thepycoach

import subprocess
import pandas as pd
import pickle
from fuzzywuzzy import process, fuzz
from sympy import symbols, Eq, solve

thepycoach

#3,Finding Surebets
#Formula to find surebets
def find_surebet(frame):
    frame[['btts_x_1', 'btts_x_2']] = frame['btts_x'].apply(lambda x: x.split('\n')).apply(pd.Series).astype(float)
    frame[['btts_y_1', 'btts_y_2']] = frame['btts_y'].apply(lambda x: x.split('\n')).apply(pd.Series).astype(float)
    frame['sure_btts1'] = (1 / frame['btts_x_1']) + (1 / frame['btts_y_2'])
    frame['sure_btts2'] = (1 / frame['btts_x_2']) + (1 / frame['btts_y_1'])
    frame = frame[['Teams_x', 'btts_x', 'Teams_y', 'btts_y', 'sure_btts1', 'sure_btts2']]
    frame = frame[(frame['sure_btts1'] < 1) | (frame['sure_btts2'] < 1)]
    frame.reset_index(drop=True, inplace=True)

thepycoach

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import pandas as pd
import time
import pickle
import re

thepycoach

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
import pandas as pd
import pickle

options = Options()

thepycoach

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.ui import Select
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
import pandas as pd
import pickle

thepycoach

def beat_bookies(odds1, odds2, total_stake):
    x, y = symbols('x y')
    eq1 = Eq(x + y - total_stake, 0) # total_stake = x + y
    eq2 = Eq((odds2*y) - odds1*x, 0) # odds1*x = odds2*y
    stakes = solve((eq1,eq2), (x, y))
    total_investment = stakes[x] + stakes[y]
    profit1 = odds1*stakes[x] - total_stake
    profit2 = odds2*stakes[y] - total_stake
    benefit1 = f'{profit1 / total_investment * 100:.2f}%'
    benefit2 = f'{profit2 / total_investment * 100:.2f}%'

thepycoach

import subprocess
import pandas as pd
import pickle
from fuzzywuzzy import process, fuzz
from sympy import symbols, Eq, solve

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

thepycoach

headers = {   
    'authority': 'www.google.com',
    'content-length': '0',
    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36',
    'content-type': 'text/plain;charset=UTF-8',
    'accept': '*/*',
    'referer': 'https://www.google.com/',
    'accept-language': 'en-US,en;q=0.9',
  }

thepycoach

def calculate_real_odds(home_team, away_team, df_past_years, market):
    df_past_years = df_past_years.assign(total_goals=df_past_years['home_goals'] + df_past_years['away_goals'])
    df_past_years['Over/Under'] = np.where(df_past_years['total_goals']>2, 'Over 2.5', 'Under 2.5')
    dict_markets = {'Over/Under':['Over 2.5', 'Under 2.5']}
    df_real_odds = df_past_years[(df_past_years['home_team']==home_team)|(df_past_years['away_team']==away_team)].groupby(market).count()[['total_goals']]
    option1 = df_real_odds.loc[dict_markets[market][0], 'total_goals']
    option2 = df_real_odds.loc[dict_markets[market][1], 'total_goals']
    percentage_odds_over = option1 / (option1 + option2)
    real_odds = round(1/percentage_odds_over, 2)
    return real_odds

thepycoach

dict_leagues = {'Spanish La Liga':'SP1', 'German Bundesliga':'D1', 
                'English Premier League':'E0', 'Italian Serie A':'I1'}
dict_historical_data = {}

for league in dict_leagues:
    frames = []
    for i in range(15, 21):
        df = pd.read_csv("http://www.football-data.co.uk/mmz4281/"+str(i)+str(i+1)+"/"+dict_leagues[league]+".csv")
        df = df[['Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG']]
        df = df.rename(columns={'HomeTeam':'home_team', 'AwayTeam':'away_team','FTHG': 'home_goals', 'FTAG': 'away_goals'})