(range(1, thisSSN+1)))]['GP'].sum()
lastYASSNGP = self.SCI.loc[(code, lastYASSNYYYY, list(range(1, lastYASSNMM+1)))]['GP'].sum()
GrossMarginYoY =0 if lastYASSNGP == 0 else (thisYASSNGP/lastYASSNGP)-1
score5 = 5 if GrossMarginYoY>0 else 0
#6.OP季增率>上季=5
thisSSNOP = self.SCI.loc[(code, thisSSNYYYY, thisSSN)]['OP']
lastSSNOP = self.SCI.loc[(code, lastSSNYYYY, lastSSN)]['OP']
OperatingProfitQoQ = 0 if lastSSNOP == 0 else (thisSSNOP/lastSSNOP)-1
score6 = 5 if OperatingProfitQoQ>0 else 0
#7.OP年增率>去年同季=5
thisYASSNOP = self.SCI.loc[(code, thisSSNYYYY, list(range(1, thisSSN+1)))]['OP'].sum()
lastYASSNOP = self.SCI.loc[(code, lastYASSNYYYY, list(range(1, lastYASSNMM+1)))]['OP'].sum()
OperatingProfitYoY = 0 if lastYASSNOP == 0 else (thisYASSNOP/lastYASSNOP)-1
score7 = 5 if OperatingProfitYoY>0 else 0
#8.5年營業活動現金流量>0=5
ls5YCASHO = self.FSA.loc[(code, list(range(thisYYYY-4, thisYYYY+1))),'CFR']*self.BS.loc[(code, list(range(thisYYYY-4, thisYYYY+1)), 4)]['CL']
score8 = 0 if False in list(ls5YCASHO>0) else 5
#9.5年OP>0=5
ls5YOCF = self.SCI.loc[(code, list(range(thisYYYY-4, thisYYYY+1)), ),'OP'].groupby('yr').sum()
score9 = 0 if False in list(ls5YOCF>0) else 5
#10.5年本期淨利>0=5
ls5NetProfit = self.SCI.loc[(code, list(range(thisYYYY-4, thisYYYY+1)), ),'NPBT'].groupby('yr').sum()
score10 = 0 if False in list(ls5NetProfit>0) else 5
#11.5年CD>0=5
ls5EarnM = self.DIV.loc[(code, list(range(thisYYYY-4, thisYYYY+1))), 'CD']
score11 = 0 if False in list(ls5EarnM>0) else 5
#12.CR>100%=5
CURR = self.FSA.loc[(code, thisYYYY),'CR']
score12 =5 if CURR>100 else 0
#13.負債比率<50%=5
Debt2AssetsRatio = self.FSA.loc[(code, thisYYYY),'DR']
score13 =5 if Debt2AssetsRatio<50 else 0
#14.PER(越低)=15
score14 = -1
PEScore=[40, 30, 25, 20, 15, 12, 10]
try:
PE = float(self.DQ.loc[(code, currDate), 'PER'])
PER = [x<PE for x in PEScore]
for i in range(len(PER)):
if PER[i]:
score14 = i*2.5
break
if score14<0:
score14 = 15
except:
PE = 0
score14 = 0
#15.股價淨值比(越低)=5
CP = float(self.DQ.loc[(code, currDate), 'CP'])
CD = float(self.DIV.loc[(code, thisYYYY), 'CD'])
RNper = float(self.BS.loc[(code, thisSSNYYYY, thisSSN), 'RNper'])
score15 = -1
PBScore=[8.5, 6, 4, 2.5, 1.5, 1]
if RNper == 0:
PB = -1
score15 = 0
else:
PB = CP/RNper
PBR = [x<PB for x in PBScore]
for i in range(len(PBR)):
if PBR[i]:
score15 = i*2.5
break
if score15 < 0:
score15 = 5
#16.CD殖利率(越高)=10
score16 = -1
DividendYieldScore = [0, 1, 2, 2.5, 3, 4, 5, 6, 8, 10]
DividendYield = 0 if CP == 0 else CD/CP
DividendYieldR = [x>DividendYield for x in DividendYieldScore]
for i in range(len(DividendYieldR)):
if DividendYieldR[i]:
score16 = (i-1)*2.5
break
if score16<0:
score16 = 10
score = score1+score2+score3+score4+score5+score6+score7+score8+score9+score10+score11+score12+score13+score14+score15+score16
index.append([code, currDate])
#當日, 當月, 當季, 當年
data.append([thisYYMM, str(thisSSNYYYY)+'/'+str(thisSSN), thisYYYY,
self.ComInfo.loc[code,'Com'],self.ComInfo.loc[code,'IC'],MonthMoM,MonthYAYoY, CumYAYoY, GrossMarginQoQ, GrossMarginYoY, OperatingProfitQoQ, OperatingProfitYoY, CURR, Debt2AssetsRatio, PE, PB, DividendYield, score])
except:
continue
if len(data)>0:
dfValueStockScore = pd.DataFrame(data = data, index = pd.MultiIndex.from_tuples(index),
columns=['thisYYMM', 'thisSSN', 'thisYYYY', 'Com', 'IC', 'MonthMoM', 'MonthYAYoY', 'CumYAYoY', 'GrossMarginQoQ','GrossMarginYoY','OperatingProfitQoQ','OperatingProfitYoY','CURR','Debt2AssetsRatio','PE','PB', 'DividendYield','TWValueScore'])
dfValueStockScore.index.set_names(['Ticker', 'Date'], inplace=True)
return dfValueStockScore