The World Of Stocks
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_())
