def test_league_of_legends_contests(self):
result = client.contests(Sport.LEAGUE_OF_LEGENDS)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
"draftkings": {
"sports":
list(
map((lambda sport: {
**sport, "supported":
sport["sportId"] in SPORT_ID_TO_PYDFS_SPORT,
"positions":
SPORT_ID_TO_PYDFS_SPORT[sport["sportId"]]["positions"]
if sport["sportId"] in SPORT_ID_TO_PYDFS_SPORT else None
}),
sports()["sports"])),
"contests":
lambda sportId, sport:
({
"provider": "draftkings",
"contests": contests(sport=SPORT_ID_TO_SPORT[sportId])["contests"],
}),
"players":
lambda id, gameType: ({
"provider": "draftkings",
"players": get_draftkings_players(id)
}),
"optimize":
lambda sport, gameType: get_optimizer(
is_captain_mode(gameType), SPORT_ID_TO_PYDFS_SPORT[sport["sportId"]
]["sport"])
},
"yahoo": {
"sports":
list([{
"fullName":
def test_nba_contests(self):
result = client.contests(Sport.NBA)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_tennis_contests(self):
result = client.contests(Sport.TENNIS)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_mma_contests(self):
result = client.contests(Sport.MIXED_MARTIAL_ARTS)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_eurloeage_basketball_contests(self):
result = client.contests(Sport.EUROLEAGUE_BASKETBALL)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_cfl_contests(self):
result = client.contests(Sport.CFL)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_college_basketball_contests(self):
result = client.contests(Sport.COLLEGE_BASKETBALL)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_golf_contests(self):
result = client.contests(Sport.GOLF)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_nascar_contests(self):
result = client.contests(Sport.NASCAR)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
def test_mlb_contests(self):
result = client.contests(Sport.MLB)
self.assertIsNotNone(result)
self.assertIsNotNone(result["contests"])
self.assertIsNotNone(result["groups"])
from draft_kings.data import Sport
from draft_kings.client import contests
from draft_kings.client import available_players
from draft_kings.client import draftables
from draft_kings.client import draft_group_details
import functools
import inspect
import os
import sys
import requests
from bs4 import BeautifulSoup
from mappings import COLUMN_MAPPINGS
from mappings import NAME_CORRECTIONS
import pandas as pd
contests_nfl = contests(sport=Sport.NFL)
contests_nba = contests(sport=Sport.NBA)
contests_mlb = contests(sport=Sport.MLB)
def get_my_path():
try:
filename = __file__ # where we were when the module was loaded
except NameError: # fallback
filename = inspect.getsourcefile(get_my_path)
return os.path.realpath(filename)
def changedate_nfl():
day = datetime.datetime.now()
while day.weekday() != 6:
def DKCrawler():
dk_info = contests(sport=Sport.NBA)
if not dk_info['contests']:
print("Contests Empty!")
if not dk_info['groups']:
print("Slates Empty!")
date = dk_info['groups'][0]['starts_at'].astimezone(
pytz.timezone('America/Los_Angeles')).date()
logging.info("======== DKCrawler for date %s ========" % date)
for slate in dk_info['groups']:
slate_date = slate['starts_at'].astimezone(
pytz.timezone('America/Los_Angeles')).date()
if slate_date < date:
date = slate_date
# Crawl contest information.
CreateDirectoryIfNotExist(GetMetaDataPath() + '/draftkings/contests')
contests_dict = {'CONTEST_ID': [], 'SLATE_ID' : [], 'NAME': [], 'START_TIMESTAMP': [], 'SPORT': [], 'FANTASY_PLAYER_POINTS': [], \
'IS_GUARANTEED': [], 'IS_STARRED': [], 'IS_HEAD_TO_HEAD': [], 'IS_DOUBLE_UP': [], 'IS_FIFTY_FIFTY': [], 'TOTAL_ENTRIES': [], \
'MAXIMUM_ENTRIES': [], 'ENTRY_FEE': [], 'TOTAL_PAYOUT': []}
for contest in dk_info['contests']:
if contest['sport'] is not Sport.NBA:
continue
contest_time = contest['starts_at'].astimezone(
pytz.timezone('America/Los_Angeles'))
if contest_time.date() != date:
continue
contests_dict['CONTEST_ID'].append(contest['id'])
contests_dict['START_TIMESTAMP'].append(contest_time)
contests_dict['FANTASY_PLAYER_POINTS'].append(
contest['fantasy_player_points'])
contests_dict['SPORT'].append(contest['sport'])
contests_dict['NAME'].append(contest['name'])
contests_dict['IS_GUARANTEED'].append(contest['guaranteed'])
contests_dict['IS_STARRED'].append(contest['starred'])
contests_dict['IS_HEAD_TO_HEAD'].append(contest['head_to_head'])
contests_dict['IS_DOUBLE_UP'].append(contest['double_up'])
contests_dict['IS_FIFTY_FIFTY'].append(contest['fifty_fifty'])
contests_dict['TOTAL_ENTRIES'].append(contest['entries']['total'])
contests_dict['MAXIMUM_ENTRIES'].append(contest['entries']['maximum'])
contests_dict['ENTRY_FEE'].append(contest['entries']['fee'])
contests_dict['TOTAL_PAYOUT'].append(contest['payout'])
contests_dict['SLATE_ID'].append(contest['draft_group_id'])
contests_df = pd.DataFrame.from_dict(contests_dict)
contests_df.set_index('CONTEST_ID')
contests_df.to_csv(GetMetaDataPath() + '/draftkings/contests/%s.csv' %
date.strftime('%Y-%m-%d'))
logging.info("Crawl DraftKings Contests for %s, # of contests: %d" %
(date.strftime('%Y-%m-%d'), len(contests_df)))
# Crawl slate information.
CreateDirectoryIfNotExist(GetMetaDataPath() + '/draftkings/slates')
slates_dict = {
'SLATE_ID': [],
'SLATE_TYPE': [],
'START_TIMESTAMP': [],
'SPORT': [],
'GAMES_COUNT': []
}
for slate in dk_info['groups']:
slate_time = slate['starts_at'].astimezone(
pytz.timezone('America/Los_Angeles'))
if slate_time.date() != date:
continue
slates_dict['SLATE_ID'].append(slate['id'])
slates_dict['SLATE_TYPE'].append(
DRAFTKINGS_SLATE_TYPE[slate['contest_type_id']])
slates_dict['START_TIMESTAMP'].append(slate_time)
slates_dict['SPORT'].append(slate['sport'])
slates_dict['GAMES_COUNT'].append(slate['games_count'])
slates_df = pd.DataFrame.from_dict(slates_dict)
slates_df.set_index('SLATE_ID')
slates_df.to_csv(GetMetaDataPath() +
'/draftkings/slates/%s.csv' % date.strftime('%Y-%m-%d'))
logging.info("Crawl DraftKings Slates for %s" % date.strftime('%Y-%m-%d'))
print(slates_df)
# Crawl player information
CreateDirectoryIfNotExist(GetMetaDataPath() + '/draftkings/players')
players_dict = {
'NAME': [],
'DK_ID': [],
'POSITION': [],
'SALARY': [],
'SLATE_ID': [],
'GAME_INFO': [],
'TEAM': [],
'DKP': []
}
for slate_id in slates_dict['SLATE_ID']:
lines = urlopen(GetDKPlayersForSlateUrl(slate_id))
# Header contains lineup position
header = next(lines).decode("utf-8")
start_column = 0
end_column = header.index('Instructions') - 2
lineup_positions = header[start_column:end_column].split(',')
# Go through instruction lines
while True:
line = next(lines).decode("utf-8")
if "Position" in line:
start_column = line.index("Position")
break
# Parse players data
for line in lines:
# POST data is in bytes
line = line.decode("utf-8")
fields = line[start_column:].split(',')
# For tier slate, set salary as 1
if len(fields) == 8:
fields.insert(5, 1)
players_dict['NAME'].append(fields[2])
players_dict['DK_ID'].append(fields[3])
players_dict['POSITION'].append(fields[0])
players_dict['SALARY'].append(fields[5])
players_dict['SLATE_ID'].append(slate_id)
players_dict['GAME_INFO'].append(fields[6])
players_dict['TEAM'].append(fields[7])
players_dict['DKP'].append(fields[8])
players_df = pd.DataFrame.from_dict(players_dict)
players_df.set_index('NAME')
players_df.to_csv(GetMetaDataPath() +
'/draftkings/players/%s.csv' % date.strftime('%Y-%m-%d'))
logging.info("Crawl DraftKings Players for %s" % date.strftime('%Y-%m-%d'))
