Upload app.py

app.py

@@ -1305,7 +1305,7 @@ def display_summary_analytics(summary_df):
                 st.metric("Strategy Score", f"{display_score:.2f}%")
                 st.metric("Avg Profit per Trade (Active Tickers)", f"{avg_trade_profit:.2%}")
                 net_return_pct = avg_trade_profit * total_trades * 100
-                st.metric("Net % Return", f"{net_return_pct:.1f}%", help="The total 'Pile of Money'. Sum of all trade percentages.")
+                st.metric("Moneypile Score", f"{net_return_pct:.1f}", help="The total 'Pile of Money'. Sum of all trade percentages (Winners - Losers).")
 
                 st.text(f"Profitable Tickers: {good_tickers}")
                 st.text(f"Losing Tickers: {bad_tickers}")
@@ -3167,14 +3167,15 @@ def run_confidence_optimisation(optimise_for, find_mode, master_df, main_content
                     st.session_state.worst_confidence_setups_list = []
             
             # --- Display the table ---
-            display_df_conf = results_df.head(60)
+            # [CHANGE] Rename the column for display purposes
+            display_df_conf = results_df.head(60).rename(columns={'Net % Return': 'Moneypile Score'})
             
-            # [UPDATED] Added "Net % Return" to the formatters
+            # [CHANGE] Update formatter key to 'Moneypile Score' and remove '%' from format string
             conf_formatters = { 
                 "Avg Profit/Trade": "{:.2%}", 
                 "Ticker G/B Ratio": "{:.2f}", 
                 "Trade G/B Ratio": "{:.2f}", 
-                "Net % Return": "{:.1f}%", 
+                "Moneypile Score": "{:.1f}", 
                 "Avg Entry Conf.": "{:.1f}%", 
                 "Good Score": "{:.4f}", 
                 "Bad Score": "{:.4f}", 
@@ -3184,8 +3185,7 @@ def run_confidence_optimisation(optimise_for, find_mode, master_df, main_content
             if 'MA Slope' in display_df_conf.columns: conf_formatters['MA Slope'] = '{}'
             if 'Markov' in display_df_conf.columns: conf_formatters['Markov'] = '{}'
             
-            valid_conf_formatters = {k: (lambda val, fmt=v: fmt.format(val) if pd.notna(val) else '-')
-                                     for k, v in conf_formatters.items() if k in display_df_conf.columns}
+            valid_conf_formatters = {k: (lambda val, fmt=v: fmt.format(val) if pd.notna(val) else '-') for k, v in conf_formatters.items() if k in display_df_conf.columns}
             st.dataframe(display_df_conf.style.format(valid_conf_formatters, na_rep='-'))
 
         else:
@@ -4767,8 +4767,21 @@ def main():
         # 11. Display Confidence Optimisation Results
         elif st.session_state.get('confidence_results_df') is not None and not st.session_state.confidence_results_df.empty:
             st.subheader("📊 Confidence Setup Optimisation Results")
-            display_df_conf = st.session_state.confidence_results_df.head(60)
-            conf_formatters = { "Avg Profit/Trade": "{:.2%}", "Ticker G/B Ratio": "{:.2f}", "Trade G/B Ratio": "{:.2f}", "Avg Entry Conf.": "{:.1f}%", "Good Score": "{:.4f}", "Bad Score": "{:.4f}", "Norm. Score %": "{:.2f}%" }
+            
+            # [CHANGE] Rename the column for display
+            display_df_conf = st.session_state.confidence_results_df.head(60).rename(columns={'Net % Return': 'Moneypile Score'})
+            
+            # [CHANGE] Update formatters to match
+            conf_formatters = { 
+                "Avg Profit/Trade": "{:.2%}", 
+                "Ticker G/B Ratio": "{:.2f}", 
+                "Trade G/B Ratio": "{:.2f}", 
+                "Moneypile Score": "{:.1f}", 
+                "Avg Entry Conf.": "{:.1f}%", 
+                "Good Score": "{:.4f}", 
+                "Bad Score": "{:.4f}", 
+                "Norm. Score %": "{:.2f}%" 
+            }
             if 'MACD' in display_df_conf.columns: conf_formatters['MACD'] = '{}'
             if 'MA Slope' in display_df_conf.columns: conf_formatters['MA Slope'] = '{}'
             if 'Markov' in display_df_conf.columns: conf_formatters['Markov'] = '{}'