def markAttendance(prn,
classroom,
course_name,
date_time=datetime.now().strftime("%Y-%m-%d %H:%M:%S")):
connection = establishConnection()
cursor = connection.cursor()
cursor.execute(
"""SELECT course_code FROM course WHERE course_name = '%s'""" %
(course_name))
r = cursor.fetchone()
if not r:
print(
f'Error: Course {course_name} does not exist in the database... Check spelling'
)
return 'Failed: Unsuccessful in marking attendnace of student...'
else:
course_code = r[0]
cursor.execute("""INSERT INTO attends VALUES(%s, '%s', '%s', %s) ; """ %
(prn, date_time, classroom, course_code))
cursor.close()
connection.commit()
connection.close()
return f"Attendance of student with PRN {prn} for course {course_name} dated on {date_time} in venue {classroom} is succesfully recorded!"
def recordLectureConductedByFaculty(lecture, faculty_name, course_name):
connection = establishConnection()
cursor = connection.cursor()
cursor.execute(
""" SELECT emp_id FROM faculty WHERE faculty_name = '%s' """ %
(faculty_name))
r = cursor.fetchone()
if not r:
return 'Error: Faculty name {faculty_name} not found in the database'
else:
emp_id = r[0]
cursor.execute(
"""SELECT course_code FROM course WHERE course_name = '%s' """ %
(course_name))
r = cursor.fetchone()
if not r:
return 'Error: Course name {course_name} not found in the database'
else:
course_code = r[0]
cursor.execute("""INSERT INTO conducts VALUES('%s', '%s', %s, %s)""" %
(lecture[1], lecture[0], emp_id, course_code))
cursor.close()
connection.commit()
connection.close()
return f"Lecture conducted by faculty {faculty_name} dated {lecture[1]} in classroom {lecture[0]} is recorded in the database."
def conexao():
import connection
connection = connection.conexao()
cursor = connection.cursor()
cursor.execute("SELECT * from cnpj order by id_cnpj desc")
id_result = cursor.fetchone()
id_cnpj_in = id_result[0]
id_cnpj = id_cnpj_in + 1
return id_cnpj
def recordLecture(classroom,
date_time=datetime.now().strftime("%Y-%m-%d %H:%M:%S")):
connection = establishConnection()
cursor = connection.cursor()
cursor.execute("INSERT INTO lecture VALUES('%s', '%s')" %
(date_time, classroom))
lecture = (classroom, date_time)
cursor.close()
connection.commit()
connection.close()
return lecture
def get_booking_history():
connection = psycopg2.connect(
database="postgres",
user="******",
password="******",
host="postgres.cviulopflptv.us-east-1.rds.amazonaws.com",
port='5432')
b_history = connection.cursor()
new_dict = {}
emailid = request.args['user']
print(emailid)
userdet_query = "select ud.firstname, ud.lastname from userdetails ud where ud.emailid='{}'".format(
str(emailid))
if emailid != '':
b_history.execute(userdet_query)
user_det = b_history.fetchall()
for res in user_det:
firstname = res[0]
lastname = res[1]
book_history_query = "select tk.ticketid, ct.sourcecityname, d.destname, d.destprov, s.journeydate, s.starttime, tk.seatsbooked, tk.payment, ud.firstname, ud.lastname from tickets tk, schedule s, cities ct, destination d, userdetails ud where tk.emailid=ud.emailid and tk.scheduleid=s.scheduleid and tk.destid=d.destid and s.sourcecityid=ct.sourcecityid and tk.emailid='{}' order by s.journeydate desc".format(
str(emailid))
b_history.execute(book_history_query)
book_history_res = b_history.fetchall()
all_book_history = []
for res in book_history_res:
bk_hist_list = []
bk_hist_list.append(str(res[0]))
bk_hist_list.append(str(res[1]))
bk_hist_list.append(str(res[2]))
bk_hist_list.append(str(res[3]))
bk_hist_list.append(str(res[4]))
bk_hist_list.append(str(res[5]))
bk_hist_list.append(str(res[6]))
bk_hist_list.append(str(res[7]))
bk_hist_list.append(str(res[8]))
bk_hist_list.append(str(res[9]))
all_book_history.append(bk_hist_list)
new_dict['emailid'] = emailid
new_dict['firstname'] = firstname
new_dict['lastname'] = lastname
new_dict['all_book_history'] = all_book_history
return new_dict
else:
return new_dict
def connect(db_host, db_name, db_user, db_pwd):
for x in range(5):
print("Attempting to connect...")
try:
connection = mysql.connector.connect(host=db_host,
database=db_name,
user=db_user,
password=db_pwd)
if connection.is_connected():
db_Info = connection.get_server_info()
print("Connected to MySQL Server version ", db_Info)
cursor = connection.cursor()
cursor.execute("select database();")
record = cursor.fetchone()
print("Your connected to database: ", record)
return connection
except Error as e:
print("Error while connecting to MySQL", e)
time.sleep(5)
def confirm_signup():
user = ''
if session.get('email'):
user = session['email']
print("into signup")
otp = request.form['otp']
device = 'web'
if 'device' in request.args:
device = request.args['device']
data = {'email': user, 'otp': otp}
response = requests.post("http://3.80.19.25:3000/confirmsignup", json=data)
print(response.json())
if device != 'mobile':
if response.json() == 'SUCCESS':
connection = psycopg2.connect(
database="postgres",
user="******",
password="******",
host="postgres.cviulopflptv.us-east-1.rds.amazonaws.com",
port='5432')
schd = connection.cursor()
name = session['user_temp_name']
phone = session['phonenumber_temp']
schd.execute(
"INSERT INTO userdetails (username,firstname,lastname,sex,emailid, phone, active_status,usertype) VALUES(%s,%s,%s,%s,%s,%s,%s,%s);",
[
name, name, name, 'm/f', session['email'], phone, True,
"user"
])
connection.commit()
session['token_id'] = user
session['email'] = ''
return render_template('home.html', user=user)
else:
session['email'] = ''
return render_template('home.html', user=user)
else:
if response.json() == 'SUCCESS':
return True
else:
return False
NUM_SEQ_UNICO = int(row['NUM_SEQ_UNICO'])
#print(type(NUM_SEQ_UNICO))
DSC_IMG_URL = row['DSC_IMG_URL']
#print(type(DSC_IMG_URL))
NUM_CPF = row['NUM_CPF']
if NUM_CPF == None or NUM_CPF == '':
NUM_CPF = None
else:
NUM_CPF = int(row['NUM_CPF'])
#print(type(NUM_CPF),NUM_CPF,'cpf')
except:
print('Erro de tipo de dados')
NUM_CODIGO = str(NUM_CODIGO)
#print(type(NUM_CODIGO))
cursor = connection.cursor() # cria um cursor
cursor.execute("SELECT NUM_CODIGO from gtin_sefaz where NUM_CODIGO =" +
NUM_CODIGO) # consulta sql
result = cursor.fetchone() # busca o resultado da consulta
NUM_CODIGO = int(NUM_CODIGO)
try:
if result == None:
cursor = connection.cursor()
cursor.execute(
"SELECT seq_id from gtin_sefaz order by seq_id desc")
id_result = cursor.fetchone()
seq_id_in = id_result[0]
SEQ_ID = seq_id_in + 1
def book_ticket():
connection = psycopg2.connect(
database="postgres",
user="******",
password="******",
host="postgres.cviulopflptv.us-east-1.rds.amazonaws.com",
port='5432')
schd = connection.cursor()
cardNumber = request.form['cardNumber']
expiryMonth = request.form['expiryMonth']
expiryYear = request.form['expiryYear']
cvCode = request.form['cvCode']
number_of_travellers = request.form['passengers']
scheduleid = request.form['scheduleid']
username = request.form['user']
bus_dict = {}
active_status = True
if username != '':
if cardNumber == '1111111111111111' and expiryYear > '19' and expiryMonth > '03':
print("into if")
schd.execute(
"SELECT s.sourcecityid, s.destid, s.journeydate, s.starttime, s.busid, s.scheduleid,s.price FROM schedule s WHERE scheduleid = %s",
[scheduleid])
results = schd.fetchall()
for res in results:
bus_dict = {}
source_city_id = res[0]
destination_id = res[1]
journeydate = res[2]
starttime = res[3]
busid = res[4]
price = res[6]
bus_dict['source_city_id'] = source_city_id
bus_dict['destination_id'] = destination_id
bus_dict['journeydate'] = journeydate
bus_dict['starttime'] = starttime
bus_dict['busid'] = busid
bus_dict['scheduleid'] = res[5]
bus_dict['price'] = int(res[6]) * int(number_of_travellers)
bus_dict['username'] = username
bus_dict['passengers'] = number_of_travellers
print("Source City: " + source_city_id)
print("Destination Name: " + destination_id)
print("Bus start time: " + str(starttime))
print("Bus id: " + str(busid))
booking_time = int(time.time())
schd.execute(
"INSERT INTO tickets (emailid,destid,seatsbooked,active_status,scheduleid,payment,booking_time) VALUES (%s,%s,%s,%s,%s,%s,%s);",
[
username, destination_id,
int(number_of_travellers), active_status,
int(scheduleid), bus_dict['price'], booking_time
])
connection.commit()
schd.execute(
"UPDATE schedule SET seatsavailable = seatsavailable - %s WHERE scheduleid = %s",
[int(number_of_travellers),
int(scheduleid)])
connection.commit()
ticket_query = "select tk.ticketid, ud.firstname, ud.lastname, ud.emailid, ct.sourcecityname, d.destname, d.destprov, s.journeydate, s.starttime, tk.seatsbooked, tk.payment from userdetails ud, tickets tk, destination d, schedule s, cities ct where ud.emailid=tk.emailid and tk.destid=d.destid and tk.scheduleid=s.scheduleid and s.sourcecityid=ct.sourcecityid and tk.emailid='{}' order by booking_time desc Limit 1".format(
username)
schd.execute(ticket_query)
ticket_results = schd.fetchall()
for res in ticket_results:
pdf_ticketid = str(res[0])
pdf_name = str(res[1])
pdf_email = str(res[3])
pdf_source = str(res[4])
pdf_dest = str(res[5]) + ", " + str(res[6])
pdf_departure_date = str(res[7])
pdf_departure_time = str(res[8])
pdf_seatsbooked = str(res[9])
pdf_payment = "$" + str(res[10])
pdfName = username.split(
'@')[0] + '_' + pdf_ticketid + '_ticket.pdf'
pdfTitle = "Hi " + pdf_name + "! Here's your ticket."
bookingReference = 'Ticket ID#: ' + pdf_ticketid
name = 'Email: ' + pdf_email
source = 'From: ' + pdf_source
destination = 'To: ' + pdf_dest
departureDay = 'Departure Day: ' + pdf_departure_date
departureTime = 'Departure Time: ' + pdf_departure_time
numberOfTravellers = 'Number Of Travellers: ' + pdf_seatsbooked
amount_paid = 'Amount paid: ' + pdf_payment
contactUs = 'Customer Support: [email protected]'
pdf = canvas.Canvas(pdfName)
pdf.setTitle(pdfTitle)
pdf.setFont("Helvetica-Bold", 28)
pdf.drawCentredString(280, 800, 'Travel Ticket')
pdf.line(170, 790, 400, 790)
pdf.setFont("Courier", 14)
pdf.drawString(50, 740, pdfTitle)
pdf.drawString(50, 720, bookingReference)
pdf.drawString(50, 700, name)
pdf.drawString(50, 680, source)
pdf.drawString(50, 660, destination)
pdf.drawString(50, 640, departureDay)
pdf.drawString(50, 620, departureTime)
pdf.drawString(50, 600, numberOfTravellers)
pdf.drawString(50, 580, amount_paid)
pdf.setFont("Courier-Bold", 12)
pdf.drawString(50, 100, contactUs)
pdf.save()
pdf_name = pdfName
msg = EmailMessage()
from_ = "*****@*****.**"
pwd = "canatour@123"
msg['Subject'] = "Booking Successful - Canatour"
msg['From'] = from_
msg['To'] = username
msg.set_content(
'Please find attached your travel ticket. Hope you enjoy the journey.'
)
with open(pdf_name, 'rb') as f:
file_data = f.read()
file_name = f.name
msg.add_attachment(file_data,
maintype='application',
subtype='octet-stream',
filename=file_name)
with smtplib.SMTP_SSL('smtp.gmail.com') as smtp:
smtp.login(from_, pwd)
smtp.send_message(msg)
return bus_dict
else:
print("into else")
return bus_dict
else:
return "No user logged in"
connection = connection.conexao()
PATH = 'GS1BRcopia.xml'
tree = ET.parse(PATH)
root = tree.getroot()
id_segmento = 0
id_familia = 0
id_classe = 0
id_bloco = 0
for child in root.findall("segment"):
cursor = connection.cursor()
cursor.execute("SELECT * from gpc_segmento order by id_segmento desc")
id_result = cursor.fetchone()
id_segmento_in = id_result[0]
id_segmento = id_segmento_in + 1
gpc_segmento_id_segmento = id_segmento
'''Mostra todos os segmentos'''
if 'code' in child.keys():
segmento = []
s = child.items()
segmento.append(s)
cod_segmento = segmento[0][0][1]
desc_segmento = segmento[0][1][1]
print('segmento: ',cod_segmento,desc_segmento)
'''INSERE SEGMENTO - tabela gpc_segmento'''
import os
import sys
import site
import timelib
import settings
# Set this to the path to your python library
sys.path.append('/path/to/django/lib')
import connection, transaction
cursor = connection.cursor()
def convert_chars(text):
conv_table = [
["\xc3\x84\xc3\xba", "“"],
["\xc3\x84\xc3\xb9", "”"],
["\xc3\x84\xc3\xae", "—"],
["\xe2\x80\x9a\xc3\x84\xc3\xb4", "’"],
["\xe2\x88\x9a\xc2\xba", "ü"],
["\xe2\x80\x9a\xc3\x84\xc2\xa2", "•"],
["\xe2\x80\x9a\xc3\x84\xc2\xb6", "…"],
["\xe2\x80\x9a\xc3\x84\xe2\x89\xa4","′"],
["\xe2\x88\x9a\xc2\xa9","é"],
["\xe2\x88\x9a\xc2\xb0","á"],
["\xe2\x80\x9a\xc3\x84\xc3\xb2","‘"],
["\xe2\x80\x9a\xc3\x84\xe2\x89\xa5","″"],
["\xe2\x88\x9a\xe2\x89\xa0","í"],
["\xe2\x80\x9a\xc3\x84\xc3\xb2","‘"],
["\xe2\x88\x9a\xc3\x9f","ç"],
["\xe2\x88\x9a\xe2\x89\xa5","ó"],
def neuralcalculations(connection, ticker, date):
date_parts = date.split("-") # Explode Date
year = date_parts[0] # Grab The Date
month = date_parts[1] # Grab The Date
day = date_parts[2] # Grab The Date
Today = datetime.today().strftime('%Y-%m-%d') # Grab Todays Date
Entry = random.randint(2000000000, 60000000000)
DayOfWeek = day # Day of Week
TickerSymbol = ticker #Ticker Symbol
day_name = datetime.strptime(
(date), '%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
trading_day = 0
if (day_name == "Monday"):
trading_day = 0.2
elif (day_name == "Tuesday"):
trading_day = 0.4
elif (day_name == "Wednesday"):
trading_day = 0.6
elif (day_name == "Thursday"):
trading_day = 0.8
elif (day_name == "Friday"):
trading_day = 1
else:
print("Fatal Error: Invalid Date Input Received"
) # Kill Program, Request Not Allowed
sys.exit("Fatal Error")
if ((month == "3") or (month == "4") or (month == "5")):
trading_season = 0.25
elif ((month == "6") or (month == "7") or (month == "8")):
trading_season = 0.50
elif ((month == "9") or (month == "10") or (month == "11")):
trading_season = 0.75
elif ((month == "12") or (month == "1") or (month == "2")):
trading_season = 1.0
else:
print("Fatal Error: Could Not Calculate " + day_name +
" Accurately") # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error")
# Calculate Moving Average 200 Trading Days * From Date *
# ***************************************************************************
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s ORDER BY Date DESC"
cursor.execute(sql, (ticker))
connection.commit()
MovingAvgBig = cursor.fetchall()
days_list = rangedatelist(
date, 286) # Get List of Backdates In This Range (Including Weekends)
MAB_Total = 0.0
MAB_Averg = 0.0
MAB_Count = 0
for day in MovingAvgBig:
MAB_Closing = day['Closing_Price']
MAB_Date = day['Date']
MAB_Date = datetime.strptime(MAB_Date,
'%Y-%m-%d') # Convert to Datetime
if ((MAB_Date in days_list) and (MAB_Count < 200)):
MAB_Count = MAB_Count + 1
MAB_Total = MAB_Total + float(MAB_Closing)
print("Counter Check: ", MAB_Count)
MAB_Averg = (MAB_Total / MAB_Count)
MAB_Averg = round(MAB_Averg, 2)
print("200 Day Moving Average: ", MAB_Averg)
update.large_moving_avg(connection, MAB_Averg, ticker)
# Calculate Moving Average 200 Trading Days * From Date *
# ***************************************************************************
# Calculate Moving Average 5 Trading Days * From Date *
# ***************************************************************************
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s ORDER BY Date DESC"
cursor.execute(sql, (ticker))
connection.commit()
MovingAvgLittle = cursor.fetchall()
drop_today = datetime.strptime(date, '%Y-%m-%d')
back_today = drop_today - timedelta(days=1)
back_today = back_today.strftime('%Y-%m-%d')
days_list = rangedatelist(
back_today,
7) # Get List of Backdates In This Range (Including Weekends)
MAL_Total = 0.0
MAL_Averg = 0.0
MAL_Count = 0
for day in MovingAvgLittle:
MAL_Closing = day['Closing_Price']
MAL_Date = day['Date']
MAL_Date = datetime.strptime(MAL_Date,
'%Y-%m-%d') # Convert to Datetime
if ((MAL_Date in days_list) and (MAL_Count < 5)):
MAL_Count = MAL_Count + 1
MAL_Total = MAL_Total + float(MAL_Closing)
print("Counter Check: ", MAL_Count)
MAL_Averg = (MAL_Total / MAL_Count)
MAL_Averg = round(MAL_Averg, 2)
print("5 Day Moving Average: ", MAL_Averg)
update.small_moving_avg(connection, MAL_Averg, ticker)
# Calculate Moving Average 5 Trading Days * From Date *
# ***************************************************************************
# Calculate Volume Average 90 Trading Days * From Date *
# ***************************************************************************
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s ORDER BY Date DESC"
cursor.execute(sql, (ticker))
connection.commit()
MovingAvgVolume = cursor.fetchall()
days_list = rangedatelist(
date, 130) # Get List of Backdates In This Range (Including Weekends)
MAV_Total = 0.0
MAV_Averg = 0.0
MAV_Count = 0
for day in MovingAvgVolume:
MAV_Volume = day['Volume']
MAV_Date = day['Date']
MAV_Date = datetime.strptime(MAV_Date,
'%Y-%m-%d') # Convert to Datetime
if ((MAV_Date in days_list) and (MAV_Count < 90)):
MAV_Count = MAV_Count + 1
MAV_Total = MAV_Total + float(MAV_Volume)
print("Counter Check: ", MAV_Count)
MAV_Averg = (MAV_Total / MAV_Count)
MAV_Averg = round(MAV_Averg)
print("Average Volume 90 Days: ", MAV_Averg)
update.avg_volume_long(connection, MAV_Averg, ticker)
# Calculate Volume Average 90 Trading Days * From Date *
# ***************************************************************************
# Calculate # of Tweets From Today *******************************************
DailyTweets_Count = 0
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
today_tweets = cursor.fetchall()
DailyTweets_Count = cursor.rowcount
update.today_tweets(connection, DailyTweets_Count, ticker)
# Calculate # Of Tweets From Today *******************************************
# Calculate Sentiment For The Day
# ***************************************************************************
# SHORT TERM SENTIMENT ANALYSIS
# --------------------------------------------
# Short Term Sentiment Scale
# 0 Extremelly Negative Sentiment <-40
# 0.1 -40 - -30
# 0.2 -30 - -20
# 0.3 -20 - -10
# 0.4 -10 - -0
# 0.5 Neutral 0
# 0.6 0 - 10
# 0.7 10 - 20
# 0.8 20 - 30
# 0.9 30 - 40
# 1 Significant Positive Sentiment >50
# --------------------------------------------
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
DaySentiment = cursor.fetchall()
Daily_Sentiment = 0
Daily_Count = 0.001
for tweet in DaySentiment:
Temp_Likes = 0
Temp_Likes = float(tweet["Total_Likes"])
Temp_Sentiment = 0
Temp_Sentiment = float(tweet["Tweet_Sentiment"])
Daily_Count = Daily_Count + 1
while (Temp_Likes != 0):
if ((Temp_Sentiment <= 90)
and (Temp_Sentiment >= -90)): # Prevent Overbounds
Temp_Sentiment = (Temp_Sentiment * 0.15) + Temp_Sentiment
Temp_Likes = Temp_Likes - 1
Daily_Sentiment = Daily_Sentiment + Temp_Sentiment
SentimentNormalized = (Daily_Sentiment / Daily_Count
) # Calculate One Day Sentiment
SentimentNormalized = round(SentimentNormalized)
if (SentimentNormalized < -40):
SentimentNormalized = float(0.0)
elif ((SentimentNormalized >= -40) and (SentimentNormalized < -30)):
SentimentNormalized = float(0.1)
elif ((SentimentNormalized >= -30) and (SentimentNormalized < -20)):
SentimentNormalized = float(0.2)
elif ((SentimentNormalized >= -20) and (SentimentNormalized < -10)):
SentimentNormalized = float(0.3)
elif ((SentimentNormalized >= -10) and (SentimentNormalized < 0)):
SentimentNormalized = float(0.4)
elif (SentimentNormalized == 0):
SentimentNormalized = float(0.5)
elif ((SentimentNormalized > 0) and (SentimentNormalized < 10)):
SentimentNormalized = float(0.6)
elif ((SentimentNormalized >= 10) and (SentimentNormalized < 20)):
SentimentNormalized = float(0.7)
elif ((SentimentNormalized >= 20) and (SentimentNormalized < 30)):
SentimentNormalized = float(0.8)
elif ((SentimentNormalized >= 30) and (SentimentNormalized <= 40)):
SentimentNormalized = float(0.9)
elif (SentimentNormalized > 40):
SentimentNormalized = float(1.0)
else:
print("Fatal Error: Could Not Calculate Sentiment Accurately")
sys.exit("Fatal Error")
print("Sentiment For " + day_name + "", SentimentNormalized)
# Calculate Sentiment For The Day *******************************************
# Calculate Sentiment + Volume Average 30 Actual Days * From Date *
# ***************************************************************************
# LONG TERM SENTIMENT ANALYSIS
# --------------------------------------------
# Calculate and Normalize 30 Day Sentiment Average
# Long Term Sentiment Scale
# 0 General Negative Sentiment <95
# 0.1 96
# 0.2 97
# 0.3 98
# 0.4 99
# 0.5 Average 100
# 0.6 101
# 0.7 102
# 0.8 103
# 0.9 104
# 1 General Positive Sentiment >105
# --------------------------------------------
days_list = rangedatelist(
date, 29) # Get List of Backdates In This Range (Including Weekends)
SAB_Total = 0.0
SAB_Averg = 0.0
SAB_Count = 0
TVA_Count = 0 # Daily Tweet Volume Count
TVA_Days = 0.001 # Daily Tweet Volume Total Days
TVA_Total = 0 # Tweet Volume Average Total
for day in days_list: # For Each Day In Range, Calculate Sentiment Analysis
TVA_Days = TVA_Days + 1
SAB_Count = SAB_Count + 1
day = day.strftime('%Y-%m-%d')
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, day))
connection.commit()
one_day = cursor.fetchall()
Daily_Sentiment = 0
Daily_Count = 0.001
for tweet in one_day:
TVA_Count = TVA_Count + 1
Temp_Likes = 0
Temp_Likes = float(tweet["Total_Likes"])
Temp_Sentiment = 0
Temp_Sentiment = float(tweet["Tweet_Sentiment"])
Daily_Count = Daily_Count + 1
while (Temp_Likes != 0):
if ((Temp_Sentiment <= 90)
and (Temp_Sentiment >= -90)): # Prevent Overbounds
Temp_Sentiment = (Temp_Sentiment * 0.15) + Temp_Sentiment
Temp_Likes = Temp_Likes - 1
Daily_Sentiment = Daily_Sentiment + Temp_Sentiment
if (Daily_Sentiment == 0):
TVA_Days = TVA_Days - 1 # Fix Averages If No Tweets On That Day
Tweet_Sentiment = (Daily_Sentiment / Daily_Count
) # Calculate One Day Sentiment
SAB_Total = SAB_Total + Tweet_Sentiment # Add One Day Sentiment To SAB Total
TVA_Total = (TVA_Count / TVA_Days)
SAB_Averg = (SAB_Total / SAB_Count)
SAB_Averg = round(SAB_Averg)
SAB_Averg = SAB_Averg + 100
print("30 Day Sentiment", SAB_Averg)
update.avg_sentiment_short(connection, SAB_Averg, ticker)
print("30 Day Avg Tweets", TVA_Total)
update.avg_tweets_short(connection, TVA_Total, ticker)
if (SAB_Averg <= 95):
SentimentLongTerm = float(0.0)
elif (SAB_Averg == 96):
SentimentLongTerm = float(0.1)
elif (SAB_Averg == 97):
SentimentLongTerm = float(0.2)
elif (SAB_Averg == 98):
SentimentLongTerm = float(0.3)
elif (SAB_Averg == 99):
SentimentLongTerm = float(0.4)
elif (SAB_Averg == 100):
SentimentLongTerm = float(0.5)
elif (SAB_Averg == 101):
SentimentLongTerm = float(0.6)
elif (SAB_Averg == 102):
SentimentLongTerm = float(0.7)
elif (SAB_Averg == 103):
SentimentLongTerm = float(0.8)
elif (SAB_Averg == 104):
SentimentLongTerm = float(0.9)
elif (SAB_Averg >= 105):
SentimentLongTerm = float(1.0)
else:
print("Fatal Error: Could Not Calculate Sentiment Accurately")
sys.exit("Fatal Error")
# Calculate Sentiment + Volume Average 30 Actual Days * From Date *
# ***************************************************************************
# Grab Pricing For Two Trading Days Requested and Following
# ***************************************************************************
if ((day_name == "Saturday") or (day_name == "Sunday")):
print("Error: You cannot request a neural programming for a weekend")
else:
safety_check = 0
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
prices_on_date = cursor.fetchall()
safety_check = cursor.rowcount
if (safety_check == 0):
print("Error: No Date Record Exists For The Date Requested")
sys.exit("Fatal Error #202") # Kill Program, Bad User Input
else:
for pricing in prices_on_date:
ThisDay_Volume = pricing["Volume"]
ThisDay_Closing = pricing["Closing_Price"]
ThisDay_HighPr = pricing["High_Price"]
ThisDay_LowPr = pricing["Low_Price"]
print("Lookup Volume: ", ThisDay_Volume)
print("Lookup High: ", ThisDay_HighPr)
print("Lookup Low: ", ThisDay_LowPr)
print("Lookup Close: ", ThisDay_Closing)
start_date = datetime.strptime(date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if (no_weekends == "Saturday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-2)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error #203")
elif (no_weekends == "Sunday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error #204")
print("Next Day: ", no_weekends)
print("Next Day: ", forward_date)
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, forward_date))
connection.commit()
prices_on_forward_date = cursor.fetchall()
safety_check = cursor.rowcount
NextDay_Volume = 0.01
NextDay_HighPr = 0.01
NextDay_LowPr = 0.01
NextDay_Closing = 0.01
for pricing in prices_on_forward_date:
NextDay_Volume = pricing["Volume"]
NextDay_Closing = pricing["Closing_Price"]
NextDay_HighPr = pricing["High_Price"]
NextDay_LowPr = pricing["Low_Price"]
if (NextDay_Closing == 0.01):
# For some reason no price record exists for this date, due to Market Holiday Possibly, Push Forward Again
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if (no_weekends == "Saturday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-2)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error #205")
elif (no_weekends == "Sunday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error #206")
print("New Forward Date Selected: ", forward_date)
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, forward_date))
connection.commit()
prices_on_forward_date = cursor.fetchall()
safety_check = cursor.rowcount
for pricing in prices_on_forward_date:
NextDay_Volume = pricing["Volume"]
NextDay_Closing = pricing["Closing_Price"]
NextDay_HighPr = pricing["High_Price"]
NextDay_LowPr = pricing["Low_Price"]
if (NextDay_Closing == 0.01):
print("Fatal Error: Forward Date Price Data Not Available")
sys.exit("Fatal Error #207")
print("Next Day-----------------------", no_weekends)
print("Lookup Volume: ", NextDay_Volume)
print("Lookup High: ", NextDay_HighPr)
print("Lookup Low: ", NextDay_LowPr)
print("Lookup Close: ", NextDay_Closing)
# Grab Pricing For Two Trading Days Requested and Following
# ***************************************************************************
# TRADING VOLUME ANALYSIS
# ***************************************************************************
# --------------------------------------------
# Calculate and Normalize Volume Trading
# Trading Volume Scale
# 0 Unusually Low Volume 0,1
# 0.1 2,3
# 0.2 4,5
# 0.3 6,7
# 0.4 8,9
# 0.5 Average 10
# 0.6 11,12
# 0.7 13,14
# 0.8 15,16
# 0.9 17,18
# 1 Unusually High Volume 19,20
# --------------------------------------------
ThisDay_Volume = int(ThisDay_Volume)
MAV_Averg = int(MAV_Averg)
if (ThisDay_Volume > MAV_Averg):
Sub_Process = (ThisDay_Volume / MAV_Averg)
Sub_Process = 10 * Sub_Process
VolumeNormalized = Sub_Process
if (Sub_Process > 20): # Maximum Volume Increase Is Double
Sub_Process = 20
VolumeNormalized = Sub_Process
elif (Sub_Process < 10): # This shouldn't ever happen
Sub_Process = 10
VolumeNormalized = Sub_Process
elif (ThisDay_Volume < MAV_Averg):
Sub_Process = (ThisDay_Volume / MAV_Averg)
Sub_Process = 10 * Sub_Process
VolumeNormalized = Sub_Process
if (Sub_Process < 0): # No Negatives Allowed
Sub_Process = 0
VolumeNormalized = Sub_Process
elif (Sub_Process > 10): # This shouldn't ever happen
Sub_Process = 10
VolumeNormalized = Sub_Process
else:
VolumeNormalized = 10
VolumeNormalized = round(VolumeNormalized)
if ((VolumeNormalized == 0) or (VolumeNormalized == 1)):
VolumeNormalized = float(0.0)
elif ((VolumeNormalized == 2) or (VolumeNormalized == 3)):
VolumeNormalized = float(0.1)
elif ((VolumeNormalized == 4) or (VolumeNormalized == 5)):
VolumeNormalized = float(0.2)
elif ((VolumeNormalized == 6) or (VolumeNormalized == 7)):
VolumeNormalized = float(0.3)
elif ((VolumeNormalized == 8) or (VolumeNormalized == 9)):
VolumeNormalized = float(0.4)
elif (VolumeNormalized == 10):
VolumeNormalized = float(0.5)
elif ((VolumeNormalized == 11) or (VolumeNormalized == 12)):
VolumeNormalized = float(0.6)
elif ((VolumeNormalized == 13) or (VolumeNormalized == 14)):
VolumeNormalized = float(0.7)
elif ((VolumeNormalized == 15) or (VolumeNormalized == 16)):
VolumeNormalized = float(0.8)
elif ((VolumeNormalized == 17) or (VolumeNormalized == 18)):
VolumeNormalized = float(0.9)
elif ((VolumeNormalized == 19) or (VolumeNormalized == 20)):
VolumeNormalized = float(1.0)
else:
print("Fatal Error: Could Not Calculate Volume Accurately")
sys.exit("Fatal Error")
# ***************************************************************************
# Calculate and Normalize Daily Sentiment
# ***************************************************************************
# SHORT TERM TWEET VOLUME
# --------------------------------------------
# Calculate and Normalize Tweet Volume
# Tweet Volume Scale
# 0 Unusually Low Volume 0,1
# 0.1 2,3
# 0.2 4,5
# 0.3 6,7
# 0.4 8,9
# 0.5 Average 10
# 0.6 11,12
# 0.7 13,14
# 0.8 15,16
# 0.9 17,18
# 1 Unusually High Volume 19,20
# --------------------------------------------
DailyTweets_Count = int(DailyTweets_Count)
TVA_Total = int(TVA_Total)
if (DailyTweets_Count > MAV_Averg):
Sub_Process = (DailyTweets_Count / TVA_Total)
Sub_Process = 10 * Sub_Process
TweetsNormalized = Sub_Process
if (Sub_Process > 20): # Maximum Volume Increase Is Double
Sub_Process = 20
TweetsNormalized = Sub_Process
elif (Sub_Process < 10): # This shouldn't ever happen
Sub_Process = 10
TweetsNormalized = Sub_Process
elif (DailyTweets_Count < TVA_Total):
Sub_Process = (DailyTweets_Count / TVA_Total)
Sub_Process = 10 * Sub_Process
TweetsNormalized = Sub_Process
if (Sub_Process < 0): # No Negatives Allowed
Sub_Process = 0
TweetsNormalized = Sub_Process
elif (Sub_Process > 10): # This shouldn't ever happen
Sub_Process = 10
TweetsNormalized = Sub_Process
else:
TweetsNormalized = 10
TweetsNormalized = round(TweetsNormalized)
if ((TweetsNormalized == 0) or (TweetsNormalized == 1)):
TweetsNormalized = float(0.0)
elif ((TweetsNormalized == 2) or (TweetsNormalized == 3)):
TweetsNormalized = float(0.1)
elif ((TweetsNormalized == 4) or (TweetsNormalized == 5)):
TweetsNormalized = float(0.2)
elif ((TweetsNormalized == 6) or (TweetsNormalized == 7)):
TweetsNormalized = float(0.3)
elif ((TweetsNormalized == 8) or (TweetsNormalized == 9)):
TweetsNormalized = float(0.4)
elif (TweetsNormalized == 10):
TweetsNormalized = float(0.5)
elif ((TweetsNormalized == 11) or (TweetsNormalized == 12)):
TweetsNormalized = float(0.6)
elif ((TweetsNormalized == 13) or (TweetsNormalized == 14)):
TweetsNormalized = float(0.7)
elif ((TweetsNormalized == 15) or (TweetsNormalized == 16)):
TweetsNormalized = float(0.8)
elif ((TweetsNormalized == 17) or (TweetsNormalized == 18)):
TweetsNormalized = float(0.9)
elif ((TweetsNormalized == 19) or (TweetsNormalized == 20)):
TweetsNormalized = float(1.0)
else:
print("Fatal Error: Could Not Calculate Volume Accurately")
sys.exit("Fatal Error")
# Calculate and Normalize Volume Trading
# ***************************************************************************
# Calculate Results From The Next Day
# ***************************************************************************
# 0.0 Stock price decreased
# 0.2 Stock price decreased by <1%
# 0.3
# 0.4
# ———————————————————
# 0.4
# 0.5
# 0.6 Stock price increased by <1%
# 1.0 Stock price increased
ThisDay_Closing = float(ThisDay_Closing)
NextDay_Closing = float(NextDay_Closing)
if (ThisDay_Closing <= NextDay_Closing): # Stock price increased
change_percent = ((
(NextDay_Closing - ThisDay_Closing) / ThisDay_Closing) * 100)
if (change_percent > 1.0):
Output1 = 1.0
Output2 = 1.0
else:
Output1 = 0.7
Output2 = 0.7
if (ThisDay_Closing > NextDay_Closing): # Stock price decreased
change_percent = ((
(ThisDay_Closing - NextDay_Closing) / ThisDay_Closing) * 100)
if (change_percent > 1.0):
Output1 = 0.0
Output2 = 0.0
else:
Output1 = 0.3
Output2 = 0.3
if (ThisDay_Closing <= NextDay_Closing): # Stock price increased
change_percent = ((
(NextDay_Closing - ThisDay_Closing) / ThisDay_Closing) * 100)
if (change_percent > 2.0):
Output3 = 1.0
Output4 = 1.0
else:
Output3 = 0.7
Output4 = 0.7
if (ThisDay_Closing > NextDay_Closing): # Stock price decreased
change_percent = ((
(ThisDay_Closing - NextDay_Closing) / ThisDay_Closing) * 100)
if (change_percent > 2.0):
Output3 = 0.0
Output4 = 0.0
else:
Output3 = 0.3
Output4 = 0.3
if (ThisDay_Closing <= NextDay_Closing): # Stock price increased
Output5 = 1.0
Output6 = 1.0
if (ThisDay_Closing > NextDay_Closing): # Stock price decreased
Output5 = 0.0
Output6 = 0.0
if (ThisDay_Closing > MAB_Averg):
AboveBigMoving = 1
else:
AboveBigMoving = 0
if (ThisDay_Closing < MAL_Averg):
BelowLittleMoving = 1
else:
BelowLittleMoving = 0
# Calculate Results From The Next Day
# ***************************************************************************
# Algorithm Results
# ***************************************************************************
print("\n\n")
print("Neural Network Normalized Data: ")
print("----------------------------------------------")
print("Entry ", Entry)
print("Volume Normalized: [0,20] ", VolumeNormalized)
print("Tweets Normalized: [0,20] ", TweetsNormalized)
print("Sentiment Analysis: [0,200] ", SentimentNormalized)
print("Above Big Moving: [0,1] ", AboveBigMoving)
print("Below Little Moving: [0,1] ", BelowLittleMoving)
print("Trading Day: [1,5] ", trading_day)
print("Trading Season: [Sp, Su, Fa, Wi] ", trading_season)
print("Moving Avg Sentiment: [0,200] ", SAB_Averg)
print("Results: [", Output1, ",", Output2, "]", " Up1%/Up/Down/Down1%")
print("Results: [", Output3, ",", Output4, "]", " Up2%/Up/Down/Down2%")
print("Results: [", Output5, ",", Output6, "]", " Up/Down")
try:
with connection.cursor() as cursor:
sql = "SELECT * FROM `NeuralNetwork` WHERE `Date`=%s AND Ticker=%s"
cursor.execute(sql, (date, ticker))
result = cursor.fetchone()
if result is None:
print("Success: Inserting Neural Data Set: ", ticker)
with connection.cursor() as cursor:
sql = "INSERT INTO `NeuralNetwork` (`Date`, `Entry`, `TradingDay`, `TradingSeason`, `VolumeNormalized`, `AboveBigMoving`, `BelowLittleMoving`, `TweetsVolumeNormalized`, `Sentiment`, `Ticker`, `Output1`, `Output2`, `Output3`, `Output4`, `Output5`, `Output6`, `Sentiment30`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
cursor.execute(
sql, (date, Entry, trading_day, trading_season,
VolumeNormalized, AboveBigMoving, BelowLittleMoving,
TweetsNormalized, SentimentNormalized, ticker,
Output1, Output2, Output3, Output4, Output5, Output6,
SentimentLongTerm))
connection.commit()
update.last_input(connection, ticker)
else:
print("Error: Neural Data Set Exists For The Date ", ticker, date)
finally:
print("::: SAVING DATA ::: DO NOT CLOSE CONNECTION :::")
def neuralcalculations(connection, ticker, date):
try:
network = Network.load("neuralnetwork.json")
except Exception as e:
print("#101 Error: Neural Network Does Not Exist")
sys.exit("Fatal Error") # Kill Program, Bad User Input
date_parts = date.split("-") # Explode Date
year = date_parts[0] # Grab The Date
month = date_parts[1] # Grab The Date
day = date_parts[2] # Grab The Date
Today = datetime.today().strftime('%Y-%m-%d') # Grab Todays Date
Entry = random.randint(2000000000, 60000000000)
DayOfWeek = day # Day of Week
TickerSymbol = ticker # Ticker Symbol
day_name = datetime.strptime(
(date), '%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
trading_day = 0
if (day_name == "Monday"):
trading_day = 0.2
elif (day_name == "Tuesday"):
trading_day = 0.4
elif (day_name == "Wednesday"):
trading_day = 0.6
elif (day_name == "Thursday"):
trading_day = 0.8
elif (day_name == "Friday"):
trading_day = 1
else:
print("#102 Fatal Error: Invalid Date Input Received"
) # Kill Program, Request Not Allowed
sys.exit("Fatal Error")
print(month)
if ((month == "03") or (month == "04") or (month == "05")):
trading_season = 0.25
elif ((month == "06") or (month == "07") or (month == "08")):
trading_season = 0.50
elif ((month == "09") or (month == "10") or (month == "11")):
trading_season = 0.75
elif ((month == "12") or (month == "01") or (month == "02")):
trading_season = 1.0
else:
print("#103 Fatal Error: Could Not Calculate " + day_name +
" Accurately") # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error")
# Run this from Price records which will be updated LIVE
# Calculate Live Stock Information *******************************************
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockPrices` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
live_data = cursor.fetchall()
for live in live_data:
stock_price_live = live["Price"]
stock_price_incr = live["Increase"]
stock_price_volm = live["Volume"]
MAV_Averg = live["AvgVolumeNinety"]
MAL_Averg = live["ShortMovingAvg"]
MAB_Averg = live["LongMovingAvg"]
# Calculate Live Stock Information *******************************************
# Calculate # of Tweets From Today *******************************************
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
today_tweets = cursor.fetchall()
DailyTweets_Count = cursor.rowcount
# Calculate # Of Tweets From Today *******************************************
# Calculate Sentiment For The Day
# ***************************************************************************
# SHORT TERM SENTIMENT ANALYSIS
# --------------------------------------------
# Short Term Sentiment Scale
# 0 Extremelly Negative Sentiment <-40
# 0.1 -40 - -30
# 0.2 -30 - -20
# 0.3 -20 - -10
# 0.4 -10 - -0
# 0.5 Neutral 0
# 0.6 0 - 10
# 0.7 10 - 20
# 0.8 20 - 30
# 0.9 30 - 40
# 1 Significant Positive Sentiment >50
# --------------------------------------------
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, date))
connection.commit()
DaySentiment = cursor.fetchall()
Daily_Sentiment = 0
Daily_Count = 0.001
for tweet in DaySentiment:
Temp_Likes = 0
Temp_Likes = float(tweet["Total_Likes"])
Temp_Sentiment = 0
Temp_Sentiment = float(tweet["Tweet_Sentiment"])
Daily_Count = Daily_Count + 1
while (Temp_Likes != 0):
if ((Temp_Sentiment <= 90)
and (Temp_Sentiment >= -90)): # Prevent Overbounds
Temp_Sentiment = (Temp_Sentiment * 0.15) + Temp_Sentiment
Temp_Likes = Temp_Likes - 1
Daily_Sentiment = Daily_Sentiment + Temp_Sentiment
SentimentNormalized = (Daily_Sentiment / Daily_Count
) # Calculate One Day Sentiment
SentimentNormalized = round(SentimentNormalized)
if (SentimentNormalized < -40):
SentimentNormalized = float(0.0)
elif ((SentimentNormalized >= -40) and (SentimentNormalized < -30)):
SentimentNormalized = float(0.1)
elif ((SentimentNormalized >= -30) and (SentimentNormalized < -20)):
SentimentNormalized = float(0.2)
elif ((SentimentNormalized >= -20) and (SentimentNormalized < -10)):
SentimentNormalized = float(0.3)
elif ((SentimentNormalized >= -10) and (SentimentNormalized < 0)):
SentimentNormalized = float(0.4)
elif (SentimentNormalized == 0):
SentimentNormalized = float(0.5)
elif ((SentimentNormalized > 0) and (SentimentNormalized < 10)):
SentimentNormalized = float(0.6)
elif ((SentimentNormalized >= 10) and (SentimentNormalized < 20)):
SentimentNormalized = float(0.7)
elif ((SentimentNormalized >= 20) and (SentimentNormalized < 30)):
SentimentNormalized = float(0.8)
elif ((SentimentNormalized >= 30) and (SentimentNormalized <= 40)):
SentimentNormalized = float(0.9)
elif (SentimentNormalized > 40):
SentimentNormalized = float(1.0)
else:
print("#104 Fatal Error: Could Not Calculate Sentiment Accurately")
sys.exit("Fatal Error")
print("Sentiment For " + day_name + "", SentimentNormalized)
# Calculate Sentiment For The Day *******************************************
# Calculate Prediction Date *************************************************
start_date = datetime.strptime(date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if (no_weekends == "Saturday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-2)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("#105 Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error")
elif (no_weekends == "Sunday"):
start_date = datetime.strptime(forward_date, '%Y-%m-%d')
forwardgo = start_date - timedelta(days=-1)
forward_date = forwardgo.strftime('%Y-%m-%d')
no_weekends = datetime.strptime(
(forward_date),
'%Y-%m-%d').strftime('%A') # Grab Name of Requested Date
if ((no_weekends == "Saturday") or (no_weekends == "Sunday")):
print("#106 Fatal Error: Could Not Calculate Date Accurately"
) # Kill Program, Something Went Very Wrong
sys.exit("Fatal Error")
forward_date_id = datetime.strptime((forward_date),
'%Y-%m-%d').strftime('%d')
# Calculate Prediction Date *************************************************
# Calculate Sentiment + Tweet Volume Average 30 Actual Days * From Date *
# ***************************************************************************
# LONG TERM SENTIMENT ANALYSIS
# --------------------------------------------
# Calculate and Normalize 30 Day Sentiment Average
# Long Term Sentiment Scale
# 0 General Negative Sentiment <95
# 0.1 96
# 0.2 97
# 0.3 98
# 0.4 99
# 0.5 Average 100
# 0.6 101
# 0.7 102
# 0.8 103
# 0.9 104
# 1 General Positive Sentiment >105
# --------------------------------------------
days_list = rangedatelist(
date, 29) # Get List of Backdates In This Range (Including Weekends)
SAB_Total = 0.0
SAB_Averg = 0.0
SAB_Count = 0
TVA_Count = 0 # Daily Tweet Volume Count
TVA_Days = 0.001 # Daily Tweet Volume Total Days
TVA_Total = 0 # Tweet Volume Average Total
for day in days_list: # For Each Day In Range, Calculate Sentiment Analysis
TVA_Days = TVA_Days + 1
SAB_Count = SAB_Count + 1
day = day.strftime('%Y-%m-%d')
with connection.cursor() as cursor:
sql = "SELECT * FROM `StockTweets` WHERE `Ticker`=%s AND `Date`=%s"
cursor.execute(sql, (ticker, day))
connection.commit()
one_day = cursor.fetchall()
Daily_Sentiment = 0
Daily_Count = 0.001
for tweet in one_day:
TVA_Count = TVA_Count + 1
Temp_Likes = 0
Temp_Likes = float(tweet["Total_Likes"])
Temp_Sentiment = 0
Temp_Sentiment = float(tweet["Tweet_Sentiment"])
Daily_Count = Daily_Count + 1
while (Temp_Likes != 0):
if ((Temp_Sentiment <= 90)
and (Temp_Sentiment >= -90)): # Prevent Overbounds
Temp_Sentiment = (Temp_Sentiment * 0.15) + Temp_Sentiment
Temp_Likes = Temp_Likes - 1
Daily_Sentiment = Daily_Sentiment + Temp_Sentiment
if (Daily_Sentiment == 0):
TVA_Days = TVA_Days - 1 # Fix Averages If No Tweets On That Day
Tweet_Sentiment = (Daily_Sentiment / Daily_Count
) # Calculate One Day Sentiment
SAB_Total = SAB_Total + Tweet_Sentiment # Add One Day Sentiment To SAB Total
TVA_Total = (TVA_Count / TVA_Days)
SAB_Averg = (SAB_Total / SAB_Count)
SAB_Averg = round(SAB_Averg)
SAB_Averg = SAB_Averg + 100
print("30 Day Sentiment", SAB_Averg)
print("30 Day Avg Tweets", TVA_Total)
if (SAB_Averg <= 95):
SentimentLongTerm = float(0.0)
elif (SAB_Averg == 96):
SentimentLongTerm = float(0.1)
elif (SAB_Averg == 97):
SentimentLongTerm = float(0.2)
elif (SAB_Averg == 98):
SentimentLongTerm = float(0.3)
elif (SAB_Averg == 99):
SentimentLongTerm = float(0.4)
elif (SAB_Averg == 100):
SentimentLongTerm = float(0.5)
elif (SAB_Averg == 101):
SentimentLongTerm = float(0.6)
elif (SAB_Averg == 102):
SentimentLongTerm = float(0.7)
elif (SAB_Averg == 103):
SentimentLongTerm = float(0.8)
elif (SAB_Averg == 104):
SentimentLongTerm = float(0.9)
elif (SAB_Averg >= 105):
SentimentLongTerm = float(1.0)
else:
print("#201 Fatal Error: Could Not Calculate Sentiment Accurately")
sys.exit("Fatal Error")
# Calculate Sentiment + Volume Average 30 Actual Days * From Date *
# ***************************************************************************
# TRADING VOLUME ANALYSIS
# ***************************************************************************
# --------------------------------------------
# Calculate and Normalize Volume Trading
# Trading Volume Scale
# 0 Unusually Low Volume 0,1
# 0.1 2,3
# 0.2 4,5
# 0.3 6,7
# 0.4 8,9
# 0.5 Average 10
# 0.6 11,12
# 0.7 13,14
# 0.8 15,16
# 0.9 17,18
# 1 Unusually High Volume 19,20
# --------------------------------------------
ThisDay_Volume = int(stock_price_volm)
MAV_Averg = int(MAV_Averg) # Pulled From LiveDatabase
if (ThisDay_Volume > MAV_Averg):
Sub_Process = (ThisDay_Volume / MAV_Averg)
Sub_Process = 10 * Sub_Process
VolumeNormalized = Sub_Process
if (Sub_Process > 20): # Maximum Volume Increase Is Double
Sub_Process = 20
VolumeNormalized = Sub_Process
elif (Sub_Process < 10): # This shouldn't ever happen
Sub_Process = 10
VolumeNormalized = Sub_Process
elif (ThisDay_Volume < MAV_Averg):
Sub_Process = (ThisDay_Volume / MAV_Averg)
Sub_Process = 10 * Sub_Process
VolumeNormalized = Sub_Process
if (Sub_Process < 0): # No Negatives Allowed
Sub_Process = 0
VolumeNormalized = Sub_Process
elif (Sub_Process > 10): # This shouldn't ever happen
Sub_Process = 10
VolumeNormalized = Sub_Process
else:
VolumeNormalized = 10
VolumeNormalized = round(VolumeNormalized)
if ((VolumeNormalized == 0) or (VolumeNormalized == 1)):
VolumeNormalized = float(0.0)
elif ((VolumeNormalized == 2) or (VolumeNormalized == 3)):
VolumeNormalized = float(0.1)
elif ((VolumeNormalized == 4) or (VolumeNormalized == 5)):
VolumeNormalized = float(0.2)
elif ((VolumeNormalized == 6) or (VolumeNormalized == 7)):
VolumeNormalized = float(0.3)
elif ((VolumeNormalized == 8) or (VolumeNormalized == 9)):
VolumeNormalized = float(0.4)
elif (VolumeNormalized == 10):
VolumeNormalized = float(0.5)
elif ((VolumeNormalized == 11) or (VolumeNormalized == 12)):
VolumeNormalized = float(0.6)
elif ((VolumeNormalized == 13) or (VolumeNormalized == 14)):
VolumeNormalized = float(0.7)
elif ((VolumeNormalized == 15) or (VolumeNormalized == 16)):
VolumeNormalized = float(0.8)
elif ((VolumeNormalized == 17) or (VolumeNormalized == 18)):
VolumeNormalized = float(0.9)
elif ((VolumeNormalized == 19) or (VolumeNormalized == 20)):
VolumeNormalized = float(1.0)
else:
print("#202 Fatal Error: Could Not Calculate Volume Accurately")
sys.exit("Fatal Error")
# ***************************************************************************
# Calculate and Normalize Daily Sentiment
# ***************************************************************************
# SHORT TERM TWEET VOLUME
# --------------------------------------------
# Calculate and Normalize Tweet Volume
# Tweet Volume Scale
# 0 Unusually Low Volume 0,1
# 0.1 2,3
# 0.2 4,5
# 0.3 6,7
# 0.4 8,9
# 0.5 Average 10
# 0.6 11,12
# 0.7 13,14
# 0.8 15,16
# 0.9 17,18
# 1 Unusually High Volume 19,20
# --------------------------------------------
DailyTweets_Count = int(DailyTweets_Count)
TVA_Total = int(TVA_Total)
if (DailyTweets_Count > MAV_Averg):
Sub_Process = (DailyTweets_Count / TVA_Total)
Sub_Process = 10 * Sub_Process
TweetsNormalized = Sub_Process
if (Sub_Process > 20): # Maximum Volume Increase Is Double
Sub_Process = 20
TweetsNormalized = Sub_Process
elif (Sub_Process < 10): # This shouldn't ever happen
Sub_Process = 10
TweetsNormalized = Sub_Process
elif (DailyTweets_Count < TVA_Total):
Sub_Process = (DailyTweets_Count / TVA_Total)
Sub_Process = 10 * Sub_Process
TweetsNormalized = Sub_Process
if (Sub_Process < 0): # No Negatives Allowed
Sub_Process = 0
TweetsNormalized = Sub_Process
elif (Sub_Process > 10): # This shouldn't ever happen
Sub_Process = 10
TweetsNormalized = Sub_Process
else:
TweetsNormalized = 10
TweetsNormalized = round(TweetsNormalized)
if ((TweetsNormalized == 0) or (TweetsNormalized == 1)):
TweetsNormalized = float(0.0)
elif ((TweetsNormalized == 2) or (TweetsNormalized == 3)):
TweetsNormalized = float(0.1)
elif ((TweetsNormalized == 4) or (TweetsNormalized == 5)):
TweetsNormalized = float(0.2)
elif ((TweetsNormalized == 6) or (TweetsNormalized == 7)):
TweetsNormalized = float(0.3)
elif ((TweetsNormalized == 8) or (TweetsNormalized == 9)):
TweetsNormalized = float(0.4)
elif (TweetsNormalized == 10):
TweetsNormalized = float(0.5)
elif ((TweetsNormalized == 11) or (TweetsNormalized == 12)):
TweetsNormalized = float(0.6)
elif ((TweetsNormalized == 13) or (TweetsNormalized == 14)):
TweetsNormalized = float(0.7)
elif ((TweetsNormalized == 15) or (TweetsNormalized == 16)):
TweetsNormalized = float(0.8)
elif ((TweetsNormalized == 17) or (TweetsNormalized == 18)):
TweetsNormalized = float(0.9)
elif ((TweetsNormalized == 19) or (TweetsNormalized == 20)):
TweetsNormalized = float(1.0)
else:
print("#203 Fatal Error: Could Not Calculate Volume Accurately")
sys.exit("Fatal Error")
# Calculate and Normalize Volume Trading
# ***************************************************************************
# ***************************************************************************
# Check Above or Below Moving Averages
if (stock_price_live > MAB_Averg):
AboveBigMoving = 1
else:
AboveBigMoving = 0
if (stock_price_live < MAL_Averg):
BelowLittleMoving = 1
else:
BelowLittleMoving = 0
# Check Above or Below Moving Averages
# ***************************************************************************
# Algorithm Results
# ***************************************************************************
print("\n\n")
print("Neural Network Normalized Data: ")
print("----------------------------------------------")
print("Entry ", Entry)
print("Volume Normalized: [0,1] ", VolumeNormalized)
print("Tweets Normalized: [0,1] ", TweetsNormalized)
print("Sentiment Analysis: [0,1] ", SentimentNormalized)
print("Above Big Moving: [0,1] ", AboveBigMoving)
print("Below Little Moving: [0,1] ", BelowLittleMoving)
print("Trading Day: [0,1] ", trading_day)
print("Trading Season: [Sp, Su, Fa, Wi] ", trading_season)
print("Moving Avg Sentiment: [0,200] ", SAB_Averg)
outputs = network.process([
trading_day, trading_season, VolumeNormalized, AboveBigMoving,
BelowLittleMoving, VolumeNormalized, SentimentNormalized,
SentimentLongTerm
])
result1 = round(outputs[0], 2)
result2 = round(outputs[1], 2)
print("Results")
print("--------------------------------------------------")
print("Prediction: [", result1, ",", result2, "]")
print("\n")
try:
with connection.cursor() as cursor:
sql = "SELECT * FROM `Predictions` WHERE `PredictionDate`=%s AND Ticker=%s"
cursor.execute(sql, (forward_date, ticker))
result = cursor.fetchone()
if result is None:
print("Success: Inserting Prediction To Records: ", ticker)
with connection.cursor() as cursor:
sql = "INSERT INTO `Predictions` (`Date`, `PredictionDate`, `Day_ID`, `PredictionDateName`, `Ticker`, `InitialPrice`, `Entry`, `Pred_Output1`, `Pred_Output2`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)"
cursor.execute(
sql, (Today, forward_date, forward_date_id, no_weekends,
ticker, stock_price_live, Entry, result1, result2))
connection.commit()
else:
print("Success: Updating Prediction In Records: ", ticker)
unique_entry = result["Entry"]
cursor = connection.cursor()
sql = "UPDATE Predictions SET Date=%s WHERE Entry=%s"
cursor.execute(sql, (Today, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET InitialPrice=%s WHERE Entry=%s"
cursor.execute(sql, (stock_price_live, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET PredictionDate=%s WHERE Entry=%s"
cursor.execute(sql, (forward_date, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET PredictionDateName=%s WHERE Entry=%s"
cursor.execute(sql, (no_weekends, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET Pred_Output1=%s WHERE Entry=%s"
cursor.execute(sql, (result1, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET Pred_Output2=%s WHERE Entry=%s"
cursor.execute(sql, (result2, unique_entry))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET Day_ID=%s WHERE Entry=%s"
cursor.execute(sql, (forward_date_id, unique_entry))
connection.commit()
finally:
# Adjusted for possibility that network is not fully trained yet.
# Fine tune this as time goes forward
if ((result1 >= 0.0) and (result1 <= 0.15)):
approx = 0.0
elif ((result1 > 0.15) and (result1 < 0.45)):
approx = 0.3
elif ((result1 >= 0.45) and (result1 < 0.55)):
approx = -1.0
elif ((result1 >= 0.55) and (result1 < 0.85)):
approx = 0.7
elif ((result1 >= 0.85) and (result1 <= 1.00)):
approx = 1.0
if (approx == 0.0):
prediction_event = "Significant Decrease"
elif (approx == 0.3):
prediction_event = "Decrease"
elif (approx == 0.7):
prediction_event = "Increase"
elif (approx == 1.0):
prediction_event = "Significant Increase"
elif (approx == -1.0):
prediction_event = "Inconclusive"
confidence = 0
boost = 0.0
if (AboveBigMoving == 1):
boost = boost + 0.13
if (BelowLittleMoving == 1):
boost = boost + 0.13
if (approx == 0.0):
if (result1 == 0.0):
confidence = 10
if (result1 == 0.01):
confidence = 9
if (result1 == 0.02):
confidence = 9
if (result1 == 0.03):
confidence = 9
if (result1 == 0.04):
confidence = 8
if (result1 == 0.05):
confidence = 8
if (result1 == 0.06):
confidence = 7
if (result1 == 0.07):
confidence = 7
if (result1 == 0.08):
confidence = 6
if (result1 == 0.09):
confidence = 6
if (result1 == 0.10):
confidence = 6
if (result1 == 0.11):
confidence = 6
if (result1 == 0.12):
confidence = 6
if (result1 == 0.13):
confidence = 6
if (result1 == 0.14):
confidence = 6
if (result1 == 0.15):
confidence = 6
if (approx == 0.3):
if (result1 == 0.16):
confidence = 5
if (result1 == 0.17):
confidence = 5
if (result1 == 0.18):
confidence = 5
if (result1 == 0.19):
confidence = 5
if (result1 == 0.20):
confidence = 5
if (result1 == 0.21):
confidence = 5
if (result1 == 0.22):
confidence = 6
if (result1 == 0.23):
confidence = 6
if (result1 == 0.24):
confidence = 7
if (result1 == 0.25):
confidence = 7
if (result1 == 0.26):
confidence = 7
if (result1 == 0.27):
confidence = 8
if (result1 == 0.28):
confidence = 8
if (result1 == 0.29):
confidence = 9
if (result1 == 0.30):
confidence = 9
if (result1 == 0.31):
confidence = 9
if (result1 == 0.32):
confidence = 8
if (result1 == 0.33):
confidence = 7
if (result1 == 0.34):
confidence = 6
if (result1 == 0.35):
confidence = 6
if (result1 == 0.36):
confidence = 5
if (result1 == 0.37):
confidence = 5
if (result1 == 0.38):
confidence = 4
if (result1 == 0.39):
confidence = 3
if (result1 == 0.40):
confidence = 3
if (result1 == 0.41):
confidence = 2
if (result1 == 0.42):
confidence = 2
if (result1 == 0.43):
confidence = 1
if (result1 == 0.44):
confidence = 1
if (approx == -1.0):
if (result1 == 0.45):
confidence = 0
if (result1 == 0.46):
confidence = 0
if (result1 == 0.47):
confidence = 0
if (result1 == 0.48):
confidence = 0
if (result1 == 0.49):
confidence = 0
if (result1 == 0.50):
confidence = 0
if (result1 == 0.51):
confidence = 0
if (result1 == 0.52):
confidence = 0
if (result1 == 0.53):
confidence = 0
if (result1 == 0.54):
confidence = 0
if (approx == 0.7):
if (result1 == 0.55):
confidence = 1
if (result1 == 0.56):
confidence = 1
if (result1 == 0.57):
confidence = 2
if (result1 == 0.58):
confidence = 2
if (result1 == 0.59):
confidence = 2
if (result1 == 0.60):
confidence = 3
if (result1 == 0.61):
confidence = 3
if (result1 == 0.62):
confidence = 4
if (result1 == 0.63):
confidence = 5
if (result1 == 0.64):
confidence = 5
if (result1 == 0.65):
confidence = 6
if (result1 == 0.66):
confidence = 7
if (result1 == 0.67):
confidence = 8
if (result1 == 0.68):
confidence = 8
if (result1 == 0.69):
confidence = 9
if (result1 == 0.70):
confidence = 9
if (result1 == 0.71):
confidence = 9
if (result1 == 0.72):
confidence = 8
if (result1 == 0.73):
confidence = 8
if (result1 == 0.74):
confidence = 7
if (result1 == 0.75):
confidence = 7
if (result1 == 0.76):
confidence = 6
if (result1 == 0.77):
confidence = 6
if (result1 == 0.78):
confidence = 6
if (result1 == 0.79):
confidence = 5
if (result1 == 0.80):
confidence = 5
if (result1 == 0.81):
confidence = 5
if (result1 == 0.82):
confidence = 5
if (result1 == 0.83):
confidence = 5
if (result1 == 0.84):
confidence = 5
if (approx == 1.0):
if (result1 == 0.85):
confidence = 5
if (result1 == 0.86):
confidence = 5
if (result1 == 0.87):
confidence = 5
if (result1 == 0.88):
confidence = 5
if (result1 == 0.89):
confidence = 5
if (result1 == 0.90):
confidence = 5
if (result1 == 0.91):
confidence = 6
if (result1 == 0.92):
confidence = 6
if (result1 == 0.93):
confidence = 6
if (result1 == 0.94):
confidence = 6
if (result1 == 0.95):
confidence = 7
if (result1 == 0.96):
confidence = 8
if (result1 == 0.97):
confidence = 9
if (result1 == 0.98):
confidence = 9
if (result1 == 0.99):
confidence = 9
if (result1 == 1.00):
confidence = 9
confidence = (confidence + (confidence * boost))
cursor = connection.cursor()
sql = "UPDATE Stocks SET Prediction=%s WHERE Ticker=%s"
cursor.execute(sql, (prediction_event, ticker))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Stocks SET Prediction_Day=%s WHERE Ticker=%s"
cursor.execute(sql, (no_weekends, ticker))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Stocks SET Confidence=%s WHERE Ticker=%s"
cursor.execute(sql, (confidence, ticker))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET Confidence=%s WHERE PredictionDate=%s AND Ticker=%s"
cursor.execute(sql, (confidence, forward_date, ticker))
connection.commit()
cursor = connection.cursor()
sql = "UPDATE Predictions SET Prediction=%s WHERE PredictionDate=%s AND Ticker=%s"
cursor.execute(sql, (prediction_event, forward_date, ticker))
connection.commit()
print("::: SAVING DATA ::: DO NOT CLOSE CONNECTION :::")
network = Network()
network.add_layer(
8, 8, Network.ACTIVATION_SIGMOID) # Hidden Layer, 10 Neurons, 8 inputs
network.add_layer(
2, 8, Network.ACTIVATION_SIGMOID) # Output Layer, 2 Neurons, 8 inputs
print("Creating Neural Network...")
ITERATIONS = 500 # Number of iterations per training session
LEARN_RATE = 0.03 # The rate the network learns on each iteration
THRESHOLD = 0.001 # If this precision is reached, the training session is instantly complete
# Perform a quick training session
for i in range(0, ITERATIONS, 1):
error = 0
print("Learning to fly...")
with connection.cursor() as cursor:
sql = "SELECT * FROM `NeuralNetwork` ORDER BY Date DESC LIMIT 150"
cursor.execute(sql, ())
connection.commit()
NeuralTraining = cursor.fetchall()
for set in NeuralTraining:
print("Finding hidden Robots...")
train = [
set["TradingDay"], set["TradingSeason"], set["VolumeNormalized"],
set["AboveBigMoving"], set["BelowLittleMoving"],
set["TweetsVolumeNormalized"], set["Sentiment"], set["Sentiment30"]
]
Output1 = set["Output1"]
Output2 = set["Output2"]
error += network.train(train, [Output1, Output2], LEARN_RATE)
