Update V3 Dash

app.py

@@ -20,29 +20,133 @@ import numpy as np
 import datetime as dt
 import matplotlib.pyplot as plt
 from pathlib import Path
+import time
+import plotly.graph_objects as go
+import plotly.io as pio
+from PIL import Image
 
 import streamlit as st
 import plotly.express as px
 import altair as alt
 import dateutil.parser
-import copy
+from  matplotlib.colors import LinearSegmentedColormap
 
 
 # +
+class color:
+    PURPLE = '\033[95m'
+    CYAN = '\033[96m'
+    DARKCYAN = '\033[36m'
+    BLUE = '\033[94m'
+    GREEN = '\033[92m'
+    YELLOW = '\033[93m'
+    RED = '\033[91m'
+    BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
+    END = '\033[0m'
+    
+@st.experimental_memo    
+def print_PL(amnt, thresh, extras = "" ):
+    if amnt > 0:
+        return color.BOLD + color.GREEN + str(amnt) + extras + color.END
+    elif amnt < 0:
+        return color.BOLD + color.RED + str(amnt)+ extras + color.END
+    elif np.isnan(amnt):
+        return str(np.nan)
+    else:
+        return str(amnt + extras)
+    
+@st.experimental_memo      
+def get_headers(logtype):
+    otimeheader = ""
+    cheader = ""
+    plheader = ""
+    fmat = '%Y-%m-%d %H:%M:%S'
+    
+    if logtype == "ByBit":
+        otimeheader = 'Create Time'
+        cheader = 'Contracts'
+        plheader = 'Closed P&L'
+        fmat = '%Y-%m-%d %H:%M:%S'
+        
+    if logtype == "BitGet":
+        otimeheader = 'Date'
+        cheader = 'Futures'
+        plheader = 'Realized P/L'
+        fmat = '%Y-%m-%d %H:%M:%S'  
+        
+    if logtype == "MEXC":
+        otimeheader = 'Trade time'
+        cheader = 'Futures'
+        plheader = 'closing position'
+        fmat = '%Y/%m/%d %H:%M'
+        
+    if logtype == "Binance":
+        otimeheader = 'Date'
+        cheader = 'Symbol'
+        plheader = 'Realized Profit'
+        fmat = '%Y-%m-%d %H:%M:%S'
+        
+    #if logtype == "Kucoin":
+    #    otimeheader = 'Time'
+    #    cheader = 'Contract'
+    #    plheader = ''
+    #    fmat = '%Y/%m/%d %H:%M:%S' 
+
+        
+    if logtype == "Kraken":
+        otimeheader = 'time'
+        cheader = 'asset'
+        plheader = 'amount'
+        fmat = '%Y-%m-%d %H:%M:%S.%f'  
+        
+    if logtype == "OkX":
+        otimeheader = '\ufeffOrder Time'
+        cheader = '\ufeffInstrument'
+        plheader = '\ufeffPL'
+        fmat = '%Y-%m-%d %H:%M:%S'        
+    
+    return otimeheader.lower(), cheader.lower(), plheader.lower(), fmat
+    
+@st.experimental_memo    
+def get_coin_info(df_coin, principal_balance,plheader):
+    numtrades = int(len(df_coin))
+    numwin = int(sum(df_coin[plheader] > 0))
+    numloss = int(sum(df_coin[plheader] < 0))
+    winrate = np.round(100*numwin/numtrades,2)
+    
+    grosswin = sum(df_coin[df_coin[plheader] > 0][plheader])
+    grossloss = sum(df_coin[df_coin[plheader] < 0][plheader])
+    if grossloss != 0:
+        pfactor = -1*np.round(grosswin/grossloss,2)
+    else: 
+        pfactor = np.nan
+    
+    cum_PL = np.round(sum(df_coin[plheader].values),2)
+    cum_PL_perc = np.round(100*cum_PL/principal_balance,2)
+    mean_PL = np.round(sum(df_coin[plheader].values/len(df_coin)),2)
+    mean_PL_perc = np.round(100*mean_PL/principal_balance,2)
+    
+    return numtrades, numwin, numloss, winrate, pfactor, cum_PL, cum_PL_perc, mean_PL, mean_PL_perc
+
 @st.experimental_memo
 def get_hist_info(df_coin, principal_balance,plheader):
     numtrades = int(len(df_coin))
     numwin = int(sum(df_coin[plheader] > 0))
     numloss = int(sum(df_coin[plheader] < 0))
-    winrate = int(np.round(100*numwin/numtrades,2))
+    if numtrades != 0:
+        winrate = int(np.round(100*numwin/numtrades,2))
+    else: 
+        winrate = np.nan
     
     grosswin = sum(df_coin[df_coin[plheader] > 0][plheader])
     grossloss = sum(df_coin[df_coin[plheader] < 0][plheader])
-    if grossloss !=0:
+    if grossloss != 0:
         pfactor = -1*np.round(grosswin/grossloss,2)
     else: 
         pfactor = np.nan
     return numtrades, numwin, numloss, winrate, pfactor
+
 @st.experimental_memo
 def get_rolling_stats(df, lev, otimeheader, days):
     max_roll = (df[otimeheader].max() - df[otimeheader].min()).days
@@ -58,273 +162,564 @@ def get_rolling_stats(df, lev, otimeheader, days):
     else:
         rolling_perc = np.nan
     return 100*rolling_perc
+@st.experimental_memo
+def cc_coding(row):
+    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
+def ctt_coding(row):
+    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2023-01-02 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
 
 @st.experimental_memo
+def my_style(v, props=''):
+    props = 'color:red' if v < 0 else 'color:green'
+    return props
+
 def filt_df(df, cheader, symbol_selections):
-    """
-        Inputs: df (pd.DataFrame), cheader (str) and symbol_selections (list[str]).
-        
-        Returns a filtered pd.DataFrame containing only data that matches symbol_selections (list[str])
-        from df[cheader].
-    """
     
     df = df.copy()
     df = df[df[cheader].isin(symbol_selections)]
 
     return df
 
-@st.experimental_memo
-def my_style(v, props=''):
-    props = 'color:red' if v < 0 else 'color:green'
-    return props
-
-@st.experimental_memo
-def cc_coding(row):
-    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
+def tv_reformat(close50filename):
+    try:
+        data = pd.read_csv(open(close50filename,'r'), sep='[,|\t]', engine='python')
+    except: 
+        data = pd.DataFrame([])
+    
+    if data.empty:
+        return data 
+    else:
+        entry_df = data[data['Type'].str.contains("Entry")]
+        exit_df = data[data['Type'].str.contains("Exit")]
 
+        entry_df.index = range(len(entry_df))
+        exit_df.index = range(len(exit_df))
 
-@st.cache(ttl=24*3600, allow_output_mutation=True)
-def load_data(filename, otimeheader,fmat):
-    df = pd.read_csv(open(filename,'r'), sep='\t') # so as not to mutate cached value 
-    df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
-#    df.insert(1, 'Signal', ['Long']*len(df)) 
+        df = pd.DataFrame([], columns=['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %'])
+        
+        df['Signal'] = [string.split(' ')[1] for string in entry_df['Type']]
+        df['Trade'] = entry_df.index
+        df['Entry Date'] = entry_df['Date/Time']
+        df['Buy Price'] = entry_df['Price USDT']
+
+        df['Sell Price'] = exit_df['Price USDT']
+        df['Exit Date'] = exit_df['Date/Time']
+        df['P/L per token'] = df['Sell Price'] - df['Buy Price']
+        df['P/L %'] = exit_df['Profit %']
+        df['Drawdown %'] = exit_df['Drawdown %']
+        df['Close 50'] = [int(i == "Close 50% of Position") for i in exit_df['Signal']]
+        df = df.sort_values(['Entry Date','Close 50'], ascending = [False, True])
+        df.index = range(len(df))
+        
+        df.loc[df['Close 50'] == 1, 'Exit Date'] = np.copy(df.loc[df[df['Close 50'] == 1].index.values -1]['Exit Date'])
+
+        grouped_df = df.groupby('Entry Date').agg({'Signal' : 'first', 'Entry Date': 'min', 'Buy Price':'mean',
+                                 'Sell Price' : 'mean',
+                                 'Exit Date': 'max',
+                                 'P/L per token': 'mean', 
+                                 'P/L %' : 'mean'})
+
+        grouped_df.insert(0,'Trade', range(len(grouped_df)))
+        grouped_df.index = range(len(grouped_df))
+        return grouped_df
+
+def load_data(filename, otimeheader, fmat):
+    df = pd.read_csv(open(filename,'r'), sep='\t') # so as not to mutate cached value
+    close50filename = filename.split('.')[0] + '-50.' + filename.split('.')[1]
+    df2 = tv_reformat(close50filename)
+    
+    if filename == "CT-Trade-Log.csv":
+        df.columns = ['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
+        df.insert(1, 'Signal', ['Long']*len(df)) 
+    elif filename == "CC-Trade-Log.csv" or "PB-Trade-Log.csv":
+        df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
+    else: 
+        df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %']
+        
+    if filename != "CT-Toasted-Trade-Log.csv":
+        df['Signal'] = df['Signal'].str.replace(' ', '', regex=True)
+        df['Buy Price'] = df['Buy Price'].str.replace('$', '', regex=True)
+        df['Sell Price'] = df['Sell Price'].str.replace('$', '', regex=True)
+        df['Buy Price'] = df['Buy Price'].str.replace(',', '', regex=True)
+        df['Sell Price'] = df['Sell Price'].str.replace(',', '', regex=True)
+        df['P/L per token'] = df['P/L per token'].str.replace('$', '', regex=True)
+        df['P/L per token'] = df['P/L per token'].str.replace(',', '', regex=True)
+        df['P/L %'] = df['P/L %'].str.replace('%', '', regex=True)
+
+        df['Buy Price'] = pd.to_numeric(df['Buy Price'])
+        df['Sell Price'] = pd.to_numeric(df['Sell Price'])
+        df['P/L per token'] = pd.to_numeric(df['P/L per token'])
+        df['P/L %'] = pd.to_numeric(df['P/L %'])
+    
+    if df2.empty: 
+        df = df
+    else:
+        df = pd.concat([df,df2], axis=0, ignore_index=True)
+    
+    if filename == "CT-Trade-Log.csv":
+        df['Signal'] = ['Long']*len(df)
     
-    df['Buy Price'] = df['Buy Price'].str.replace('$', '', regex=True)
-    df['Sell Price'] = df['Sell Price'].str.replace('$', '', regex=True)
-    df['Buy Price'] = df['Buy Price'].str.replace(',', '', regex=True)
-    df['Sell Price'] = df['Sell Price'].str.replace(',', '', regex=True)
-    df['P/L per token'] = df['P/L per token'].str.replace('$', '', regex=True)
-    df['P/L per token'] = df['P/L per token'].str.replace(',', '', regex=True)
-    df['P/L %'] = df['P/L %'].str.replace('%', '', regex=True)
-
-    df['Buy Price'] = pd.to_numeric(df['Buy Price'])
-    df['Sell Price'] = pd.to_numeric(df['Sell Price'])
-    df['P/L per token'] = pd.to_numeric(df['P/L per token'])
-    df['P/L %'] = pd.to_numeric(df['P/L %'])
-
     dateheader = 'Date'
     theader = 'Time'
-
+    
     df[dateheader] = [tradetimes.split(" ")[0] for tradetimes in df[otimeheader].values]
     df[theader] = [tradetimes.split(" ")[1] for tradetimes in df[otimeheader].values]
 
     df[otimeheader]= [dateutil.parser.parse(date+' '+time)
-                              for date,time in zip(df[dateheader],df[theader])]
-
+                                  for date,time in zip(df[dateheader],df[theader])]
     df[otimeheader] = pd.to_datetime(df[otimeheader])
     df['Exit Date'] = pd.to_datetime(df['Exit Date'])
     df.sort_values(by=otimeheader, inplace=True)
-
+    
     df[dateheader] = [dateutil.parser.parse(date).date() for date in df[dateheader]]
     df[theader] = [dateutil.parser.parse(time).time() for time in df[theader]]
-    df['Trade'] = [i+1 for i in range(len(df))] #reindex
+    df['Trade'] = df.index + 1 #reindex
     
-    return df
-
-def runapp():
-    bot_selections = "Pure Bread"
-    otimeheader = 'Entry Date'
-    plheader = 'P/L %'
-    fmat = '%Y-%m-%d %H:%M:%S'
-    dollar_cap = 100000.00
-    fees = .075/100
-    st.header(f"{bot_selections} Performance Dashboard :bread: :moneybag:")
-    st.write("Welcome to the Trading Bot Dashboard by BreadBytes! You can use this dashboard to track " +
-                 "the performance of our trading bots.")
- #   st.sidebar.header("FAQ")
-
- #   with st.sidebar.subheader("FAQ"):
- #       st.write(Path("FAQ_README.md").read_text())
-    st.subheader("Choose your settings:")
-    no_errors = True
-    
-    data = load_data("PB-Trade-Log.csv",otimeheader,fmat)
-    df = data.copy(deep=True)
+    if filename == "CT-Trade-Log.csv":
+        df['DCA'] = np.nan
+
+        for exit in pd.unique(df['Exit Date']):
+            df_exit = df[df['Exit Date']==exit]
+            if dateutil.parser.parse(str(exit)) < dateutil.parser.parse('2023-02-07 13:00:00'):
+                for i in range(len(df_exit)):
+                    ind = df_exit.index[i]
+                    df.loc[ind,'DCA'] = i+1
+                    
+            else: 
+                for i in range(len(df_exit)):
+                    ind = df_exit.index[i]
+                    df.loc[ind,'DCA'] = i+1.1
+    return df 
+
+
+def get_sd_df(sd_df, sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance):
+    sd = 2*.00026
+    # ------ Standard Dev. Calculations. 
+    if bot_selections == "Cinnamon Toast":
+        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
+        sd_df['DCA %'] = sd_df['DCA'].map(dca_map)
+        sd_df['Calculated Return % (+)'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']*(1+df['Signal'].map(signal_map)*sd) - df['Buy Price']*(1-df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1-df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
+        sd_df['Calculated Return % (-)'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']*(1-df['Signal'].map(signal_map)*sd)-df['Buy Price']*(1+df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1+df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
+        sd_df['DCA'] = np.floor(sd_df['DCA'].values)
+
+        sd_df['Return Per Trade (+)'] = np.nan
+        sd_df['Return Per Trade (-)'] = np.nan
+        sd_df['Balance used in Trade (+)'] = np.nan
+        sd_df['Balance used in Trade (-)'] = np.nan
+        sd_df['New Balance (+)'] = np.nan
+        sd_df['New Balance (-)'] = np.nan
+
+        g1 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (+)'].reset_index(name='Return Per Trade (+)')
+        g2 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (-)'].reset_index(name='Return Per Trade (-)')
+        sd_df.loc[sd_df['DCA']==1.0,'Return Per Trade (+)'] = 1+lev*g1['Return Per Trade (+)'].values
+        sd_df.loc[sd_df['DCA']==1.0,'Return Per Trade (-)'] = 1+lev*g2['Return Per Trade (-)'].values
+
+        sd_df['Compounded Return (+)'] = sd_df['Return Per Trade (+)'].cumprod()
+        sd_df['Compounded Return (-)'] = sd_df['Return Per Trade (-)'].cumprod()
+        sd_df.loc[sd_df['DCA']==1.0,'New Balance (+)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df.loc[sd_df['DCA']==1.0,'Compounded Return (+)']]
+        sd_df.loc[sd_df['DCA']==1.0,'Balance used in Trade (+)'] = np.concatenate([[principal_balance], sd_df.loc[sd_df['DCA']==1.0,'New Balance (+)'].values[:-1]])
+
+        sd_df.loc[sd_df['DCA']==1.0,'New Balance (-)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df.loc[sd_df['DCA']==1.0,'Compounded Return (-)']]
+        sd_df.loc[sd_df['DCA']==1.0,'Balance used in Trade (-)'] = np.concatenate([[principal_balance], sd_df.loc[sd_df['DCA']==1.0,'New Balance (-)'].values[:-1]])
+    else: 
+        sd_df['Calculated Return % (+)'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']*(1+df['Signal'].map(signal_map)*sd) - df['Buy Price']*(1-df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1-df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
+        sd_df['Calculated Return % (-)'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']*(1-df['Signal'].map(signal_map)*sd)-df['Buy Price']*(1+df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1+df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
+        sd_df['Return Per Trade (+)'] = np.nan
+        sd_df['Return Per Trade (-)'] = np.nan
+
+        g1 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (+)'].reset_index(name='Return Per Trade (+)')
+        g2 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (-)'].reset_index(name='Return Per Trade (-)')
+        sd_df['Return Per Trade (+)'] = 1+lev*g1['Return Per Trade (+)'].values
+        sd_df['Return Per Trade (-)'] = 1+lev*g2['Return Per Trade (-)'].values
+
+        sd_df['Compounded Return (+)'] = sd_df['Return Per Trade (+)'].cumprod()
+        sd_df['Compounded Return (-)'] = sd_df['Return Per Trade (-)'].cumprod()
+        sd_df['New Balance (+)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df['Compounded Return (+)']]
+        sd_df['Balance used in Trade (+)'] = np.concatenate([[principal_balance], sd_df['New Balance (+)'].values[:-1]])
+
+        sd_df['New Balance (-)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df['Compounded Return (-)']]
+        sd_df['Balance used in Trade (-)'] = np.concatenate([[principal_balance], sd_df['New Balance (-)'].values[:-1]])
+
+    sd_df['Net P/L Per Trade (+)'] = (sd_df['Return Per Trade (+)']-1)*sd_df['Balance used in Trade (+)']
+    sd_df['Cumulative P/L (+)'] = sd_df['Net P/L Per Trade (+)'].cumsum()
+
+    sd_df['Net P/L Per Trade (-)'] = (sd_df['Return Per Trade (-)']-1)*sd_df['Balance used in Trade (-)']
+    sd_df['Cumulative P/L (-)'] = sd_df['Net P/L Per Trade (-)'].cumsum()
+    return sd_df
+
+def runapp() -> None:
+            bot_selections = "Pure Bread"
+            otimeheader = 'Exit Date'
+            fmat = '%Y-%m-%d %H:%M:%S'
+            fees = .075/100
     
-    dateheader = 'Date'
-    theader = 'Time'
+            st.header(f"{bot_selections} Performance Dashboard :bread: :moneybag:")
+            no_errors = True
+            st.write("Welcome to the Trading Bot Dashboard by BreadBytes! You can use this dashboard to track " +
+                 "the performance of our trading bots.")
 
-    with st.form("user input", ):
-        if no_errors:
-            with st.container():
-                col1, col2 = st.columns(2)
-                with col1:
-                    try:
-                        startdate = st.date_input("Start Date", value=pd.to_datetime(df[otimeheader]).min())
-                    except:
-                        st.error("Please select your exchange or upload a supported trade log file.")
-                        no_errors = False 
-                with col2:
-                    try:
-                        enddate = st.date_input("End Date", value=datetime.today())
-                    except:
-                        st.error("Please select your exchange or upload a supported trade log file.")
-                        no_errors = False 
-                #st.sidebar.subheader("Customize your Dashboard")
-
-                if no_errors and (enddate < startdate): 
-                    st.error("End Date must be later than Start date. Please try again.")
-                    no_errors = False 
-            with st.container(): 
-                col1,col2 = st.columns(2) 
-                with col2:
-                    lev = st.number_input('Leverage', min_value=1, value=1, max_value= 3, step=1)
-                with col1:
-                    principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
-
-        #hack way to get button centered 
-        c = st.columns(9)
-        with c[4]: 
-            submitted = st.form_submit_button("Get Cookin'!")     
+            if bot_selections == "Cinnamon Toast":
+                lev_cap = 5
+                dollar_cap = 1000000000.00
+                data = load_data("CT-Trade-Log.csv",otimeheader, fmat)
+            if bot_selections == "French Toast":
+                lev_cap = 3
+                dollar_cap = 10000000000.00
+                data = load_data("FT-Trade-Log.csv",otimeheader, fmat)
+            if bot_selections == "Short Bread":
+                lev_cap = 5
+                dollar_cap = 1000000000.00
+                data = load_data("SB-Trade-Log.csv",otimeheader, fmat)
+            if bot_selections == "Cosmic Cupcake":
+                lev_cap = 3
+                dollar_cap = 1000000000.00
+                data = load_data("CC-Trade-Log.csv",otimeheader, fmat)
+            if bot_selections == "Pure Bread":
+                lev_cap = 3
+                dollar_cap = 1000000000.00
+                data = load_data("PB-Trade-Log.csv",otimeheader, fmat)
             
-    signal_map = {'Long': 1, 'Short':-1} # 1 for long #-1 for short
-
-    df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+            df = data.copy(deep=True)
 
-    
-    if submitted and principal_balance * lev > dollar_cap:
-        lev = np.floor(dollar_cap/principal_balance)
-        st.error(f"WARNING: (Starting Balance)*(Leverage) exceeds the ${dollar_cap} limit. Using maximum available leverage of {lev}")
+            dateheader = 'Date'
+            theader = 'Time'
+            
+            st.subheader("Choose your settings:")
+            with st.form("user input", ):
+                if no_errors:
+                    with st.container():
+                        col1, col2 = st.columns(2)
+                        with col1:
+                            try:
+                                startdate = st.date_input("Start Date", value=pd.to_datetime(df[otimeheader]).min())
+                            except:
+                                st.error("Please select your exchange or upload a supported trade log file.")
+                                no_errors = False 
+                        with col2:
+                            try:
+                                enddate = st.date_input("End Date", value=datetime.today())
+                            except:
+                                st.error("Please select your exchange or upload a supported trade log file.")
+                                no_errors = False 
+                        #st.sidebar.subheader("Customize your Dashboard")
+
+                        if no_errors and (enddate < startdate): 
+                            st.error("End Date must be later than Start date. Please try again.")
+                            no_errors = False 
+                    with st.container(): 
+                        col1,col2 = st.columns(2) 
+                        with col2:
+                            lev = st.number_input('Leverage', min_value=1, value=1, max_value= lev_cap, step=1)
+                        with col1:
+                            principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
                 
-    if submitted and no_errors:
-        df = df[(df[dateheader] >= startdate) & (df[dateheader] <= enddate)]
-
-        if len(df) == 0:
-                st.error("There are no available trades matching your selections. Please try again!")
-                no_errors = False
-        if no_errors:
-            df['Return Per Trade'] = 1+lev*df['Calculated Return %'].values
-
-            df['Compounded Return'] = df['Return Per Trade'].cumprod()
-            df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
-            df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
-            df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
-            df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
-            cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
-
-            effective_return = 100*((cum_pl - principal_balance)/principal_balance)
-
-            st.header(f"{bot_selections} Results")
-            if len(bot_selections) > 1:
-                st.metric(
-                    "Total Account Balance",
-                    f"${cum_pl:.2f}",
-                    f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
-                )
-
-            st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
-
-            df['Per Trade Return Rate'] = df['Return Per Trade']-1
-
-            totals = pd.DataFrame([], columns = ['# of Trades', 'Wins', 'Losses', 'Win Rate', 'Profit Factor'])
-            data = get_hist_info(df.drop('Drawdown %', axis=1).dropna(), principal_balance,'Per Trade Return Rate')
-            totals.loc[len(totals)] = list(i for i in data)
-
-            totals['Cum. P/L'] = cum_pl-principal_balance
-            totals['Cum. P/L (%)'] = 100*(cum_pl-principal_balance)/principal_balance
-            #results_df['Avg. P/L'] = (cum_pl-principal_balance)/results_df['# of Trades'].values[0]
-            #results_df['Avg. P/L (%)'] = 100*results_df['Avg. P/L'].values[0]/principal_balance
-
-            if df.empty:
-                st.error("Oops! None of the data provided matches your selection(s). Please try again.")
-            else:
-                #st.dataframe(totals.style.format({'# of Trades': '{:.0f}','Wins': '{:.0f}','Losses': '{:.0f}','Win Rate': '{:.2f}%','Profit Factor' : '{:.2f}', 'Avg. P/L (%)': '{:.2f}%', 'Cum. P/L (%)': '{:.2f}%', 'Cum. P/L': '{:.2f}', 'Avg. P/L': '{:.2f}'})
-            #.text_gradient(subset=['Win Rate'],cmap="RdYlGn", vmin = 0, vmax = 100)\
-            #.text_gradient(subset=['Profit Factor'],cmap="RdYlGn", vmin = 0, vmax = 2), use_container_width=True)
-                for row in totals.itertuples():
-                    col1, col2, col3, col4 = st.columns(4)
-                    c1, c2, c3, c4 = st.columns(4)
-                    with col1:
-                        st.metric(
-                            "Total Trades",
-                            f"{row._1:.0f}",
-                        )
-                    with c1:
-                        st.metric(
-                            "Profit Factor",
-                            f"{row._5:.2f}",
-                        )
-                    with col2: 
-                        st.metric(
-                            "Wins",
-                            f"{row.Wins:.0f}",
-                        )
-                    with c2:
-                        st.metric(
-                            "Cumulative P/L",
-                            f"${row._6:.2f}",
-                            f"{row._7:.2f} %",
-                        )
-                    with col3: 
-                        st.metric(
-                            "Losses",
-                            f"{row.Losses:.0f}",
-                        )
-                    with c3:
-                        st.metric(
-                        "Rolling 7 Days",
-                            "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                            f"{get_rolling_stats(df,lev, otimeheader, 7):.2f}%",
-                        )
-                        st.metric(
-                        "Rolling 30 Days",
-                            "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                            f"{get_rolling_stats(df,lev, otimeheader, 30):.2f}%",
-                        )
-
-                    with col4: 
-                        st.metric(
-                            "Win Rate",
-                            f"{row._4:.1f}%",
-                        )
-                    with c4:
-                        st.metric(
-                        "Rolling 90 Days",
-                            "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                            f"{get_rolling_stats(df,lev, otimeheader, 90):.2f}%",
-                        )
-                        st.metric(
-                        "Rolling 180 Days",
-                            "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                            f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
-                        )
+                if bot_selections == "Cinnamon Toast":
+                    st.write("Choose your DCA setup (for trades before 02/07/2023)")
+                    with st.container():
+                        col1, col2, col3, col4 = st.columns(4)
+                        with col1: 
+                            dca1 = st.number_input('DCA 1 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                        with col2: 
+                            dca2 = st.number_input('DCA 2 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                        with col3: 
+                            dca3 = st.number_input('DCA 3 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                        with col4: 
+                            dca4 = st.number_input('DCA 4 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                    st.write("Choose your DCA setup (for trades on or after 02/07/2023)")
+                    with st.container():
+                        col1, col2 = st.columns(2)
+                        with col1: 
+                            dca5 = st.number_input('DCA 1 Allocation', min_value=0, value=50, max_value= 100, step=1)
+                        with col2: 
+                            dca6 = st.number_input('DCA 2 Allocation', min_value=0, value=50, max_value= 100, step=1)
+
+                #hack way to get button centered 
+                c = st.columns(9)
+                with c[4]: 
+                    submitted = st.form_submit_button("Get Cookin'!")           
+
+            if submitted and principal_balance * lev > dollar_cap:
+                lev = np.floor(dollar_cap/principal_balance)
+                st.error(f"WARNING: (Starting Balance)*(Leverage) exceeds the ${dollar_cap} limit. Using maximum available leverage of {lev}")
+
+            if submitted and no_errors:
+                df = df[(df[dateheader] >= startdate) & (df[dateheader] <= enddate)]
+                signal_map = {'Long': 1, 'Short':-1}  
+            
+                
+                if len(df) == 0:
+                        st.error("There are no available trades matching your selections. Please try again!")
+                        no_errors = False
                         
-    if submitted: 
-        grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                 'Sell Price' : 'max',
-                                 'Net P/L Per Trade': 'mean', 
-                                 'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2)})
-        grouped_df.index = range(1, len(grouped_df)+1)
-        grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
-                                   'Net P/L Per Trade':'Net P/L', 
-                                   'Calculated Return %':'P/L %'}, inplace=True)        
-    else:
-        grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                 'Sell Price' : 'max',
-                                 'P/L per token' : 'mean', 
-                                 'Calculated Return %' : lambda x: np.round(100*x.sum(),2)})
-        grouped_df.index = range(1, len(grouped_df)+1)
-        grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
-                                   'P/L per token':'Net P/L', 
-                                   'Calculated Return %':'P/L %'}, inplace=True)     
-    
-    st.subheader("Trade Logs")
-    grouped_df['Entry Date'] = pd.to_datetime(grouped_df['Entry Date'])
-    grouped_df['Exit Date'] = pd.to_datetime(grouped_df['Exit Date'])
-    st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.4f}', 'Sell Price': '${:.4f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
-    .apply(cc_coding, axis=1)\
-    .applymap(my_style,subset=['Net P/L'])\
-    .applymap(my_style,subset=['P/L %'])\
-                 ,use_container_width=True)
-    new_title = '<div style="text-align: right;"><span style="background-color:lightgrey;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span> Backtest Data</div>'
-    st.markdown(new_title, unsafe_allow_html=True)
-    
+                if no_errors:
+                    if bot_selections == "Cinnamon Toast":
+                        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
+                        df['DCA %'] = df['DCA'].map(dca_map)
+                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+                        df['DCA'] = np.floor(df['DCA'].values)
+                        
+                        df['Return Per Trade'] = np.nan
+                        df['Balance used in Trade'] = np.nan
+                        df['New Balance'] = np.nan
+
+                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
+                        df.loc[df['DCA']==1.0,'Return Per Trade'] = 1+lev*g['Return Per Trade'].values
+                        
+                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
+                        df.loc[df['DCA']==1.0,'New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df.loc[df['DCA']==1.0,'Compounded Return']]
+                        df.loc[df['DCA']==1.0,'Balance used in Trade'] = np.concatenate([[principal_balance], df.loc[df['DCA']==1.0,'New Balance'].values[:-1]])
+                    else: 
+                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+                        df['Return Per Trade'] = np.nan
+                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
+                        df['Return Per Trade'] = 1+lev*g['Return Per Trade'].values
+                        
+                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
+                        df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
+                        df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
+                    df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
+                    df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
+                    
+                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake" or bot_selections == "Pure Bread":
+                        cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
+                        #cum_sdp = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
+                        #cum_sdm = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
+                    else: 
+                        cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
+                        #cum_sdp = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
+                        #cum_sdm = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
+                    #sd = 2*.00026
+                    #sd_df = get_sd_df(get_sd_df(df.copy(), sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance)
+                    
+                    effective_return = 100*((cum_pl - principal_balance)/principal_balance)
+
+                    st.header(f"{bot_selections} Results")
+                    with st.container(): 
+                        
+                        if len(bot_selections) > 1:
+                            col1, col2 = st.columns(2)
+                            with col1: 
+                                st.metric(
+                                    "Total Account Balance",
+                                    f"${cum_pl:.2f}",
+                                    f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
+                                )
+
+#                             with col2:
+#                                 st.write("95% of trades should fall within this 2 std. dev. range.")
+#                                 st.metric(
+#                                     "High Range (+ 2 std. dev.)",
+#                                     f"", #${cum_sdp:.2f}
+#                                     f"{100*(cum_sdp-principal_balance)/(principal_balance):.2f} %",
+#                                 )
+#                                 st.metric(
+#                                     "Low Range (- 2 std. dev.)",
+#                                     f"" ,#${cum_sdm:.2f}"
+#                                     f"{100*(cum_sdm-principal_balance)/(principal_balance):.2f} %",
+#                                 )
+                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake" or  bot_selections == "Pure Bread":
+                        #st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
+                        dfdata = df.drop('Drawdown %', axis=1).dropna()
+                        #sd_df = sd_df.drop('Drawdown %', axis=1).dropna()
+                    else: 
+                        #st.line_chart(data=df.dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
+                        dfdata = df.dropna()
+                        #sd_df = sd_df.dropna()
+                        
+                    # Create figure
+                    fig = go.Figure()
+
+                    pyLogo = Image.open("logo.png")
+                    
+#                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (+)'],line_shape='spline',
+#                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'),  showlegend = False)
+#                                   )
+                    
+#                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (-)'],
+#                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'), line_shape='spline',
+#                              fill='tonexty', 
+#                              fillcolor = 'rgba(31, 119, 200,.2)', name = '+/- Standard Deviation')
+#                                  )
+
+                    # Add trace
+                    fig.add_trace(
+                        go.Scatter(x=dfdata['Exit Date'], y=np.round(dfdata['Cumulative P/L'].values,2), line_shape='spline', 
+                                   line = {'smoothing': 1.0, 'color' : 'rgba(31, 119, 200,.8)'},
+                                   name='Cumulative P/L')
+                    )
+                    buyhold  = (principal_balance/dfdata['Buy Price'][dfdata.index[0]])*(dfdata['Buy Price']-dfdata['Buy Price'][dfdata.index[0]])
+                    fig.add_trace(go.Scatter(x=dfdata['Exit Date'], y=np.round(buyhold.values,2), line_shape='spline', 
+                                             line = {'smoothing': 1.0, 'color' :'red'}, name = 'Buy & Hold Return')
+                    )
+
+                    fig.add_layout_image(
+                            dict(
+                                source=pyLogo,
+                                xref="paper",
+                                yref="paper",
+                                x = 0.05, #dfdata['Exit Date'].astype('int64').min() // 10**9, 
+                                y = .85, #dfdata['Cumulative P/L'].max(),
+                                sizex= .9, #(dfdata['Exit Date'].astype('int64').max() - dfdata['Exit Date'].astype('int64').min()) // 10**9,
+                                sizey= .9, #(dfdata['Cumulative P/L'].max() - dfdata['Cumulative P/L'].min()),
+                                sizing="contain",
+                                opacity=0.2, 
+                            layer = "below")
+                    )
+                    
+                    #style layout 
+                    fig.update_layout(
+                        height = 600,
+                        xaxis=dict(
+                            title="Exit Date", 
+                            tickmode='array',
+                        ),
+                        yaxis=dict(
+                            title="Cumulative P/L"
+                        ) ) 
+                    
+                    st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
+                    st.write()
+                    df['Per Trade Return Rate'] = df['Return Per Trade']-1
+
+                    totals = pd.DataFrame([], columns = ['# of Trades', 'Wins', 'Losses', 'Win Rate', 'Profit Factor'])
+                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake" or bot_selections == "Pure Bread":
+                        data = get_hist_info(df.drop('Drawdown %', axis=1).dropna(), principal_balance,'Per Trade Return Rate')
+                    else: 
+                        data = get_hist_info(df.dropna(), principal_balance,'Per Trade Return Rate')
+                    totals.loc[len(totals)] = list(i for i in data)
+
+                    totals['Cum. P/L'] = cum_pl-principal_balance
+                    totals['Cum. P/L (%)'] = 100*(cum_pl-principal_balance)/principal_balance
+                    
+                    if df.empty:
+                        st.error("Oops! None of the data provided matches your selection(s). Please try again.")
+                    else:
+                        with st.container():
+                            for row in totals.itertuples():
+                                col1, col2, col3, col4= st.columns(4)
+                                c1, c2, c3, c4 = st.columns(4)
+                                with col1:
+                                    st.metric(
+                                        "Total Trades",
+                                        f"{row._1:.0f}",
+                                    )
+                                with c1:
+                                    st.metric(
+                                        "Profit Factor",
+                                        f"{row._5:.2f}",
+                                    )
+                                with col2: 
+                                    st.metric(
+                                        "Wins",
+                                        f"{row.Wins:.0f}",
+                                    )
+                                with c2:
+                                    st.metric(
+                                        "Cumulative P/L",
+                                        f"${row._6:.2f}",
+                                        f"{row._7:.2f} %",
+                                    )
+                                with col3: 
+                                    st.metric(
+                                        "Losses",
+                                        f"{row.Losses:.0f}",
+                                    )
+                                with c3:
+                                    st.metric(
+                                    "Rolling 7 Days",
+                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
+                                        f"{get_rolling_stats(df,lev, otimeheader, 7):.2f}%",
+                                    )
+                                    st.metric(
+                                    "Rolling 30 Days",
+                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
+                                        f"{get_rolling_stats(df,lev, otimeheader, 30):.2f}%",
+                                    )
+
+                                with col4: 
+                                    st.metric(
+                                        "Win Rate",
+                                        f"{row._4:.1f}%",
+                                    )
+                                with c4:
+                                    st.metric(
+                                    "Rolling 90 Days",
+                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
+                                        f"{get_rolling_stats(df,lev, otimeheader, 90):.2f}%",
+                                    )
+                                    st.metric(
+                                    "Rolling 180 Days",
+                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
+                                        f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
+                                    )
+            
+            if bot_selections == "Cinnamon Toast":
+                if submitted:                     
+                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
+                                             'Sell Price' : 'max',
+                                             'Net P/L Per Trade': 'mean', 
+                                             'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2), 
+                                             'DCA': lambda x: int(np.floor(x.max()))})
+                    grouped_df.index = range(1, len(grouped_df)+1)
+                    grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
+                                               'Net P/L Per Trade':'Net P/L', 
+                                               'Calculated Return %':'P/L %'}, inplace=True)
+                else:
+                    dca_map = {1: 25/100, 2: 25/100, 3: 25/100, 4: 25/100, 1.1: 50/100, 2.1: 50/100}
+                    df['DCA %'] = df['DCA'].map(dca_map)
+                    df['Calculated Return %'] = (df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade
+                        
+                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
+                                             'Sell Price' : 'max',
+                                             'P/L per token': 'mean', 
+                                             'Calculated Return %' : lambda x: np.round(100*x.sum(),2), 
+                                             'DCA': lambda x: int(np.floor(x.max()))})
+                    grouped_df.index = range(1, len(grouped_df)+1)
+                    grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
+                                               'Calculated Return %':'P/L %',
+                                               'P/L per token':'Net P/L'}, inplace=True)
+                
+            else: 
+                if submitted: 
+                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
+                                             'Sell Price' : 'max',
+                                             'Net P/L Per Trade': 'mean', 
+                                             'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2)})
+                    grouped_df.index = range(1, len(grouped_df)+1)
+                    grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
+                                               'Net P/L Per Trade':'Net P/L', 
+                                               'Calculated Return %':'P/L %'}, inplace=True)        
+                else: 
+                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
+                                             'Sell Price' : 'max',
+                                             'P/L per token': 'mean', 
+                                             'P/L %':'mean'})
+                    grouped_df.index = range(1, len(grouped_df)+1)
+                    grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
+                                               'P/L per token':'Net P/L'}, inplace=True)
+            st.subheader("Trade Logs")
+            grouped_df['Entry Date'] = pd.to_datetime(grouped_df['Entry Date'])
+            grouped_df['Exit Date'] = pd.to_datetime(grouped_df['Exit Date'])
+            if bot_selections == "Cosmic Cupcake" or bot_selections == "CT Toasted":
+                coding = cc_coding if bot_selections == "Cosmic Cupcake" else ctt_coding
+                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
+                .apply(coding, axis=1)\
+                .applymap(my_style,subset=['Net P/L'])\
+                .applymap(my_style,subset=['P/L %']), use_container_width=True)
+                # new_title = '<div style="text-align: right;"><span style="background-color:lightgrey;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span> Not Live Traded</div>'
+                # st.markdown(new_title, unsafe_allow_html=True)
+            else: 
+                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
+                .applymap(my_style,subset=['Net P/L'])\
+                .applymap(my_style,subset=['P/L %']), use_container_width=True)
+                
+#             st.subheader("Checking Status")
+#             if submitted:
+#                 st.dataframe(sd_df)
+                                
 if __name__ == "__main__":
     st.set_page_config(
         "Trading Bot Dashboard",
         layout="wide",
     )
     runapp()
-# -
-
-