def RunAStrategy(self):
theStartDate = datetime.datetime.strptime(self.StartDate, '%Y-%m-%d')
theEndDate = datetime.datetime.strptime(self.EndDate, '%Y-%m-%d')
currentDate = theStartDate
while currentDate <= theEndDate:
if len(self.CurrentList) < self.StockNumber:
BuyRes = self.GetBuyList(currentDate.strftime('%Y-%m-%d'))
for anitem in BuyRes:
self.BuyAStock(currentDate.strftime('%Y-%m-%d'), anitem[0],
anitem[1])
if len(self.CurrentList) == self.StockNumber:
break
removed = []
for i in range(len(self.CurrentList)):
asymbol = self.CurrentList.keys()[i]
[SellOrNot,
SellPrice] = self.SellNow(asymbol,
currentDate.strftime('%Y-%m-%d'))
if SellOrNot:
self.SellAStock(asymbol, SellPrice,
currentDate.strftime('%Y-%m-%d'))
removed.append(asymbol)
for anitem in removed:
del self.CurrentList[anitem]
currentDate += timedelta(days=1)
thecurrentdata = MyPredictDB.GetAllData(self.EndDate, 1)
totalmoney = 0
for anitem in self.CurrentList:
thedata = self.CurrentList[anitem]
totalnum = 0
for adata in thedata:
totalnum += adata[0]
print('We are holding ' + str(totalnum) + ' ' + anitem)
print('The current price is ' + str(thecurrentdata[anitem][2][0]))
themoney = totalnum * thecurrentdata[anitem][2][0]
totalmoney += themoney
print('we have remain cash:' + str(self.RemainMoney))
print('we have total money:' + str(self.RemainMoney + totalmoney))
print('Percent is ' +
str((self.RemainMoney + totalmoney) / self.InitMoney))
def PrepareLearningData(StockFile):
alist = PrepareData.GetBigCompany(StockFile)
#alist = ['aapl','aap','orcl','f','azo']
AllData = MyPredictDB.GetAllDataFromAList(alist, '2018-02-22', 600)
GetPicturesAndLabels(AllData,KnownDays=KnownDays,PredictDays=10,StepDays=20,UpThreshold=0.05)
def PreparePredictData(StockFile,TheDate):
'''
@param TheDate: 2018-02-02
'''
###
# delete the old predict files first
###
thedir = './data/Stock/predict/'
for root, subdir, files in os.walk(thedir):
for afile in files:
os.remove(os.path.join(thedir,afile))
alist = PrepareData.GetBigCompany(StockFile)
#alist = ['aapl']
AllData = MyPredictDB.GetAllDataFromAList(alist, TheDate, KnownDays+1)
plt = myplt()
convert = mdates.strpdate2num('%Y-%m-%d %H:%M:%S')
'''
'''
# get all data
#BigLists = ['AAPL']
#print BigLists
#AllData = MyPredictDB.GetAllDataFromAList(BigLists, '2017-07-21', 400)
TotalSymbolNumber = len(AllData)
CurrentSymbolNumber = 0
for asymbol in AllData:
print(asymbol+'\t'+str(CurrentSymbolNumber)+'/'+str(TotalSymbolNumber))
TheData = AllData[asymbol]
dates = []
for anitem in TheData[0]:
dates.append(convert(anitem.strftime('%Y-%m-%d %H:%M:%S')))
dates.reverse()
TheData[0].reverse()
TheData[1].reverse()
TheData[2].reverse()
TheData[3].reverse()
TheData[4].reverse()
TheData[5].reverse()
TheData[1] = _fix_zero_data(TheData[1])
TheData[2] = _fix_zero_data(TheData[2])
TheData[3] = _fix_zero_data(TheData[3])
TheData[4] = _fix_zero_data(TheData[4])
i = 0
TotalNum = len(dates)
f=open(os.path.join(thedir,asymbol+'.txt'),'w')
while i+KnownDays<TotalNum:
KnownDates = dates[i:i+KnownDays]
KnownOpen = TheData[1][i:i+KnownDays]
KnownClose = TheData[2][i:i+KnownDays]
KnownHigh = TheData[3][i:i+KnownDays]
KnownLow = TheData[4][i:i+KnownDays]
KnownVolume = TheData[5][i:i+KnownDays]
savename = os.path.join(thedir,asymbol+'_'+TheData[0][i+KnownDays-1].strftime('%Y-%m-%d')+'.png')
plt.drawData(KnownDates, KnownOpen, KnownClose,KnownHigh,KnownLow,KnownVolume,True,savename)
lastClose = KnownClose[-1]
if lastClose==0.0:
print ('Something is wrong')
print (asymbol)
print (KnownClose)
print (KnownDates)
return
f.write(asymbol+'_'+TheData[0][i+KnownDays-1].strftime('%Y-%m-%d')+':::1:::0.0\n')
i+=1
f.close()
CurrentSymbolNumber+=1
def __init__(self,
InitMoney,
StartDate,
EndDate,
AllTimeHighPeriod=200,
AllTimeLowPeriod=10,
StockList=[],
StockNumber=3):
'''
@param AllTimeHighPeriod: one year all time high or one month all time high? the unit is days
@param DownPercent: must >0 and <1
'''
SimulationBaseClass.__init__(self, InitMoney, StartDate, EndDate,
StockNumber)
self.AllTimeLowPeriod = AllTimeLowPeriod
self.AllList = StockList
# Get all data
theStartDate = datetime.datetime.strptime(self.StartDate,
'%Y-%m-%d').date()
theEndDate = datetime.datetime.strptime(self.EndDate,
'%Y-%m-%d').date()
totalPeriod = (theEndDate - theStartDate).days
print("Getting all data")
self.AllData = MyPredictDB.GetAllDataFromAList(
self.AllList, EndDate, AllTimeHighPeriod + totalPeriod)
print("Calculating all time highs")
self.AllTimeHigh = {}
self.AllTimeLow = {}
RemoveData = []
for anitem in self.AllData:
# get all time high
alltimehigh = {}
alltimelow = {}
TheStockData = self.AllData[anitem]
allClosePrices = []
allDates = []
# the index of start date in the array
# the front, the near date. the end, the previous date
startIndex = -1
i = 0
allClosePrices = TheStockData[2]
allDates = TheStockData[0]
for i in range(len(allDates)):
if theStartDate == allDates[i]:
startIndex = i
break
if startIndex == -1:
#print("the start date is not in the database")
RemoveData.append(anitem)
continue
try:
for i in range(startIndex + 1):
lastIndex = min(i + 1 + AllTimeHighPeriod,
len(allClosePrices))
lastLowIndex = min(i + 1 + self.AllTimeLowPeriod,
len(allClosePrices))
#print lastIndex
#print i
#print allClosePrices
if allClosePrices[i] > max(
allClosePrices[i + 1:lastIndex]):
alltimehigh[allDates[i].strftime('%Y-%m-%d')] = True
else:
alltimehigh[allDates[i].strftime('%Y-%m-%d')] = False
if allClosePrices[i] < min(
allClosePrices[i + 1:lastLowIndex]):
alltimelow[allDates[i].strftime('%Y-%m-%d')] = True
else:
alltimelow[allDates[i].strftime('%Y-%m-%d')] = False
od = collections.OrderedDict(
sorted(alltimehigh.items(), reverse=True))
self.AllTimeHigh[anitem] = od
odlow = collections.OrderedDict(
sorted(alltimelow.items(), reverse=True))
self.AllTimeLow[anitem] = odlow
except:
if anitem in self.AllTimeHigh:
del self.AllTimeHigh[anitem]
if anitem in self.AllTimeLow:
del self.AllTimeLow[anitem]
RemoveData.append(anitem)
for anitem in RemoveData:
del self.AllData[anitem]
def __init__(self,InitMoney,StartDate,EndDate,AllTimeHighPeriod=200,AllTimeLowPeriod =30,VibrationPeriod = 10,\
StockList = [],StockNumber = 3, Leverage=5,VibrationRatio=3.5, BeTesting=False):
'''
@param AllTimeHighPeriod: one year all time high or one month all time high? the unit is days
@param DownPercent: must >0 and <1
'''
SimulationBaseClass.__init__(self, InitMoney, StartDate, EndDate,StockNumber)
self.VibrationPeriod = VibrationPeriod
self.VibrationRatio = VibrationRatio
self.Leverage = Leverage
self.RemainMoney = self.InitMoney*self.Leverage
self.TrueMoney = InitMoney
self.Margin = 0.0
self.Commission = 15
self.AllList = StockList
self.TrueMoneyList = {}
# Get all data
theStartDate = datetime.datetime.strptime(self.StartDate,'%Y-%m-%d').date()
theEndDate = datetime.datetime.strptime(self.EndDate,'%Y-%m-%d').date()
totalPeriod = (theEndDate-theStartDate).days
self.SPYData = []
print("Getting all data")
if not BeTesting:
self.AllData = MyPredictDB.GetAllDataFromAList(self.AllList,EndDate,AllTimeHighPeriod+totalPeriod)
else:
self.SPYData = GetSPXInfo()
self.AllData = {}
ReadStockHistoryFromCSV(self.StartDate,self.EndDate,AllTimeHighPeriod,self.AllData)
print("Calculating all time highs")
self.AllTimeHigh = {}
self.AllTimeLow = {}
self.AllTimeLowPeriod = AllTimeLowPeriod
self.Vibration = {}
RemoveData = []
for anitem in self.AllData:
# get all time high
alltimehigh = {}
alltimelow = {}
AVibration = {}
AMeanVibration = {}
ADayVibration = {}
TheStockData = self.AllData[anitem]
allClosePrices = []
allDates = []
# the index of start date in the array
# the front, the near date. the end, the previous date
startIndex = -1
i = 0
allOpenPrices = TheStockData[1]
allClosePrices = TheStockData[2]
allHighPrices = TheStockData[3]
allLowPrices = TheStockData[4]
###
#sometimes there is 0 in the data, this is an error. We fix it using a simple method.
self._fixZeroValues(allOpenPrices)
self._fixZeroValues(allClosePrices)
self._fixZeroValues(allHighPrices)
self._fixZeroValues(allLowPrices)
###
#allVibration = numpy.abs(numpy.array(allClosePrices)-numpy.array(allOpenPrices))
PreClosePrices = allClosePrices[1:]
PreClosePrices.append(0.0)
HMinPreClose = numpy.array(allHighPrices)-numpy.array(PreClosePrices)
PreCloseMinL = numpy.array(PreClosePrices) - numpy.array(allLowPrices)
HighMinLow = numpy.array(allHighPrices)-numpy.array(allLowPrices)
allVibration = []
for viIndex in range(len(HMinPreClose)):
allVibration.append(numpy.max([HMinPreClose[viIndex],PreCloseMinL[viIndex],HighMinLow[viIndex]]))
allDates = TheStockData[0]
for i in range(len(allDates)):
if theStartDate==allDates[i]:
startIndex = i
break
if startIndex==-1:
#print("the start date is not in the database")
RemoveData.append(anitem)
continue
try:
for i in range(startIndex+1):
lastIndex = min(i+1+AllTimeHighPeriod,len(allClosePrices))
lastVibrationIndex = min(i+1+self.VibrationPeriod,len(allClosePrices))
lastLowIndex = min(i+1+self.AllTimeLowPeriod,len(allClosePrices))
#print lastIndex
#print i
#print allClosePrices
currentStringFormatDate = allDates[i].strftime('%Y-%m-%d')
if allClosePrices[i]>max(allClosePrices[i+1:lastIndex]):
alltimehigh[currentStringFormatDate]=True
else:
alltimehigh[currentStringFormatDate]=False
#calculate vibration
AMeanVibration[currentStringFormatDate] = numpy.mean(allVibration[i+1:lastVibrationIndex])
#ADayVibration[currentStringFormatDate] = allHighPrices[i]-allLowPrices[i]
ADayVibration[currentStringFormatDate] = numpy.max([allHighPrices[i]-allLowPrices[i],allHighPrices[i]-allClosePrices[i+1],allClosePrices[i+1]-allLowPrices[i]])
if ADayVibration[currentStringFormatDate]>self.VibrationRatio*AMeanVibration[currentStringFormatDate] and allClosePrices[i]<allOpenPrices[i]:
AVibration[currentStringFormatDate] = True
else:
AVibration[currentStringFormatDate] = False
if allClosePrices[i]<min(allClosePrices[i+1:lastLowIndex]):
alltimelow[allDates[i].strftime('%Y-%m-%d')] = True
else:
alltimelow[allDates[i].strftime('%Y-%m-%d')] = False
od = collections.OrderedDict(sorted(alltimehigh.items(),reverse=True))
self.AllTimeHigh[anitem] = od
odVibration = collections.OrderedDict(sorted(AVibration.items(),reverse=True))
odlowMean = collections.OrderedDict(sorted(AMeanVibration.items(),reverse=True))
odlowADay = collections.OrderedDict(sorted(ADayVibration.items(),reverse=True))
self.Vibration[anitem] = [odVibration,odlowMean,odlowADay]
odlow = collections.OrderedDict(sorted(alltimelow.items(),reverse=True))
self.AllTimeLow[anitem] = odlow
except:
if anitem in self.AllTimeHigh:
del self.AllTimeHigh[anitem]
if anitem in self.Vibration:
del self.Vibration[anitem]
if anitem in self.AllTimeLow:
del self.AllTimeLow[anitem]
RemoveData.append(anitem)
for anitem in RemoveData:
del self.AllData[anitem]