def Main(args):
if (len(args) != 3 and len(args) != 4):
PrintUsage()
return
#Test if file exists
try:
open(args[0])
except Exception as e:
print('Error opening file: ' + args[0])
print(str(e))
PrintUsage()
return
#Test validity of start date string
try:
datetime.strptime(args[1], '%Y-%m-%d').timestamp()
except Exception as e:
print(e)
print('Error parsing date: ' + args[1])
PrintUsage()
return
#Test validity of end date string
try:
datetime.strptime(args[2], '%Y-%m-%d').timestamp()
except Exception as e:
print('Error parsing date: ' + args[2])
PrintUsage()
return
#Test validity of final optional argument
if (len(args) == 4):
predPrd = args[3].upper()
if (predPrd == 'D'):
predPrd = 'daily'
elif (predPrd == 'W'):
predPrd = 'weekly'
elif (predPrd == 'M'):
predPrd = 'monthly'
else:
PrintUsage()
return
else:
predPrd = 'daily'
#Everything looks okay; proceed with program
#Grab the data frame
D = ParseData(args[0])
#The number of previous days of data used
#when making a prediction
numPastDays = 16
PlotData(D)
#Number of neurons in the input layer
i = numPastDays * 7 + 1
#Number of neurons in the output layer
o = D.shape[1] - 1
#Number of neurons in the hidden layers
h = int((i + o) / 2)
#The list of layer sizes
#layers = [('F', h), ('AF', 'tanh'), ('F', h), ('AF', 'tanh'), ('F', o)]
#R = ANNR([i], layers, maxIter = 1000, tol = 0.01, reg = 0.001, verbose = True)
R = KNeighborsRegressor(n_neighbors=5)
sp = StockPredictor(R, nPastDays=numPastDays)
#Learn the dataset and then display performance statistics
sp.Learn(D)
sp.TestPerformance()
#Perform prediction for a specified date range
P = sp.PredictDate(args[1], args[2], predPrd)
#Keep track of number of predicted results for plot
n = P.shape[0]
#Append the predicted results to the actual results
D = P.append(D)
#Predicted results are the first n rows
PlotData(D, range(n + 1))
return (P, n)
def Main(args):
if (len(args) != 3 and len(args) != 4):
PrintUsage()
return
#Obtain CSV from Internet
print(
'\n############# Downloading Historical Data from Internet #############'
)
file = args[0] + '.csv'
str2 = 'http://real-chart.finance.yahoo.com/table.csv?s=' + args[
0] + '.NS&d=02&e=16&f=2017&g=d&a=0&b=1&c=1996&ignore=.csv'
f = open(file, 'wb')
f.write(requests.get(str2).content)
f.close()
print('\n############# Download Complete ##############')
#Test if file exists
try:
open(file)
except Exception as e:
print('Error opening file: ' + file)
print(str(e))
PrintUsage()
return
#Test validity of start date string
try:
datetime.strptime(args[1], '%Y-%m-%d').timestamp()
except Exception as e:
print('Error parsing date: ' + args[1])
PrintUsage()
return
#Test validity of end date string
try:
datetime.strptime(args[2], '%Y-%m-%d').timestamp()
except Exception as e:
print('Error parsing date: ' + args[2])
PrintUsage()
return
#Test validity of final optional argument
if (len(args) == 4):
predPrd = args[3].upper()
if (predPrd == 'D'):
predPrd = 'daily'
elif (predPrd == 'W'):
predPrd = 'weekly'
elif (predPrd == 'M'):
predPrd = 'monthly'
else:
PrintUsage()
return
else:
predPrd = 'daily'
print('\n############# Processing on Data ##############')
#Everything looks okay; proceed with program
D = ParseData(file)
#The number of previous days of data used
#when making a prediction
numPastDays = 20
PlotData(D, args[0])
#Number of neurons in the input layer
i = numPastDays * 7 + 1
#Number of neurons in the output layer
o = D.shape[1] - 1
#Number of neurons in the hidden layers
h = int((i + o) / 2)
#The list of layer sizes
layers = [i, h, h, o]
#Type of Regressor Used
#R = RandomForestRegressor(n_estimators = 5)
R = MLPR(layers, maxItr=1000, tol=0.40, reg=0.001, verbose=True)
sp = StockPredictor(R, nPastDays=numPastDays)
#Learn the dataset and then display performance statistics
sp.Learn(D)
#sp.TestPerformance()
#Perform prediction for a specified date range
P, R = sp.PredictDate(args[1], args[2], predPrd)
print('\n############# Predection is on the way ##############')
#Keep track of number of predicted results for plot
n = P.shape[0]
#Append the predicted results to the actual results
D = P.append(D)
#Predicted results are the first n rows
PlotData(D, args[0], range(n + 1))
return (R, n)
def main():
predictor = StockPredictor()
predictor.train(period="Year")
print("Year prediction:", predictor.predict(2020))
predictor.plot_prediction(period="Year", numeric_value=2020)
predictor.train(period="Month")
print("Month prediction:", predictor.predict(9))
predictor.plot_prediction(period="Month", numeric_value=9)
predictor.train(period="Day")
print("Day prediction:", predictor.predict(20))
predictor.plot_prediction(period="Day", numeric_value=20)
def Main(args):
if(len(args) != 3 and len(args) != 4):
PrintUsage()
return
#Test if file exists
try:
open(args[0])
except Exception as e:
print('Error opening file: ' + args[0])
print(str(e))
PrintUsage()
return
#Test validity of start date string
try:
datetime.strptime(args[1], '%Y-%m-%d').timestamp()
except Exception as e:
print('Error parsing date: ' + args[1])
PrintUsage()
return
#Test validity of end date string
try:
datetime.strptime(args[2], '%Y-%m-%d').timestamp()
except Exception as e:
print('Error parsing date: ' + args[2])
PrintUsage()
return
#Test validity of final optional argument
if(len(args) == 4):
predPrd = args[3].upper()
if(predPrd == 'D'):
predPrd = 'daily'
elif(predPrd == 'W'):
predPrd = 'weekly'
elif(predPrd == 'M'):
predPrd = 'monthly'
else:
PrintUsage()
return
else:
predPrd = 'daily'
#Everything looks okay; proceed with program
#Grab the data frame
D = ParseData(args[0])
#The number of previous days of data used
#when making a prediction
numPastDays = 15
#PlotData(D)
#Number of neurons in the input layer
i = numPastDays * 7 + 1
#Number of neurons in the output layer
o = D.shape[1] - 1
#Number of neurons in the hidden layers
h = int((i + o) / 2)
#The list of layer sizes
layers = [i, h, h, h, h,h,h, o]
R = MLPR(layers, maxItr = 100000, tol = 0.01, reg = 0.001, verbose = True, batchSize=200)
#R = KNeighborsRegressor(n_neighbors = 7)
sp = StockPredictor(R, nPastDays = numPastDays)
#Learn the dataset and then display performance statistics
size=len(D)
trainingData =DataFrame.reset_index(D[5:size], drop=True)
print(trainingData)
sp.Learn(trainingData)
sp.TestPerformance()
#Perform prediction for a specified date range
P= sp.PredictDate(args[1], args[2], predPrd)
#Keep track of number of predicted results for plot
print("done")
print(P)
n = P.shape[0]
print("nnnnn")
print(n)
#Append the predicted results to the actual results
#D = P.append(D)
#Predicted results are the first n rows
PlotTestData(P, D[0:5])
return (P, n)