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import streamlit as st
st.set_page_config(layout="wide")
for name in dir():
if not name.startswith('_'):
del globals()[name]
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import random
import gc
@st.cache_resource
def init_conn():
scope = ['https://www.googleapis.com/auth/spreadsheets',
"https://www.googleapis.com/auth/drive"]
credentials = {
"type": "service_account",
"project_id": "sheets-api-connect-378620",
"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
"client_id": "106625872877651920064",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
}
gc_con = gspread.service_account_from_dict(credentials)
return gc_con
gcservice_account = init_conn()
freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
@st.cache_resource(ttl = 300)
def load_player_projections():
sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=1401252991')
worksheet = sh.worksheet('Player_Level_ROO')
load_display = pd.DataFrame(worksheet.get_all_records())
load_display.replace('', np.nan, inplace=True)
raw_display = load_display.dropna(subset=['Median'])
raw_display = raw_display[raw_display['Type'] == 'Basic']
dk_raw_display = raw_display[raw_display['Site'] == 'Draftkings']
fd_raw_display = raw_display[raw_display['Site'] == 'Fanduel']
dk_ids = dict(zip(dk_raw_display['Player'], dk_raw_display['player_id']))
fd_ids = dict(zip(fd_raw_display['Player'], fd_raw_display['player_id']))
return dk_raw_display, fd_raw_display, dk_ids, fd_ids
dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = load_player_projections()
static_exposure = pd.DataFrame(columns=['Player', 'count'])
overall_exposure = pd.DataFrame(columns=['Player', 'count'])
def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port):
SimVar = 1
Sim_Winners = []
fp_array = FinalPortfolio.values
if insert_port == 1:
up_array = CleanPortfolio.values
# Pre-vectorize functions
vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
if insert_port == 1:
vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
st.write('Simulating contest on frames')
while SimVar <= Sim_size:
if insert_port == 1:
fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
elif insert_port == 0:
fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
sample_arrays1 = np.c_[
fp_random,
np.sum(np.random.normal(
loc=vec_projection_map(fp_random[:, :-5]),
scale=vec_stdev_map(fp_random[:, :-5])),
axis=1)
]
if insert_port == 1:
sample_arrays2 = np.c_[
up_array,
np.sum(np.random.normal(
loc=vec_up_projection_map(up_array[:, :-5]),
scale=vec_up_stdev_map(up_array[:, :-5])),
axis=1)
]
sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
else:
sample_arrays = sample_arrays1
final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
Sim_Winners.append(best_lineup)
SimVar += 1
return Sim_Winners
def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth, site_var):
RunsVar = 1
seed_depth_def = seed_depth1
Strength_var_def = Strength_var
strength_grow_def = strength_grow
Teams_used_def = Teams_used
Total_Runs_def = Total_Runs
st.write('Creating Seed Frames')
if site_var == 'Draftkings':
while RunsVar <= seed_depth_def:
if RunsVar <= 3:
FieldStrength = Strength_var_def
FinalPortfolio, maps_dict = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio2, maps_dict2 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
maps_dict.update(maps_dict2)
elif RunsVar > 3 and RunsVar <= 4:
FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
FinalPortfolio3, maps_dict3 = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio4, maps_dict4 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
maps_dict.update(maps_dict3)
maps_dict.update(maps_dict4)
elif RunsVar > 4:
FieldStrength = 1
FinalPortfolio5, maps_dict5 = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio6, maps_dict6 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
maps_dict.update(maps_dict5)
maps_dict.update(maps_dict6)
RunsVar += 1
elif site_var == 'Fanduel':
while RunsVar <= seed_depth_def:
if RunsVar <= 3:
FieldStrength = Strength_var_def
FinalPortfolio, maps_dict = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio2, maps_dict2 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
maps_dict.update(maps_dict2)
elif RunsVar > 3 and RunsVar <= 4:
FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
FinalPortfolio3, maps_dict3 = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio4, maps_dict4 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
maps_dict.update(maps_dict3)
maps_dict.update(maps_dict4)
elif RunsVar > 4:
FieldStrength = 1
FinalPortfolio5, maps_dict5 = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio6, maps_dict6 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
maps_dict.update(maps_dict5)
maps_dict.update(maps_dict6)
RunsVar += 1
return FinalPortfolio_export, maps_dict
def create_overall_dfs(pos_players, table_name, dict_name, pos):
if pos == "UTIL":
pos_players = pos_players.sort_values(by='Value', ascending=False)
table_name_raw = pos_players.reset_index(drop=True)
overall_table_name = table_name_raw.head(round(len(table_name_raw)))
overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
elif pos != "UTIL":
table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True)
overall_table_name = table_name_raw.head(round(len(table_name_raw)))
overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
return overall_table_name, overall_dict_name
def get_overall_merged_df():
ref_dict = {
'pos':['C', 'W', 'D', 'UTIL'],
'pos_dfs':['C_Table', 'W_Table', 'D_Table', 'UTIL_Table'],
'pos_dicts':['c_dict', 'w_dict', 'd_dict', 'util_dict']
}
for i in range(0,4):
ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
return ref_dict
def calculate_range_var(count, min_val, FieldStrength, field_growth):
var = round(len(count[0]) * FieldStrength)
var = max(var, min_val)
var += round(field_growth)
return min(var, len(count[0]))
def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
full_pos_player_dict = get_overall_merged_df()
g_baselines = raw_baselines[raw_baselines['Position'] == 'G']
g_baselines = g_baselines.drop_duplicates(subset='Team')
max_var = len(g_baselines[g_baselines['Position'] == 'G'])
field_growth_rounded = round(field_growth)
ranges_dict = {}
if site_var1 == 'Draftkings':
# Calculate ranges
for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 30], ['C', 'W', 'D', 'UTIL']):
count = create_overall_dfs(pos_players, df, dict_val, key)
ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
if max_var <= 10:
ranges_dict['g_range'] = round(max_var)
elif max_var > 10 and max_var <= 16:
ranges_dict['g_range'] = round(max_var / 1.5)
elif max_var > 16:
ranges_dict['g_range'] = round(max_var / 2)
# Generate random portfolios
rng = np.random.default_rng()
total_elements = [2, 3, 2, 1, 1]
keys = ['c', 'w', 'd', 'g', 'util']
all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)]
RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'])
RandomPortfolio['User/Field'] = 0
elif site_var1 == 'Fanduel':
# Calculate ranges
for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 30], ['C', 'W', 'D', 'UTIL']):
count = create_overall_dfs(pos_players, df, dict_val, key)
ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded)
if max_var <= 10:
ranges_dict['g_range'] = round(max_var)
elif max_var > 10 and max_var <= 16:
ranges_dict['g_range'] = round(max_var)
elif max_var > 16:
ranges_dict['g_range'] = round(max_var)
# Generate random portfolios
rng = np.random.default_rng()
total_elements = [2, 2, 2, 2, 1]
keys = ['c', 'w', 'd', 'util', 'g']
all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)]
RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G'])
RandomPortfolio['User/Field'] = 0
return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
def get_correlated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
sizesplit = round(Total_Sample_Size * sharp_split)
RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
reset_index(drop=True)
RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W3s'] = RandomPortfolio['W3'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W3p'] = RandomPortfolio['W3'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W3o'] = RandomPortfolio['W3'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortArray = RandomPortfolio.to_numpy()
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))]
RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1)
RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own'])
RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
if insert_port == 1:
CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']),
CleanPortfolio['C2'].map(maps_dict['Salary_map']),
CleanPortfolio['W1'].map(maps_dict['Salary_map']),
CleanPortfolio['W2'].map(maps_dict['Salary_map']),
CleanPortfolio['W3'].map(maps_dict['Salary_map']),
CleanPortfolio['D1'].map(maps_dict['Salary_map']),
CleanPortfolio['D2'].map(maps_dict['Salary_map']),
CleanPortfolio['G'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL'].map(maps_dict['Salary_map'])
]).astype(np.int16)
if insert_port == 1:
CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']),
CleanPortfolio['C2'].map(up_dict['Projection_map']),
CleanPortfolio['W1'].map(up_dict['Projection_map']),
CleanPortfolio['W2'].map(up_dict['Projection_map']),
CleanPortfolio['W3'].map(up_dict['Projection_map']),
CleanPortfolio['D1'].map(up_dict['Projection_map']),
CleanPortfolio['D2'].map(up_dict['Projection_map']),
CleanPortfolio['G'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL'].map(up_dict['Projection_map'])
]).astype(np.float16)
if insert_port == 1:
CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']),
CleanPortfolio['C2'].map(maps_dict['Own_map']),
CleanPortfolio['W1'].map(maps_dict['Own_map']),
CleanPortfolio['W2'].map(maps_dict['Own_map']),
CleanPortfolio['W3'].map(maps_dict['Own_map']),
CleanPortfolio['D1'].map(maps_dict['Own_map']),
CleanPortfolio['D2'].map(maps_dict['Own_map']),
CleanPortfolio['G'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL'].map(maps_dict['Own_map'])
]).astype(np.float16)
if site_var1 == 'Draftkings':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
elif site_var1 == 'Fanduel':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']]
return RandomPortfolio, maps_dict
def get_uncorrelated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
sizesplit = round(Total_Sample_Size * sharp_split)
RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
reset_index(drop=True)
RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W3s'] = RandomPortfolio['W3'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W3p'] = RandomPortfolio['W3'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W3o'] = RandomPortfolio['W3'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortArray = RandomPortfolio.to_numpy()
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))]
RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1)
RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own'])
RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
if insert_port == 1:
CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']),
CleanPortfolio['C2'].map(maps_dict['Salary_map']),
CleanPortfolio['W1'].map(maps_dict['Salary_map']),
CleanPortfolio['W2'].map(maps_dict['Salary_map']),
CleanPortfolio['W3'].map(maps_dict['Salary_map']),
CleanPortfolio['D1'].map(maps_dict['Salary_map']),
CleanPortfolio['D2'].map(maps_dict['Salary_map']),
CleanPortfolio['G'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL'].map(maps_dict['Salary_map'])
]).astype(np.int16)
if insert_port == 1:
CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']),
CleanPortfolio['C2'].map(up_dict['Projection_map']),
CleanPortfolio['W1'].map(up_dict['Projection_map']),
CleanPortfolio['W2'].map(up_dict['Projection_map']),
CleanPortfolio['W3'].map(up_dict['Projection_map']),
CleanPortfolio['D1'].map(up_dict['Projection_map']),
CleanPortfolio['D2'].map(up_dict['Projection_map']),
CleanPortfolio['G'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL'].map(up_dict['Projection_map'])
]).astype(np.float16)
if insert_port == 1:
CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']),
CleanPortfolio['C2'].map(maps_dict['Own_map']),
CleanPortfolio['W1'].map(maps_dict['Own_map']),
CleanPortfolio['W2'].map(maps_dict['Own_map']),
CleanPortfolio['W3'].map(maps_dict['Own_map']),
CleanPortfolio['D1'].map(maps_dict['Own_map']),
CleanPortfolio['D2'].map(maps_dict['Own_map']),
CleanPortfolio['G'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL'].map(maps_dict['Own_map'])
]).astype(np.float16)
if site_var1 == 'Draftkings':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
elif site_var1 == 'Fanduel':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']]
return RandomPortfolio, maps_dict
def get_correlated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
sizesplit = round(Total_Sample_Size * sharp_split)
RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
reset_index(drop=True)
RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTIL1s'] = RandomPortfolio['UTIL1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTIL2s'] = RandomPortfolio['UTIL2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTIL1p'] = RandomPortfolio['UTIL1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTIL2p'] = RandomPortfolio['UTIL2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTIL1o'] = RandomPortfolio['UTIL1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTIL2o'] = RandomPortfolio['UTIL2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortArray = RandomPortfolio.to_numpy()
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))]
RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1)
RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own'])
RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
if insert_port == 1:
CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']),
CleanPortfolio['C2'].map(maps_dict['Salary_map']),
CleanPortfolio['W1'].map(maps_dict['Salary_map']),
CleanPortfolio['W2'].map(maps_dict['Salary_map']),
CleanPortfolio['D1'].map(maps_dict['Salary_map']),
CleanPortfolio['D2'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL1'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL2'].map(maps_dict['Salary_map']),
CleanPortfolio['G'].map(maps_dict['Salary_map'])
]).astype(np.int16)
if insert_port == 1:
CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']),
CleanPortfolio['C2'].map(up_dict['Projection_map']),
CleanPortfolio['W1'].map(up_dict['Projection_map']),
CleanPortfolio['W2'].map(up_dict['Projection_map']),
CleanPortfolio['D1'].map(up_dict['Projection_map']),
CleanPortfolio['D2'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL1'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL2'].map(up_dict['Projection_map']),
CleanPortfolio['G'].map(up_dict['Projection_map'])
]).astype(np.float16)
if insert_port == 1:
CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']),
CleanPortfolio['C2'].map(maps_dict['Own_map']),
CleanPortfolio['W1'].map(maps_dict['Own_map']),
CleanPortfolio['W2'].map(maps_dict['Own_map']),
CleanPortfolio['D1'].map(maps_dict['Own_map']),
CleanPortfolio['D2'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL1'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL2'].map(maps_dict['Own_map']),
CleanPortfolio['G'].map(maps_dict['Own_map'])
]).astype(np.float16)
if site_var1 == 'Draftkings':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
elif site_var1 == 'Fanduel':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']]
return RandomPortfolio, maps_dict
def get_uncorrelated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
sizesplit = round(Total_Sample_Size * sharp_split)
RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(gs_dict)), dtype="string[pyarrow]")
RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist()
RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\
reset_index(drop=True)
RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTIL1s'] = RandomPortfolio['UTIL1'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['UTIL2s'] = RandomPortfolio['UTIL2'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTIL1p'] = RandomPortfolio['UTIL1'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['UTIL2p'] = RandomPortfolio['UTIL2'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTIL1o'] = RandomPortfolio['UTIL1'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['UTIL2o'] = RandomPortfolio['UTIL2'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
RandomPortArray = RandomPortfolio.to_numpy()
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))]
RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))]
RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1)
RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own'])
RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
if insert_port == 1:
CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']),
CleanPortfolio['C2'].map(maps_dict['Salary_map']),
CleanPortfolio['W1'].map(maps_dict['Salary_map']),
CleanPortfolio['W2'].map(maps_dict['Salary_map']),
CleanPortfolio['D1'].map(maps_dict['Salary_map']),
CleanPortfolio['D2'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL1'].map(maps_dict['Salary_map']),
CleanPortfolio['UTIL2'].map(maps_dict['Salary_map']),
CleanPortfolio['G'].map(maps_dict['Salary_map'])
]).astype(np.int16)
if insert_port == 1:
CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']),
CleanPortfolio['C2'].map(up_dict['Projection_map']),
CleanPortfolio['W1'].map(up_dict['Projection_map']),
CleanPortfolio['W2'].map(up_dict['Projection_map']),
CleanPortfolio['D1'].map(up_dict['Projection_map']),
CleanPortfolio['D2'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL1'].map(up_dict['Projection_map']),
CleanPortfolio['UTIL2'].map(up_dict['Projection_map']),
CleanPortfolio['G'].map(up_dict['Projection_map'])
]).astype(np.float16)
if insert_port == 1:
CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']),
CleanPortfolio['C2'].map(maps_dict['Own_map']),
CleanPortfolio['W1'].map(maps_dict['Own_map']),
CleanPortfolio['W2'].map(maps_dict['Own_map']),
CleanPortfolio['D1'].map(maps_dict['Own_map']),
CleanPortfolio['D2'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL1'].map(maps_dict['Own_map']),
CleanPortfolio['UTIL2'].map(maps_dict['Own_map']),
CleanPortfolio['G'].map(maps_dict['Own_map'])
]).astype(np.float16)
if site_var1 == 'Draftkings':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
elif site_var1 == 'Fanduel':
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True)
RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']]
return RandomPortfolio, maps_dict
tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
with tab1:
with st.container():
col1, col2 = st.columns([3, 3])
with col1:
st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.")
proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
if proj_file is not None:
try:
proj_dataframe = pd.read_csv(proj_file)
proj_dataframe = proj_dataframe.dropna(subset='Median')
proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
try:
proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
except:
pass
except:
proj_dataframe = pd.read_excel(proj_file)
proj_dataframe = proj_dataframe.dropna(subset='Median')
proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
try:
proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
except:
pass
st.table(proj_dataframe.head(10))
player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
with col2:
st.info("The Portfolio file for Draftkings must contain only columns in order and explicitly named: 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', and 'UTIL'. The Portfolio file for Fanduel must contain only columns in order and explicitly named: 'C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', and 'G'. Upload your projections first to avoid an error message.")
portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
if portfolio_file is not None:
try:
portfolio_dataframe = pd.read_csv(portfolio_file)
except:
portfolio_dataframe = pd.read_excel(portfolio_file)
try:
try:
try:
portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "W3", "D1", "D2", "G", "UTIL"]
split_portfolio = portfolio_dataframe
split_portfolio[['C1', 'C1_ID']] = split_portfolio.C1.str.split("(", n=1, expand = True)
split_portfolio[['C2', 'C2_ID']] = split_portfolio.C2.str.split("(", n=1, expand = True)
split_portfolio[['W1', 'W1_ID']] = split_portfolio.W1.str.split("(", n=1, expand = True)
split_portfolio[['W2', 'W2_ID']] = split_portfolio.W2.str.split("(", n=1, expand = True)
split_portfolio[['W3', 'W3_ID']] = split_portfolio.W3.str.split("(", n=1, expand = True)
split_portfolio[['D1', 'D1_ID']] = split_portfolio.D1.str.split("(", n=1, expand = True)
split_portfolio[['D2', 'D2_ID']] = split_portfolio.D2.str.split("(", n=1, expand = True)
split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True)
split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True)
split_portfolio['C1'] = split_portfolio['C1'].str.strip()
split_portfolio['C2'] = split_portfolio['C2'].str.strip()
split_portfolio['W1'] = split_portfolio['W1'].str.strip()
split_portfolio['W2'] = split_portfolio['W2'].str.strip()
split_portfolio['W3'] = split_portfolio['W3'].str.strip()
split_portfolio['D1'] = split_portfolio['D1'].str.strip()
split_portfolio['D2'] = split_portfolio['D2'].str.strip()
split_portfolio['G'] = split_portfolio['G'].str.strip()
split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
st.table(split_portfolio.head(10))
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['W3'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict),
split_portfolio['UTIL'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['W3'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict),
split_portfolio['UTIL'].map(player_proj_dict)])
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['W3'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict),
split_portfolio['UTIL'].map(player_own_dict)])
except:
portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "D1", "D2", "UTIL1", "UTIL2", "G"]
split_portfolio = portfolio_dataframe
split_portfolio[['C1_ID', 'C1']] = split_portfolio.C1.str.split(":", n=1, expand = True)
split_portfolio[['C2_ID', 'C2']] = split_portfolio.C2.str.split(":", n=1, expand = True)
split_portfolio[['W1_ID', 'W1']] = split_portfolio.W1.str.split(":", n=1, expand = True)
split_portfolio[['W2_ID', 'W2']] = split_portfolio.W2.str.split(":", n=1, expand = True)
split_portfolio[['D1_ID', 'D1']] = split_portfolio.D1.str.split(":", n=1, expand = True)
split_portfolio[['D2_ID', 'D2']] = split_portfolio.D2.str.split(":", n=1, expand = True)
split_portfolio[['UTIL1_ID', 'UTIL1']] = split_portfolio.UTIL1.str.split(":", n=1, expand = True)
split_portfolio[['UTIL2_ID', 'UTIL2']] = split_portfolio.UTIL2.str.split(":", n=1, expand = True)
split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True)
split_portfolio['C1'] = split_portfolio['C1'].str.strip()
split_portfolio['C2'] = split_portfolio['C2'].str.strip()
split_portfolio['W1'] = split_portfolio['W1'].str.strip()
split_portfolio['W2'] = split_portfolio['W2'].str.strip()
split_portfolio['D1'] = split_portfolio['D1'].str.strip()
split_portfolio['D2'] = split_portfolio['D2'].str.strip()
split_portfolio['UTIL1'] = split_portfolio['UTIL1'].str.strip()
split_portfolio['UTIL2'] = split_portfolio['UTIL2'].str.strip()
split_portfolio['G'] = split_portfolio['G'].str.strip()
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['UTIL1'].map(player_salary_dict),
split_portfolio['UTIL2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['UTIL1'].map(player_proj_dict),
split_portfolio['UTIL2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict)])
st.table(split_portfolio.head(10))
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['UTIL1'].map(player_own_dict),
split_portfolio['UTIL2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict)])
except:
try:
portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "W3", "D1", "D2", "G", "UTIL"]
split_portfolio = portfolio_dataframe
st.table(split_portfolio.head(10))
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['W3'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict),
split_portfolio['UTIL'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['W3'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict),
split_portfolio['UTIL'].map(player_proj_dict)])
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['W3'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict),
split_portfolio['UTIL'].map(player_own_dict)])
except:
portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "D1", "D2", "UTIL1", "UTIL2", "G"]
split_portfolio = portfolio_dataframe
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['UTIL1'].map(player_salary_dict),
split_portfolio['UTIL2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['UTIL1'].map(player_proj_dict),
split_portfolio['UTIL2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict)])
st.table(split_portfolio.head(10))
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['UTIL1'].map(player_own_dict),
split_portfolio['UTIL2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict)])
except:
try:
split_portfolio = portfolio_dataframe
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['W3'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict),
split_portfolio['UTIL'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['W3'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict),
split_portfolio['UTIL'].map(player_proj_dict)])
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['W3'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict),
split_portfolio['UTIL'].map(player_own_dict)])
except:
split_portfolio = portfolio_dataframe
split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict),
split_portfolio['C2'].map(player_salary_dict),
split_portfolio['W1'].map(player_salary_dict),
split_portfolio['W2'].map(player_salary_dict),
split_portfolio['D1'].map(player_salary_dict),
split_portfolio['D2'].map(player_salary_dict),
split_portfolio['UTIL1'].map(player_salary_dict),
split_portfolio['UTIL2'].map(player_salary_dict),
split_portfolio['G'].map(player_salary_dict)])
split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict),
split_portfolio['C2'].map(player_proj_dict),
split_portfolio['W1'].map(player_proj_dict),
split_portfolio['W2'].map(player_proj_dict),
split_portfolio['D1'].map(player_proj_dict),
split_portfolio['D2'].map(player_proj_dict),
split_portfolio['UTIL1'].map(player_proj_dict),
split_portfolio['UTIL2'].map(player_proj_dict),
split_portfolio['G'].map(player_proj_dict)])
split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict),
split_portfolio['C2'].map(player_own_dict),
split_portfolio['W1'].map(player_own_dict),
split_portfolio['W2'].map(player_own_dict),
split_portfolio['D1'].map(player_own_dict),
split_portfolio['D2'].map(player_own_dict),
split_portfolio['UTIL1'].map(player_own_dict),
split_portfolio['UTIL2'].map(player_own_dict),
split_portfolio['G'].map(player_own_dict)])
gc.collect()
with tab2:
col1, col2 = st.columns([1, 7])
with col1:
if st.button("Load/Reset Data", key='reset1'):
st.cache_data.clear()
for key in st.session_state.keys():
del st.session_state[key]
dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = load_player_projections()
slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'))
site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
if site_var1 == 'Draftkings':
if slate_var1 == 'User':
raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
elif slate_var1 != 'User':
raw_baselines = dk_roo_raw[dk_roo_raw['Type'] == 'Basic']
elif site_var1 == 'Fanduel':
if slate_var1 == 'User':
raw_baselines = proj_dataframe
elif slate_var1 != 'User':
raw_baselines = fd_roo_raw[fd_roo_raw['Type'] == 'Basic']
st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1')
if insert_port1 == 'Yes':
insert_port = 1
elif insert_port1 == 'No':
insert_port = 0
contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
if contest_var1 == 'Small':
Contest_Size = 1000
elif contest_var1 == 'Medium':
Contest_Size = 5000
elif contest_var1 == 'Large':
Contest_Size = 10000
strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
if strength_var1 == 'Not Very':
sharp_split = .33
Strength_var = .50
scaling_var = 5
elif strength_var1 == 'Average':
sharp_split = .50
Strength_var = .25
scaling_var = 10
elif strength_var1 == 'Very':
sharp_split = .75
Strength_var = .01
scaling_var = 15
Sort_function = 'Median'
Sim_function = 'Projection'
if Contest_Size <= 1000:
strength_grow = .01
elif Contest_Size > 1000 and Contest_Size <= 2500:
strength_grow = .025
elif Contest_Size > 2500 and Contest_Size <= 5000:
strength_grow = .05
elif Contest_Size > 5000 and Contest_Size <= 20000:
strength_grow = .075
elif Contest_Size > 20000:
strength_grow = .1
field_growth = 100 * strength_grow
with col2:
with st.container():
if st.button("Simulate Contest"):
with st.container():
for key in st.session_state.keys():
del st.session_state[key]
if slate_var1 == 'User':
initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
# Define the calculation to be applied
def calculate_own(position, own, mean_own, factor, max_own=75):
return np.where((position == 'G') & (own - mean_own >= 0),
own * (factor * (own - mean_own) / 100) + mean_own,
own)
# Set the factors based on the contest_var1
factor_qb, factor_other = {
'Small': (10, 5),
'Medium': (6, 3),
'Large': (3, 1.5),
}[contest_var1]
# Apply the calculation to the DataFrame
initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'G' else factor_other), axis=1)
initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75)
initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum())
# Drop unnecessary columns and create the final DataFrame
Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
elif slate_var1 != 'User':
# Copy only the necessary columns
initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
# Define the calculation to be applied
def calculate_own(position, own, mean_own, factor, max_own=75):
return np.where((position == 'G') & (own - mean_own >= 0),
own * (factor * (own - mean_own) / 100) + mean_own,
own)
# Set the factors based on the contest_var1
factor_qb, factor_other = {
'Small': (10, 5),
'Medium': (6, 3),
'Large': (3, 1.5),
}[contest_var1]
# Apply the calculation to the DataFrame
initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'G' else factor_other), axis=1)
initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75)
initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum())
# Drop unnecessary columns and create the final DataFrame
Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
if site_var1 == 'Draftkings':
if insert_port == 1:
st.table(portfolio_dataframe)
UserPortfolio = portfolio_dataframe[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']]
elif insert_port == 0:
UserPortfolio = pd.DataFrame(columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'])
elif site_var1 == 'Fanduel':
if insert_port == 1:
UserPortfolio = portfolio_dataframe[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']]
elif insert_port == 0:
UserPortfolio = pd.DataFrame(columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G'])
Overall_Proj.replace('', np.nan, inplace=True)
Overall_Proj = Overall_Proj.dropna(subset=['Median'])
Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000)))
Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2
Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False)
Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own'])
Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0]
Overall_Proj['Floor_raw'] = Overall_Proj['Median'] * .25
Overall_Proj['Ceiling_raw'] = Overall_Proj['Median'] * 2
Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'G', Overall_Proj['Median'] * .5, Overall_Proj['Floor_raw'])
Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'D', Overall_Proj['Median'] * .1, Overall_Proj['Floor_raw'])
Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'G', Overall_Proj['Median'] * 1.75, Overall_Proj['Ceiling_raw'])
Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'D', Overall_Proj['Median'] * 1.75, Overall_Proj['Ceiling_raw'])
Overall_Proj['STDev'] = Overall_Proj['Median'] / 3
Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
Teams_used = Teams_used.reset_index()
Teams_used['team_item'] = Teams_used['index'] + 1
Teams_used = Teams_used.drop(columns=['index'])
Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
team_list = Teams_used['Team'].to_list()
item_list = Teams_used['team_item'].to_list()
FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
if FieldStrength < 0:
FieldStrength = Strength_var
field_split = Strength_var
for checkVar in range(len(team_list)):
Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
cs_raw = Overall_Proj[Overall_Proj.Position.str.contains('C')]
cs_raw.dropna(subset=['Median']).reset_index(drop=True)
cs_raw = cs_raw.reset_index(drop=True)
cs_raw = cs_raw.sort_values(by=['Own', 'Median'], ascending=False)
ws_raw = Overall_Proj[Overall_Proj.Position.str.contains("W")]
ws_raw.dropna(subset=['Median']).reset_index(drop=True)
ws_raw = ws_raw.reset_index(drop=True)
ws_raw = ws_raw.sort_values(by=['Own', 'Value'], ascending=False)
ds_raw = Overall_Proj[Overall_Proj.Position == 'D']
ds_raw.dropna(subset=['Median']).reset_index(drop=True)
ds_raw = ds_raw.reset_index(drop=True)
ds_raw = ds_raw.sort_values(by=['Own', 'Value'], ascending=False)
gs_raw = Overall_Proj[Overall_Proj.Position == 'G']
gs_raw = gs_raw[gs_raw['Median'] > 0]
gs_raw.dropna(subset=['Median']).reset_index(drop=True)
gs_raw = gs_raw.reset_index(drop=True)
gs_raw = gs_raw.sort_values(by=['Own', 'Median'], ascending=False)
gs = gs_raw.head(round(len(gs_raw)))
gs = gs.assign(Var = range(0,len(gs)))
gs_dict = pd.Series(gs.Player.values, index=gs.Var).to_dict()
pos_players = pd.concat([cs_raw, ws_raw, ds_raw])
pos_players.dropna(subset=['Median']).reset_index(drop=True)
pos_players = pos_players.reset_index(drop=True)
if insert_port == 1:
try:
# Initialize an empty DataFrame for Raw Portfolio
Raw_Portfolio = pd.DataFrame()
# Loop through each position and split the data accordingly
if site_var1 == 'Draftkings':
positions = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']
elif site_var1 == 'Fanduel':
positions = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']
for pos in positions:
temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True)
temp_df.columns = [pos, 'Drop']
Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1)
# Select only necessary columns and strip white spaces
CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip())
CleanPortfolio.reset_index(inplace=True)
CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
CleanPortfolio.drop(columns=['index'], inplace=True)
CleanPortfolio.replace('', np.nan, inplace=True)
CleanPortfolio.dropna(subset=['G'], inplace=True)
# Create frequency table for players
cleaport_players = pd.DataFrame(
np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
columns=['Player', 'Freq']
).sort_values('Freq', ascending=False).reset_index(drop=True)
cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
# Merge and update nerf_frame
nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
nerf_frame[col] *= 0.90
except:
CleanPortfolio = UserPortfolio.reset_index()
CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
CleanPortfolio.drop(columns=['index'], inplace=True)
# Replace empty strings and drop rows with NaN in 'QB' column
CleanPortfolio.replace('', np.nan, inplace=True)
CleanPortfolio.dropna(subset=['G'], inplace=True)
# Create frequency table for players
cleaport_players = pd.DataFrame(
np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:10].values, return_counts=True)),
columns=['Player', 'Freq']
).sort_values('Freq', ascending=False).reset_index(drop=True)
cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
# Merge and update nerf_frame
nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
nerf_frame[col] *= 0.90
elif insert_port == 0:
CleanPortfolio = UserPortfolio
cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
nerf_frame = Overall_Proj
ref_dict = {
'pos':['C', 'W', 'D', 'UTIL'],
'pos_dfs':['C_Table', 'W_Table', 'D_Table', 'UTIL_Table'],
'pos_dicts':['c_dict', 'w_dict', 'd_dict', 'util_dict']
}
maps_dict = {
'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)),
'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)),
'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)),
'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)),
'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)),
'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)),
'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)),
'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)),
'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team))
}
up_dict = {
'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)),
'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)),
'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)),
'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)),
'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)),
'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)),
'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)),
'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)),
'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
}
FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth, site_var1)
Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
# Initial setup
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
# Type Casting
type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
del FinalPortfolio, insert_port, type_cast_dict
# Sorting
st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
# Data Copying
st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
# Data Copying
st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
# Conditional Replacement
if site_var1 == 'Draftkings':
columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']
elif site_var1 == 'Fanduel':
columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']
if site_var1 == 'Draftkings':
replace_dict = dkid_dict
elif site_var1 == 'Fanduel':
replace_dict = fdid_dict
for col in columns_to_replace:
st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
del replace_dict, Sim_Winner_Frame, Sim_Winners
st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)),
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map'])
st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100
st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(2500)
st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own']
st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map'])
for checkVar in range(len(team_list)):
st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list)
# st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int)
# st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(2500)
# st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own']
# st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list)
# st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int)
# st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/2500
# st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own']
# st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list)
# st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int)
# st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/2500
# st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own']
# st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Team'].replace(item_list, team_list)
# st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[6]].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.te_freq['Freq'] = st.session_state.te_freq['Freq'].astype(int)
# st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.te_freq['Salary'] = st.session_state.te_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.te_freq['Proj Own'] = st.session_state.te_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/2500
# st.session_state.te_freq['Edge'] = st.session_state.te_freq['Exposure'] - st.session_state.te_freq['Proj Own']
# st.session_state.te_freq['Team'] = st.session_state.te_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.te_freq['Team'] = st.session_state.te_freq['Team'].replace(item_list, team_list)
# st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[7]].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int)
# st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/2500
# st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own']
# st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list)
# st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,8:9].values, return_counts=True)),
# columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
# st.session_state.dst_freq['Freq'] = st.session_state.dst_freq['Freq'].astype(int)
# st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map'])
# st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map'])
# st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100
# st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/2500
# st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own']
# st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map'])
# for checkVar in range(len(team_list)):
# st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Team'].replace(item_list, team_list)
with st.container():
if 'player_freq' in st.session_state:
player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
if player_split_var2 == 'Specific Players':
find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
elif player_split_var2 == 'Full Players':
find_var2 = st.session_state.player_freq.Player.values.tolist()
if player_split_var2 == 'Specific Players':
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
if player_split_var2 == 'Full Players':
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
if 'Sim_Winner_Display' in st.session_state:
st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
if 'Sim_Winner_Export' in st.session_state:
st.download_button(
label="Export Full Frame",
data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
file_name='NFL_consim_export.csv',
mime='text/csv',
)
with st.container():
# tab1 = st.tabs(['Overall Exposures'])
# tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
# with tab1:
if 'player_freq' in st.session_state:
st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
st.download_button(
label="Export Exposures",
data=st.session_state.player_freq.to_csv().encode('utf-8'),
file_name='player_freq_export.csv',
mime='text/csv',
)
# with tab2:
# if 'qb_freq' in st.session_state:
# st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.qb_freq.to_csv().encode('utf-8'),
# file_name='qb_freq_export.csv',
# mime='text/csv',
# )
# with tab3:
# if 'rb_freq' in st.session_state:
# st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.rb_freq.to_csv().encode('utf-8'),
# file_name='rb_freq_export.csv',
# mime='text/csv',
# )
# with tab4:
# if 'wr_freq' in st.session_state:
# st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.wr_freq.to_csv().encode('utf-8'),
# file_name='wr_freq_export.csv',
# mime='text/csv',
# )
# with tab5:
# if 'te_freq' in st.session_state:
# st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.te_freq.to_csv().encode('utf-8'),
# file_name='te_freq_export.csv',
# mime='text/csv',
# )
# with tab6:
# if 'flex_freq' in st.session_state:
# st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.flex_freq.to_csv().encode('utf-8'),
# file_name='flex_freq_export.csv',
# mime='text/csv',
# )
# with tab7:
# if 'dst_freq' in st.session_state:
# st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
# st.download_button(
# label="Export Exposures",
# data=st.session_state.dst_freq.to_csv().encode('utf-8'),
# file_name='dst_freq_export.csv',
# mime='text/csv',
# )
del gcservice_account
del dk_roo_raw, fd_roo_raw
del dkid_dict, fdid_dict
del static_exposure, overall_exposure
del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth
del raw_baselines
del freq_format
gc.collect() |