def ps3838_scrap_single():
os.system('node scrap_ps3838_single_standalone.js')
try:
df_single = pd.DataFrame.from_csv('../dataset/local/df_single.xls')
dict_odds = df_single.to_dict(orient='index')
except:
df_single = pd.DataFrame()
dict_odds = {}
list_single = glob('../dataset/local/single*.html')
list_single.sort()
for single in list_single:
print single
soup = BeautifulSoup(open(single), "html.parser")
### GMT
GMT = soup.find('span', {"id": "current-time"}).text.strip()
GMT_value = GMT[GMT.find('GMT+') + 4:-3]
GMT_to_add = 1 - int(GMT_value)
###
money_line_list = soup.find_all('div', {"class": "OneXTwo_0 "})
if money_line_list != []:
money_line_list = money_line_list[0]
ligue_list = money_line_list.find_all('div', {"class": "o_league"})
for i, ligue in enumerate(ligue_list):
money_line_ligue_list = money_line_list.find_all(
'table', {"class": "o_table events"})[i]
match_list = []
match_list = money_line_ligue_list.find_all(
'tr',
{"class": "status_I"}) + money_line_ligue_list.find_all(
'tr', {"class": "status_O"})
sport = single.split('_')[-1].split('.')[0]
for match in match_list:
if match.find_all('td', {"class": "o_A_draw "}) != []:
match_odd = match.find('td', {"class": "o_A_draw"})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
bet_X = 0
team_X_id = 0
match_odd = match.find('td', {"class": "o_B_draw"})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
else:
match_odd = match.find('td', {"class": "o_A "})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_odd = match.find('td', {"class": "o_draw"})
_ = match_odd.find('span', {
"class": "o_left"
}).text.strip()
bet_X = match_odd.find('span', {
"class": "o_right "
}).text.strip()
team_X_id = match_odd.find('a')['id']
match_odd = match.find('td', {"class": "o_B "})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_date = str(datetime.now().year) + ' ' + match.find(
'span', {
"class": "DateTime"
}).text
match_name = team_home + '_' + team_away + '_' + match_date
ligue_name = ligue.text.strip()
team_home = encode_decode(team_home)
team_away = encode_decode(team_away)
match_name = encode_decode(match_name)
### ADJUST TO GMT
match_date = date_ajustement_str(match_date, GMT_to_add)
dict_odds.update({
match_name: {
'match_date': match_date,
'team_home': team_home,
'team_home_id': team_home_id,
'team_away': team_away,
'team_away_id': team_away_id,
'team_X_id': team_X_id,
'bet_1': bet_1,
'bet_X': bet_X,
'bet_2': bet_2,
'sport': sport,
'ligue': ligue_name,
}
})
# print sport
dict_odds_new = {}
for k, v in dict_odds.iteritems():
encode_decode(k)
dict_odds_new.update({k: v})
df_single = pd.DataFrame.from_dict(dict_odds_new, orient='index')
df_single.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
###
df_single.bet_1 = df_single.bet_1.astype(float)
df_single.bet_2 = df_single.bet_2.astype(float)
df_single['bet_diff'] = df_single.bet_1 - df_single.bet_2
df_single['prediction'] = df_single.bet_diff.apply(lambda x: "1"
if x < 0 else "2")
df_single['bet_diff'] = abs(df_single.bet_1 - df_single.bet_2)
df_single['min_bet'] = df_single[['bet_1', 'bet_2']].min(axis=1)
###
df_single.match_date = date_ajustement(df_single.match_date)
df_single.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
"""
###
df_single.match_date = df_single.match_date.apply(lambda x : datetime.strptime(x.replace('Nov','11'), '%Y %m %d%H:%M') if str(x)[5:8]=='Nov' else x)
df_single.match_date = df_single.match_date.apply(lambda x : datetime.strptime(x.replace('nov.','11'), '%Y %d %m%H:%M') if str(x)[8:12]=='nov.' else x)
df_single.match_date = df_single.match_date.apply(lambda x : datetime.strptime(x.replace('Dec','12'), '%Y %m %d%H:%M') if str(x)[5:8]=='Dec' else x)
df_single.match_date = df_single.match_date.apply(lambda x : datetime.strptime(x.replace('dec.','12'), '%Y %d %m%H:%M') if str(x)[8:12]=='dec.' else x)
"""
#### filter live
#ee
#df_single.match_date = df_single.match_date.apply(lambda x : x if isinstance(x, basestring)!=True else 0)
#df_single = df_single[df_single.match_date != 0]
df_single.to_csv('../dataset/local/df_single.xls', encoding='utf-8')
return GMT_to_add
def ps3838_scrap_parlay():
url_season_page = 'https://www.ps3838.com/en/euro/sports/mix-parlay'
save_season_page = '../dataset/local/mix_parlay'
# os.system('node scrap_ps3838_parlay_standalone.js ' + url_season_page + ' ' + save_season_page)
# =============================================================================
#
# =============================================================================
try:
df_parlay = pd.DataFrame.from_csv('../dataset/local/df_parlay.xls')
dict_odds = df_parlay.to_dict(orient='index')
except:
df_parlay = pd.DataFrame()
dict_odds = {}
list_parlay = glob('../dataset/local/mix_parlay*.*')
list_parlay.sort()
for parlay in list_parlay:
print parlay
soup = BeautifulSoup(open(parlay), "html.parser")
money_line_list = soup.find_all('div', {"class": "OneXTwo_0"})
if money_line_list != []:
money_line_list = money_line_list[0]
ligue_list = money_line_list.find_all('div', {"class": "o_league"})
for i, ligue in enumerate(ligue_list):
money_line_ligue_list = money_line_list.find_all(
'table', {"class": "o_table events"})[i]
### GMT
GMT = soup.find('span', {"id": "current-time"}).text.strip()
GMT_value = GMT[GMT.find('GMT+') + 4:-3]
GMT_to_add = int(GMT_value) - 1
match_list = []
match_list = money_line_ligue_list.find_all(
'tr',
{"class": "status_I"}) + money_line_ligue_list.find_all(
'tr', {"class": "status_O"})
sport = parlay.split('_')[-1].split('.')[0]
###
sport_filter = soup.find('tr', {"id": "sportFilter"})
sport_filter_li = sport_filter.find_all('li')
for z, li in enumerate(sport_filter_li):
if str(li).find("selected") != -1:
sport_id = z + 1
# try:
for match in match_list:
if match.find_all('td', {"class": "o_A_draw "}) != []:
match_odd = match.find('td', {"class": "o_A_draw"})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
bet_X = 0
team_X_id = 0
match_odd = match.find('td', {"class": "o_B_draw"})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
else:
match_odd = match.find('td', {"class": "o_A "})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_odd = match.find('td', {"class": "o_draw"})
_ = match_odd.find('span', {
"class": "o_left"
}).text.strip()
bet_X = match_odd.find('span', {
"class": "o_right "
}).text.strip()
team_X_id = match_odd.find('a')['id']
match_odd = match.find('td', {"class": "o_B "})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_date = str(datetime.now().year) + ' ' + match.find(
'span', {
"class": "DateTime"
}).text
match_name = team_home + '_' + team_away + '_' + match_date
ligue_name = ligue.text.strip()
team_home = encode_decode(team_home)
team_away = encode_decode(team_away)
match_name = encode_decode(match_name)
match_date = date_ajustement_str(match_date, GMT_to_add)
dict_odds.update({
match_name: {
'match_date': match_date,
'team_home': team_home,
'team_home_id': team_home_id,
'team_away': team_away,
'team_away_id': team_away_id,
'team_X_id': team_X_id,
'bet_1': bet_1,
'bet_X': bet_X,
'bet_2': bet_2,
'sport': sport,
'sport_id': sport_id,
'ligue': ligue_name,
}
})
# print sport
# except:
# pass
dict_odds_new = {}
for k, v in dict_odds.iteritems():
encode_decode(k)
dict_odds_new.update({k: v})
df_parlay = pd.DataFrame.from_dict(dict_odds_new, orient='index')
df_parlay.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
###
df_parlay.bet_1 = df_parlay.bet_1.astype(float)
df_parlay.bet_2 = df_parlay.bet_2.astype(float)
df_parlay['bet_diff'] = df_parlay.bet_1 - df_parlay.bet_2
df_parlay['prediction'] = df_parlay.bet_diff.apply(lambda x: "1"
if x < 0 else "2")
df_parlay['bet_diff'] = abs(df_parlay.bet_1 - df_parlay.bet_2)
df_parlay['min_bet'] = df_parlay[['bet_1', 'bet_2']].min(axis=1)
###
df_parlay.match_date = date_ajustement(df_parlay.match_date)
df_parlay.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
###
"""
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('Nov','11'), '%Y %m %d%H:%M') if str(x)[5:8]=='Nov' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('nov.','11'), '%Y %d %m%H:%M') if str(x)[8:12]=='nov.' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('Dec','12'), '%Y %m %d%H:%M') if str(x)[5:8]=='Dec' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('dec.','12'), '%Y %d %m%H:%M') if str(x)[8:12]=='dec.' else x)
"""
df_parlay.to_csv('../dataset/local/df_parlay.xls', encoding='utf-8')
# =============================================================================
#
# =============================================================================
try:
df_parlay = pd.DataFrame.from_csv('../dataset/local/df_parlay.xls')
dict_odds = df_parlay.to_dict(orient='index')
except:
df_parlay = pd.DataFrame()
dict_odds = {}
list_parlay = glob('../dataset/local/mix_parlay*.*')
list_parlay.sort()
for parlay in list_parlay:
print parlay
soup = BeautifulSoup(open(parlay), "html.parser")
money_line_list = soup.find_all('div', {"class": "OneXTwo_0"})
if money_line_list != []:
money_line_list = money_line_list[0]
ligue_list = money_line_list.find_all('div', {"class": "o_league"})
for i, ligue in enumerate(ligue_list):
money_line_ligue_list = money_line_list.find_all(
'table', {"class": "o_table events"})[i]
### GMT
GMT = soup.find('span', {"id": "current-time"}).text.strip()
GMT_value = GMT[GMT.find('GMT+') + 4:-3]
GMT_to_add = 1 - int(GMT_value)
match_list = []
match_list = money_line_ligue_list.find_all(
'tr',
{"class": "status_I"}) + money_line_ligue_list.find_all(
'tr', {"class": "status_O"})
sport = parlay.split('_')[-1].split('.')[0]
###
sport_filter = soup.find('tr', {"id": "sportFilter"})
sport_filter_li = sport_filter.find_all('li')
for z, li in enumerate(sport_filter_li):
if str(li).find("selected") != -1:
sport_id = z + 1
for match in match_list:
if match.find_all('td', {"class": "o_A_draw "}) != []:
match_odd = match.find('td', {"class": "o_A_draw"})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
bet_X = 0
team_X_id = 0
match_odd = match.find('td', {"class": "o_B_draw"})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
else:
match_odd = match.find('td', {"class": "o_A "})
team_home = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_home_id = match_odd.find('a')['id']
bet_1 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_odd = match.find('td', {"class": "o_draw"})
_ = match_odd.find('span', {
"class": "o_left"
}).text.strip()
bet_X = match_odd.find('span', {
"class": "o_right "
}).text.strip()
team_X_id = match_odd.find('a')['id']
match_odd = match.find('td', {"class": "o_B "})
team_away = match_odd.find('span', {
"class": "o_left"
}).text.strip()
team_away_id = match_odd.find('a')['id']
bet_2 = match_odd.find('span', {
"class": "o_right "
}).text.strip()
match_date = str(datetime.now().year) + ' ' + match.find(
'span', {
"class": "DateTime"
}).text
match_name = team_home + '_' + team_away + '_' + match_date
ligue_name = ligue.text.strip()
team_home = encode_decode(team_home)
team_away = encode_decode(team_away)
match_name = encode_decode(match_name)
match_date = date_ajustement_str(match_date, GMT_to_add)
dict_odds.update({
match_name: {
'match_date': match_date,
'team_home': team_home,
'team_home_id': team_home_id,
'team_away': team_away,
'team_away_id': team_away_id,
'team_X_id': team_X_id,
'bet_1': bet_1,
'bet_X': bet_X,
'bet_2': bet_2,
'sport': sport,
'sport_id': sport_id,
'ligue': ligue_name,
}
})
# print sport
dict_odds_new = {}
for k, v in dict_odds.iteritems():
encode_decode(k)
dict_odds_new.update({k: v})
df_parlay = pd.DataFrame.from_dict(dict_odds_new, orient='index')
df_parlay.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
###
df_parlay.bet_1 = df_parlay.bet_1.astype(float)
df_parlay.bet_2 = df_parlay.bet_2.astype(float)
df_parlay['bet_diff'] = df_parlay.bet_1 - df_parlay.bet_2
df_parlay['prediction'] = df_parlay.bet_diff.apply(lambda x: "1"
if x < 0 else "2")
df_parlay['bet_diff'] = abs(df_parlay.bet_1 - df_parlay.bet_2)
df_parlay['min_bet'] = df_parlay[['bet_1', 'bet_2']].min(axis=1)
df_parlay.match_date = date_ajustement(df_parlay.match_date)
df_parlay.drop_duplicates(
subset=['match_date', 'team_away', 'team_home', 'sport'], inplace=True)
###
"""
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('Nov','11'), '%Y %m %d%H:%M') if str(x)[5:8]=='Nov' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('nov.','11'), '%Y %d %m%H:%M') if str(x)[8:12]=='nov.' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('Dec','12'), '%Y %m %d%H:%M') if str(x)[5:8]=='Dec' else x)
df_parlay.match_date = df_parlay.match_date.apply(lambda x : datetime.strptime(x.replace('dec.','12'), '%Y %d %m%H:%M') if str(x)[8:12]=='dec.' else x)
"""
df_parlay.to_csv('../dataset/local/df_parlay.xls', encoding='utf-8')
os.system('mkdir -p ../model/local')
# =============================================================================
# SCRAP
# =============================================================================
ps3838_scrap_parlay()
GMT_to_add = ps3838_scrap_single()
ps3838_scrap_result(GMT_to_add)
# =============================================================================
#
# =============================================================================
###
df_parlay = pd.DataFrame.from_csv('../dataset/local/df_parlay.xls',
encoding='utf-8')
df_parlay.team_home = df_parlay.team_home.apply(lambda x: encode_decode(x))
df_result = pd.DataFrame.from_csv('../dataset/local/df_result.xls',
encoding='utf-8')
###
df_single = pd.DataFrame.from_csv('../dataset/local/df_single.xls',
encoding='utf-8')
df_single.sport = df_single.sport.apply(lambda x: x.lower().replace('-', ' '))
df_result_single = df_result.copy()
df_result_single.sport = df_result_single.sport.apply(
lambda x: x.lower().replace('-', ' '))
###
df_merge_parlay = pd.merge(df_parlay,
df_result,
how='inner',
def ps3838_scrap_result(GMT_to_add):
df_parlay = pd.DataFrame.from_csv('../dataset/local/df_parlay.xls',
encoding='utf-8')
list_sport = df_parlay.sport.unique().tolist()
list_sport = ','.join(list_sport)
try:
df_result = pd.DataFrame.from_csv('../dataset/local/df_result.xls')
dict_result = df_result.to_dict(orient='index')
except:
df_result = pd.DataFrame()
dict_result = {}
os.system('node scrap_ps3838_results_standalone.js "' + str(list_sport) +
'"')
list_result = glob('../dataset/local/result_mix_parlay*.*')
list_result.sort()
for result in list_result:
soup = BeautifulSoup(open(result), "html.parser")
result_list = soup.find('div', {"class": "result"})
match_list = soup.find_all('tr')
sport = soup.find('th', {"colspan": "5"}).text
# print sport
for match in match_list:
teams = match.find_all('h6')
if teams != []:
if str(match).find('display:none') == -1:
match_date = str(datetime.now().year) + ' ' + match.find(
'td', {
"class": "date"
}).text
match_date = datetime.strptime(
match_date,
'%Y %m/%d %H:%M') + timedelta(hours=GMT_to_add)
score = match.find('td', {"class": "period"}).text
team_home = teams[0].text
team_away = teams[1].text
match_name = team_home + '_' + team_away + '_' + sport
team_home = encode_decode(team_home)
team_away = encode_decode(team_away)
dict_result.update({
match_name: {
'match_date': match_date,
'team_home': team_home,
'team_away': team_away,
'sport': sport,
'score': score,
}
})
dict_result_new = {}
for k, v in dict_result.iteritems():
encode_decode(k)
dict_result_new.update({k: v})
df_result = pd.DataFrame.from_dict(dict_result_new, orient='index')
df_result = pd.DataFrame.from_dict(dict_result, orient='index')
df_result.team_away = df_result.team_away.apply(lambda x: encode_decode(x))
df_result.team_home = df_result.team_home.apply(lambda x: encode_decode(x))
#df_result.reset_index(drop=False, inplace=True)
#df_result['index'] = df_result['index'] + '_' + df_result.sport
#df_result.set_index('index', inplace=True)
#df_result.match_date = df_result.match_date.apply(lambda x : datetime.strptime(x, '%Y %m/%d %H:%M'))
df_result.score = df_result.score.apply(lambda x: x.strip())
df_result = df_result[df_result.score.apply(lambda x: x.find('-') != -1)]
df_result = df_result[df_result.score.apply(
lambda x: x.split('-')[0] != '')]
df_result['winner'] = df_result.score
df_result.winner = df_result.winner.apply(
lambda x: '0' if int(x.split('-')[0]) == int(x.split('-')[1]) else '1'
if int(x.split('-')[0]) > int(x.split('-')[1]) else '2'
if int(x.split('-')[0]) < int(x.split('-')[1]) else 99)
df_result.drop_duplicates(inplace=True)
df_result.to_csv('../dataset/local/df_result.xls', encoding='utf-8')
def dashboard(dict_parameter_sport, GMT_to_add):
# =============================================================================
#
# =============================================================================
###
df_parlay = pd.DataFrame.from_csv('../dataset/local/df_parlay.xls', encoding='utf-8')
df_parlay.team_home = df_parlay.team_home.apply(lambda x : encode_decode(x))
df_result = pd.DataFrame.from_csv('../dataset/local/df_result.xls', encoding='utf-8')
###
df_futur_bet = pd.DataFrame.from_csv('../dataset/local/df_futur_bet.xls', encoding='utf-8')
###
df_single = pd.DataFrame.from_csv('../dataset/local/df_single.xls', encoding='utf-8')
df_single.sport = df_single.sport.apply(lambda x : x.lower().replace('-',' '))
df_result_single = df_result.copy()
df_result_single.dropna(inplace=True)
df_result_single.sport = df_result_single.sport.apply(lambda x : x.lower().replace('-',' '))
###
df_merge = pd.merge(df_parlay, df_result, how='inner', on=['match_date','sport','team_home'])
df_merge = df_merge[['match_date','sport','ligue','bet_1','bet_2','bet_X','bet_diff','min_bet','winner','prediction','score']]
df_merge['good_pred'] = 0
df_merge.good_pred[df_merge.prediction == df_merge.winner] = 1
df_merge['bad_pred'] = 0
df_merge.bad_pred[df_merge.prediction != df_merge.winner] = 1
df_merge.match_date = df_merge.match_date.apply(lambda x : datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
###
df_merge_single = pd.merge(df_single, df_result_single, how='inner', on=['match_date','sport','team_home'])
df_merge_single = df_merge_single[['match_date','sport','ligue','bet_1','bet_2','bet_X','bet_diff','min_bet','winner','prediction','team_home']]
df_merge_single['good_pred'] = 0
df_merge_single.good_pred[df_merge_single.prediction == df_merge_single.winner] = 1
df_merge_single['bad_pred'] = 0
df_merge_single.bad_pred[df_merge_single.prediction != df_merge_single.winner] = 1
df_merge_single.match_date = df_merge_single.match_date.apply(lambda x : datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
# =============================================================================
# Prepare bet SINGLE
# =============================================================================
try:
df_betting_single_done = pd.DataFrame.from_csv('../dataset/local/df_real_betting_single.xls', encoding='utf-8')
list_already_bet_single = df_betting_single_done.team_to_bet_id.unique().tolist()
except:
list_already_bet_single = []
df_betting_single_done['prediction'] = 2
df_betting_single_done.prediction[df_betting_single_done.team_to_bet == df_betting_single_done.team_home] = 1
df_merge_single_bet = pd.merge(df_betting_single_done, df_result_single, how='inner', on=['match_date','sport','team_home'])
list_already_result = df_merge_single_bet.team_to_bet_id.unique().tolist()
df_merge_single_bet = df_merge_single_bet[['match_date','sport','ligue','bet_diff','min_bet','bet_X','mode_bet','prediction','winner','team_home']]
df_merge_single_bet['good_pred'] = 0
df_merge_single_bet.good_pred[(df_merge_single_bet.prediction == df_merge_single_bet.winner)] = 1
df_merge_single_bet['bad_pred'] = 0
df_merge_single_bet.bad_pred[df_merge_single_bet.good_pred == 0] = 1
df_merge_single_bet.match_date = df_merge_single_bet.match_date.apply(lambda x : datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
df_merge_single_bet.sort_values('match_date', ascending=False, inplace=True)
try:
df_single_ongoing = df_betting_single_done[~(df_betting_single_done.team_to_bet_id.isin(list_already_result))]
df_single_ongoing = df_single_ongoing[['match_date','sport','ligue','bet_diff','min_bet','bet_X','mode_bet','prediction','team_to_bet']]
df_single_ongoing.sort_values('match_date', ascending=False, inplace=True)
except:
df_single_ongoing = pd.DataFrame()
# df_merge_single_bet = df_merge_single_bet[df_merge_single_bet.sport == 'soccer']
# =============================================================================
# 1 bet
# =============================================================================
result = 0
cave = 0
mise = 20
num_good_pred = 0
num_good_draw_pred = 0
num_bad_pred = 0
total_nbr_bet = 0
bankroll = 1000
min_cave = 0
max_cave = 0
total_result = 0
total_cave = 0
df_win = pd.DataFrame()
df_loss = pd.DataFrame()
for item in range(len(df_merge_single_bet)):
total_nbr_bet = total_nbr_bet + 1
min_bet = df_merge_single_bet.min_bet.iloc[item]+0.0000001
bet_X = df_merge_single_bet.bet_X.iloc[item]+0.0000001
bet_DC = 1/((1/min_bet)+(1/bet_X))
bet_DNB = (1-(1/bet_X))*min_bet
bet_WNB = (1-(1/min_bet))*bet_X
if df_merge_single_bet.mode_bet.iloc[item] == 'DC':
cave = cave + mise
result = result - mise
if df_merge_single_bet.good_pred.iloc[item] == 1 or df_merge_single_bet.winner.iloc[item] == 0:
result = result + bet_DC*mise
num_good_draw_pred = num_good_draw_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
else:
num_bad_pred = num_bad_pred + 1
df_loss = pd.concat((df_loss, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.mode_bet.iloc[item] == 'DNB':
cave = cave + mise
result = result - mise
if df_merge_single_bet.good_pred.iloc[item] == 1:
result = result + bet_DNB*mise
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.winner.iloc[item] == 0:
result = result + mise
num_good_draw_pred = num_good_draw_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.good_pred.iloc[item] == 0 and df_merge_single_bet.winner.iloc[item] != 0:
num_bad_pred = num_bad_pred + 1
df_loss = pd.concat((df_loss, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.mode_bet.iloc[item] == 'WNB':
cave = cave + mise
result = result - mise
if df_merge_single_bet.good_pred.iloc[item] == 1:
result = result + mise
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.winner.iloc[item] == 0:
result = result + bet_WNB*mise
num_good_draw_pred = num_good_draw_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.good_pred.iloc[item] == 0 and df_merge_single_bet.winner.iloc[item] != 0:
num_bad_pred = num_bad_pred + 1
df_loss = pd.concat((df_loss, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.mode_bet.iloc[item] == 'X':
cave = cave + mise*2
result = result - mise*2
if df_merge_single_bet.good_pred.iloc[item] == 1 and df_merge_single_bet.winner.iloc[item] != 0:
result = result + df_merge_single_bet.min_bet.iloc[item]*mise
num_good_draw_pred = num_good_draw_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.winner.iloc[item] == 0:
result = result + df_merge_single_bet.bet_X.iloc[item]*mise
num_good_draw_pred = num_good_draw_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.good_pred.iloc[item] == 0 and df_merge_single_bet.winner.iloc[item] != 0:
num_bad_pred = num_bad_pred + 1
df_loss = pd.concat((df_loss, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
if df_merge_single_bet.mode_bet.iloc[item] == 'S':
cave = cave + mise
result = result - mise
if df_merge_single_bet.good_pred.iloc[item] == 1:
result = result + df_merge_single_bet.min_bet.iloc[item]*mise
num_good_pred = num_good_pred + 1
df_win = pd.concat((df_win, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
else:
num_bad_pred = num_bad_pred + 1
df_loss = pd.concat((df_loss, pd.DataFrame(df_merge_single_bet.iloc[item]).T))
bankroll = bankroll + result
total_result = total_result + result
total_cave = total_cave + cave
if total_result < min_cave:
min_cave = total_result
if total_result > max_cave:
max_cave = total_result
# for item in range(len(df_merge_single_bet)):
# if (df_merge_single_bet.min_bet.iloc[item] > 1.):
# if df_merge_single_bet.mode_bet.iloc[item] == 'X':
# cave = cave + mise*2
# result = result - mise*2
#
# if df_merge_single_bet.good_pred.iloc[item] == 1 and df_merge_single_bet.winner.iloc[item] != 0:
# result = result + df_merge_single_bet.min_bet.iloc[item]*mise
# num_good_pred = num_good_pred + 1
#
# if df_merge_single_bet.winner.iloc[item] == 0:
# result = result + df_merge_single_bet.bet_X.iloc[item]*mise
# num_good_pred = num_good_pred + 1
#
# if df_merge_single_bet.good_pred.iloc[item] == 0 and df_merge_single_bet.winner.iloc[item] != 0:
# num_bad_pred = num_bad_pred + 1
#
#
# if df_merge_single_bet.mode_bet.iloc[item] == 'S':
# cave = cave + mise
# result = result - mise
# if df_merge_single_bet.good_pred.iloc[item] == 1:
# result = result + df_merge_single_bet.min_bet.iloc[item]*mise
# num_good_pred = num_good_pred + 1
# else:
# num_bad_pred = num_bad_pred + 1
###
df_futur_bet.set_index('match_date', drop=True, inplace=True)
df_futur_bet = df_futur_bet[['sport', 'team_home', 'mode_bet', 'min_bet']]
df_futur_bet.columns = ['sport', 'team home', 'mode', 'bet']
df_futur_bet.sort_index(ascending=False, inplace=True)
df_futur_bet.to_csv('../dataset/local/dashboard_bet_futur.csv')
###
date_min = datetime.now()-timedelta(hours=6)
df_single_ongoing.match_date = df_single_ongoing.match_date.apply(lambda x : datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
df_single_ongoing = df_single_ongoing[df_single_ongoing.match_date > date_min]
df_single_ongoing.set_index('match_date', drop=True, inplace=True)
df_single_ongoing = df_single_ongoing[['sport', 'team_to_bet', 'mode_bet', 'min_bet']]
df_single_ongoing.columns = ['sport', 'team to bet', u'mode', 'bet']
df_single_ongoing.to_csv('../dataset/local/dashboard_bet_ongoing.csv', encoding='utf-8')
###
df_merge_single_bet_view = df_merge_single_bet.copy()
df_merge_single_bet_view.good_pred[(df_merge_single_bet_view.mode_bet == 'WNB') & (df_merge_single_bet_view.good_pred == 1)] = '-'
df_merge_single_bet_view.good_pred[(df_merge_single_bet_view.mode_bet == 'WNB') & (df_merge_single_bet_view.winner == 0)] = 1
df_merge_single_bet_view.set_index('match_date', drop=True, inplace=True)
df_merge_single_bet_view = df_merge_single_bet_view[['sport', 'team_home', 'mode_bet', 'min_bet', 'good_pred']]
df_merge_single_bet_view.columns = ['sport', 'team home', 'mode', 'bet', 'good']
df_merge_single_bet_view.sort_index(ascending=True, inplace=True)
df_merge_single_bet_view.to_csv('../dataset/local/dashboard_results.csv', encoding='utf-8')
###
try:
soup = BeautifulSoup(open('../dataset/local/PS3838.html'), "html.parser")
PS_money_ongoing = soup.find('span', { "id" : "account-balance"}).text
dict_status = {
'0_Heure ':str((datetime.now()+timedelta(hours=GMT_to_add)).strftime('%d %B %Y, %H:%M:%S')),
'1_Cave ':str(PS_money_ongoing).replace(' ','_'),
'2_Nbr total bet ':str(total_nbr_bet)+'_',
}
df_status = pd.DataFrame.from_dict(dict_status, orient='index')
df_status.columns = ['-']
df_status.sort_index(ascending=False, inplace=True)
df_status.to_csv('../dataset/local/dashboard_status.csv')
except:
dict_status = {
'0_Heure ':str((datetime.now()+timedelta(hours=GMT_to_add)).strftime('%d %B %Y, %H:%M:%S')),
'1_Cave ':str('erreur'),
'2_Nbr total bet ':str(total_nbr_bet)+'_',
}
df_status = pd.DataFrame.from_dict(dict_status, orient='index')
df_status.columns = ['-']
df_status.sort_index(ascending=False, inplace=True)
df_status.to_csv('../dataset/local/dashboard_status.csv')
pass
orig_stdout = sys.stdout
f = open('../dataset/local/dashbord.txt', 'w')
sys.stdout = f
try:
print('******** ON GOING **********')
print 'Heure : ', str((datetime.now()+timedelta(hours=GMT_to_add)).strftime('%d %B %Y, %H:%M:%S'))
print 'Bet OnGoing : ', len(df_single_ongoing)
print 'Cave OnGoing : ', len(df_single_ongoing)*mise
print(tabulate(df_single_ongoing, headers='keys', tablefmt='psql'))
print('\n\n')
print('******** STATS **********')
print 'Mise : ', int(mise), 'euros'
print 'Number Good pred : ', num_good_pred
print 'Number Draw pred : ', num_good_draw_pred
print 'Number Bad pred : ', num_bad_pred
print 'Perc Win Bet : ', round((num_good_pred+num_good_draw_pred)/float(num_good_pred+num_good_draw_pred+num_bad_pred+0.00001)*100,2), '%'
print('\n')
print 'GAIN TOTAL : ', int(total_result), 'euros'
print 'INVEST TOTAL : ', int(total_cave), 'euros'
print 'NBR BET TOTAL : ', total_nbr_bet
print('\n')
print 'BANKROLL : ', int(bankroll), 'euros'
print 'ROI cave : ', round(total_result/float(total_cave)*100,2), '%'
print 'ROC cave : ', round(total_result/float(bankroll)*100,2), '%'
print 'MIN CAVE : ', int(min_cave), 'euros ==>', int(round(min_cave/mise))
print 'MAX CAVE : ', int(max_cave), 'euros ==>', int(round(max_cave/mise))
print ''
try:
list_sport_win_loss = list(set(df_win.sport.unique().tolist()+df_loss.sport.unique().tolist()))
for sport in list_sport_win_loss:
df_win['mod'] = df_win.min_bet/dict_parameter_sport['option'][sport]['mod_value']
df_win['mod'] = df_win['mod'].apply(lambda x: int(x))
df_loss['mod'] = df_loss.min_bet/dict_parameter_sport['option'][sport]['mod_value']
df_loss['mod'] = df_loss['mod'].apply(lambda x: int(x))
df_win['gain'] = 0
df_win.gain[df_win.mode_bet == 'S'] = (df_win.min_bet[df_win.mode_bet == 'S']-1) * mise
df_win.gain[(df_win.mode_bet == 'WNB') & (df_win.winner == 0)] = (df_win.bet_X[(df_win.mode_bet == 'WNB') & (df_win.winner == 0)]*(1-(1/df_win.min_bet[(df_win.mode_bet == 'WNB') & (df_win.winner == 0)]))-1) * mise
df_loss['gain'] = -mise
total = str(len(df_win[df_win.sport == sport]) + len(df_loss[df_loss.sport == sport]))
total_gain = str(int(df_win[df_win.sport == sport]['gain'].sum() + df_loss[df_loss.sport == sport]['gain'].sum()))
print sport, '=> ', total.rjust(4), 'bet /', total_gain.rjust(4), 'euros /', round(100*len(df_win[df_win.sport == sport])/float(len(df_win[df_win.sport == sport])+len(df_loss[df_loss.sport == sport])),2),'%'
list_mod_win_loss = list(set(df_win[u'mod'][df_win.sport == sport].unique().tolist()+df_loss[u'mod'][df_loss.sport == sport].unique().tolist()))
for mod in list_mod_win_loss:
print '*', round(mod*0.1,1), '-', round((mod+1)*0.1,1), '=>', str(len(df_win[df_win[u'mod'] == mod][df_win.sport == sport])+len(df_loss[df_loss[u'mod'] == mod][df_loss.sport == sport])).rjust(4), 'bet /', str(int(sum(df_win['gain'][df_win[u'mod'] == mod])+sum(df_loss['gain'][df_loss[u'mod'] == mod]))).rjust(4) , 'euros /', round(100*len(df_win[df_win[u'mod'] == mod][df_win.sport == sport])/float(len(df_win[df_win[u'mod'] == mod][df_win.sport == sport])+len(df_loss[df_loss[u'mod'] == mod][df_loss.sport == sport])),1),'%'
except:
pass
print('\n\n')
print('******** RESULTS **********')
print(tabulate(df_merge_single_bet_view.iloc[0:10], headers='keys', tablefmt='psql'))
print('\n\n')
print('******** FUTURS 12H **********')
print(tabulate(df_futur_bet, headers='keys', tablefmt='psql'))
print('\n\n')
print('******** OPTIONS **********')
print(json.dumps(dict_parameter_sport, indent=4, sort_keys=True))
print('*************************************')
aa = pd.concat((df_win,df_loss))
aa['date_day'] = aa.match_date.apply(lambda x: x.strftime("%Y %m %d %H"))
aaa = pd.DataFrame(aa.groupby('date_day').gain.sum())
aaa['result'] = aaa.gain.cumsum()
aaa.to_csv('../dataset/local/result_soccer.csv')
except Exception, e:
print e
pass
