def cross_check_teams(table: webdriver, bets_db: list) -> (int, tuple):
preds_list = table.find_elements_by_xpath('.//tr[@class="ng-scope"]')
teams_web = []
preds_details = []
for pred in preds_list:
match = pred.find_element_by_xpath('.//td[6]').text
team1, team2 = match.strip().split(' - ')
quote = float(pred.find_element_by_xpath('.//td[10]').text)
result = pred.find_element_by_xpath('.//td[11]').text
label_element = pred.find_element_by_xpath(
'.//div[contains(@class,"ng-scope")]')
label = label_element.get_attribute('ng-switch-when')
teams_web.append(team1)
teams_web.append(team2)
preds_details.append((team1, team2, quote, result, label))
teams_web.sort()
for bet_db_id, _ in bets_db:
teams_db = dbf.db_select(table='predictions',
columns=['team1', 'team2'],
where=f'bet_id = {bet_db_id}')
teams_db = [t for i in teams_db for t in i]
teams_db.sort()
if teams_web == teams_db:
return bet_db_id, preds_details
else:
continue
return 0, []
def system_simulation(combs_to_play: dict):
bet_ids = dbf.db_select(table='bets',
columns=['id'],
where='result != "Unknown" AND euros > 0')
all_bets = [dbf.db_select(table='predictions',
columns=['quote', 'label'],
where=f'bet_id = {b_id}') for b_id in bet_ids]
all_bets = [[(q, 1) if lb == 'WINNING' else (q, 0) for q, lb in bet]
for bet in all_bets]
bets_as_system = []
for i, bet in enumerate(all_bets, 1):
size = len(bet)
win = 0
played = 0
for j in range(1, size+1):
if j not in combs_to_play:# and j != size:
continue
euros_per_bet = combs_to_play[j]
comb = list(combinations(bet, j))
for c in comb:
win += np.prod(np.array(c).flatten())*euros_per_bet
played += euros_per_bet
bets_as_system.append(win - played)
_, ax = plt.subplots(figsize=(25, 6))
bal = [sum(bets_as_system[:i]) for i in range(1, len(bets_as_system)+1)]
plt.plot(bal)
# Money played and money won for real, day by day
real_mn_bet, real_mn_won = get_money_and_prizes()
# Day by day balance
real_trend = get_trend(euros_played=real_mn_bet, euros_won=real_mn_won)
plt.plot(real_trend)
wins = np.array([np.prod(i) for i in all_bets])
wins = np.argwhere(wins > 0)
for w in wins:
plt.axvline(x=w, c='r', alpha=.3)
return
def get_data(use_combo: bool) -> pd.DataFrame:
cols = ['date', 'team1', 'team2', 'league', 'bet_alias', 'quote', 'label']
data = dbf.db_select(table='simulations', columns=cols, where='')
df = pd.DataFrame(data, columns=cols)
if not use_combo:
df = df[~df['bet_alias'].str.contains('+', regex=False)].copy()
df.replace({'WINNING': 1, 'LOSING': 0}, inplace=True)
return df
def get_money_and_prizes():
money_bet = dbf.db_select(
table='bets',
columns=['euros', 'prize', 'result'],
where='status = "Placed" AND result != "Unknown"')
euros, prizes, labels = zip(*money_bet)
euros = np.array(euros)
labels = [True if i == 'WINNING' else False for i in labels]
prizes = np.multiply(np.array(prizes), np.array(labels))
return euros[euros > 0], prizes[euros > 0]
def get_bets_as_df() -> pd.DataFrame:
cols = ['date', 'euros', 'prize', 'result']
bets = dbf.db_select(table='bets',
columns=cols,
where='result != "Unknown"')
bets = pd.DataFrame(bets, columns=cols)
bets.index += 1
bets['date'] = pd.to_datetime(bets['date'], infer_datetime_format=True)
return bets
def scrape_all_quotes() -> None:
"""
Download all the quotes from the website and save them in the database.
"""
browser = None
leagues = dbf.db_select(table='leagues', columns=['name'], where='')
for league in leagues:
start = time.time()
browser = scrape_league_quotes(brow=browser, league_name=league)
m, s = utl.time_needed(start)
cfg.LOGGER.info(f'FILL DB WITH QUOTES - {league} aggiornata: {m}:{s}')
browser.quit()
utl.remove_matches_without_quotes()
def get_preds_as_df() -> pd.DataFrame:
cols = [
'id', 'bet_id', 'user', 'date', 'team1', 'team2', 'league',
'bet_alias', 'quote', 'result', 'label'
]
preds = dbf.db_select(table='predictions',
columns=cols,
where='result != "NULL"')
preds = pd.DataFrame(preds, columns=cols)
preds.set_index('id', drop=True, inplace=True)
preds.index.name = None
preds['date'] = pd.to_datetime(preds['date'], infer_datetime_format=True)
return preds
def all_fields_and_bets(panel: webdriver) -> [(str, webdriver)]:
# Select all fields we want to scrape
fields_in_db = dbf.db_select(table='fields', columns=['name'], where='')
fields_names = [f for i in fields_in_db for f, _ in (i.split('_'), )]
fields_names = set(fields_names)
all_fields_path = './/div[@class="market-info"]/div'
all_bets_path = './/div[@class="market-selections"]'
fields = panel.find_elements_by_xpath(all_fields_path)
bets = panel.find_elements_by_xpath(all_bets_path)
field_bets = []
for field, bet_group in zip(fields, bets):
field_name = field.get_attribute('innerText').upper().strip()
if field_name in fields_names:
field_bets.append((field_name, bet_group))
return field_bets
def score(which) -> None:
if which == 'general':
year1, year2 = 2017, 2030
else:
year1, year2 = which.split('-')
from_ = datetime.strptime(f'{year1}-08-01 00:00:00', '%Y-%m-%d %H:%M:%S')
to_ = datetime.strptime(f'{year2}-07-31 00:00:00', '%Y-%m-%d %H:%M:%S')
tmp = preds[(from_ <= preds['date']) & (preds['date'] <= to_)]
names = [name for name in players]
indices = np.array([compute_index(tmp, name) for name in names])
data = sorted(zip(names, indices), key=lambda x: x[1], reverse=True)
names, indices = zip(*data)
indices = normalize_indices(raw_indices=indices)
ratio = [get_user_ratio(tmp, name) for name in names]
perc = [get_user_perc(tmp, name) for name in names]
quotes = [get_user_avg_quotes(tmp, name) for name in names]
avg_quote, avg_quoteW = zip(*quotes)
colors = [get_user_color(name) for name in names]
fig, ax = plt.subplots(figsize=(9, 7))
bars = plt.bar(range(5),
indices,
0.5,
color=colors,
edgecolor='black',
linewidth=.8,
clip_on=False)
plt.xticks(range(5), names, fontsize=16)
plt.ylim(0, 1.35)
plt.box(on=None)
plt.tick_params(axis='x', which='both', bottom=False, labelbottom=True)
plt.tick_params(axis='y', which='both', left=False, labelleft=False)
plt.title(which, fontsize=16, fontweight='bold', style='italic')
last_update = dbf.db_select(table='last_results_update',
columns=['message'],
where='')[0]
plt.text(0.81, 0.98, str(last_update), transform=ax.transAxes, fontsize=8)
for i, bar in enumerate(bars):
text = f'{ratio[i]}\n{perc[i]}%\n{avg_quote[i]}\n{avg_quoteW[i]}'
plt.text(bar.get_x() + bar.get_width() / 2,
indices[i] + 0.03,
text,
ha='center',
va='bottom',
fontsize=12,
style='italic')
for i, bar in enumerate(bars):
text = f'{indices[i]}'
plt.text(bar.get_x() + bar.get_width() / 2,
indices[i] + 0.23,
text,
ha='center',
va='bottom',
fontsize=14,
fontweight='bold')
for bar in bars:
if not bar.get_height():
bar.set_linewidth(0)
plt.text(.8,
.9,
'Score',
horizontalalignment='center',
transform=ax.transAxes,
fontweight='bold',
fontsize=14)
expl = 'Win/Total\n%\nAvg Quote\nAvg Quote WIN'
plt.text(.8,
.75,
expl,
horizontalalignment='center',
transform=ax.transAxes,
style='italic',
fontsize=12)
plt.savefig(f'score_{which}.png', dpi=120, bbox_inches='tight')
plt.gcf().clear()
def get_user_color(nickname: str) -> str:
color = dbf.db_select(table='people',
columns=['color'],
where=f'nick = "{nickname}"')[0]
return color
def get_people() -> list:
return dbf.db_select(table='people', columns=['nick'], where='')
