def GetLastTradeDate():
end_date = date.today()
count = w.tdayscount(end_date)
if count.Data == 0 or datetime.now().hour <= 15:
return w.tdaysoffset(-1, end_date).Data[0][0].date()
return end_date
def func11():
yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d")
df_industry=pd.read_csv('industry.csv')
last_date_industry = time.strftime("%Y-%m-%d",time.strptime(str(int(df_industry.iloc[-1,0])),"%Y%m%d"))
day_gap_industry = w.tdayscount(last_date_industry, yesterday).Data[0][0]-1
print 11
w_wsd_data={}
stock_ls=[]
for stock in list(df_industry.columns.values)[1:] :
stock_ls.append(stock)
w_wsd_data[stock] = w.wsd(stock, 'close', (w.tdaysoffset(1,last_date_industry).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
print w_wsd_data
f=open('industry.csv','a')
for i in xrange(day_gap_industry):
this_day = w.tdaysoffset(i+1,last_date_industry)
ls = []
this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
for stock in stock_ls:
ls.append(w_wsd_data[stock].Data[0][i])
f.write(this_day_csv)
for stock_data in ls:
f.write(",")
f.write(str(stock_data))
f.write("\n")
f.close()
print 'finish'
def func2():
yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d")
df_openA=pd.read_csv('openA.csv')
last_date_openA = time.strftime("%Y-%m-%d",time.strptime(str(int(df_openA.iloc[-1,0])),"%Y%m%d"))
day_gap_openA = w.tdayscount(last_date_openA, yesterday).Data[0][0]-1
print 2
w_wsd_data={}
stock_ls=[]
for stock in universe:
if stock[0]=='1':
stock = stock + '.SZ'
else:
stock += '.SH'
stock_ls.append(stock)
w_wsd_data[stock] = w.wsd(stock, 'open', (w.tdaysoffset(1,last_date_openA).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
for i in xrange(day_gap_openA):
this_day = w.tdaysoffset(i+1,last_date_openA)
ls = []
this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
for stock in stock_ls:
ls.append(w_wsd_data[stock].Data[0][i])
f=open('openA.csv','a')
f.write(this_day_csv)
for stock_data in ls:
f.write(",")
f.write(str(stock_data))
f.write("\n")
f.close()
print 'finish'
def func9():
yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d")
df_discount_ratio_M = pd.read_csv('discount_ratio_M.csv')
last_date_discount_ratio_M = time.strftime("%Y-%m-%d",time.strptime(str(int(df_discount_ratio_M.iloc[-1,0])),"%Y%m%d"))
day_gap_discount_ratio_M = w.tdayscount(last_date_discount_ratio_M, yesterday).Data[0][0]-1
w_wsd_data={}
print 9
stock_ls=[]
for stock in universe:
if stock[0]=='1':
stock = stock + '.SZ'
else:
stock = stock + '.SH'
mA_code = w.wsd(stock, 'fund_smfcode', yesterday).Data[0][0]
stock_ls.append(mA_code)
w_wsd_data[mA_code] = w.wsd(mA_code, 'anal_tdiscountratio', (w.tdaysoffset(1,last_date_discount_ratio_M).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
for i in xrange(day_gap_discount_ratio_M):
this_day = w.tdaysoffset(i+1,last_date_discount_ratio_M)
ls = []
this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
for stock in stock_ls:
ls.append(w_wsd_data[stock].Data[0][i])
f=open('discount_ratio_M.csv','a')
f.write(this_day_csv)
for stock_data in ls:
f.write(",")
f.write(str(stock_data))
f.write("\n")
f.close()
print 'finish'
def volitility(code, T, numOfYearBefore):
EndDay = datetime.date.today()
BeginDay = EndDay - relativedelta(years=numOfYearBefore)
LogRet = RevisedLogRet(code, BeginDay, EndDay)
days = w.tdayscount(EndDay - relativedelta(months=T), EndDay,
"").Data[0][0]
vol = (LogRet.rolling(window=days, center=False).std() *
np.sqrt(252)).dropna().iloc[-1][0]
return vol
def get_tdays_count(self, begin_date, end_date, style=""):
"""
获取日期间的周期数
输入参数：
begin_date 起始日期
end_date 截止日期
style 日期类型
函数返回：
返回周期数N
返回类型：int
"""
option = {"Days": style}
td = w.tdayscount(begin_date, end_date, **option)
return td.Data[0][0]
def func6():
yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d")
df_closeB=pd.read_csv('closeB.csv')
last_date_closeB = time.strftime("%Y-%m-%d",time.strptime(str(int(df_closeB.iloc[-1,0])),"%Y%m%d"))
day_gap_closeB = w.tdayscount(last_date_closeB, yesterday).Data[0][0]-1
print 6
w_wsd_data={}
stock_ls=[]
for stock in universe:
if stock =='150073':
stock='150075.SZ'
else:
if stock[0]=='1':
stock = str(int(stock[:6])+1) + '.SZ'
else:
stock = str(int(stock[:6])+1) + '.SH'
stock_ls.append(stock)
w_wsd_data[stock] = w.wsd(stock, 'close', (w.tdaysoffset(1,last_date_closeB).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
# print 1
print stock
#if w_wsd_data[stock].Data[0][0]==None:
for i in xrange(day_gap_closeB):
this_day = w.tdaysoffset(i+1,last_date_closeB)
ls = []
this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
for stock in stock_ls:
ls.append(w_wsd_data[stock].Data[0][i])
f=open('closeB.csv','a')
f.write(this_day_csv)
for stock_data in ls:
f.write(",")
f.write(str(stock_data))
f.write("\n")
f.close()
print 'finish'
def tdays_count(begin_date, end_date):
w.start()
result = w.tdayscount(begin_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d"), "").Data[0][0]
return result
def updatePal(palPath=None):
start = time.time()
md.init('18201141877', 'Wqxl7309')
if not w.isconnected():
w.start()
palPath = r'E:\bqfcts\bqfcts\data\Paltest' if palPath is None else palPath
tempFilePath = os.path.join(palPath,'temp_files')
if not os.path.exists(tempFilePath):
os.mkdir(tempFilePath)
matName = 'data_20150701_now.mat'
savedPal = h5py.File(os.path.join(palPath,matName))
# print(read_cell(savedPal,'sec_names'))
nextTrd = dt.datetime.strptime(str(int(savedPal['nexttrd'][0][0])),'%Y%m%d')
nextTrdStr = nextTrd.strftime('%Y-%m-%d')
updateTime = dt.datetime(nextTrd.year,nextTrd.month,nextTrd.day,15,30,0)
if updateTime > dt.datetime.now():
print('not update time yet')
return
else:
availableDateStr = md.get_last_dailybars('SHSE.000001')[0].strtime[:10]
if int(availableDateStr.replace('-','')) <= int(nextTrdStr.replace('-','')):
print('new data not avaliable yet')
return
else:
print('will update from {0} to {1}'.format(nextTrdStr,availableDateStr))
betweenDays = [tdt.strtime[:10] for tdt in md.get_calendar('SHSE',nextTrdStr,availableDateStr)]
if nextTrdStr!=availableDateStr: # 避免同一日期重复
betweenDays.append(availableDateStr)
betweenDaysNumber = [int(tdt.replace('-','')) for tdt in betweenDays]
newDateNum = len(betweenDaysNumber)
# 更新前 先备份数据
backupPath = os.path.join(palPath,'backup')
cpResult = os.system(r'COPY {0} {1} /Y'.format(os.path.join(palPath,matName),os.path.join(backupPath,matName)))
assert cpResult==0,'backup failed'
gmDateFmt = 'yyyy-mm-dd'
# update indice
indiceNames = ['sh','hs300','zz500','sz50']
indiceCodes = ['000001','000300','000905','000016']
symbols = ','.join(['SHSE.{}'.format(sbl) for sbl in indiceCodes])
indiceBars = md.get_dailybars(symbols,nextTrdStr,availableDateStr)
for dumi,idx in enumerate(indiceNames):
bars = indiceBars[dumi::4]
idxret = np.array([bar.close for bar in bars])/np.array([bar.pre_close for bar in bars]) - 1
idxArray = np.array([betweenDaysNumber,
[bar.open for bar in bars],
[bar.high for bar in bars],
[bar.low for bar in bars],
[bar.close for bar in bars],
[bar.volume for bar in bars],
[bar.amount for bar in bars],
idxret
])
# newIndex = np.column_stack([savedPal['index_{}'.format(idx)][:], idxArray])
pd.DataFrame(np.transpose(idxArray)).to_csv(os.path.join(tempFilePath,'index_{}.csv'.format(idx)),index=False,header=False)
# update stock info
nCut = savedPal['N_cut'][0][0] # 6000
nEnd = savedPal['N_end'][0][0] # last end date id ex.6732
stockNames = read_cell(savedPal, 'stockname')
savedStkcdsGM = ['.'.join([stk[-2:]+'SE',stk[:6]]) for stk in stockNames]
savedStkNum = len(stockNames)
listedStkcdsWind = w.wset('sectorconstituent','date={};sectorid=a001010100000000'.format(availableDateStr)).Data[1]
newStkcdsWind = sorted(list(set(listedStkcdsWind) - set(stockNames)))
if newStkcdsWind:
stockNames.extend( newStkcdsWind )
newStkIpos = [int(tdt.strftime('%Y%m%d')) for tdt in w.wss(newStkcdsWind, 'ipo_date').Data[0]]
newIpoIds = [(w.tdayscount(nextTrd,str(ipo)).Data[0][0]+nEnd) for ipo in newStkIpos]
newStockip = pd.DataFrame([[int(newStkcdsWind[dumi][:6]), newStkIpos[dumi], newIpoIds[dumi],0,0,0,0,0] for dumi in range(len(newStkcdsWind))])
newStockip.to_csv( os.path.join(tempFilePath,'stockip.csv'),index=False,header=False )
else:
pd.DataFrame([]).to_csv(os.path.join(tempFilePath, 'stockip.csv'), index=False, header=False)
newStkcdsGm = ['.'.join([stk[-2:]+'SE',stk[:6]]) for stk in newStkcdsWind]
allStkcdsGM = savedStkcdsGM + newStkcdsGm # 全体股票包含已退市 与pal行数相同
# allSecNames = pd.DataFrame(w.wss(stockNames,'sec_name').Data[0])
allInstruments = md.get_instruments('SZSE', 1, 0) + md.get_instruments('SHSE', 1, 0)
allInstrumentsDF = pd.DataFrame([[inds.symbol, inds.sec_name] for inds in allInstruments],columns=['symbol','sec_name']).set_index('symbol')
allSecNames = allInstrumentsDF.loc[allStkcdsGM,'sec_name']
allSecNames.to_csv( os.path.join(tempFilePath, 'sec_names.csv'), index=False, header=False )
pd.DataFrame(newStkcdsWind).to_csv( os.path.join(tempFilePath, 'stockname.csv'), index=False, header=False )
# update trade info
pages = ['date','open','high','low','close','volume','amount','pctchg','flow_a_share','total_share','adjfct','adjprc','isst']
newPal = {}
for page in pages:
newPal[page] = pd.DataFrame(np.zeros([len(allStkcdsGM), newDateNum]),index=allStkcdsGM,columns=betweenDays)
lastPal = pd.DataFrame(savedPal['Pal'][:,-1,:],columns=savedStkcdsGM)
barsDaily = md.get_dailybars(','.join(allStkcdsGM), nextTrdStr, availableDateStr)
for bar in barsDaily:
tdt = bar.strtime[:10]
stk = '.'.join([bar.exchange,bar.sec_id])
newPal['date'].loc[stk, tdt] = int(tdt.replace('-',''))
newPal['open'].loc[stk, tdt] = bar.open
newPal['high'].loc[stk, tdt] = bar.high
newPal['low'].loc[stk, tdt] = bar.low
newPal['close'].loc[stk, tdt] = bar.close
newPal['volume'].loc[stk, tdt] = bar.volume
newPal['amount'].loc[stk, tdt] = bar.amount
newPal['pctchg'].loc[stk, tdt] = bar.close/bar.pre_close - 1
# 计算自算复权因子 ： 前一日收盘价*(1+当日收益率)/当日收盘价 s.t. （当日收盘价*当日复权因子）/前一日收盘价 = 1+ret
# 若当日没有交易 ： 沿用前一日 复权因子 循环外处理
# 若前一日没有交易 前一日收盘价 特殊处理：
# 当日有交易 ： 取停牌前最后一个交易日的 收盘价
# 当日没交易 没有退市 ： 沿用前一日复权因子 循环外处理
# 当日没交易 已经退市 ： 沿用前一日复权因子 循环外处理
# 若新股上市第一天 ： 复权因子为1
if stk in newStkcdsGm:
newPal['adjfct'].loc[stk, tdt] = 1
else:
noTrdLast = (lastPal.loc[0, stk] == 0) if tdt == nextTrdStr else (newPal['date'].loc[stk, betweenDays[betweenDays.index(tdt) - 1]] == 0)
if noTrdLast: # 前一日没交易 今日有交易（否则不应出现在bars里面）
lastBar = md.get_last_n_dailybars(stk, 2, end_time=tdt)[-1]
newPal['adjfct'].loc[stk, tdt] = lastPal.loc[15, stk] * lastBar.close * (1 + newPal['pctchg'].loc[stk, tdt]) / bar.close
else:
preClose = lastPal.loc[4,stk] if tdt==nextTrdStr else newPal['close'].loc[stk,betweenDays[betweenDays.index(tdt)-1]]
newPal['adjfct'].loc[stk, tdt] = lastPal.loc[15, stk] * preClose * (1 + newPal['pctchg'].loc[stk, tdt]) / bar.close
for dumi,tdt in enumerate(betweenDays):
idx = newPal['adjfct'].loc[:,tdt]==0
idx = idx.values
if tdt==nextTrdStr:
newPal['adjfct'].loc[idx[:savedStkNum], tdt] = lastPal.loc[15,:].values[idx[:savedStkNum]]
else:
newPal['adjfct'].loc[idx, tdt] = newPal['adjfct'].loc[idx, betweenDays[dumi-1]]
newPal['adjprc'] = newPal['adjfct']*newPal['close']
shareBar = md.get_share_index(','.join(allStkcdsGM), nextTrdStr, availableDateStr)
for bar in shareBar:
tdt = bar.pub_date
stk = bar.symbol
newPal['flow_a_share'].loc[stk, tdt] = bar.flow_a_share
newPal['total_share'].loc[stk, tdt] = bar.total_share
isST = np.array([int('ST' in sn) for sn in allSecNames.values])
newPal['isst'] = pd.DataFrame(np.repeat(np.reshape(isST,(isST.shape[0],1)),len(betweenDays),axis=1), index=allStkcdsGM, columns=betweenDays)
for page in newPal:
newPal[page].to_csv(os.path.join(tempFilePath,'{}.csv'.format(page)),index=False,header=False )
print('Pal temp files update finished with {0} stocks and {1} days in {2} seconds '.format(len(newStkcdsWind),len(betweenDays),time.time() - start))
]
r = 0.038
l = len(code)
# df = pd.DataFrame(columns = [u'公司名称',u'品种',u'品种代码',
# u'期权类型',u'行权价',u'到期日/交易期限',
# u'最小交易单位',u'买价',u'卖价',
# u'标的价格',u'报价日期'])
df = pd.DataFrame(columns=[
u'报价日期', u'品种', u'品种代码', u'期权类型', u'标的价格', u'行权价', u'到期日/交易期限',
u'最小交易单位', u'买价', u'卖价', u'', u'25%分位波动率', u'50%分位波动率', u'75%分位波动率',
u'今日波动率', u'买价波动率', u'卖价波动率', u'对冲成本波动率', u'设定卖价溢出波动率', u'设定买价溢出波动率'
])
T = w.tdayscount(datetime.date.today(),
datetime.date.today() + relativedelta(months=1),
"").Data[0][0]
for i in range(l):
OriTickData = w.wss(code[i], "mfprice").Data[0][0]
tick = float(re.search(r'\d+(\.\d+)?', OriTickData).group(0))
S0 = w.wsq(code[i], "rt_last").Data[0][0]
K = S0
a = BidAsk(code[i], tick, T, r)
df1 = a.QuoteOneSpecies(S0, K)
df = df.append(df1)
print(code[i])
# bookname = u'场外期权报价-渤海融盛'
def ttradedayscount(begt, endt):
w.start()
days = w.tdayscount(begt, endt).Data[0]
return days[0]
__author__ = 'aming.tao'
from WindPy import w
from datetime import *
w.start()
data=w.wsd("600000.SH","close,amt","2013-04-30", datetime.today()-timedelta(1))#取浦发银行收盘价等信
data=w.wsd("600000.SH","close,amt", datetime.today()-timedelta(100))#
data=w.wsi("600000.SH","close,amt","2015-10-01 9:00:00")#取浦发银行分钟收盘价等信息
data=w.wst("600000.SH","open", datetime.today()-timedelta(0,2*3600), datetime.now())#取浦发银行tick数据信息
data=w.wss("600000.SH,000001.SZ","eps_ttm,orps,surpluscapitalps","rptDate=20121231")#取浦发银行等财务数据信息
data=w.wset("SectorConstituent",u"date=20130608;sector=全部A股")#取全部A 股股票代码、名称信息
w.wset("IndexConstituent","date=20130608;windcode=000300.SH;field=wind_code,i_weight")#取沪深300 指数中股票代码和权重
w.wset("TradeSuspend","startdate=20130508;enddate=20130608;field=wind_code,sec_name,suspend_type,suspend_reason")#取停牌信息
w.wset("SectorConstituent",u"date=20130608;sector=风险警示股票;field=wind_code,sec_name")#取ST 股票等风险警示股票信息
w.tdays("2013-05-01","2013-06-08")#返回5 月1 日到6 月8 日之间的交易日序列
w.tdays("2013-05-01")#返回5 月1 日到当前时间的交易日序列
w.tdaysoffset(-5,"2013-05-01")#返回5 月1 日前推五个交易日的日期，返回2013-4-19
w.tdaysoffset(-5)#返回当前时间前推五个交易日的日期
w.tdayscount("2013-05-01","2013-06-08")#返回5 月1 日到6 月8 日之间的交易日序列长度，为27
w.stop()