x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] y = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] z = [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000] def SlopeD1(x, y, z, slope1, slope2, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope1 * x[i]) + (slope2 * y[i]) + yInt difference = (z[i] - predicted) * x[i] total += difference returnValue = (-2/length) * total return returnValue def SlopeD2(x, y, z, slope1, slope2, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope1 * x[i]) + (slope2 * y[i]) + yInt difference = (z[i] - predicted) * y[i] total += difference returnValue = (-2/length) * total return returnValue def InterceptD(x, y, z, slope1, slope2, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope1 * x[i]) + (slope2 *y[1]) + yInt difference = (z[i] - predicted) total += difference returnValue = (-2/length) * total return returnValue m1 = 0 m2 = 0 c = 0 l = 0.000001 iterations = 99999 for i in range(iterations): mm1 = SlopeD1(x, y, z, m1, m2 ,c) mm2 = SlopeD2(x, y, z, m1, m2 ,c) yint = InterceptD(x, y, z, m1, m2 ,c) m1 = m1 - (l * mm1) m2 = m2 - (l * mm2) c = c - (l * yint) #print(m1, m2, c) print ('(', end ='' ) print(m1, end=', ') print(m2, end=', ') print(c, end=')')

x = [1, 5, 3, 4, 7, 9, 12, 13, 15, 16, 17, 4, 5, 2, 10, 23, 25] y = [5, 12, 23, 14, 17, 8, 20, 21, 25, 38, 42, 10, 13, 7, 23, 50, 55] def SlopeD(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) * x[i] total += difference returnValue = (-2/length) * total return returnValue def InterceptD(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) total += difference returnValue = (-2/length) * total return returnValue m = 0 c = 0 l = 0.000001 iterations = 99999 for i in range(iterations): slope = SlopeD(x, y, m ,c) intercept = InterceptD(x, y, m ,c) m = m - (l * slope) c = c - (l * intercept) print (m, c)

import numpy as np import matplotlib.pyplot as plt def estimate_coef(x, y): n = np.size(x) m_x, m_y = np.mean(x), np.mean(y) SS_xy = np.sum(y*x) - n*m_y*m_x SS_xx = np.sum(x*x) - n*m_x*m_x b_1 = SS_xy / SS_xx b_0 = m_y - b_1*m_x return(b_0, b_1) def plot_regression_line(x, y, b): plt.scatter(x, y, color = "m", marker = "o", s = 30) y_pred = b[0] + b[1]*x plt.plot(x, y_pred, color = "g") plt.xlabel('x') plt.ylabel('y') plt.show() def main(): x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12]) b = estimate_coef(x, y) print("Estimated coefficients:\nb_0 = {} \" \nb_1 = {}".format(b[0], b[1]")) plot_regression_line(x, y, b) if __name__ == "__main__": main()

x = [15, 9, 12, 1, 10, 11, 4, 16, 2, 30, 4, 15, 18, 12, 14] y = [13 ,19, 16, 15, 24, 7, 18, 23, 28, 2, 26, 12, 18, 24, 17] def DerivativeSlope(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) * x[i] total += difference returnValue = (-2/length) * total return returnValue def DerivativeIntercept(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) total += difference returnValue = (-2/length) * total return returnValue m = 0 c = 0 l = 0.000001 iterations = 99999 for i in range(iterations): slope = DerivativeSlope(x, y, m ,c) intercept = DerivativeIntercept(x, y, m ,c) m = m - (l * slope) c = c - (l * intercept) print (m, c)

import math def cluster(table, maximum, k): for i in range(k): max1 = 10000 a = table[i] for f in range(k): if i !=f: b = table[f] distance =0 for i in range(3): distance += math.sqrt((a[i][0]-b[i][0])**2 + (a[i][1]-b[i][1])**2 + (a[i][2]-b[i][2])**2) if distance<max1: max1 = distance hello = f if max1 < maximum: indeces = (i, hello) return (indeces) def avg(a, b, table): e = table[a] f = table[b] new = [] for i in range(3): g = e[i] h = f[i] new.append(((g[0]+h[0])/2, (g[1]+h[1])/2,(g[2]+h[2])/2)) return (new) def recursion(table, k): if k ==2: table = avg(0, 1, table) return (table) else: maximum = 10000000 a= cluster(table, maximum, k) index1= a[0] index2= a[1] #print (index1, index2) newCluster = avg(index1, index2, table) table[index1] = newCluster table.pop(index2) return recursion(table, k-1) table = [[(1, 5,7), (1, 6, 2), (5, 3, 1)], [(2, 3, 4), (1, 2, 7), (2, 1, -5)],[(-1, -1, -1), (0, 0, 0), (3, 3, 3)], [(2, 12, 3), (2, 4, 1), (2, 4, 5)]] a = recursion(table, 4) print (str(a[0]) + " and " + str(a[1]) + " and " + str(a[2]))

import math traindata = [(1,1,"blue"), (2,2,"blue"), (3,3,"black"), (4,4,"blue"), (5,5,"black"), (4,3,"blue"), (4,10,"black"), (5,5,"black"), (6,2,"blue"), (1,7,"black"), (4,9,"blue"), (5,8,"blue")] testdata = [] testdatax = int(input("Enter the x coordinate of your testing data: ")) testdatay = int(input("Enter the y coordinate of your testing data: ")) n = int(input("Enter the number of nearest neighbors you wish to calculate: ")) testdata.append(testdatax) testdata.append(testdatay) distances = [] for x in traindata: a = x[0] b = x[1] distance = math.sqrt((abs(a - testdatax))^2 + (abs(b-testdatay))^2) distances.append(distance) mindistances = [] indices = [] while len(mindistances) < n: mindistance = min(distances) mindistances.append(mindistance) u = distances.index(mindistance) indices.append(u) distances[u] = float('inf') colors = [] for m in indices: value = traindata[m] color = value[2] colors.append(color) blues = [] blacks = [] for k in colors: if k == "blue": blues.append(k) elif k == "black": blacks.append(k) if len(blues)> len(blacks): print("Blue") elif len(blacks)> len(blues): print("Black")

import math import statistics import random centroids = [(random.randint(0, 12), random.randint(0, 12), random.randint(0, 12), "blue"), (random.randint(0, 12), random.randint(0, 12), random.randint(0, 12), "green"), (random.randint(0, 12), random.randint(0, 12), random.randint(0, 12), "red")] data = [(3,5 , 10), (4, 12, 1), (7, 8, 5), (2, 6, 11), (3, 1, 4), (4, 1, 7), (1, 2, 3), (5, 1, 3), (6, 2, 3)] length2 = len(data) NewData = [] def FindClosest(centroid, xvalue, yvalue, zvalue): base = 100000 length = len(centroid) for i in range(length): a = centroids[i] x = a[0] y = a[1] z = a[2] distance = math.sqrt(abs(xvalue - x)**2 + abs(yvalue-y)**2 + abs(zvalue-z)**2) if distance < base: base = distance place = i return (place) def ChooseColor(place, centroids): a = centroids[place] color = a[3] return (color) for i in range(length2): a = data[i] xvalue = a[0] yvalue = a[1] zvalue = a[2] NewData.append((xvalue, yvalue, zvalue, ChooseColor(FindClosest(centroids, xvalue, yvalue, zvalue), centroids))) for i in range(len(NewData)): a = NewData[i] print ("(" + str(a[0]) + ", " + str(a[1]) + ", " + str(a[2]) + ") , color = " + a[3])

import sys from PyQt5.QtCore import pyqtSlot from PyQt5.QtWidgets import QApplication, QDialog, QMainWindow from PyQt5.uic import loadUi from PyQt5 import QtWidgets import quandl import math import dash import dash_core_components as dcc import dash_html_components as html import threading import numpy as np import datetime from sklearn.linear_model import LinearRegression from sklearn import preprocessing, model_selection, svm from sklearn.svm import SVR class MainWindow(QMainWindow): #link the UI def __init__(self): super(MainWindow, self).__init__() loadUi('homepage.ui', self) quandl.ApiConfig.api_key = "Ui3JT8HDDV3vnADUsD2k" #call the fill combobox function x=self.fillCombobox() #call the function to take the text from the combobox self.pushButton_2.clicked.connect(self.averageStock) #call the function to graph a stock self.pushButton_2.clicked.connect(self.visual) self.pushButton_3.clicked.connect(self.visual) self.pushButton_4.clicked.connect(self.visual) self.pushButton_5.clicked.connect(self.visual) #call the function to calculate calculate correlation self.pushButton_5.clicked.connect(self.calcCoCo) #call the function to graph correlation data self.pushButton.clicked.connect(self.graph) self.pushButton_4.clicked.connect(self.visual2) self.pushButton_3.clicked.connect(self.graph2) self.pushButton_4.clicked.connect(self.graph3) #call the function to graph related data self.pushButton_5.clicked.connect(self.visual3) self.pushButton_7.clicked.connect(self.futureDate) self.pushButton_8.clicked.connect(self.futureStocks) self.pushButton_9.clicked.connect(self.volatility) #function for filling the comboboxes def fillCombobox(self): companies=['WIKI/AAPL', 'WIKI/GOOGL', 'WIKI/TSLA', 'WIKI/IBM', 'WIKI/WMT', 'WIKI/T', 'WIKI/AMZN', 'WIKI/INTC', 'WIKI/NKE', 'WIKI/EA'] #fill the stock comboboxes for company in companies: self.comboBox.addItem(company) self.comboBox_2.addItem(company) self.comboBox_3.addItem(company) self.comboBox_4.addItem(company) self.comboBox_22.addItem(company) self.comboBox_19.addItem(company) self.comboBox_24.addItem(company) #fill the year comboboxes for i in range(2007, 2019): self.comboBox_6.addItem(str(i)) self.comboBox_7.addItem(str(i)) self.comboBox_8.addItem(str(i)) self.comboBox_25.addItem(str(i)) self.comboBox_26.addItem(str(i)) self.comboBox_9.addItem(str(i)) self.comboBox_11.addItem(str(i)) self.comboBox_12.addItem(str(i)) self.comboBox_18.addItem(str(i)) self.comboBox_14.addItem(str(i)) for i in range(2019, 2030): self.comboBox_23.addItem(str(i)) for i in range(13): if i < 10: self.comboBox_21.addItem("0" + str(i)) self.comboBox_17.addItem("0" + str(i)) else: self.comboBox_21.addItem(str(i)) self.comboBox_17.addItem(str(i)) for i in range(32): if i < 10: self.comboBox_15.addItem("0" + str(i)) self.comboBox_20.addItem("0" + str(i)) else: self.comboBox_15.addItem(str(i)) self.comboBox_20.addItem(str(i)) for i in range(10000, 100000, 10000): self.comboBox_16.addItem(str(i)) #function for taking the text from the comboboxes def averageStock(self): company=self.comboBox.currentText() year_start=int(self.comboBox_8.currentText()) year_end=int(self.comboBox_9.currentText()) listC = [] totalC = 0 for i in range(year_start, year_end+1): df = quandl.get(company, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() listC.append(y) totalC += y avgC = totalC / len(listC) self.textEdit_27.setText(str(avgC)) #functions to graph a stock def visual(self): threading.Thread(target=self.graph, daemon=True).start() def graph(self): #make the list for the y values company=self.comboBox_4.currentText() year_start=int(self.comboBox_7.currentText()) year_end=int(self.comboBox_6.currentText()) listC = [] totalC = 0 for i in range(year_start, year_end+1): print(i) df = quandl.get(company, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() listC.append(y) #make the list for the x values years=[] for i in range(year_start, year_end+1): years.append(i) #make the graph app = dash.Dash() app.layout = html.Div(children=[ html.H1(children=str(company)+' Graph'), html.Div(children='Graph of '+str(company)+' In The Years '+str(year_start)+' through '+str(year_end) ), dcc.Graph( id='ui-graphicvisualization', figure={ 'data': [ {'x': years, 'y': listC, 'type': 'line', 'name': company}, ], 'layout': { 'title': str(company) } } ) ]) app.run_server(port=1111) #function to calculate correlation def futureDate(self): sday = self.comboBox_15.currentText() smonth = self.comboBox_21.currentText() syear = self.comboBox_18.currentText() eday = self.comboBox_20.currentText() emonth = self.comboBox_17.currentText() eyear = self.comboBox_14.currentText() valueInvested = self.comboBox_16.currentText() inputStock = self.comboBox_22.currentText() startDay = quandl.get(str(inputStock), start_date = str(syear) + "-" + str(smonth) + "-" + str(sday) , end_date = str(syear) + "-" + str(smonth) + "-" + str(sday)) endDay = quandl.get(str(inputStock), start_date = str(eyear) + "-" + str(emonth) + "-" + str(eday) , end_date = str(eyear) + "-" + str(emonth) + "-" + str(eday)) x = startDay["High"].mean() y = endDay["High"].mean() percentChange = (y-x)/x finalAmt = int(valueInvested) * (1 + percentChange) self.textEdit_28.setText(str(finalAmt)) def futureStocks(self): company=self.comboBox_19.currentText() years=[] prices=[] for i in range(2007,2019): #make a list of years from 2007 to 2018 years.append(i) #make a list of averages for each year df=quandl.get(company, start_date=str(i)+"-01-01", end_date=str(i)+"-01-10") y=df["High"].mean() prices.append(y) print (prices) #create variable for third parameter of predict_stock_prices function futureYear=int(self.comboBox_23.currentText()) x=(futureYear-2018)*365 print (x) def predict_stock_prices(years, prices, x): years = np.reshape(years, (len(years),1 )) prices = np.reshape(prices, (len(prices),1)) svr_rbf = SVR(kernel = 'rbf', C=1e3, gamma=0.1) svr_rbf.fit(years, prices) return svr_rbf.predict(x)[0] predicted_prices = predict_stock_prices(years, prices, [[x]]) self.textEdit_32.setText(str(predicted_prices)) def volatility(self): inputStock = self.comboBox_24.currentText() year_start=int(self.comboBox_25.currentText()) year_end=int(self.comboBox_26.currentText()) listC = [] totalC = 0 num = 0 for i in range(year_start, year_end+1): df = quandl.get(inputStock, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() listC.append(y) totalC += y num += 1 avgC = totalC / len(listC) diffs= [] for i in listC: poop = (avgC - i) ** 2 diffs.append(poop) hello = 0 for i in diffs: hello += int(i) volatility = float((hello / len(listC))** 0.5) self.textEdit_35.setText(str(volatility)) def calcCoCo(self): inputCompany=self.comboBox_2.currentText() company=self.comboBox_3.currentText() year_start=int(self.comboBox_11.currentText()) year_end=int(self.comboBox_12.currentText()) listInput = [] totalInput = 0 listComp = [] totalComp = 0 for i in range(year_start, year_end+1): df = quandl.get(str(inputCompany), start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") df2 = quandl.get(str(company), start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() x= df2["High"].mean() listInput.append(y) totalInput += y listComp.append(x) totalComp += x avgInput = totalInput / len(listInput) avgComp = totalComp / len(listComp) listCompSquared = [] listInputSquared = [] mult = [] #to find the standard devations, u nee to find bot lists squared for i in listInput: listInputSquared.append(i**2) for i in listComp: listCompSquared.append(i**2) #to print out the multiplication of both the values in both lists, set a varaibe for both equal to the i'th number in each list. When i is 2 in the for loop, the variable will be list[i] for i in range(0, len(listInput)): itemInput = listInput[i] itemComp = listComp[i] mult.append(itemComp * itemInput) #find the average of he squared list sumPowInput = sum(listInputSquared) avgSquareInput = sumPowInput / len(listInputSquared) sumPowComp = sum(listCompSquared) avgSquareComp = sumPowComp / len(listCompSquared) sumMult = sum(mult) avgMult = sum(mult)/len(mult) varInput = avgSquareInput - (avgInput **2 ) varComp = avgSquareComp - (avgComp **2 ) coVar = (avgMult) - (avgInput * avgComp) CoCo = coVar / (math.sqrt(varInput * varComp)) self.textEdit_26.setText(str(CoCo)) #functions for graphing correlation between two stocks def visual2(self): threading.Thread(target=self.graph2, daemon=True).start() def graph2(self): inputCompany=self.comboBox_2.currentText() company=self.comboBox_3.currentText() year_start=int(self.comboBox_11.currentText()) year_end=int(self.comboBox_12.currentText()) listInput = [] totalInput = 0 listComp = [] totalComp = 0 for i in range(year_start, year_end+1): df = quandl.get(inputCompany, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") df2 = quandl.get(company, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() x= df2["High"].mean() listInput.append(y) totalInput += y listComp.append(x) totalComp += x avgInput = totalInput / len(listInput) avgComp = totalComp / len(listComp) listCompSquared = [] listInputSquared = [] mult = [] #to find the standard devations, u nee to find bot lists squared for i in listInput: listInputSquared.append(i**2) for i in listComp: listCompSquared.append(i**2) #to print out the multiplication of both the values in both lists, set a varaibe for both equal to the i'th number in each list. When i is 2 in the for loop, the variable will be list[i] for i in range(0, len(listInput)): itemInput = listInput[i] itemComp = listComp[i] mult.append(itemComp * itemInput) #find the average of he squared list sumPowInput = sum(listInputSquared) avgSquareInput = sumPowInput / len(listInputSquared) sumPowComp = sum(listCompSquared) avgSquareComp = sumPowComp / len(listCompSquared) sumMult = sum(mult) avgMult = sum(mult)/len(mult) varInput = avgSquareInput - (avgInput **2 ) varComp = avgSquareComp - (avgComp **2 ) coVar = (avgMult) - (avgInput * avgComp) CoCo = coVar / (math.sqrt(varInput * varComp)) #make the x list consisting of the years years=[] for i in range(year_start, year_end+1): years.append(i) app = dash.Dash() app.layout = html.Div(children=[ html.H1(children='Correlation Coefficients'), html.Div(children=''' Graphs each company with eachother '''), dcc.Graph( id='example-graph-5', figure={ 'data': [ {'x': years, 'y': listInput, 'type': 'line', 'name': inputCompany}, {'x': years, 'y': listComp, 'type': 'line', 'name': company}, ], 'layout': { 'title': 'Correlation= '+str(CoCo) } } ), ]) app.run_server(port=2222) #functions for graphing correlation between first stock and list of other stocks def visual3(self): threading.Thread(target=self.graph3, daemon=True).start() def graph3(self): inputCompany=self.comboBox_4.currentText() year_start=int(self.comboBox_7.currentText()) year_end=int(self.comboBox_6.currentText()) #list of companies to loop through companies=['WIKI/AAPL', 'WIKI/GOOGL', 'WIKI/TSLA', 'WIKI/IBM', 'WIKI/WMT', 'WIKI/T', 'WIKI/AMZN', 'WIKI/INTC', 'WIKI/NKE', 'WIKI/EA'] #dictionary with each company and its correlation coefficient stockList=[] absstock=[] absstockDiction={} randstockDiction={} stockList=[] absstock=[] absstockDiction={} randstockDiction={} avgValuesDiction={} #calculate correlation for each company and append the values to a dictionary for company in companies: listInput = [] totalInput = 0 listComp = [] totalComp = 0 for i in range(year_start, year_end+1): df = quandl.get(inputCompany, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") df2 = quandl.get(company, start_date = str(i)+"-01-01", end_date = str(i)+"-12-31") y = df["High"].mean() x= df2["High"].mean() listInput.append(y) totalInput += y listComp.append(x) totalComp += x avgInput = totalInput / len(listInput) avgComp = totalComp / len(listComp) listCompSquared = [] listInputSquared = [] mult = [] for i in listInput: listInputSquared.append(i**2) for i in listComp: listCompSquared.append(i**2) for i in range(0, len(listInput)): itemInput = listInput[i] itemComp = listComp[i] mult.append(itemComp * itemInput) sumPowInput = sum(listInputSquared) avgSquareInput = sumPowInput / len(listInputSquared) sumPowComp = sum(listCompSquared) avgSquareComp = sumPowComp / len(listCompSquared) sumMult = sum(mult) avgMult = sum(mult)/len(mult) varInput = avgSquareInput - (avgInput **2 ) varComp = avgSquareComp - (avgComp **2 ) coVar = (avgMult) - (avgInput * avgComp) CoCo = coVar / (math.sqrt(varInput * varComp)) #append values to dictionaries/lists randstockDiction[company]=CoCo absstockDiction[abs(CoCo)]=company absstock.append(abs(CoCo)) avgValuesDiction[inputCompany]=listInput avgValuesDiction[company]=listComp #make final sorted dictionary absstock=sorted(absstock) i=9 while i>=0: comp=absstockDiction[absstock[i]] stockList.append([comp,randstockDiction[comp]]) i=i-1 #graph the related data years=[] for i in range(year_start, year_end+1): years.append(i) app = dash.Dash() app.layout = html.Div(children=[ html.H1(children='Correlation Coefficients: Related Data'), html.Div(children=''' Graphs each company with eachother and finds the correlation coefficient. '''), dcc.Graph( id='example-graph-6', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[0][0]], 'type': 'line', 'name': stockList[0][0]}, ], 'layout': { 'title': str(stockList[0][0]) } } ), dcc.Graph( id='example-graph-7', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[1][0]], 'type': 'line', 'name': stockList[1][0]}, ], 'layout': { 'title': str(stockList[1][0]) } } ), dcc.Graph( id='example-graph-8', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[2][0]], 'type': 'line', 'name': stockList[2][0]}, ], 'layout': { 'title': str(stockList[2][0]) } } ), dcc.Graph( id='example-graph-9', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[3][0]], 'type': 'line', 'name': stockList[3][0]}, ], 'layout': { 'title': str(stockList[3][0]) } } ), dcc.Graph( id='example-graph-10', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[4][0]], 'type': 'line', 'name': stockList[4][0]}, ], 'layout': { 'title': str(stockList[4][0]) } } ), dcc.Graph( id='example-graph-11', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[5][0]], 'type': 'line', 'name': stockList[5][0]}, ], 'layout': { 'title': str(stockList[5][0]) } } ), dcc.Graph( id='example-graph-12', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[6][0]], 'type': 'line', 'name': stockList[6][0]}, ], 'layout': { 'title': str(stockList[6][0]) } } ), dcc.Graph( id='example-graph-13', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[7][0]], 'type': 'line', 'name': stockList[7][0]}, ], 'layout': { 'title': str(stockList[7][0]) } } ), dcc.Graph( id='example-graph-14', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[8][0]], 'type': 'line', 'name': stockList[8][0]}, ], 'layout': { 'title': str(stockList[8][0]) } } ), dcc.Graph( id='example-graph-15', figure={ 'data': [ {'x': years, 'y': avgValuesDiction[inputCompany], 'type': 'line', 'name': inputCompany}, {'x': years, 'y': avgValuesDiction[stockList[9][0]], 'type': 'line', 'name': stockList[9][0]}, ], 'layout': { 'title': str(stockList[9][0]) } } ) ]) app.run_server(port=3333) app = QApplication(sys.argv) widget = MainWindow() widget.show() sys.exit(app.exec_())