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):