def optimize_lineup(df, good_list, alright_list, meh_list):
optimizer = get_optimizer(Site.FANDUEL, Sport.BASKETBALL)
optimizer.load_players_from_csv("fanduel.csv")
df['name'] = df.apply(lambda row: (str.split(row['name'], ' Jr.')[0]), axis=1)
for player in optimizer.players:
if player.full_name in df['name'].unique():
projected_score = df.loc[
(df['name'] == player.full_name) & (df['team'] == NBA_TEAMS.find_NBA_abrev(player.team))][
'Projected Score'].tolist()
if len(projected_score) > 0:
player.fppg = projected_score[0]
else:
player.fppg = 0
# print(f"{player.full_name:}{projected_score}")
else:
player.fppg = 0
optimizer.set_min_salary_cap(60000)
optimizer.set_max_repeating_players(2)
optimizer.add_stack(TeamStack(2, for_teams=good_list))
optimizer.add_stack(TeamStack(2, for_teams=alright_list))
optimizer.add_stack(TeamStack(1, for_teams=meh_list))
# for lineup in optimizer.optimize(n=50):
# print(lineup)
# print(lineup.players) # list of players
# print(lineup.fantasy_points_projection)
# print(lineup.salary_costs)
exporter = CSVLineupExporter(optimizer.optimize(n=50))
exporter.export('lineups.csv')
print("Lineups optimized and exported!")
def dk_lineup(request):
import requests
# check if most recent lineups were already created.
from pydfs_lineup_optimizer import get_optimizer, Site, Sport, CSVLineupExporter
project = os.environ["PROJECT_ID"]
bucket = os.environ["BUCKET"]
dataset_base = os.environ["DATASET_BASE"]
dataset_dfs = os.environ["DATASET_DFS"]
today = (datetime.now() - timedelta(hours=4)).strftime('%Y-%m-%d')
request_json = request.get_json(silent=True)
starting_lineups = "https://us-central1-{}.cloudfunctions.net/starting_lineups"
r = requests.post(starting_lineups.format(project))
if request_json and "n_lineups" in request_json:
n_lineups = int(request_json.get("n_lineups"))
if n_lineups > 100:
n_lineups = 100
else:
n_lineups = 100
df = get_draftkings_predictions(project=project,
dataset_base=dataset_base,
dataset_dfs=dataset_dfs,
dt=today.replace("-", ""))
df.to_csv("/tmp/mlb_dk.csv", index=False)
optimizer = get_optimizer(Site.DRAFTKINGS, Sport.BASEBALL)
optimizer.load_players_from_csv("/tmp/mlb_dk.csv")
exporter = CSVLineupExporter(optimizer.optimize(n_lineups))
exporter.export("/tmp/lineups.csv")
# lineups = pd.DataFrame()
# cols = ["pos", "first", "last", "position", "team", "opp", "fppg", "salary"]
# i=1
# for lineup in optimizer.optimize(n=n_lineups):
# lineup_list = lineup.printer.print_lineup(lineup).split("\n")[0:10]
# lineup_df = pd.concat([pd.DataFrame(dict(zip(cols,re.split("\s{1,}",x.strip())[1:9])),index=[0])
# for x in lineup_list], axis=0, ignore_index=True)
# lineup_df["lineup_number"] = i
# i+=1
# lineups = pd.concat([lineups, lineup_df], ignore_index=True)
#
# # lineups.columns = ["pos", "first", "last", "position", "team", "opp", "projection", "salary", "lineup_number"]
# lineups.to_csv("/tmp/lineups.csv", index=False)
upload_blob(bucket_name=bucket,
source_file_name="/tmp/lineups.csv",
destination_blob_name="lineups/daily_dk_lineups.csv")
upload_blob(bucket_name=bucket,
source_file_name="/tmp/lineups.csv",
destination_blob_name="lineups/mlb/dk_lineups_{}.csv".format(
today.replace("-", "")))
lineup_link = "https://storage.cloud.google.com/{bucket}/lineups/daily_dk_lineups.csv"
return lineup_link.format(bucket=bucket, dt=today.replace("-", ""))
def save_click():
init_dir = ""
if (selected_sport.get() == 1):
init_dir = '/home/pete/Documents/dk_lineups'
elif (selected_sport.get() == 2):
init_dir = '/home/pete/Documents/nhl/dk_lineups'
results = filedialog.asksaveasfilename(initialdir=init_dir,
title='Save File',
initialfile='results.csv')
LockPlayers()
ExcludePlayers()
exporter = CSVLineupExporter(optimizer.optimize(int(num_lineups.get())))
exporter.export(results)
DraftKingsRefromatting(results)
save_button.state(['disabled'])
"dsSalary",
str(universe)]]
players = list()
for index, row in data.iterrows():
player = Player(
row["draftstarsID"],
row['playerName'].split(" ")[0],
row['playerName'].split(" ")[1],
[row['draftstarsPosition']] if str(row['draftstarsPosition2']) == "nan" else [row['draftstarsPosition'],
row['draftstarsPosition2']],
row["team"],
row["dsSalary"],
row[str(universe)]
)
players.append(player)
return players
@staticmethod
def _import_data(filename):
data = pd.read_csv(filename)
return data
if __name__ == "__main__":
generator = GenerateDraftstarsLineups()
lineups = generator.process()
lineup_generator = chain.from_iterable(lineups)
exporter = CSVLineupExporter(lineup_generator)
exporter.export("universeLineups.csv")
print("CSV exported")
SITE = Site.DRAFTKINGS
elif platform == 'draftkings-showdown':
SITE = Site.DRAFTKINGS_CAPTAIN_MODE
elif platform == 'draftkings-tiers':
SITE = Site.DRAFTKINGS_TIERS
elif platform == 'fanduel':
SITE = Site.FANDUEL
# Sport
if sport == 'nfl':
SPORT = Sport.FOOTBALL
elif sport == 'nba':
SPORT = Sport.BASKETBALL
# make optimizer
# class CustomPuLPSolver(PuLPSolver):
# LP_SOLVER = PULP_CBC_CMD(threads=8, options=['preprocess off'], msg=False)
# LP_SOLVER = PULP_CHOCO_CMD()
# define optimizer
# optimizer = get_optimizer(site = SITE, sport = SPORT, solver=CustomPuLPSolver)
optimizer = get_optimizer(site=SITE, sport=SPORT)
optimizer.load_players_from_csv(input_string)
optimizer.set_max_repeating_players(int(maxrepplyr))
# export
exporter = CSVLineupExporter(
optimizer.optimize(n=int(nlineups), max_exposure=float(maxexp)))
output_string = str(
input_string[:input_string.rfind('/') + 1]) + 'optimized.csv'
exporter.export(output_string)
from pydfs_lineup_optimizer import get_optimizer, Site, Sport, CSVLineupExporter
import pandas
week = 'week4'
tournament = 'thur-mon'
csv_player_path = "/home/pete/Documents/dk_player_exports/WEEK/TOURNAMENT/DKSalaries_ffa.csv"
lineups_file = "/home/pete/Documents/dk_lineups/WEEK/TOURNAMENT/results.csv"
csv_player_path = csv_player_path.replace("WEEK", week).replace(
"TOURNAMENT", tournament)
lineups_file = lineups_file.replace("WEEK",
week).replace("TOURNAMENT", tournament)
optimizer = get_optimizer(Site.DRAFTKINGS, Sport.FOOTBALL)
optimizer.load_players_from_csv(csv_player_path)
number_of_lineups = 20
for lineup in optimizer.optimize(number_of_lineups):
print(lineup)
# print(lineup.players)
# print(lineup.fantasy_points_projection)
# print(lineup.salary_costs)
exporter = CSVLineupExporter(optimizer.optimize(number_of_lineups))
exporter.export(lineups_file)
# TODO: reformat the headers to match the approriate upload order for Draft Kings (QB, RB, RB, WR, WR, WR, TE, FLEX, DST)
optimizer = get_optimizer(Site.DRAFTKINGS_CAPTAIN_MODE, Sport.FOOTBALL)
optimizer.load_players_from_csv("DKSalaries1pmshowdown.csv")
#optimizer.add_stack(TeamStack(3))
#optimizer.add_stack(TeamStack(3, for_positions=['QB', 'WR']))
#optimizer.add_stack(PositionsStack(['QB', 'WR', 'WR']))
#optimizer.force_positions_for_opposing_team(('QB', 'WR'))
#optimizer.restrict_positions_for_same_team(('RB', 'RB'))
#optimizer.restrict_positions_for_opposing_team(['QB'], ['DST'])
optimizer.set_deviation(0.05, 0.3)
optimizer.set_max_repeating_players(5)
for player in optimizer.players:
if player.fppg < 1:
optimizer.remove_player(player)
#mekale = optimizer.get_player_by_name('Phillip Walker')
#optimizer.add_player_to_lineup(mekale)
#dc_wrs = []
#for name in ['Eli Rogers', 'Rashad Ross', 'Deandre Thompkins', 'Malachi Dupre']:
# for p in optimizer.find_players(name):
# dc_wrs.append(p)
#group = PlayersGroup(dc_wrs, min_from_group=1)
#optimizer.add_stack(Stack([cardale, group]))
optimizer.restrict_positions_for_opposing_team(['K'], ['K'])
exporter = CSVLineupExporter(
optimizer.optimize(LINES, randomness=True, generate_exposures=True))
exporter.export('result1pm.csv')
optimizer.player_exposures.write_exposures_csv(total_lineups=LINES,
csv_filename='exposures1pm.csv')
player_include[-1 + (i % (1 + len(player_include)))]
) # find player with specified name in your optimizer
#second_player = optimizer.get_player_by_id('12864605') # find player with player id
optimizer.add_player_to_lineup(player) # lock this player in lineup
#optimizer.add_player_to_lineup(second_player)
if (math.floor(i / (1 + len(player_include))) > 0):
player = optimizer.get_player_by_name(
player_exclude[math.floor(-1 + (i / (1 + len(player_include))))])
optimizer.remove_player(player)
#optimizer.restore_player(player)
if i == 0:
exporter = CSVLineupExporter(optimizer.optimize(lineups))
else:
exporter = JohnCSVLineupExporter(optimizer.optimize(lineups))
exporter.export(resultlocation)
#%%
for lineup in optimizer.optimize(n=1):
print(lineup)
print(lineup.players) # list of players
print(lineup.fantasy_points_projection)
print(lineup.salary_costs)
#%%
#put in right format
from pydfs_lineup_optimizer import get_optimizer, Site, Sport, CSVLineupExporter
import pandas
import math
import csv
filelocation = r"C:\Users\admin\Downloads\DKSalaries_pga.csv"
def create_optimizer(request):
if request.method == 'POST':
form = OptimizerForm(request.POST)
form2 = SlateForm(request.POST)
if form2.is_valid() and 'change_slate' in request.POST:
slate = form2.cleaned_data['slate']
form = OptimizerForm()
form2 = SlateForm(initial={'slate': slate})
df = get_daily_roster(
Path(
'//home/ubuntu/Fantasy-Fire/website/optimizer/prediction.csv'
))
# df = create_predictions(df)
df = create_predictions(
df,
slate=Path(
'//home/ubuntu/Fantasy-Fire/website/optimizer/Slates/' +
slate + '.csv'))
df['Min Exposure'] = 0
df['Max Exposure'] = 1
df['Value'] = round(df['Predicted_FP'] / (df['Salary'] / 1000), 2)
df.insert(0, "Include", '', True)
for ind in df.index:
df['Predicted_FP'][
ind] = "<input type='number' step='any' form='optimizer' name='fantasy_points_" + str(
ind) + "' value=" + str(
round(df['Predicted_FP'][ind],
2)) + " id='id_predicted_fp'>"
# df['Predicted FP'] = df['Predicted FP'].apply(lambda x: round(float(x), 2))
# df['Predicted FP'] = df['Predicted FP'].apply(
# lambda x: "<input type='text' form='optimizer' name='fantasy_points_" + df['Name'] + "' value=" + str(
# x) + " id='id_predicted_fp'>")
df['Min Exposure'][
ind] = "<input type='number' step='any' name='min_exposure_" + str(
ind) + "' value=" + str(0) + ">"
df['Max Exposure'][
ind] = "<input type='number' step='any' name='max_exposure_" + str(
ind) + "' value=" + str(1) + ">"
df['Include'][
ind] = "<input type='checkbox' id='child' name='include_" + str(
ind) + "' checked>"
df = df[[
'Include', 'Name', 'ID', 'Roster Position', 'Salary',
'TeamAbbrev', 'Opp', 'Predicted_FP', 'Min Exposure',
'Max Exposure', 'Value'
]]
df.rename(columns={
'Include':
"<input type='checkbox' id='parent' onclick='checkAll()' checked>"
},
inplace=True)
html_table = df.to_html(
index=False,
justify='left',
escape=False,
table_id='slateData',
classes=[
'table w3-table-all table-bordered table-striped table-hover table-responsive table-sm searchable sortable tablesorter tablesorter-default, container-fluid'
])
return render(
request, 'optimizer/optimizer.html', {
'form': form,
'player_table': html_table,
'form2': form2,
'slate': slate
})
if form.is_valid():
slate = form2.cleaned_data['slate']
min_salary = form.cleaned_data['min_salary']
max_exposure = form.cleaned_data['max_exposure']
no_lineups = form.cleaned_data['no_lineups']
deviation = form.cleaned_data['deviation']
generation_type = form.cleaned_data['generation_type']
with_replacement = form.cleaned_data['with_replacement']
if "Showdown" in slate:
optimizer = get_optimizer(Site.DRAFTKINGS_CAPTAIN_MODE,
Sport.BASKETBALL)
else:
optimizer = get_optimizer(Site.DRAFTKINGS, Sport.BASKETBALL)
df = pd.read_csv(
Path("//home/ubuntu/Fantasy-Fire/website/optimizer/Slates/" +
slate + ".csv"))
df2 = pd.read_csv(
Path(
r"//home/ubuntu/Fantasy-Fire/website/optimizer/prediction.csv"
))
result = df.merge(df2, left_on='Name', right_on='name', how='left')
# result = result.drop(
# columns=['AvgPointsPerGame'])
result = result.fillna(0)
includes = []
fantasy_points = []
min_exposures = []
max_exposures = []
deviations = []
for key, value in request.POST.items():
if 'include' in key:
includes.append(key)
elif 'fantasy_points' in key:
fantasy_points.append(value)
elif 'min_exposure' in key:
min_exposures.append(value)
elif 'max_exposure' in key:
max_exposures.append(value)
elif 'deviation' in key:
deviations.append(value)
result['Min Exposure'] = 0
result['Max Exposure'] = 1
for ind in result.index:
result['AvgPointsPerGame'][ind] = fantasy_points[ind]
if int(max_exposure) != int(max_exposures[ind]) and int(
max_exposures[ind]) > 1:
result['Max Exposure'][ind] = int(max_exposures[ind]) / 100
result['Min Exposure'][ind] = int(min_exposures[ind]) / 100
# result = result.rename(columns={'max_exposure': 'Max Exposure', 'min_exposure': 'Min Exposure'})
result.to_csv(
Path(
"//home/ubuntu/Fantasy-Fire/website/optimizer/Predictions.csv"
))
optimizer.load_players_from_csv(
Path(
'//home/ubuntu/Fantasy-Fire/website/optimizer/Predictions.csv'
))
optimizer.set_deviation(0, int(deviation) / 100)
optimizer.set_min_salary_cap(min_salary)
for player in optimizer.players:
id = player.id
playa = optimizer.get_player_by_id(id)
optimizer.remove_player(playa)
for i in includes:
id = str(result['ID'][int(i[i.find('_') + 1:])])
player = optimizer.get_player_by_id(id)
optimizer.restore_player(player)
exporter = CSVLineupExporter(
optimizer.optimize(no_lineups,
randomness=generation_type,
with_replacement=with_replacement,
max_exposure=max_exposure / 100))
exporter.export(
Path('//home/ubuntu/Fantasy-Fire/website/optimizer/lineups.csv'
))
with open(
'//home/ubuntu/Fantasy-Fire/website/optimizer/lineups.csv'
) as myfile:
response = HttpResponse(myfile, content_type='text/csv')
response[
'Content-Disposition'] = 'attachment; filename=lineups.csv'
return response
else:
form = OptimizerForm()
form2 = SlateForm(initial={'slate': 'Main_Slate'})
df = get_daily_roster(
Path(
'//home/ubuntu/Fantasy-Fire/website/optimizer/prediction.csv'))
# df = create_predictions(df)
df = create_predictions(
df,
slate=Path(
'//home/ubuntu/Fantasy-Fire/website/optimizer/Slates/Main_Slate.csv'
))
df['Min Exposure'] = 0
df['Max Exposure'] = 1
df['Value'] = round(df['Predicted_FP'] / (df['Salary'] / 1000), 2)
df.insert(0, "Include", '', True)
for ind in df.index:
df['Predicted_FP'][
ind] = "<input type='number' step='any' form='optimizer' name='fantasy_points_" + str(
ind) + "' value=" + str(round(
df['Predicted_FP'][ind], 2)) + " id='id_predicted_fp'>"
# df['Predicted FP'] = df['Predicted FP'].apply(lambda x: round(float(x), 2))
# df['Predicted FP'] = df['Predicted FP'].apply(
# lambda x: "<input type='text' form='optimizer' name='fantasy_points_" + df['Name'] + "' value=" + str(
# x) + " id='id_predicted_fp'>")
df['Min Exposure'][
ind] = "<input type='number' step='any' name='min_exposure_" + str(
ind) + "' value=" + str(0) + ">"
df['Max Exposure'][
ind] = "<input type='number' step='any' name='max_exposure_" + str(
ind) + "' value=" + str(1) + ">"
df['Include'][
ind] = "<input type='checkbox' id='child' name='include_" + str(
ind) + "' checked>"
df = df[[
'Include', 'Name', 'ID', 'Roster Position', 'Salary', 'TeamAbbrev',
'Opp', 'Predicted_FP', 'Min Exposure', 'Max Exposure', 'Value'
]]
df.rename(columns={
'Include':
"<input type='checkbox' id='parent' onclick='checkAll()' checked>"
},
inplace=True)
# df['Value'] = df['Predicted_FP'] / (df['Salary'] / 1000)
html_table = df.to_html(
index=False,
justify='left',
escape=False,
table_id='slateData',
classes=[
'table w3-table-all table-bordered table-striped table-hover table-responsive table-sm searchable sortable tablesorter tablesorter-default, container-fluid'
])
# 'table table-bordered table-striped'])
return render(
request, 'optimizer/optimizer.html', {
'form': form,
'player_table': html_table,
'form2': form2,
'slate': 'Main_Slate'
})
#optimizer.force_positions_for_opposing_team(('QB', 'WR'))
#optimizer.add_stack(PositionsStack(['QB', 'WR']))
optimizer.restrict_positions_for_same_team(('RB', 'RB'))
optimizer.force_positions_for_opposing_team(('QB', 'WR'))
#optimizer.force_positions_for_opposing_team(('QB', 'WR'))
#optimizer.add_stack(TeamStack(2, for_positions=['QB', 'WR'], for_teams=['DAL', 'HOU', 'DC']))
optimizer.restrict_positions_for_opposing_team(['QB'], ['DST'])
optimizer.set_deviation(0.02, 0.35)
optimizer.set_max_repeating_players(5)
for player in optimizer.players:
if player.fppg < 1:
optimizer.remove_player(player)
elif 'WR' in player.positions:
player.min_deviation = 0.25
player.max_deviation = 0.55
elif 'QB' in player.positions:
player.min_deviation = 0.15
player.max_deviation = 0.35
#mekale = optimizer.get_player_by_name('Mekale McKay')
#optimizer.add_player_to_lineup(mekale)
exporter = CSVLineupExporter(
optimizer.optimize(LINES, randomness=True, generate_exposures=True))
exporter.export('result.csv')
optimizer.player_exposures.write_exposures_csv(total_lineups=LINES)
#exporter = CSVLineupExporter(optimizer.optimize_lineups(lineups, generate_exposures=True))
#exporter.export('result.csv')
#optimizer.player_exposures.write_exposures_csv(total_lineups=LINES)
# for lineup in optimizer.optimize(n=10, randomness=True):
# optimizer.add_player_to_lineup(drummond) # lock this player in lineup
# optimizer.add_player_to_lineup(curry) # lock this player in lineup
# optimizer.add_player_to_lineup(gordon)
# optimizer.add_player_to_lineup(robinson)
# # optimizer.remove_player_from_lineup(player1)
# optimizer.remove_player(burks)
# optimizer.restore_player(player2)
# player1.max_exposure = 0.5 # set max exposure
# young.min_exposure = 0.3 # set min exposure
# gordon.min_exposure = 0.6 # set min exposure
# harden.min_exposure = 0.1
# curry.min_exposure = 0.7
# draymond.fppg = 100
# chriss.min_exposure = 0.1
# robinson.min_exposure = 0.3
# reddish.fppg = 19
# print(lineup)
# print(lineup.players) # list of players
# print(lineup.fantasy_points_projection)
# print(lineup.salary_costs)
optimizer.set_deviation(0, 0.1) # changing defaults of 0.06 and 0.12
optimizer.set_min_salary_cap(49500)
# optimizer.set_max_repeating_players(5) #restricts how many unique players each team must have
# optimizer.set_team_stacking([3, 3]) #in this case, two teams must have at least 3 players present
# optimizer.set_players_from_one_team({'GS': 2, 'LAC': 2})
exporter = CSVLineupExporter(optimizer.optimize(300, randomness=True))
exporter.export(
r'C:\Users\Charlie\Documents\GitHub\Fantasy-Fire\website\optimizer\lineups.csv'
)