for dataPoint in dataCollection.find({
'datetime': {
'$gt': endDate,
'$lt': endValidationDate
},
'ticker': 'AUDUSD'
}).sort("datetime"):
validData.append(dataPoint["data"])
print "#### Data length:", len(data)
print "#### Valid data length:", len(validData)
dataLength = len(data)
validDataLength = len(validData)
pool = genome.Pool(db)
data = numpy.array(data).astype(numpy.float32)
data_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(data_gpu, data)
validData = numpy.array(validData).astype(numpy.float32)
validData_gpu = cuda.mem_alloc(validData.nbytes)
cuda.memcpy_htod(validData_gpu, validData)
#### transfer data array and winner table to GPU
while True:
trees = []
for x in range(poolSize):
trees.append(genome.randomTree(treeLength))
#plt.plot(plotX, plot2)
#plt.show()
print 'plot done'
#data = []
#for dataPoint in dataCollection.find({'datetime': {'$gt': startDate , '$lt': endDate }, 'ticker': 'AUDUSD' }).sort("datetime"):
# data.append( dataPoint["data"] )
#print "#### Data length:", len(data)
#dataLength = len(data)
dataLength = dataTimeSize
pool = genome.Pool(db, 'sell', 'AUDUSD', endDate)
data = numpy.array(data).astype(numpy.float32)
printFreeMemory()
print "Data size ", data.nbytes/1024, " KB"
printFreeMemory()
data_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(data_gpu, data)
trees = []
for x in range(poolSize):
trees.append( genome.randomTree(treeLength) )
for dataPoint in dataCollection.find({
'datetime': {
'$gt': startDate,
'$lt': endDate
},
'ticker': secExchange
}).sort("datetime"):
dataSec.append(dataPoint["data"])
print "#### Primary Data length:", len(data)
print "#### Secondary Data length:", len(dataSec)
dataLength = len(data)
dataLengthSec = len(dataSec)
pool = genome.Pool(db, 'buy', primExchange, endDate)
data = numpy.array(data).astype(numpy.float32)
data_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(data_gpu, data)
dataSec = numpy.array(data).astype(numpy.float32)
dataSec_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(dataSec_gpu, dataSec)
while True:
trees = []
for x in range(poolSize):
trees.append(genome.randomTree(treeLength))
'ticker': 'AUDUSD'
}).sort("datetime"):
data.append(dataPoint["data"])
print data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5]
#for dataPoint in dataCollection.find({'datetime': {'$gt': endDate , '$lt': endValidationDate }, 'ticker': 'AUDUSD' }).sort("datetime"):
# validData.append( dataPoint["data"] )
print "#### Data length:", len(data)
print "#### Valid data length:", len(validData)
dataLength = len(data)
validDataLength = len(validData)
pool = genome.Pool(db, 'buy', 'AUDUSD', endDate)
data = numpy.array(data).astype(numpy.float32)
data_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(data_gpu, data)
#validData = numpy.array(validData).astype(numpy.float32)
#validData_gpu = cuda.mem_alloc(validData.nbytes)
#cuda.memcpy_htod(validData_gpu, validData)
#### transfer data array and winner table to GPU
while True:
trees = []
for x in range(poolSize):