def main():
import sys
import time
sys.path.append('/home/jdw/UM2020Spring/M567/GetStockData/')
import StockFunctions as gsf
#First we get some data. This function returns an array of times and prices.
#First set the length of time to get.
numHours = 6
numSec = numHours * 360
#The variable sleepTime is the number of seconds to sleep before getting
#another stock price.
sleepTime = 10
#Let us get the data and time.
dt = time.localtime()
yearStr = str('{0:02d}'.format(dt.tm_year))
monthStr = str('{0:02d}'.format(dt.tm_mon))
dayStr = str('{0:02d}'.format(dt.tm_mday))
hourStr = str('{0:02d}'.format(dt.tm_hour))
minuteStr = str('{0:02d}'.format(dt.tm_min))
dateTime = (yearStr + monthStr + dayStr + hourStr + minuteStr)
#The name of the stock we are looking at.
stockAbbrev = 'tsla' #Tesla
#Now get the data from the IEX server.
data = gsf.query(numSec, sleepTime, stockAbbrev, dateTime)
#Now plot the data.
succeed = gsf.visualizeDailyStockPrice(data, stockAbbrev, dateTime)
def writeFile(LST, LSP, tag):
# breakpoint()
import csv, time
import numpy as np
import os.path
from os import path
import sys
sys.path.append('/home/jdw/UM2020Spring/M567/Functions/')
import StockFunctions as sf
timeNonzero = np.nonzero(LST)
t_vals = time.localtime(LST[timeNonzero[0][0]] / 1000.0)
#Convert the integers to strings and prepend 0's to those values which
#are less than 10.
year = str('{0:04d}'.format(t_vals[0]))
month = str('{0:02d}'.format(t_vals[1]))
day = str('{0:02d}'.format(t_vals[2]))
hour = str('{0:02d}'.format(t_vals[3]))
minute = str('{0:02d}'.format(t_vals[4]))
#Create a string for the date and time that the data were collected.
dateTime = year + month + day + hour + minute
stockDir = sf.GetStockNames(tag)
dirpath = '/home/jdw/UM2020Spring/M567/Data/' + stockDir + '/'
fname = 'StockPrice_' + str(tag) + dateTime + '.txt'
filename = dirpath + fname
#Write the data to a file.
if (path.exists(filename)):
#Append to the file.
with open(filename, mode='a') as f:
stock_writer = csv.writer(f, delimiter=',')
for i in range(len(LST)):
stock_writer.writerow([LST[i], LSP[i]])
else:
#Write the file.
with open(filename, mode='w') as f:
stock_writer = csv.writer(f, delimiter=',')
for i in range(len(LST)):
stock_writer.writerow([LST[i], LSP[i]])
return True
def main() :
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
import math
import argparse #A parser for command line options.
import sys
import csv
import glob
#Since python cannot figure out my path I append my function directory to the path.
sys.path.append('/home/jdw/UM2020Spring/M567/Functions/')
#Now import StockFunctions.py.
import StockFunctions as sf
#Create an argparse object.
# parser = argparse.ArgumentParser()
#Add some optional arguments.
#Get the name of the company for which the stock prices were obtained.
# stockNameHelp = 'Input the name of a company whose stock prices you want to visualize.'
# parser.add_argument('-s','--stockName', help = stockNameHelp, default = 'Google')
#Get the year the stock prices were obtained.
# yearHelp = 'Input the year the stock price was obtained. Value is integer.'
# parser.add_argument('-y', '--year', help = yearHelp, default = 2020, type = int)
#Get the month the stock prices were obtained.
# monthHelp = 'Input the month the stock price was obtained. Value is integer.'
# parser.add_argument('-m', '--month', help = monthHelp, default = 3, type = int)
#Get the day the stock prices were obtained.
# dayHelp = 'Input the day the stock price was obtained. Value is integer.'
# parser.add_argument('-d', '--day', help = dayHelp, default = 2, type = int)
#Get the input directory for the plot.
# inputHelp = 'Input the full path of the directory in which the data is found.'
# defaultInputDir = '/home/jdw/UM2020Spring/M567/Data/Google/'
# parser.add_argument('-D','--Directory', help = inputHelp, default = defaultInputDir)
#Get the output directory for the plot.
# outputHelp = 'Input the full path of the directory in which to place the output file.'
# defaultOutputDir = '/home/jdw/UM2020Spring/M567/Plots/'
# parser.add_argument('-P','--Plots', help = outputHelp, default = defaultOutputDir)
#Set the input arguments to the args object.
# args = parser.parse_args()
#Make up the input and output file names.
# stockAbbrev = sf.GetStockAbbrev(args.stockName)
# datetime = str(args.year) + str('{0:02d}'.format(args.month)) + str('{0:02d}'.format(args.day))
#Choose a company whose stock price you want to visualize.
stockName = 'Verizon'
stockAbbrev = sf.GetStockAbbrev(stockName)
directory = ('/home/jdw/UM2020Spring/M567/Data/' + stockName +
'/StockPrice_' + stockAbbrev + '*.txt')
infiles = glob.glob(directory)
for filename in infiles :
if(filename[-4:] == '.txt') :
datetime = filename[-16 : -4]
month = filename[-12 : -10]
month = month.lstrip('0')
day = filename[-10 : -8]
day = day.lstrip('0')
year = filename[-16 : -12]
#Create a output file name to where the plot will be saved.
outfilepath = '/home/jdw/UM2020Spring/M567/Data/' + stockName + '/'
outfilename = stockName + 'DailyStockPrice' + datetime + '.pdf'
outfile = outfilepath + outfilename
#Open and read in the saved data. We need to use infile[0] because glob returns a list.
with open(filename) as csv_file :
csv_reader = csv.reader(csv_file, delimiter = ',')
line_count = 0
dateTime = []
price = []
for row in csv_reader :
dateTime.append(float(row[0]))
price.append(float(row[1]))
#End of the for loop - for row in csv_reader:
#End of the with/open loop.
numHalfHours = 13
dpperhalfhour = math.ceil(len(price)/numHalfHours)
tickval = np.zeros(8)
for i in range(1, 8) :
tickval[i] = dpperhalfhour*(2*i - 1)
#End of the for loop - for i in range(1, 8) :
#Create a time vector.
t = np.arange(1, len(price) + 1)
#Get the date for the data.
monthNameStr = sf.convertMonthNumToMonthName(int(month))
dateStr = (monthNameStr + ' ' + day + ' ' + year)
#Create a title string.
titlestr = ('Stock Price vs Time for ' + dateStr + ' - ' + stockName)
#Lets make sure we have a clean canvas.
plt.close('all')
plt.figure()
plt.plot(t, price, 'b')
plt.grid('on')
plt.title(titlestr, fontsize = 9)
plt.ylabel('Stock Price(Dollars)')
plt.xlabel('Time')
plt.xticks(tickval, ('7:30 ',
'8:00AM','9:00AM','10:00AM','11:00AM','12:00PM','1:00PM','2:00PM'),
fontsize = 7)
#Save the plot to a file.
pp = PdfPages(outfile)
pp.savefig()
pp.close()
plt.cla()
plt.clf()