update sb and ft for drawdown %

app.py

@@ -583,8 +583,12 @@ def runapp() -> None:
                         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
 
+                    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
+                    else: 
+                        cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
+                        
                     effective_return = 100*((cum_pl - principal_balance)/principal_balance)
 
                     st.header(f"{bot_selections} Results")
@@ -594,17 +598,23 @@ def runapp() -> None:
                             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)
+                    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)
+                    else: 
+                        st.line_chart(data=df.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')
+                    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: