app updates and fees update

app.py

@@ -281,7 +281,7 @@ def get_sd_df(sd_df, sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fee
     return sd_df
 
 def runapp() -> None:
-            bot_selections = "Pumpernickel"
+            bot_selections = "Pure Bread"
             otimeheader = 'Exit Date'
             fmat = '%Y-%m-%d %H:%M:%S'
             fees = .075/100
@@ -295,6 +295,10 @@ def runapp() -> None:
                 lev_cap = 5
                 dollar_cap = 1000000000.00
                 data = load_data('history.csv', 'Pumpernickel Test', 'Bybit Futures', otimeheader, fmat)
+            if bot_selections == "Pure Bread":
+                lev_cap = 5
+                dollar_cap = 1000000000.00
+                data = load_data('history.csv', 'Pure Bread Test', 'Bybit Futures', otimeheader, fmat)
             
             df = data.copy(deep=True)
 
@@ -349,7 +353,7 @@ def runapp() -> None:
                 if bot_selections == "Pumpernickel":
                     dca_map = {1: 1/3, 2: 1/3, 3: 1/3}
                     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['Calculated Return %'] = (df['DCA %'])*(df['Signal'].map(signal_map)*(df['Sell Price']-df['Buy Price'])/df['Buy Price'])-2*fees #accounts for fees on open and close of trade 
                     df['DCA'] = np.floor(df['DCA'].values)
 
                     df['Return Per Trade'] = np.nan
@@ -363,7 +367,7 @@ def runapp() -> None:
                     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['Calculated Return %'] = (df['Signal'].map(signal_map)*(df['Sell Price']-df['Buy Price'])/df['Buy Price'])-2*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
@@ -542,15 +546,21 @@ def runapp() -> None:
                                          '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',
+                grouped_df.rename(columns={'Buy Price':'Buy Price',
                                            'Net P/L Per Trade':'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':'${:.2f}', 'P/L %':'{:.2f}%'})\
-            .applymap(my_style,subset=['Net P/L'])\
-            .applymap(my_style,subset=['P/L %']), use_container_width=True)
+            if bot_selections == "Pumpernickel":  
+                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.4f}', 'Sell Price': '${:.4f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
+                .applymap(my_style,subset=['Net P/L'])\
+                .applymap(my_style,subset=['P/L %']), use_container_width=True)
+            else: 
+                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','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: