def get_last_n_dailybars(symbol, n):
end_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
VAR = md.get_last_n_dailybars(symbol, n, end_time)
z = len(VAR)
var = []
for i in range(z):
var.append(VAR[z-1-i])
ret = bar_topd(var,'date')
return ret
def get_last_n_dailybars(symbol, n):
end_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
VAR = md.get_last_n_dailybars(symbol, n, end_time)
z = len(VAR)
var = []
for i in range(z):
var.append(VAR[z - 1 - i])
ret = bar_topd(var, 'date')
return ret
def read_last_n_kline(symbol_list, weeks_in_seconds, count, end_time):
# 连接本地终端时，td_addr为localhost:8001,
if (td.init('*****@*****.**', 'zyj2590@1109', 'strategy_1') == 0):
# 类结构体转成dataframe
columns = [
'endtime', 'open', 'high', 'low', 'close', 'volume', 'amount'
]
bars = 0
is_daily = (weeks_in_seconds == 240 * 60)
data_list = [] # pd.DataFrame(None, columns=columns)
'''
todo 整批股票读取有问题，数据取不全，放弃
stocks = ''
for x in symbol_list:
stocks+=','+x
read_days=int(count*weeks_in_seconds/240/60)+1
start_date=md.get_calendar('SZSE',
datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S')
-datetime.timedelta(days=read_days),end_time)[0].strtime
start_date=start_date[:10] +' 09:30:00'
while start_date<end_time:
bars=md.get_bars(stocks[1:], weeks_in_seconds, start_date, end_time)
'''
for stock in symbol_list:
#now = '[{0}] read k line'.format(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
#print(now,stock)
kdata = []
# 返回结果是bar类数组
if is_daily:
bars = md.get_last_n_dailybars(stock, count, end_time)
else:
bars = md.get_last_n_bars(stock, weeks_in_seconds, count,
end_time)
for bar in bars:
if is_daily:
kdata.append([
int(bar.utc_time), bar.open, bar.high, bar.low,
bar.close, bar.volume, bar.amount
])
else:
kdata.append([
int(bar.utc_endtime), bar.open, bar.high, bar.low,
bar.close, bar.volume, bar.amount
])
if len(bars) > 0:
kdata = pd.DataFrame(kdata, columns=columns)
kdata = kdata.sort_values(by='endtime', ascending=False)
data_list.append({'code': stock, 'kdata': kdata})
return data_list
def holdlist_format(self,
date=None,
prctype='close',
outdir=None,
source='wind'):
""" 提取标准格式, 与get_totval 平行，不会互相调用 """
if date is None:
date = dt.datetime.today()
if source == 'wind':
####### 万得数据源 #######
w.start()
if date is None:
date = dt.datetime.today()
elif source == 'gm':
###### 掘金数据源 #######
md.init('18201141877', 'Wqxl7309')
if prctype == 'settle':
prctype = 'settle_price' # 转为掘金格式
holdnum = self.get_holdnum(date=date)
holding = pd.DataFrame()
for strat in self._logdir:
stratinfo = strat.split('_')
cttype = stratinfo[1].upper()
montype = stratinfo[0]
name = RawHoldingFutures.get_contracts_ours(date=date,
cttype=cttype)[montype]
code = RawHoldingStocks.addfix(name)
num = holdnum[strat]
multi = self._multiplier[cttype]
if source == 'wind':
############# wind 数据源
prc = w.wsd('.'.join([name, 'CFE']), prctype, date,
date).Data[0][0]
elif source == 'gm':
######### 掘金数据
lastbar = md.get_last_n_dailybars(
symbol='.'.join(['CFFEX', name]),
n=1,
end_time=date.strftime('%Y-%m-%d'))[0]
prc = eval('.'.join(['lastbar', prctype]))
#### 紧急措施 手动
# prc = 6069.2 if name=='IC1707' else 5880.6
holdlist = pd.DataFrame(
[[code, name, num, multi, prc]],
columns=['code', 'name', 'num', 'multi', 'prc'])
#holdlist['val'] = holdlist['num']*holdlist['multi']*holdlist['prc']
holding = holding.append(holdlist, ignore_index=True)
holding = holding[holding['num'] != 0]
if outdir:
holding.to_csv(outdir, header=True, index=False)
else:
return holding
r = md.get_last_n_bars('SHSE.600000',60,10)
print('get_last_n_bars(10): ', len(r))
#提取日频数据
r = md.get_dailybars(
'SHSE.600000',
'2015-05-01 00:00:00',
'2015-05-20 23:59:59')
print('get_dailybars: ', len(r))
#提取dailybar快照
r = md.get_last_dailybars('SHSE.600000,')
print('get_last_dailybars: ', len(r))
#提取最新N笔dailybar数据
r = md.get_last_n_dailybars('SHSE.600000', 10)
print('get_last_n_dailybars(10): ', len(r))
#提取交易代码
r = md.get_instruments('SHSE', 1, 1)
print('get_instruments: ', len(r))
#根据期货品种提取交易代码
r = md.get_instruments_by_name('ag')
print('get_instruments_by_name', len(r))
#提取指数的成分股代码
r = md.get_constituents('SHSE.000001')
print('get_constituents', len(r))
#按时间周期提取FinancialIndex
def check_update(self,checkDate,outputPath = r'E:\stocks_data_min\StocksMinDB\check_reports'):
"""
对比 checkDate 对应的两个数据库，应满足:
当天股票数量一致
每只股票 Kbar 数量一致
两个库的交易日期一致
输出：report
当日较前日比，增加的新股
当日股票数量
是否有更新失败的股票，如果有是哪些
"""
start = time.time()
if isinstance(checkDate,dt.datetime):
checkDate = checkDate.strftime('%Y%m%d')
today = dt.datetime.today().strftime('%Y%m%d')
assert checkDate>='19990726' and checkDate<=today
print('***************************')
print('Checking data base on date {}'.format(checkDate))
print('***************************')
bars = md.get_last_n_dailybars('SHSE.000001', 2, end_time=checkDate)
preDate = ''.join(bars[1].strtime.split('T')[0].split('-'))
###### check by_day ######
connByDay = self.byDayDb._getConn_()
cursorByDay = connByDay.cursor()
### trd dates ###
cursorByDay.execute('SELECT date FROM trddates')
byDayTrddates = set([trd[0] for trd in cursorByDay.fetchall()])
cursorByDay.execute('SELECT stkcd,count(*) FROM stkmin_{} GROUP BY stkcd'.format(checkDate))
### stocks ###
byDayBars = pd.DataFrame(cursorByDay.fetchall(),columns=['stkcd','barnum'])
checkDateStocks = set(byDayBars['stkcd'].values)
if checkDate>'19990726':
cursorByDay.execute('SELECT DISTINCT stkcd FROM stkmin_{}'.format(preDate))
preDateStocks = set([stk[0] for stk in cursorByDay.fetchall()])
else:
preDateStocks = set()
newStocks = checkDateStocks - preDateStocks
print('\n[+]{0} new stocks listed on date {1}'.format(len(newStocks),checkDate))
print(newStocks)
###### check by_stk ######
connByStk = self.byStkDb._getConn_()
cursorByStk = connByStk.cursor()
### trd dates ###
cursorByStk.execute('SELECT date FROM trddates')
byStkTrddates = set([trd[0] for trd in cursorByStk.fetchall()])
moreTrdByDay = byDayTrddates - byStkTrddates
moreTrdByStk = byStkTrddates - byDayTrddates
if (moreTrdByDay | moreTrdByStk):
print('\n[-]Different trdate dates')
print('more in by day:',moreTrdByDay)
print('more in by stk:',moreTrdByStk)
else:
print('\n[+]Trade dates matched between two databases')
### stocks ###
cursorByStk.execute('SHOW TABLES')
allStocks = set([int(tb[0].split('_')[1][2:]) for tb in cursorByStk.fetchall() if tb[0]!='trddates'])
lostNewStocks = newStocks - allStocks
if lostNewStocks:
print('\n[-]Following new stocks NOT updated in stocks_data_min_by_stock')
print(lostNewStocks)
else:
print('\n[+]New stocks matched between two databases on date {}'.format(checkDate))
lostCheckDateStocks = checkDateStocks - allStocks
if lostCheckDateStocks:
print('\n[-]Following stocks NOT updated in stocks_data_min_by_stock on date {}'.format(checkDate))
print(lostCheckDateStocks)
else:
print('\n[+]All stocks matched between two databases on date {}'.format(checkDate))
### check each stock ###
missBarStocks = []
for stk in sorted(list(checkDateStocks)):
# stkstr = str(stk)
# if stk>=600000:
# stkstr = 'sh'+stkstr
# else:
# stklen = len(stkstr)
# if stklen<6:
# stkstr = 'sz'+'0'*(6-stklen)+stkstr
# else:
# stkstr = 'sz'+stkstr
stkstr = self.stkcd_int_trans(stkint=stk)
cursorByStk.execute('SELECT count(*) FROM stkmin_{0} WHERE date={1}'.format(stkstr,checkDate))
barnumByStk = cursorByStk.fetchall()[0][0]
barnumByDay = byDayBars.loc[byDayBars['stkcd']==stk,'barnum'].values[0]
if barnumByDay==barnumByStk:
print('[+]Stock {0} bar num matched on date {1}'.format(stkstr,checkDate))
else:
missBarStocks.append([stk,barnumByStk,barnumByDay])
if missBarStocks:
print('\n[-] Missing bars between two databases on date {}'.format(checkDate))
missTable = pd.DataFrame(missBarStocks,columns=['stkcd','barnumByStk','barnumByDay'])
print(missTable)
missTable.to_csv(os.path.join(outputPath,'check_report_{}.csv'.format(checkDate)),index=False)
else:
print('\n[+] All bars of {0} stocks matched between two databases on date {1}'.format(len(checkDateStocks),checkDate))
print('\nCheck finished on date {0} with {1} seconds'.format(checkDate,time.time()-start))
r = md.get_bars(
'CFFEX.IF1512',
60,
'2015-05-01 09:30:00',
'2015-05-10 09:31:00',
)
print('get_bars: ', len(r))
r = md.get_last_bars('CFFEX.IF1512,', 60)
print('get_last_bars: ', len(r))
r = md.get_last_n_bars(
'CFFEX.IF1512',
60,
10)
print('get_last_n_bars(10): ', len(r))
r = md.get_dailybars(
'CFFEX.IF1512',
'2015-05-01 00:00:00',
'2015-05-20 23:59:59')
print('get_dailybars: ', len(r))
r = md.get_last_dailybars('CFFEX.IF1512,')
print('get_last_dailybars: ', len(r))
r = md.get_last_n_dailybars('CFFEX.IF1512', 10)
print('get_last_n_dailybars(10): ', len(r))
input()
def check_update(
self,
checkDate,
outputPath=r'E:\stocks_data_min\StocksMinDB\check_reports'):
"""
对比 checkDate 对应的两个数据库，应满足:
当天股票数量一致
每只股票 Kbar 数量一致
两个库的交易日期一致
输出：report
当日较前日比，增加的新股
当日股票数量
是否有更新失败的股票，如果有是哪些
"""
start = time.time()
if isinstance(checkDate, dt.datetime):
checkDate = checkDate.strftime('%Y%m%d')
today = dt.datetime.today().strftime('%Y%m%d')
assert checkDate >= '19990726' and checkDate <= today
print('***************************')
print('Checking data base on date {}'.format(checkDate))
print('***************************')
bars = md.get_last_n_dailybars('SHSE.000001', 2, end_time=checkDate)
preDate = ''.join(bars[1].strtime.split('T')[0].split('-'))
###### check by_day ######
connByDay = self.byDayDb._getConn_()
cursorByDay = connByDay.cursor()
### trd dates ###
cursorByDay.execute('SELECT date FROM trddates')
byDayTrddates = set([trd[0] for trd in cursorByDay.fetchall()])
cursorByDay.execute(
'SELECT stkcd,count(*) FROM stkmin_{} GROUP BY stkcd'.format(
checkDate))
### stocks ###
byDayBars = pd.DataFrame(cursorByDay.fetchall(),
columns=['stkcd', 'barnum'])
checkDateStocks = set(byDayBars['stkcd'].values)
if checkDate > '19990726':
cursorByDay.execute(
'SELECT DISTINCT stkcd FROM stkmin_{}'.format(preDate))
preDateStocks = set([stk[0] for stk in cursorByDay.fetchall()])
else:
preDateStocks = set()
newStocks = checkDateStocks - preDateStocks
print('\n[+]{0} new stocks listed on date {1}'.format(
len(newStocks), checkDate))
print(newStocks)
###### check by_stk ######
connByStk = self.byStkDb._getConn_()
cursorByStk = connByStk.cursor()
### trd dates ###
cursorByStk.execute('SELECT date FROM trddates')
byStkTrddates = set([trd[0] for trd in cursorByStk.fetchall()])
moreTrdByDay = byDayTrddates - byStkTrddates
moreTrdByStk = byStkTrddates - byDayTrddates
if (moreTrdByDay | moreTrdByStk):
print('\n[-]Different trdate dates')
print('more in by day:', moreTrdByDay)
print('more in by stk:', moreTrdByStk)
else:
print('\n[+]Trade dates matched between two databases')
### stocks ###
cursorByStk.execute('SHOW TABLES')
allStocks = set([
int(tb[0].split('_')[1][2:]) for tb in cursorByStk.fetchall()
if tb[0] != 'trddates'
])
lostNewStocks = newStocks - allStocks
if lostNewStocks:
print(
'\n[-]Following new stocks NOT updated in stocks_data_min_by_stock'
)
print(lostNewStocks)
else:
print('\n[+]New stocks matched between two databases on date {}'.
format(checkDate))
lostCheckDateStocks = checkDateStocks - allStocks
if lostCheckDateStocks:
print(
'\n[-]Following stocks NOT updated in stocks_data_min_by_stock on date {}'
.format(checkDate))
print(lostCheckDateStocks)
else:
print('\n[+]All stocks matched between two databases on date {}'.
format(checkDate))
### check each stock ###
missBarStocks = []
for stk in sorted(list(checkDateStocks)):
# stkstr = str(stk)
# if stk>=600000:
# stkstr = 'sh'+stkstr
# else:
# stklen = len(stkstr)
# if stklen<6:
# stkstr = 'sz'+'0'*(6-stklen)+stkstr
# else:
# stkstr = 'sz'+stkstr
stkstr = self.stkcd_int_trans(stkint=stk)
cursorByStk.execute(
'SELECT count(*) FROM stkmin_{0} WHERE date={1}'.format(
stkstr, checkDate))
barnumByStk = cursorByStk.fetchall()[0][0]
barnumByDay = byDayBars.loc[byDayBars['stkcd'] == stk,
'barnum'].values[0]
if barnumByDay == barnumByStk:
print('[+]Stock {0} bar num matched on date {1}'.format(
stkstr, checkDate))
else:
missBarStocks.append([stk, barnumByStk, barnumByDay])
if missBarStocks:
print('\n[-] Missing bars between two databases on date {}'.format(
checkDate))
missTable = pd.DataFrame(
missBarStocks, columns=['stkcd', 'barnumByStk', 'barnumByDay'])
print(missTable)
missTable.to_csv(os.path.join(
outputPath, 'check_report_{}.csv'.format(checkDate)),
index=False)
else:
print(
'\n[+] All bars of {0} stocks matched between two databases on date {1}'
.format(len(checkDateStocks), checkDate))
print('\nCheck finished on date {0} with {1} seconds'.format(
checkDate,
time.time() - start))
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))