app.py

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import csv 
import pandas as pd
from datetime import datetime, timedelta
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
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))
    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:
        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
    
    if max_roll >= days:
        rollend = df[otimeheader].max()-timedelta(days=days)
        rolling_df = df[df[otimeheader] >= rollend]

        if len(rolling_df) > 0:
            rolling_perc = rolling_df['Return Per Trade'].dropna().cumprod().values[-1]-1
        else: 
            rolling_perc = np.nan
    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):
    
    df = df.copy()
    df = df[df[cheader].isin(symbol_selections)]

    return df

def tv_reformat(close50filename):
    try:
        data = pd.read_csv(open('CT-Trade-Log-50.csv','r'), sep='[,|\t]', engine='python')
    except: 
        data = pd.DataFrame([])
    
    if data.empty:
        return data 
    else:
        entry_df = data[data['Type'] == "Entry Long"]
        exit_df = data[data['Type']=="Exit Long"]

        entry_df.index = range(len(entry_df))
        exit_df.index = range(len(exit_df))

        df = pd.DataFrame([], columns=['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %'])

        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.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({'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":
        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)
    
    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])]
    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'] = df.index + 1 #reindex
    
    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 = "Cinnamon Toast"
            otimeheader = 'Exit Date'
            fmat = '%Y-%m-%d %H:%M:%S'
            fees = .075/100
    
            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.")

            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 = 100000.00
                data = load_data("SB-Trade-Log.csv",otimeheader, fmat)
            if bot_selections == "Cosmic Cupcake":
                lev_cap = 3
                dollar_cap = 100000.00
                data = load_data("CC-Trade-Log.csv",otimeheader, fmat)
            if bot_selections == "CT Toasted":
                lev_cap = 5
                dollar_cap = 100000.00
                data = load_data("CT-Toasted-Trade-Log.csv",otimeheader, fmat)
            
            df = data.copy(deep=True)

            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 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 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":
                        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":
                        #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":
                        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()
# -