Create app.py

app.py

@@ -0,0 +1,358 @@
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+from itertools import combinations
+
+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 = gspread.service_account_from_dict(credentials)
+
+st.set_page_config(layout="wide")
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
+                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
+
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
+
+@st.cache_data
+def set_slate_teams():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Site_Info')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    return raw_display
+
+@st.cache_data
+def player_stat_table():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Player_Projections')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    return raw_display
+
+@st.cache_data
+def load_dk_player_projections():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('DK_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def load_fd_player_projections():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('FD_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def load_dk_stacks():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('DK_Stacks')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_data
+def load_fd_stacks():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('FD_Stacks')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+player_stats = player_stat_table()
+dk_roo_raw = load_dk_player_projections()
+fd_roo_raw = load_fd_player_projections()
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+site_slates = set_slate_teams()
+col1, col2 = st.columns([1, 5])
+
+tab1, tab2 = st.tabs(['Uploads and Info', 'Stack Finder'])
+
+with tab1:
+    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'.")
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        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)
+                  except:
+                            proj_dataframe = pd.read_excel(proj_file)
+    with col2:
+        if proj_file is not None:  
+                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        
+with tab2:
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              player_stats = player_stat_table()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'), key='slate_var1')
+        site_var1 = st.radio("What site are you playing?", ('Draftkings', 'Fanduel'), key='site_var1')
+        
+        if site_var1 == 'Draftkings':
+            if slate_var1 == 'User':
+                raw_baselines = proj_dataframe
+                qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+            elif slate_var1 != 'User':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+        elif site_var1 == 'Fanduel':
+            if slate_var1 == 'User':
+                raw_baselines = proj_dataframe
+                qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+            elif slate_var1 != 'User':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                qb_lookup = raw_baselines[raw_baselines['Position'] == 'QB']
+        split_var1 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('Full Slate Run', 'Specific Teams'), key='split_var1')
+        if split_var1 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the analysis?', options = raw_baselines['Team'].unique(), key='team_var1')
+        elif split_var1 == 'Full Slate Run':
+            team_var1 = raw_baselines.Team.unique().tolist()
+        pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
+        if pos_split1 == 'Specific Positions':
+            pos_var1 = st.multiselect('What Positions would you like to view?', options = ['QB', 'WR', 'TE'])
+        elif pos_split1 == 'All Positions':
+            pos_var1 = 'All'
+        if site_var1 == 'Draftkings':
+            max_sal1 = st.number_input('Max Salary', min_value = 5000, max_value = 50000, value = 35000, step = 100, key='max_sal1')
+        elif site_var1 == 'Fanduel':
+            max_sal1 = st.number_input('Max Salary', min_value = 5000, max_value = 35000, value = 25000, step = 100, key='max_sal1')
+        size_var1 = st.selectbox('What size of stacks are you analyzing?', options = ['QB+1', 'QB+2'])
+        if size_var1 == 'QB+1':
+            stack_size = 2
+        elif size_var1 == 'QB+2':
+            stack_size = 3
+    
+        team_dict = dict(zip(raw_baselines.Player, raw_baselines.Team))
+        proj_dict = dict(zip(raw_baselines.Player, raw_baselines.Median))
+        own_dict = dict(zip(raw_baselines.Player, raw_baselines.Own))
+        cost_dict = dict(zip(raw_baselines.Player, raw_baselines.Salary))
+        qb_dict = dict(zip(qb_lookup.Team, qb_lookup.Player))
+    
+    with col2:
+        stack_hold_container = st.empty()
+        if st.button('Run stack analysis'):
+            comb_list = []
+            if pos_split1 == 'All Positions':
+                raw_baselines = raw_baselines
+            elif pos_split1 != 'All Positions':
+                raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var1))]
+    
+            for cur_team in team_var1:
+                working_baselines = raw_baselines
+                working_baselines = working_baselines[working_baselines['Team'] == cur_team]
+                working_baselines = working_baselines[working_baselines['Position'] != 'RB']
+                working_baselines = working_baselines[working_baselines['Position'] != 'DST']
+                qb_var = qb_dict[cur_team]
+                order_list = working_baselines['Player']
+    
+                comb = combinations(order_list, stack_size)
+    
+                for i in list(comb):
+                    if qb_var in i:
+                        comb_list.append(i)
+    
+            comb_DF = pd.DataFrame(comb_list)
+            
+            if stack_size == 2:
+                comb_DF['Team'] = comb_DF[0].map(team_dict)
+    
+                comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                        comb_DF[1].map(proj_dict)])
+    
+                comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                        comb_DF[1].map(cost_dict)])
+    
+                comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                        comb_DF[1].map(own_dict)])
+            elif stack_size == 3:
+                comb_DF['Team'] = comb_DF[0].map(team_dict)
+    
+                comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                        comb_DF[1].map(proj_dict),
+                        comb_DF[2].map(proj_dict)])
+    
+                comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                        comb_DF[1].map(cost_dict),
+                        comb_DF[2].map(cost_dict)])
+    
+                comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                        comb_DF[1].map(own_dict),
+                        comb_DF[2].map(own_dict)])
+            elif stack_size == 4:
+                comb_DF['Team'] = comb_DF[0].map(team_dict)
+    
+                comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                        comb_DF[1].map(proj_dict),
+                        comb_DF[2].map(proj_dict),
+                        comb_DF[3].map(proj_dict)])
+    
+                comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                        comb_DF[1].map(cost_dict),
+                        comb_DF[2].map(cost_dict),
+                        comb_DF[3].map(cost_dict)])
+    
+                comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                        comb_DF[1].map(own_dict),
+                        comb_DF[2].map(own_dict),
+                        comb_DF[3].map(own_dict)])
+            elif stack_size == 5:
+                comb_DF['Team'] = comb_DF[0].map(team_dict)
+    
+                comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
+                        comb_DF[1].map(proj_dict),
+                        comb_DF[2].map(proj_dict),
+                        comb_DF[3].map(proj_dict),
+                        comb_DF[4].map(proj_dict)])
+    
+                comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
+                        comb_DF[1].map(cost_dict),
+                        comb_DF[2].map(cost_dict),
+                        comb_DF[3].map(cost_dict),
+                        comb_DF[4].map(cost_dict)])
+    
+                comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
+                        comb_DF[1].map(own_dict),
+                        comb_DF[2].map(own_dict),
+                        comb_DF[3].map(own_dict),
+                        comb_DF[4].map(own_dict)])
+    
+            comb_DF = comb_DF.sort_values(by='Proj', ascending=False)
+            comb_DF = comb_DF.loc[comb_DF['Salary'] <= max_sal1]
+    
+            cut_var = 0
+            
+            if stack_size == 2:
+                while cut_var <= int(len(comb_DF)):
+                    try:
+                        if int(cut_var) == 0:
+                            cur_proj = float(comb_DF.iat[cut_var, 3])
+                            cur_own = float(comb_DF.iat[cut_var, 5])
+                        elif int(cut_var) >= 1:
+                            check_own = float(comb_DF.iat[cut_var, 5])
+                            if check_own > cur_own:
+                                comb_DF = comb_DF.drop([cut_var])
+                                cur_own = cur_own
+                                cut_var = cut_var - 1
+                                comb_DF = comb_DF.reset_index()
+                                comb_DF = comb_DF.drop(['index'], axis=1)
+                            elif check_own <= cur_own:
+                                cur_own = float(comb_DF.iat[cut_var, 5])
+                                cut_var = cut_var
+                        cut_var += 1
+                    except:
+                        cut_var += 1
+            elif stack_size == 3:
+                while cut_var <= int(len(comb_DF)):
+                    try:
+                        if int(cut_var) == 0:
+                            cur_proj = float(comb_DF.iat[cut_var,4])
+                            cur_own = float(comb_DF.iat[cut_var,6])
+                        elif int(cut_var) >= 1:
+                            check_own = float(comb_DF.iat[cut_var,6])
+                            if check_own > cur_own:
+                                comb_DF = comb_DF.drop([cut_var])
+                                cur_own = cur_own
+                                cut_var = cut_var - 1
+                                comb_DF = comb_DF.reset_index()
+                                comb_DF = comb_DF.drop(['index'], axis=1)
+                            elif check_own <= cur_own:
+                                cur_own = float(comb_DF.iat[cut_var,6])
+                                cut_var = cut_var
+                        cut_var += 1
+                    except:
+                        cut_var += 1
+            elif stack_size == 4:
+                while cut_var <= int(len(comb_DF)):
+                    try:
+                        if int(cut_var) == 0:
+                            cur_proj = float(comb_DF.iat[cut_var,5])
+                            cur_own = float(comb_DF.iat[cut_var,7])
+                        elif int(cut_var) >= 1:
+                            check_own = float(comb_DF.iat[cut_var,7])
+                            if check_own > cur_own:
+                                comb_DF = comb_DF.drop([cut_var])
+                                cur_own = cur_own
+                                cut_var = cut_var - 1
+                                comb_DF = comb_DF.reset_index()
+                                comb_DF = comb_DF.drop(['index'], axis=1)
+                            elif check_own <= cur_own:
+                                cur_own = float(comb_DF.iat[cut_var,7])
+                                cut_var = cut_var
+                        cut_var += 1
+                    except:
+                        cut_var += 1
+            elif stack_size == 5:
+                while cut_var <= int(len(comb_DF)):
+                    try:
+                        if int(cut_var) == 0:
+                            cur_proj = float(comb_DF.iat[cut_var,6])
+                            cur_own = float(comb_DF.iat[cut_var,8])
+                        elif int(cut_var) >= 1:
+                            check_own = float(comb_DF.iat[cut_var,8])
+                            if check_own > cur_own:
+                                comb_DF = comb_DF.drop([cut_var])
+                                cur_own = cur_own
+                                cut_var = cut_var - 1
+                                comb_DF = comb_DF.reset_index()
+                                comb_DF = comb_DF.drop(['index'], axis=1)
+                            elif check_own <= cur_own:
+                                cur_own = float(comb_DF.iat[cut_var,8])
+                                cut_var = cut_var
+                        cut_var += 1
+                    except:
+                        cut_var += 1
+    
+            with stack_hold_container:
+                stack_hold_container = st.empty()
+                st.dataframe(comb_DF.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(comb_DF),
+                    file_name='NFL_Stack_Options_export.csv',
+                    mime='text/csv',
+            )