def analyzeSymbol(stockSymbol):
startTime = time.time()
flag = 0
trainingData = getTrainingData(stockSymbol)
network = NeuralNetwork(inputNodes=3, hiddenNodes=3, outputNodes=1)
network.train(trainingData)
# get rolling data for most recent day
network.train(trainingData)
for i in range(0, 5):
# get rolling data for most recent day
predictionData = getPredictionData(stockSymbol, flag)
returnPrice = network.test(predictionData)
# de-normalize and return predicted stock price
predictedStockPrice = denormalizePrice(returnPrice, predictionData[1],
predictionData[2])
print predictedStockPrice
flag += 1
global new_value
new_value = predictedStockPrice
return predictedStockPrice
def analyzeSymbol(stockSymbol):
startTime = time.time()
trainingData = getTrainingData(stockSymbol)
network = NeuralNetwork(inputNodes=3, hiddenNodes=3, outputNodes=1)
network.train(trainingData)
# get rolling data for most recent day
predictionData = getPredictionData(stockSymbol)
# get prediction
returnPrice = network.test(predictionData)
# de-normalize and return predicted stock price
predictedStockPrice = denormalizePrice(returnPrice, predictionData[1],
predictionData[2])
# create return object, including the amount of time used to predict
returnData = {}
returnData['price'] = predictedStockPrice
returnData['time'] = time.time() - startTime
return returnData
def analyzeSymbol():
trainingData = getTrainingData()
network = NeuralNetwork(inputNodes = 3, hiddenNodes = 5, outputNodes = 1)
model = network.train(trainingData)
# get rolling data for most recent day
predictionData = getPredictionData(0)
returnPrice = network.test(predictionData)
predictedStockPrice = denormalizePrice(returnPrice, predictionData[1], predictionData[2])
returnData = {}
returnData[0] = predictedStockPrice
return (predictedStockPrice)
def analyzeSymbol(stockSymbol):
startTime = time.time()
trainingData = getTrainingData(stockSymbol)
network = NeuralNetwork(inputNodes = 3, hiddenNodes = 3, outputNodes = 1)
network.train(trainingData)
# get rolling data for most recent day
predictionData = getPredictionData(stockSymbol)
# get prediction
returnPrice = network.test(predictionData)
# de-normalize and return predicted stock price
predictedStockPrice = denormalizePrice(returnPrice, predictionData[1], predictionData[2])
# create return object, including the amount of time used to predict
returnData = {}
returnData['price'] = predictedStockPrice
returnData['time'] = time.time() - startTime
return returnData
def analyzeId(c_id):
startTime = time.time()
count2 =0
itr = 0
if (c_id>0):
network = NeuralNetwork(inputNodes = 5, hiddenNodes = 18, outputNodes = 1) #company id >0 for individual prediciton
averageError = 1
while (averageError>0.01 and itr<2000):
#for i in range(1000):
count1 =0
resultBackProp =[]
while (count1<295):
trainingData = getTrainingData(c_id,count1)
resultBackProp.append(network.train(trainingData))
count1 = count1 +1
averageError = sum(resultBackProp)/len(resultBackProp)
print ("avgError = ", averageError)
#==================================================================
# get data for testing
testError =[]
while (count2<95):
testData = getTestData(c_id,count2)
testError.append(network.accuracyTest(testData))
count2 = count2 + 1
testErrorAverage = sum(testError)/len(testError)
#====================================================================
# get data for tomorrow's prediction
predictionData = getPredictionData(c_id)
# get prediction result
returnPrice = network.test(predictionData)
# de-normalize and return predicted stock price
final = InitialCalculation()
predictedStockPrice = final.denormalizePrice(returnPrice)
# create return object, including the accuracy of the testing
returnData ={}
returnData['price'] = predictedStockPrice
#returnData['accuracy'] = accuracy
returnData['error'] = testErrorAverage
returnData['time'] = time.time() - startTime
print(returnData)
else:
nep_network = NeuralNetwork(inputNodes = 8, hiddenNodes = 20, outputNodes = 1) #company id =0 for nepse prediciton
averageError = 1
while (averageError>0.03 ):
#for i in range(1000):
count1 =0
resultBackProp =[]
#print(getTrainingData(c_id,count1))
while (count1<95):
trainingData = getTrainingData(c_id,count1)
resultBackProp.append(nep_network.trainNepse(trainingData))
count1 = count1 +1
averageError = sum(resultBackProp)/len(resultBackProp)
print ("avgError = ", averageError)
#==================================================================
# get data for testing
# considering 50 test data, we nedd 45 loops
testError =[]
while (count2<10):
testData = getTestData(c_id,count2)
testError.append(nep_network.accuracyTestNepse(testData))
count2 = count2 + 1
testErrorAverage = sum(testError)/len(testError)
#print ("test error =", testError)
#print("te average = ", testErrorAverage)
#accuracy = (1- testErrorAverage)*100
#====================================================================
# get data for tomorrow's prediction
predictionData = getPredictionData(c_id)
# get prediction result
returnPrice = nep_network.test(predictionData)
# de-normalize and return predicted stock price
final = InitialCalculation()
predictedStockPrice = final.denormalizePriceNepse(returnPrice)
# create return object, including the accuracy of the testing
returnData ={}
returnData['price'] = predictedStockPrice
#returnData['accuracy'] = accuracy
returnData['error'] = testErrorAverage
returnData['time'] = time.time() - startTime
print(returnData)
def analyzeSymbol(name):
filename = ('uploads/'+name)
count = sum(1 for line in open(filename))
con = sql.connect("EBL.db")
cur = con.cursor()
y =name
#cur.execute ("DROP TABLE if exists bankname ")
#cur.execute ('''CREATE TABLE bankname (id INTEGER not null PRIMARY KEY, bankname)''')
# cur.execute("insert into bankname (bankname) values (?)", (y,))
#cur.execute("insert into bankname (name) values (55)")
con.commit()
cur.execute ("DROP TABLE if exists {} ".format (name))
cur.execute( '''CREATE TABLE {}(id INTEGER not null PRIMARY KEY, date DATE, ltp INTEGER, percent INTEGER, high INTEGER, low INTEGER, prediction INTEGER)'''.format(y))
#cur.execute("CREATE TABLE fuche ( id INTEGER not null PRIMARY KEY, date, volume, lamo)")
with open ('uploads/'+name) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
#print(readCSV)
for row in readCSV:
cur.execute("insert into {} (date, ltp, percent, high, low) values (?,?, ?, ?, ?)".format(y), ( row[0], row[1], row[2], row[3], row[4]))
con.commit()
#b=b+1
#INSERT INTO MyTable(MyIntColumn) VALUES(NULL);
# cur.execute("insert into {} (date, ltp, percent, high, low) values (NULL, NULL, NULL, NULL, NULL)".format(y))
# con.commit()
x = name
trainingData = getTrainingData(x)
network = NeuralNetwork(inputNodes = 3, hiddenNodes = 2, outputNodes = 1)
network.train(trainingData)
f****n = []
pussy = []
for a in range (0,count-9):
predictionData = getPredictionData(name, a)
#returnPrice = loaded_model(network.test(predictionData))
returnPrice = network.test(predictionData)
#result = loaded_model.returnPrice
predictedStockPrice = denormalizePrice(returnPrice, predictionData[1], predictionData[2])
predictedStockPrice=math.floor(predictedStockPrice)
returnData = {}
returnData[a] = predictedStockPrice
con = sql.connect("EBL.db")
cur = con.cursor()
pussy += [predictedStockPrice]
#pussy[a]=predictedStockPrice
cur.execute('update {} set prediction = ? WHERE (id = ?)'.format(y), (pussy[a], a+11,))
#cur.execute('insert into fuche (lamo) values (?) WHERE (id = 5)', (returnData[a],)) Working code
con.commit()
'''
for x in range (0,9):
cur.execute ('SELECT * FROM plcs WHERE id=(SELECT max(id)-? FROM plcs)', (x,))
print(x)
result = cur.fetchall()
for row in result:
#print (result)
print (row[0])
'''
#cur.execute ('SELECT COUNT(1) from {}'.format(y) )
# totalrow = cur.fetchone()[0]
#con.commit()
#cur.execute('UPDATE fuche set lamo = "maya" where id = (?)', (a))
# con.commit()
#print ("Return price")
#print (returnData['price'])
#print ("Prediction")
#conn = sqlite3.connect('EBL.db')
#curs = conn.cursor()
#nm = '2036-12-30'
#addr = '230'
#city = '776'
# curs.execute("INSERT INTO banks (date, value, luck) VALUES (?,?,?)",(nm, addr, returnData[a]) )
#conn.commit()
# to insert the predicted value inside the database
#print (returnData[a])
# print (b)
for bipin in range (0, 9):
xdata=[]
for r in range (0,10):
cur.execute ('SELECT * FROM {} WHERE id=(SELECT max(id)-? FROM {} )'.format(name, name), (r,))
# print(a)
result = cur.fetchall()
for row in result:
#print (result)
#print (row[0])
data = row[2]
if data==None:
data=row[6]
xdata.append(data)
# print (xdata)
fuckoff = getTimeSeriesValues(xdata, 10)
f****n = fuckoff[0][0]
Predicted = network.test(f****n)
predict = float(denormalizePrice(Predicted, f****n[1], f****n[2]))
predict=math.floor(predict)
# print ("Predicted")
# print (predict)
cur.execute("SELECT * from {} where id = (SELECT max(id) FROM {})".format(name, name))
result = cur.fetchall()
for row in result:
x = row[0]
print (x)
datee=row[1]
# print(row[1]+1)
# end_date = row[1] + datetime.timedelta(days=10)
# print (end_date)
date = datetime.strptime(row[1], "%Y-%m-%d")
modified_date = date + timedelta(days=1)
x = datetime.strftime(modified_date, "%Y-%m-%d")
# print(row[1])
cur.execute("insert into {} (date, prediction) values (?, ?)".format(name), (x, predict,))
con.commit()
return (returnData[a])
