def main(): group_str = ['large', 'medium', 'small'] objects = [] for gs in group_str: objects.append(load(gs + ".pkl")) loader = 0 for o in objects: for s in o.getShares(): try: data = s.get_historical(start, end) if data[0]['Date'] == end and \ data[-1]['Date'] == start: print "Stock valid: " + \ s.get_info()['symbol'] else: print "STOCK INVALID: " + \ s.get_info()['symbol'] except yf.YQLQueryError: print "get_hist error on: " + \ s.get_info()['symbol'] except KeyError: print "KeyError on date: " + \ s.get_info()['symbol'] except: print "Unexpecter error", sys.exc_info()[0] raise
def main():
# ewma = pandas.stats.moments.ewma
# yahoo = Share('YHOO')
try:
groups = ['small', 'medium', 'large']
object_groups = []
#yahoo = Share('YHOO')
#print yahoo.get_historical('2014-04-25', '2014-04-29')
#print sys.argv[1], sys.argv[2]
for g in groups:
object_groups.append(load(g + '.pkl'))
file_object = open("raw_data.txt","w+" )
for small_g in object_groups:
for share in small_g.shares:
# print share
data_list = small_g.shares[share].get_historical(str(sys.argv[1]), str(sys.argv[2]))
for index in range(len(data_list)):
file_object.write('Symbol: '+str(data_list[index]['Symbol'])+ ' Date : '+str(data_list[index]['Date']) + ' Volume: '+str(data_list[index]['Volume']) + ' Close : '+str(data_list[index]['Close']) +"\n")
#print(data_list[index])
# data_list = yahoo.get_historical('2014-04-25', '2014-05-29')
except IOError:
print "Error: can\'t find file or read data"
else:
print "Written content in the file successfully"
file_object.close()
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(start_date, end_date, printValue):
# ewma = pandas.stats.moments.ewma
# yahoo = Share('YHOO')
try:
#groups = ['small', 'medium', 'large']
groups = [ 'large']
object_groups = []
#yahoo = Share('YHOO')
#print yahoo.get_historical('2014-04-25', '2014-04-29')
#print sys.argv[1], sys.argv[2]
for g in groups:
object_groups.append(load(g + '.pkl'))
file_object = open("raw_data.txt","w+" )
X_label = [None]*10
Y_label = [None]*10
i = 0
for small_g in object_groups:
for share in small_g.shares:
# print share
X_label[i] = []
Y_label[i] = []
data_list = small_g.shares[share].get_historical(start_date, end_date)
for index in range(len(data_list)):
file_object.write('Symbol: '+str(data_list[index]['Symbol'])+ ' Date : '+str(data_list[index]['Date']) + ' Volume: '+str(data_list[index]['Volume']) + ' Open : '+str(data_list[index]['Open']) + ' Close : '+str(data_list[index]['Close']) + ' High : '+str(data_list[index]['High']) + ' Low : '+str(data_list[index]['Low'])+ ' Adj_Close : '+str(data_list[index]['Adj_Close']) + "\n")
if( index < (len(data_list)-9) ):
X_label[i].append([ data_list[index]['Volume'],data_list[index]['Open'],data_list[index]['Close'],data_list[index]['High'],data_list[index]['Low'],data_list[index]['Adj_Close'], ema(data_list, 10, data_list[index]['Date'])[1][0]])
j = index - 1
if data_list[index]['Close'] <= data_list[j]['Close']:
Y_label[i].append(1)
if data_list[index]['Close'] > data_list[j]['Close']:
Y_label[i].append(2)
i=+1
if(printValue):
print X_label[0]
print Y_label[0]
else:
return [X_label, Y_label]
#print(data_list[index])
# data_list = yahoo.get_historical('2014-04-25', '2014-05-29')
except IOError:
print "Error: can\'t find file or read data"
else:
print "Written content in the file successfully"
file_object.close()
def main():
largegroup = load('large.pkl')
tickers = largegroup.getTickers()
first_share = largegroup.shares[tickers[0]]
hist_data = first_share.get_historical('2014-04-01', '2014-04-29')
smas = sma(hist_data, 10)
[emas, emas_array] = ema(hist_data, 10)
print "Simple Moving Averages"
for date in smas:
print date + ": " + str(smas[date])
print "\nExponential Moving Averages"
for date in emas:
print date + ": " + str(emas[date])
#pprint(hist_data)
for v in emas_array:
print str(v)
from object_saver import load groups = ['small', 'medium', 'large'] object_groups = [] for g in groups: object_groups.append(load(g + '.pkl')) for og in object_groups: og.printShares()
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)
