def run(stockName, interval_days):
nnetwork = NeuralNetwork()
nnetwork.setSupervisedDataSetSize(9, 1)
objStock = datahandler.getStock(stockName)
neural_network_input = objStock.getData()
if debug:
print '##################################################################'
print stockName
print '##################################################################\n'
total_tests = 0
total_hits = total_hits_highs = total_hits_lows = 0
total_highs = total_lows = 0
fout_highs = open(
'./results/' + stockName + '_' + str(interval_days) + '_highs', 'w')
fout_lows = open(
'./results/' + stockName + '_' + str(interval_days) + '_lows', 'w')
fout_all = open(
'./results/' + stockName + '_' + str(interval_days) + '_all', 'w')
try:
# Sliding Window
for i in range(len(neural_network_input) - 3 * interval_days):
ret = run_day(nnetwork, neural_network_input, i, interval_days,
total_tests, total_hits, total_hits_highs,
total_hits_lows, total_highs, total_lows)
total_tests = ret[0]
total_hits = ret[1]
total_hits_highs = ret[2]
total_hits_lows = ret[3]
total_highs = ret[4]
total_lows = ret[5]
rel_highs = rel_lows = ''
if total_highs == 0:
rel_highs = ',-1'
else:
rel_highs = ',' + str(float(total_hits_highs) / total_highs)
if total_lows == 0:
rel_lows = ',-1'
else:
rel_lows = ',' + str(float(total_hits_lows) / total_lows)
rel_all = ',' + str(float(total_hits) / total_tests)
fout_highs.write(rel_highs)
fout_lows.write(rel_lows)
fout_all.write(rel_all)
except KeyboardInterrupt:
fout_highs.close()
fout_lows.close()
fout_all.close()
def run(stockName, interval_days):
nnetwork = NeuralNetwork()
nnetwork.setSupervisedDataSetSize(9, 1)
objStock = datahandler.getStock(stockName)
neural_network_input = objStock.getData()
if debug:
print '##################################################################'
print stockName
print '##################################################################\n'
total_tests=0
total_hits=total_hits_highs=total_hits_lows=0
total_highs=total_lows=0
fout_highs = open('./results/'+stockName+'_'+str(interval_days)+'_highs', 'w')
fout_lows = open('./results/'+stockName+'_'+str(interval_days)+'_lows', 'w')
fout_all = open('./results/'+stockName+'_'+str(interval_days)+'_all', 'w')
try:
# Sliding Window
for i in range(len(neural_network_input)-3*interval_days):
ret = run_day(nnetwork, neural_network_input, i, interval_days, total_tests, total_hits, total_hits_highs, total_hits_lows, total_highs, total_lows)
total_tests = ret[0]
total_hits = ret[1]
total_hits_highs = ret[2]
total_hits_lows = ret[3]
total_highs = ret[4]
total_lows = ret[5]
rel_highs=rel_lows=''
if total_highs==0:
rel_highs = ',-1'
else:
rel_highs = ','+str(float(total_hits_highs)/total_highs)
if total_lows==0:
rel_lows=',-1'
else:
rel_lows = ','+str(float(total_hits_lows)/total_lows)
rel_all = ','+str(float(total_hits)/total_tests)
fout_highs.write(rel_highs)
fout_lows.write(rel_lows)
fout_all.write(rel_all)
except KeyboardInterrupt:
fout_highs.close()
fout_lows.close()
fout_all.close()
def run(stockName, interval_days, year):
nnetwork = NeuralNetwork()
nnetwork.setSupervisedDataSetSize(9, 1)
objStock = datahandler.getStock(stockName)
neural_network_input = objStock.getData()
yearLimits = objStock.getYearLimits(year)
yearLimits[0] = max(0, yearLimits[0] - 2 * interval_days)
#print '##################################################################'
#print stockName
#print '##################################################################\n'
total_tests = 0
total_hits = total_hits_highs = total_hits_lows = 0
total_highs = total_lows = 0
# Sliding Window
#for i in range(len(neural_network_input)-3*interval_days):
for i in range(yearLimits[0], yearLimits[1] - 3 * interval_days):
ret = run_day(nnetwork, neural_network_input, i, interval_days,
total_tests, total_hits, total_hits_highs,
total_hits_lows, total_highs, total_lows)
total_tests = ret[0]
total_hits = ret[1]
total_hits_highs = ret[2]
total_hits_lows = ret[3]
total_highs = ret[4]
total_lows = ret[5]
rel_highs = rel_lows = ''
if total_highs == 0:
rel_highs = '-1'
else:
rel_highs = str(float(total_hits_highs) / total_highs)
if total_lows == 0:
rel_lows = '-1'
else:
rel_lows = str(float(total_hits_lows) / total_lows)
rel_all = str(float(total_hits) / total_tests)
#print 'All: ' + rel_all + ' High: ' + rel_highs + ' Low: ' + rel_lows
print 'Total:\n' + str(total_hits) + ' out of ' + str(total_tests)
print str(total_hits_highs) + ' out of ' + str(total_highs) + ' highs.'
print str(total_hits_lows) + ' out of ' + str(total_lows) + ' lows.' + '\n'
def run(stockName, interval_days, year):
nnetwork = NeuralNetwork()
nnetwork.setSupervisedDataSetSize(9, 1)
objStock = datahandler.getStock(stockName)
neural_network_input = objStock.getData()
yearLimits = objStock.getYearLimits(year)
yearLimits[0] = max(0, yearLimits[0]-2*interval_days)
#print '##################################################################'
#print stockName
#print '##################################################################\n'
total_tests=0
total_hits=total_hits_highs=total_hits_lows=0
total_highs=total_lows=0
# Sliding Window
#for i in range(len(neural_network_input)-3*interval_days):
for i in range(yearLimits[0], yearLimits[1]-3*interval_days):
ret = run_day(nnetwork, neural_network_input, i, interval_days, total_tests, total_hits, total_hits_highs, total_hits_lows, total_highs, total_lows)
total_tests = ret[0]
total_hits = ret[1]
total_hits_highs = ret[2]
total_hits_lows = ret[3]
total_highs = ret[4]
total_lows = ret[5]
rel_highs=rel_lows=''
if total_highs==0:
rel_highs = '-1'
else:
rel_highs = str(float(total_hits_highs)/total_highs)
if total_lows==0:
rel_lows='-1'
else:
rel_lows = str(float(total_hits_lows)/total_lows)
rel_all = str(float(total_hits)/total_tests)
#print 'All: ' + rel_all + ' High: ' + rel_highs + ' Low: ' + rel_lows
print 'Total:\n' + str(total_hits) + ' out of ' + str(total_tests)
print str(total_hits_highs) + ' out of ' + str(total_highs) + ' highs.'
print str(total_hits_lows) + ' out of ' + str(total_lows) + ' lows.' + '\n'
