def main():
parser = OptionParser()
parser.add_option("-o", "--output", dest="outfile",
help="csv filename for output")
(options, args) = parser.parse_args()
# print "Options: " + str(options.outfile)
writeToFile = False
if options.outfile != None:
writeToFile = True
filename = options.outfile
fd = open(filename, "wb")
fd.write("type,name,prediction\n")
objects = []
for s in group_strings:
objects.append( load(s + ".pkl"))
# groups = []
# for o in objects:
# groups.append(o.getShares())
group_prediction = []
for g in objects:
prediction_percent = []
for share in g.getShares():
#if share.get_info()['symbol'] != "INTU":
# continue
model = train(share, train_start, train_end,alg_type)
predict = test(share, test_start, test_end, model,alg_type)
if model == None or predict == None:
continue
prediction_percent.append(predict)
if writeToFile:
fd.write("share," + share.get_info()['symbol'] + \
"," + str(predict) + "\n")
print "Share: " + share.get_info()['symbol'] + \
"\tpredict: " + str(predict)
avg = np.mean(prediction_percent)
group_prediction.append(avg)
if writeToFile:
fd.write("group," + g.name + "," + str(avg) + "\n")
print "Group: " + g.name + "\tpredict: " + str(avg) + "\n"
fd.close()
def main():
parser = OptionParser()
parser.add_option("-o", "--output", dest="outfile",
help="csv filename for output")
parser.add_option("-p", "--predict", action="store_true",
dest="predict", default=False,
help="Run prediction metrics")
(options, args) = parser.parse_args()
# print "Options: " + str(options.outfile)
# print "Options:" + str(options.verbose)
run_predict = options.predict
writeToFile = False
if options.outfile != None:
writeToFile = True
filename = options.outfile
fd = open(filename, "wb")
fd.write("type,name,prediction\n")
else:
filename = None
fd = None
objects = []
for s in group_strings:
objects.append( load(s + ".pkl"))
# groups = []
# for o in objects:
# groups.append(o.getShares())
group_prediction = []
for g in objects:
prediction_percent = []
retry_count = 5
while retry_count > 0:
try:
for share in g.getShares():
#if share.get_info()['symbol'] != "INTU":
# continue
model = train(share, train_start, train_end)
if model == None:
continue
g.addModel(share, model)
if run_predict:
predict = test(share, test_start, test_end, model)
if predict == None:
continue
else:
predict = 0
prediction_percent.append(predict)
if writeToFile:
fd.write("share," + share.get_info()['symbol'] + \
"," + str(predict) + "\n")
try:
print "Share: " + share.get_info()['symbol'] + \
"\tpredict: " + str(predict)
except yf.YQLQueryError:
print "StockPrediction - trying to print Share: \
symbol prediction. Share: " + share.get_info()
raise
except yf.YQLQueryError:
print "StockPrediction - YQLQueryError on train and test. Share: \
" + share.get_info()
retry_count = retry_count - 1
continue
except:
print "StockPrediction - unexpectederror on train and test. Share: \
" + share.get_info()
retry_count = retry_count - 1
if retry_count == 0:
raise
else:
continue
break
avg = np.mean(prediction_percent)
group_prediction.append(avg)
if writeToFile:
fd.write("group," + g.name + "," + str(avg) + "\n")
print "Group: " + g.name + "\tpredict: " + str(avg) + "\n"
if writeToFile:
fd.close()
for g in objects:
[p1, p2] = trade(g, test_start, test_end, g.name + ".csv")
print "Group: " , g.name + "\tstart: " , str(p1) \
, "\tend: " , str(p2)
