def do_all(self):
"""
执行所有历史数据
"""
self.getTime()
if self.begintime and self.endtime is not None:
Now = self.begintime
while (Now >= self.begintime and Now <= self.endtime):
self.Now = strTostr(Now)
CollaOptions_oneday(self.Lock, self.Now)
Now = timedelta(Now, 1)
def getdayMax(self,Time=None):
"""
获取某一天汇率最大值
"""
key={'code':True}
Time_end = strTostr(timedelta(Time,17.0/24))##当天时间下午5点
Time = strTostr(Time)
initial ={'Close':0,'Time':Time}
reduces = """function(doc,prev){
if (prev.Time<doc.Time){
prev.Close = doc.Close/1.0/doc.PriceWeight;
prev.Time = doc.Time
}}"""##遍历寻找当前最大汇率值
condition={'type':'0','code':self.code,'Time':{'$lt':Time_end,'$gte':Time}}
data = self.__mongo.group('kline',key,condition,initial,reduces)
self.__mongo.close()
return data
def setrate():
from database.database import mongodb ## mongo connect
from help.help import getNow, strTostr ##get current datetime
key = {'code': True}
Time = strTostr(getNow('%Y-%m-%d'))
initial = {'Time': Time}
reduces = """function(doc,prev){
if (prev.Time>doc.Time){
prev.Time = doc.Time
}}"""
condition = {'type': '5'}
db = mongodb()
rate = db.group('kline', key, condition, initial, reduces)
rate = map(lambda x: {x['code']: x['Time'].split()[0]}, rate)
globalrate.rate = reduce(lambda x, y: dict(x, **y), rate)
db.close()
def getMax(self):
"""
获取利率最大的日期对应的日期
"""
key={'index':True}
Time = strTostr(getNow('%Y'),'%Y')
initial ={'rate':0,'datadate':Time}
reduces = """function(doc,prev){
if (prev.datadate<doc.datadate){
prev.rate = doc.rate;
prev.datadate = doc.datadate
}}"""##遍历寻找当前最大汇率值
ratetype ='12月'
condition={'ratetype':ratetype,'index':self.code}
data = self.__mongo.group('KPI',key,condition,initial,reduces)
self.__mongo.close()
return data
def gethistory(Currency):
"""获取汇率对的历史收盘价"""
data = []
try:
mongo = mongodb()
data = mongo.select('kline', {'type': '5', 'code': Currency})
mongo.close()
except:
pass
if data != []:
data = pd.DataFrame(data)
data = data[['Time', 'Close', 'PriceWeight']]
data['Close'] = data['Close'] / data['PriceWeight']
#data['High'] = data['High']/data['PriceWeight']
#data['Open'] = data['Open']/data['PriceWeight']
#data['LastClose'] = data['LastClose']/data['PriceWeight']
#data['Low'] = data['Low']/data['PriceWeight']
data['Time'] = data['Time'].map(
lambda x: strTostr(x, '%Y-%m-%d %H:%M:%S', '%Y-%m-%d'))
data = data[['Time', 'Close']]
return data