def main():
'''main function for generating portfolio'''
pp = []
choices = []
goodAlphas = [
'alpha083', 'alpha101', 'alpha024', 'alpha042', 'alpha028', 'alpha025',
'alpha018', 'alpha010', 'alpha047', 'alpha033', 'alpha009', 'alpha005',
'alpha051'
]
dist = YFI(0)
X, Y = Process.ProcessData(dist, 2)
#alphaIndex = Alphas.SingleAlpha(X, Y, 13)
for i in range(5):
result = Prediction.AvgedPredict(dist, X, Y, goodAlphas[:13], 30, 8)
print(result)
return result
#Calculate percentage return on particular month
goodAlphas = [
'alpha083', 'alpha101', 'alpha024', 'alpha042', 'alpha028', 'alpha025',
'alpha018', 'alpha010', 'alpha047', 'alpha033', 'alpha009', 'alpha005',
'alpha051'
]
alphaIndex = goodAlphas
#target = datetime.datetime(year = 2020, month = 7, day = 1)
#result = Testing.Designated(target, dist, X, Y, alphaIndex, 10)
print('Starting to train neural networks......')
n1 = timi.time()
resl = {}
ress = {}
for i in range(10):
reslo, ressh = Prediction.AvgedPredict(dist, X, Y, alphaIndex, 10, 10,
'08-01-2020')
for j in reslo:
if j in resl:
resl[j] += 1
else:
resl[j] = 1
for k in ressh:
if k in ress:
ress[k] += 1
else:
ress[k] = 1
print(str(i) + ' out of 10 done')
n2 = int(timi.time() - n1) / 60
print('Entire Training took' + str(n2) + ' min')
resl = sorted(resl.items(), key=lambda x: x[1], reverse=True)