Update app.py

app.py

@@ -487,18 +487,18 @@ def runapp() -> None:
                 #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)
-                if submitted: 
-                    st.header(f"{bot_selections} Results")
-                    with st.container(): 
+                 
+                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"${cum_pl:.2f}",
-                                    f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
-                                )
+                    if len(bot_selections) > 1:
+                        col1, col2 = st.columns(2)
+                        with col1: 
+                            st.metric(
+                                "Total Account Balance",
+                                f"${cum_pl:.2f}",
+                                f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
+                            )
                                 
                 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)
@@ -565,81 +565,80 @@ def runapp() -> None:
                 ) 
 
                 st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
-                if submitted:
-                    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}%",
-                                    )
+                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: