def cix_one(item, weight_range):
value = 0
cix_data = {}
try:
# 1 PE
index = Index.objects.get(name='沪深300', date=item.date)
print(index)
avg_pe = 13.58
# pe = interp(index.pe, [8.0159, 50], [0, 50])
pe = interp(index.pe, [avg_pe*0.6, avg_pe*2], [0, 50])
# print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
value += pe
cix_data.update({'pe': pe})
# 2 破净率
min_low_pb = 0.02
max_low_pb = 0.15
broken_net_ratio = item.broken_net_ratio
pb = interp(-broken_net_ratio, [-max_low_pb, min_low_pb], weight_range)
value += pb
cix_data.update({'broken_net': pb})
# 3 AH premium index
try:
avg_ah = 120
equity = Equity.objects.get(code='HKHSAHP', date=item.date)
ah = interp(equity.close, [avg_ah*0.7, avg_ah*1.3], weight_range)
value += ah
cix_data.update({'ah': ah})
except:
print('error')
# 4 GDP rate
print(item.gdp)
if item.gdp:
gdp = interp(item.gdp, [0.4, 1], weight_range)
value += gdp
cix_data.update({'gdp': gdp})
# 5 百元股 [0,3.6%]
g100_ratio = item.over_100_ratio
if g100_ratio:
high = interp(g100_ratio, [0, 0.036], weight_range)
value += high
cix_data.update({'over_100': high})
# 8 NHNL
n = interp(item.nhnl, [-1000, 1000], weight_range)
value += n
cix_data.update({'nhnl': n})
print(cix_data)
Market.objects(date=item.date).update_one(cix=value, cix_data=cix_data, upsert=True)
except DoesNotExist as e:
import sys, traceback
traceback.print_exc(file=sys.stdout)
def cix():
weight_range = [0, 10]
items = Market.objects().order_by('date')
for item in items:
value = 0
cix_data = {}
try:
index = Index.objects.get(name='沪深A股', date=item.date)
print(index)
pe = interp(index.pe, [10, 20], [0, 50])
# print('min_pe:{} max_pe:{} latest_pe:{} pe:{}'.format(min_pe, max_pe, latest_pe, pe))
value += pe
cix_data.update({'pe': pe})
min_low_pb = 0.02
max_low_pb = 0.15
broken_net_ratio = item.broken_net_ratio
pb = interp(-broken_net_ratio, [-max_low_pb, min_low_pb], weight_range)
value += pb
cix_data.update({'broken_net': pb})
# 3 AH premium index
equity = Equity.objects.get(code='HKHSAHP', date=item.date)
ah = interp(equity.close, [100, 130], weight_range)
value += ah
cix_data.update({'ah': ah})
# 4 GDP rate
print(item.gdp)
if item.gdp:
gdp = interp(item.gdp, [0.4, 1], weight_range)
value += gdp
cix_data.update({'gdp': gdp})
# 5 百元股 [0,3.6%]
g100_ratio = item.over_100_ratio
if g100_ratio:
high = interp(g100_ratio, [0, 0.036], weight_range)
value += high
cix_data.update({'over_100': high})
# 8 NHNL
n = interp(item.nhnl, [-1000, 1000], weight_range)
value += n
cix_data.update({'nhnl': n})
print(cix_data)
Market.objects(date=item.date).update_one(cix=value, cix_data=cix_data, upsert=True)
except DoesNotExist as e:
continue
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
data = Market.objects().order_by('date')
print(data)
# df = DataFrame(list(data))
# print df
# max_ah = df['value'].max()
# min_ah = df['value'].min()
# avg_ah = df['value'].mean()
# print('PE max:{} min:{} average:{} median:{}'.format(max_ah, min_ah, avg_ah))
serializer = MarketListSerializer({'items': data})
# content = JSONRenderer().render(serializer.data)
# print '**********content:{}'.format(content)
# json_output = json.loads(content)
# print '****json:{}'.format(json_output)
result = get_market_result(serializer)
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
data = Market.objects().order_by('date')
print(data)
# df = DataFrame(list(data))
# print df
# max_ah = df['value'].max()
# min_ah = df['value'].min()
# avg_ah = df['value'].mean()
# print('PE max:{} min:{} average:{} median:{}'.format(max_ah, min_ah, avg_ah))
serializer = MarketListSerializer({'items': data})
# content = JSONRenderer().render(serializer.data)
# print '**********content:{}'.format(content)
# json_output = json.loads(content)
# print '****json:{}'.format(json_output)
result = get_market_result(serializer)
response = Response(result, status=status.HTTP_200_OK)
return get_response_cors(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 cix():
weight_range = [0, 10]
items = Market.objects().order_by('date')
for item in items:
cix_one(item, weight_range)
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)