def hs_cei():
# url = 'http://www.hsi.com.hk/HSI-Net/static/revamp/contents/en/dl_centre/reports_stat/monthly/pe/hscei.xls'
url = 'https://www.hsi.com.hk/static/uploads/contents/en/dl_centre/monthly/pe/hscei.xls'
# r = requests.get(url)
# file_contents = io.BytesIO(r.content)
# book = xlrd.open_workbook(file_contents=file_contents.read())
book = get_excel_book(url)
# print(book)
name = 'HSCEI'
for sheet in range(book.nsheets):
sh = book.sheet_by_index(sheet)
for rx in range(sh.nrows):
row = sh.row(rx)
# df = DataFrame(row)
# print(df)
# print(row)
date = row[0].value
pe = row[1].value
# print(type(pe))
if date and pe and type(pe) == float:
py_date = xlrd.xldate.xldate_as_datetime(date, book.datemode)
# print(py_date)
date = str(py_date)
print(pd.to_datetime(date))
Index.objects(name=name, date=date).update_one(name=name, date=date, pe=pe, upsert=True)
def read_index2(code='000905'):
url = 'http://www.csindex.com.cn/uploads/file/autofile/perf/{}perf.xls'.format(code)
book = get_excel_book(url)
# print(book)
if code == '000300':
name = '沪深300'
elif code == '000905':
name = '中证500'
elif code == '000016':
name = '上证50'
for sheet in range(book.nsheets):
sh = book.sheet_by_index(sheet)
for rx in range(sh.nrows):
row = sh.row(rx)
df = DataFrame(row)
# print(df)
print(row)
print(len(row))
if len(row) > 15:
date = row[0].value
pe1 = row[15].value
pe2 = row[16].value
dividend_yield_ratio1 = row[17].value
dividend_yield_ratio2 = row[18].value
turnover = row[13].value
# # print(type(pe))
if date and turnover and pe1 and type(pe1) == float:
py_date = xlrd.xldate.xldate_as_datetime(date, book.datemode)
print(py_date)
date = str(py_date)
print(pd.to_datetime(date))
Index.objects(name=name, date=date).update_one(name=name, date=date, pe=pe1, pe_ttm=pe2,
dividend_yield_ratio=dividend_yield_ratio1,
turnover=turnover,
upsert=True)
def csi_by_type(date='2011-05-04', data_type='zy1'):
# http://115.29.204.48/syl/bk20180202.zip
day = arrow.get(date, date_format).date()
weekday = day.weekday()
# ignore weekend
if weekday == 5 or weekday == 6:
return
url = '{}industry-price-earnings-ratio?date={}&type={}'.format(csi_domain, date, data_type)
print('url***', url)
page = parse(url).getroot()
# result = etree.tostring(page)
# print(result)
xpath = '//table[@class="table table-bg p_table table-bordered table-border mb-20"]'
if data_type == 'zz1' or data_type == 'zz2' or data_type == 'zz3' or data_type == 'zz4':
xpath = '//table[@class="table table-bg p_table table-bordered table-border mb-20"]'
r = page.xpath(xpath)
# print(len(r))
tree = etree.ElementTree(r[0])
# print(etree.tostring(tree))
html_table = etree.tostring(tree)
dfs = pd.read_html(html_table, flavor='lxml')
df = dfs[0]
print(df)
# v1 = df.iloc[3][0]
# print(v1, df.iloc[3][1], df.iloc[3][2])
# print(v1, df.iloc[4][1], df.iloc[4][2])
for index, row in df.iterrows():
v0 = row.iloc[0]
v1 = row.iloc[1]
v2 = row.iloc[2]
# print(index, name, value)
print('index {} v0:{} v1:{} v2:{}***'.format(index, v0, v1, v2))
try:
if data_type == 'zy1':
# 静态市盈率
Index.objects(name=v0, date=day).update_one(name=v0, pe=v1, upsert=True)
elif data_type == 'zy2':
# 滚动市盈率
Index.objects(name=v0, date=day).update_one(name=v0, pe_ttm=v1, upsert=True)
elif data_type == 'zy3':
# 市净率
Index.objects(name=v0, date=day).update_one(name=v0, pb=v1, upsert=True)
elif data_type == 'zy4':
# 股息率
Index.objects(name=v0, date=day).update_one(name=v0, dividend_yield_ratio=v1, upsert=True)
elif data_type == 'zz1':
# 行业静态市盈率
Industry.objects(code=v0, date=day).update_one(code=v0, date=day, name=v1, pe=v2, upsert=True)
elif data_type == 'zz2':
# 行业滚动市盈率
Industry.objects(code=v0, date=day).update_one(code=v0, pe_ttm=v2, upsert=True)
elif data_type == 'zz3':
# 行业市净率
Industry.objects(code=v0, date=day).update_one(code=v0, pb=v2, upsert=True)
elif data_type == 'zz4':
# 行业股息率
Industry.objects(code=v0, date=day).update_one(code=v0, dividend_yield_ratio=v2, upsert=True)
except:
continue
def csi(date='20171228'):
# http://115.29.204.48/syl/bk20180202.zip
day = arrow.get(date,'YYYYMMDD').date()
weekday = day.weekday()
# ignore weekend
if weekday == 5 or weekday == 6:
return
url = 'http://115.29.204.48/syl/bk'+date+'.zip'
r = requests.get(url)
if r.status_code == 404:
return
# create memory file
z = zipfile.ZipFile(io.BytesIO(r.content))
# not extract to disk file here
# z.extractall()
memory_unzip_files = extract_zip(z)
# print(zip_files)
# pandas read_csv not work!
# df = pd.read_csv("bk20171228.csv")
# xls_file = pd.ExcelFile('bk20171228.xls', encoding_override="gb2312")
# xls_file = pd.read_excel('bk20171228.xls', encoding="gb2312")
for name in memory_unzip_files.keys():
book = xlrd.open_workbook(file_contents=memory_unzip_files.get(name), encoding_override="gbk")
print("The number of worksheets is {0}".format(book.nsheets))
print("Worksheet name(s): {0}".format(book.sheet_names()))
for sheet in range(book.nsheets):
sh = book.sheet_by_index(sheet)
print("{0} {1} {2}".format(sh.name, sh.nrows, sh.ncols))
for rx in range(sh.nrows):
row = sh.row(rx)
# print(row)
name = row[0].value
value = row[1].value
print(name, value)
# print(pe.replace('.', '', 1).isdigit(), type(pe))
if value.replace('.', '', 1).isdigit():
if sheet == 0:
# 静态市盈率
Index.objects(name=name, date=day).update_one(name=name, date=day, pe=value, upsert=True)
elif sheet == 1:
# 滚动市盈率
print(Index.objects(name=name, date=day))
Index.objects(name=name, date=day).update_one(name=name, pe_ttm=value, upsert=True)
elif sheet == 2:
# 板块市净率
Index.objects(name=name, date=day).update_one(name=name, pb=value, upsert=True)
elif sheet == 3:
# 板块股息率
Index.objects(name=name, date=day).update_one(name=name, dividend_yield_ratio=value, upsert=True)
def read_index2(code='000905'):
url = 'http://www.csindex.com.cn/uploads/file/autofile/perf/{}perf.xls'.format(
code)
book = get_excel_book(url)
# print(book)
if code == '000300':
name = '沪深300'
elif code == '000905':
name = '中证500'
elif code == '000016':
name = '上证50'
for sheet in range(book.nsheets):
sh = book.sheet_by_index(sheet)
for rx in range(sh.nrows):
row = sh.row(rx)
df = DataFrame(row)
# print(df)
print(row)
print(len(row))
if len(row) > 15:
date = row[0].value
pe1 = row[15].value
pe2 = row[16].value
dividend_yield_ratio1 = row[17].value
dividend_yield_ratio2 = row[18].value
turnover = row[13].value
# # print(type(pe))
if date and pe1 and type(pe1) == float:
py_date = xlrd.xldate.xldate_as_datetime(
date, book.datemode)
print(py_date)
date = str(py_date)
print(pd.to_datetime(date))
Index.objects(name=name, date=date).update_one(
name=name,
date=date,
pe=pe1,
pe_ttm=pe2,
dividend_yield_ratio=dividend_yield_ratio1,
turnover=turnover,
upsert=True)
def get(self, request, *args, **kw):
# Process any get params that you may need
# If you don't need to process get params,
# you can skip this part
name = request.GET.get('code')
items = Index.objects(name=name).order_by('date')
index_col = db.index.find({'name': name})
df = pd.DataFrame(list(index_col))
serializer = IndexListSerializer({'items': items})
# HSCEI index has no pb data
if name == 'HSCEI':
df['pb'] = 0
result = get_result(serializer, df)
response = Response(result, status=status.HTTP_200_OK)
return get_response_cors(response)
def get(self, request, *args, **kw):
# Process any get params that you may need
# If you don't need to process get params,
# you can skip this part
name = request.GET.get('code')
items = Index.objects(name=name).order_by('date')
index_col = db.index.find({'name': name})
df = pd.DataFrame(list(index_col))
serializer = IndexListSerializer({'items': items})
# HSCEI index has no pb data
if name == 'HSCEI':
df['pb'] = 0
result = get_result(serializer, df)
response = Response(result, status=status.HTTP_200_OK)
return get_response_cors(response)
def get(self, request, *args, **kw):
# Process any get params that you may need
# If you don't need to process get params,
# you can skip this part
print('*' * 15)
name = request.GET.get('name')
items = Index.objects(name=name).order_by('date')
index_col = db.index.find({'name': name})
df = pd.DataFrame(list(index_col))
serializer = IndexListSerializer({'items': items})
# print serializer.is_valid()
# print serializer.errors
content = JSONRenderer().render(serializer.data)
print('**********content:{}'.format(content))
json_output = json.loads(content)
print('****json:{}'.format(json_output))
pb_list = []
pe_list = []
for item in json_output.get('items'):
# date = arrow.get(item.get('date'), 'YYYY-MM-DD HH:mm:ss').timestamp
timestamp = arrow.get(item.get('date'),
'YYYY-MM-DD HH:mm:ss').timestamp * 1000
if item.get('pb'):
pb_list.append([timestamp, item.get('pb')])
pe_list.append([timestamp, item.get('pe')])
# HSCEI index has no pb data
if name == 'HSCEI':
df['pb'] = 0
result = {
'PB': pb_list,
'PE': pe_list,
'PB_avg': df['pb'].mean(),
'PE_avg': df['pe'].mean()
}
response = Response(result, status=status.HTTP_200_OK)
return response
def read_market(nh, nl, date):
# 破净率
low_pb = low_pb_ratio()
print(low_pb)
broken_net_ratio = low_pb[0]
broken_net = low_pb[1]
stock_count = low_pb[3]
nh_ratio = float(nh)/stock_count
nl_ratio = float(nl)/stock_count
nhnl = nh - nl
# 跌停板
dt = screen_by_pencentage(-10.11, -9.9)
dt_ratio = dt/stock_count
# 涨停板
zt = screen_by_pencentage(9.9, 10.11)
zt_ratio = zt/stock_count
zdr = zt-dt
print('dtb:{} ztb:{} zdr'.format(dt, zt, zdr))
# 仙股
penny_stocks = screen_by_price(0.1, 1)['count']
penny_stocks_ratio = penny_stocks/stock_count
# 破发率
broken_ipo_count, total_ipo, broken_ipo_rate, broken_list = broken_ipo()
# CIX范围从0到100,由10个指标组成
cix = 0
cix_data = {}
weight_range = [0, 10]
# 1 SH PE
# pe_df = avg_sh_pe('2000-1-31')
# max_pe = pe_df['PE'].max()
# min_pe = pe_df['PE'].min()
# # get latest PE DF by tail()
# # latest_pe_df = pe_df.tail(1)
# # latest_pe = latest_pe_df.iloc[0][1]
# # print 'latest PE:{}'.format(latest_pe)
# latest_sh = Index.objects(name='上海A股').order_by('-date').first()
# print('items***{}'.format(latest_sh))
# pe = interp(latest_sh.pe, [min_pe, max_pe], weight_range)
# # print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
# cix += pe
# 1 替换为沪深A股PE TODO
max_pe = 30
min_pe = 12
# get latest PE DF by tail()
# latest_pe_df = pe_df.tail(1)
# latest_pe = latest_pe_df.iloc[0][1]
# print 'latest PE:{}'.format(latest_pe)
latest_sh = Index.objects(name='沪深A股').order_by('-date').first()
print('items***{}'.format(latest_sh))
pe = interp(latest_sh.pe, [min_pe, max_pe], [0, 50])
# print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
cix += pe
cix_data.update({'pe': pe})
# 2 破净率
min_low_pb = 0.02
max_low_pb = 0.15
pb = interp(-broken_net_ratio, [-max_low_pb, min_low_pb], weight_range)
cix += pb
cix_data.update({'broken_net': pb})
# 3 AH premium index
ah_now = xueqiu('HKHSAHP')
ah_current = ah_now.current
ah = interp(ah_current, [100, 130], weight_range)
cix += ah
cix_data.update({'ah': ah})
# 4 GDP rate
rate = gdp_rate()
gdp = interp(rate, [0.4, 1], weight_range)
cix += gdp
cix_data.update({'gdp': gdp})
# 5 百元股 [0,3.6%]
high_price = high_price_ratio()
g100 = high_price[0]
g100_ratio = high_price[1]
high = interp(g100_ratio, [0, 0.036], weight_range)
cix += high
cix_data.update({'over_100': high})
# 8 NHNL
n = interp(nhnl, [-1000, 1000], weight_range)
cix += n
cix_data.update({'nhnl': n})
# 9 融资规模及占比 TODO tushare
# 10 社交媒体挖掘 TODO xueqiu
# 5 SH换手率 [1%,3%]
sh = read_index_market('SH000001')
turnover_rate = sh['turnover_rate']
turnover = interp(turnover_rate, [1, 3], weight_range)
# cix += turnover
# 6 涨跌停差额
# 7 TODO 最近一年IPO、可转债涨幅或破发率
# low price
low_price = low_price_ratio()
print('low_price***{}'.format(low_price))
print(cix_data)
# TODO cix 映射到0.5-1.5区间,代表持仓比例
Market.objects(date=get_date(date)).update_one(nh=nh, nl=nl, nhnl=nhnl, nh_ratio=nh_ratio, nl_ratio=nl_ratio,
stock_count=stock_count,
over_100=g100, over_100_ratio=g100_ratio,
penny_stocks=penny_stocks, penny_stocks_ratio=penny_stocks_ratio,
low_price_ratio=low_price,
pe=latest_sh.pe, turnover=turnover_rate,
ah=ah_current, gdp=rate, cix=cix,cix_data=cix_data,
broken_net=broken_net, broken_net_ratio=broken_net_ratio,
broken_net_stocks=low_pb[2],
dt=dt, dt_ratio=dt_ratio, zt=zt, zt_ratio=zt_ratio, zdr=zdr,
ipo=total_ipo, broken_ipo=broken_ipo_count,
broken_ipo_ratio=broken_ipo_rate,broken_ipo_list=broken_list,
upsert=True)
def read_market(nh, nl, date):
# 破净率
low_pb = low_pb_ratio()
print(low_pb)
broken_net_ratio = low_pb[0]
broken_net = low_pb[1]
stock_count = low_pb[3]
nh_ratio = float(nh) / stock_count
nl_ratio = float(nl) / stock_count
# 跌停板
dt = screen_by_pencentage(-10.11, -9.9)
dt_ratio = dt / stock_count
# 涨停板
zt = screen_by_pencentage(9.9, 10.11)
zt_ratio = zt / stock_count
zdr = zt / dt
print('dtb:{} ztb:{} zdr'.format(dt, zt, zdr))
# 仙股
penny_stocks = screen_by_price(0.1, 1)['count']
penny_stocks_ratio = penny_stocks / stock_count
# 破发率
broken_ipo_count, total_ipo, broken_ipo_rate, broken_list = broken_ipo()
cix = 0
weight_range = [0, 10]
# 1 latest SH PE
pe_df = avg_sh_pe('2000-1-31')
max_pe = pe_df['PE'].max()
min_pe = pe_df['PE'].min()
# get latest PE DF by tail()
# latest_pe_df = pe_df.tail(1)
# latest_pe = latest_pe_df.iloc[0][1]
# print 'latest PE:{}'.format(latest_pe)
latest_sh = Index.objects(name='上海A股').order_by('-date').first()
print('items***{}'.format(latest_sh))
pe = interp(latest_sh.pe, [min_pe, max_pe], weight_range)
# print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
cix += pe
# 2 破净率
min_low_pb = 0
max_low_pb = 0.1
pb = interp(-broken_net_ratio, [-max_low_pb, min_low_pb], weight_range)
cix += pb
# 3 AH premium index
ah_now = xueqiu('HKHSAHP')
ah_current = ah_now.current
ah = interp(ah_current, [100, 150], weight_range)
cix += ah
# 4 GDP rate
rate = gdp_rate()
gdp = interp(rate, [0.4, 1], weight_range)
cix += gdp
# 5 百元股 [0,3.6%]
high_price = high_price_ratio()
g100 = high_price[0]
g100_ratio = high_price[1]
high = interp(g100_ratio, [0, 0.036], weight_range)
cix += high
# 5 low price
low_price = low_price_ratio()
print('low_price***{}'.format(low_price))
# 6 SH换手率 [1%,3%]
sh = read_index_market('SH000001')
turnover_rate = sh['turnover_rate']
turnover = interp(turnover_rate, [1, 3], weight_range)
cix += turnover
Market.objects(date=get_date(date)).update_one(
nh=nh,
nl=nl,
nhnl=nh - nl,
nh_ratio=nh_ratio,
nl_ratio=nl_ratio,
stock_count=stock_count,
over_100=g100,
over_100_ratio=g100_ratio,
penny_stocks=penny_stocks,
penny_stocks_ratio=penny_stocks_ratio,
low_price_ratio=low_price,
pe=latest_sh.pe,
turnover=turnover_rate,
ah=ah_current,
gdp=rate,
cix=cix,
broken_net=broken_net,
broken_net_ratio=broken_net_ratio,
broken_net_stocks=low_pb[2],
dt=dt,
dt_ratio=dt_ratio,
zt=zt,
zt_ratio=zt_ratio,
zdr=zdr,
ipo=total_ipo,
broken_ipo=broken_ipo_count,
broken_ipo_ratio=broken_ipo_rate,
broken_ipo_list=broken_list,
upsert=True)
def read_market(nh, nl, date):
# 破净率
low_pb = low_pb_ratio()
print(low_pb)
broken_net_ratio = low_pb[0]
broken_net = low_pb[1]
stock_count = low_pb[3]
nh_ratio = float(nh)/stock_count
nl_ratio = float(nl)/stock_count
nhnl = nh - nl
# 跌停板
dt = screen_by_pencentage(-10.11, -9.9)
dt_ratio = dt/stock_count
# 涨停板
zt = screen_by_pencentage(9.9, 10.11)
zt_ratio = zt/stock_count
zdr = zt-dt
print('dtb:{} ztb:{} zdr'.format(dt, zt, zdr))
# 仙股
penny_stocks = screen_by_price(0.1, 1)['count']
penny_stocks_ratio = penny_stocks/stock_count
# 破发率
broken_ipo_count, total_ipo, broken_ipo_rate, broken_list = broken_ipo()
# CIX范围从0到100,由10个指标组成
cix = 0
cix_data = {}
weight_range = [0, 10]
# 1 SH PE
# pe_df = avg_sh_pe('2000-1-31')
# max_pe = pe_df['PE'].max()
# min_pe = pe_df['PE'].min()
# # get latest PE DF by tail()
# # latest_pe_df = pe_df.tail(1)
# # latest_pe = latest_pe_df.iloc[0][1]
# # print 'latest PE:{}'.format(latest_pe)
# latest_sh = Index.objects(name='上海A股').order_by('-date').first()
# print('items***{}'.format(latest_sh))
# pe = interp(latest_sh.pe, [min_pe, max_pe], weight_range)
# # print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
# cix += pe
# 1 替换为沪深A股PE TODO
max_pe = 30
min_pe = 12
# get latest PE DF by tail()
# latest_pe_df = pe_df.tail(1)
# latest_pe = latest_pe_df.iloc[0][1]
# print 'latest PE:{}'.format(latest_pe)
latest_sh = Index.objects(name='沪深A股').order_by('-date').first()
print('items***{}'.format(latest_sh))
pe = interp(latest_sh.pe, [min_pe, max_pe], [0, 50])
# print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
cix += pe
cix_data.update({'pe': pe})
# 2 破净率
min_low_pb = 0.02
max_low_pb = 0.15
pb = interp(-broken_net_ratio, [-max_low_pb, min_low_pb], weight_range)
cix += pb
cix_data.update({'broken_net': pb})
# 3 AH premium index
ah_now = xueqiu('HKHSAHP')
ah_current = ah_now.current
ah = interp(ah_current, [100, 130], weight_range)
cix += ah
cix_data.update({'ah': ah})
# 4 GDP rate
rate = gdp_rate()
gdp = interp(rate, [0.4, 1], weight_range)
cix += gdp
cix_data.update({'gdp': gdp})
# 5 百元股 [0,3.6%]
high_price = high_price_ratio()
g100 = high_price[0]
g100_ratio = high_price[1]
high = interp(g100_ratio, [0, 0.036], weight_range)
cix += high
cix_data.update({'over_100': high})
# 8 NHNL
n = interp(nhnl, [-1000, 1000], weight_range)
cix += n
cix_data.update({'nhnl': n})
# 9 融资规模及占比 TODO tushare
# 10 社交媒体挖掘 TODO xueqiu
# 5 SH换手率 [1%,3%]
sh = read_index_market('SH000001')
turnover_rate = sh['turnover_rate']
turnover = interp(turnover_rate, [1, 3], weight_range)
# cix += turnover
# 6 涨跌停差额
# 7 TODO 最近一年IPO、可转债涨幅或破发率
# low price
low_price = low_price_ratio()
print('low_price***{}'.format(low_price))
print(cix_data)
# TODO cix 映射到0.5-1.5区间,代表持仓比例
Market.objects(date=get_date(date)).update_one(nh=nh, nl=nl, nhnl=nhnl, nh_ratio=nh_ratio, nl_ratio=nl_ratio,
stock_count=stock_count,
over_100=g100, over_100_ratio=g100_ratio,
penny_stocks=penny_stocks, penny_stocks_ratio=penny_stocks_ratio,
low_price_ratio=low_price,
pe=latest_sh.pe, turnover=turnover_rate,
ah=ah_current, gdp=rate, cix=cix,cix_data=cix_data,
broken_net=broken_net, broken_net_ratio=broken_net_ratio,
broken_net_stocks=low_pb[2],
dt=dt, dt_ratio=dt_ratio, zt=zt, zt_ratio=zt_ratio, zdr=zdr,
ipo=total_ipo, broken_ipo=broken_ipo_count,
broken_ipo_ratio=broken_ipo_rate,broken_ipo_list=broken_list,
upsert=True)
def csi_by_type(date='2011-05-04', data_type='zy1'):
# http://115.29.204.48/syl/bk20180202.zip
day = arrow.get(date, date_format).date()
weekday = day.weekday()
# ignore weekend
if weekday == 5 or weekday == 6:
return
url = '{}industry-price-earnings-ratio?date={}&type={}'.format(
csi_domain, date, data_type)
print('url***', url)
page = parse(url).getroot()
# result = etree.tostring(page)
# print(result)
xpath = '//table[@class="table table-bg p_table table-bordered table-border mb-20"]'
if data_type == 'zz1' or data_type == 'zz2' or data_type == 'zz3' or data_type == 'zz4':
xpath = '//table[@class="table table-bg p_table table-bordered table-border mb-20"]'
r = page.xpath(xpath)
# print(len(r))
tree = etree.ElementTree(r[0])
# print(etree.tostring(tree))
html_table = etree.tostring(tree)
dfs = pd.read_html(html_table, flavor='lxml')
df = dfs[0]
print(df)
# v1 = df.iloc[3][0]
# print(v1, df.iloc[3][1], df.iloc[3][2])
# print(v1, df.iloc[4][1], df.iloc[4][2])
for index, row in df.iterrows():
v0 = row.iloc[0]
v1 = row.iloc[1]
v2 = row.iloc[2]
# print(index, name, value)
print('index {} v0:{} v1:{} v2:{}***'.format(index, v0, v1, v2))
try:
if data_type == 'zy1':
# 静态市盈率
Index.objects(name=v0, date=day).update_one(name=v0,
pe=v1,
upsert=True)
elif data_type == 'zy2':
# 滚动市盈率
Index.objects(name=v0, date=day).update_one(name=v0,
pe_ttm=v1,
upsert=True)
elif data_type == 'zy3':
# 市净率
Index.objects(name=v0, date=day).update_one(name=v0,
pb=v1,
upsert=True)
elif data_type == 'zy4':
# 股息率
Index.objects(name=v0,
date=day).update_one(name=v0,
dividend_yield_ratio=v1,
upsert=True)
elif data_type == 'zz1':
# 行业静态市盈率
Industry.objects(code=v0, date=day).update_one(code=v0,
date=day,
name=v1,
pe=v2,
upsert=True)
elif data_type == 'zz2':
# 行业滚动市盈率
Industry.objects(code=v0, date=day).update_one(code=v0,
pe_ttm=v2,
upsert=True)
elif data_type == 'zz3':
# 行业市净率
Industry.objects(code=v0, date=day).update_one(code=v0,
pb=v2,
upsert=True)
elif data_type == 'zz4':
# 行业股息率
Industry.objects(code=v0,
date=day).update_one(code=v0,
dividend_yield_ratio=v2,
upsert=True)
except:
continue
def csi_by_type(date='2011-05-04', data_type='zy1'):
# http://115.29.204.48/syl/bk20180202.zip
day = arrow.get(date, date_format).date()
weekday = day.weekday()
# ignore weekend
if weekday == 5 or weekday == 6:
return
url = 'http://www.csindex.com.cn/zh-CN/downloads/industry-price-earnings-ratio?date={}&type={}'.format(
date, data_type)
page = parse(url).getroot()
# result = etree.tostring(page)
# print(result)
r = page.xpath(
'//table[@class="table-bg p_table table-bordered table-border mb-20"]')
print(len(r))
tree = etree.ElementTree(r[0])
# print(etree.tostring(tree))
html_table = etree.tostring(tree)
dfs = pd.read_html(html_table, flavor='lxml')
df = dfs[0]
print(df)
# v1 = df.iloc[1][1]
# print(v1)
for index, row in df.iterrows():
name = row.iloc[0]
value = row.iloc[1]
print(index, name, value)
if data_type == 'zy1':
Index.objects(name=name, date=day).update_one(name=name,
pe=value,
upsert=True)
elif data_type == 'zy2':
Index.objects(name=name, date=day).update_one(name=name,
pe_ttm=value,
upsert=True)
elif data_type == 'zy3':
Index.objects(name=name, date=day).update_one(name=name,
pb=value,
upsert=True)
elif data_type == 'zy4':
Index.objects(name=name,
date=day).update_one(name=name,
dividend_yield_ratio=value,
upsert=True)
elif data_type == 'zz1':
# 行业静态市盈率
Industry.objects(code=name, date=day).update_one(code=name,
date=day,
name=name,
pe=value,
upsert=True)
elif data_type == 'zz2':
# 行业滚动市盈率
Industry.objects(code=name, date=day).update_one(code=name,
pe_ttm=value,
upsert=True)
elif data_type == 'zz2':
# 行业市净率
Industry.objects(code=name, date=day).update_one(code=name,
pb=value,
upsert=True)
elif data_type == 'zz2':
# 行业股息率
Industry.objects(code=name,
date=day).update_one(code=name,
dividend_yield_ratio=value,
upsert=True)