Upload app.py

app.py

@@ -3169,7 +3169,105 @@ def update_state():
             if main_key in st.session_state:
                 st.session_state[main_key] = st.session_state[widget_key]
 
+def convert_results_to_csv(summary_df, params):
+    """
+    Combines the Performance Summary and the Configuration Settings into a single CSV string.
+    """
+    if summary_df is None or summary_df.empty:
+        return ""
+
+    # 1. Get Performance Data (First Row)
+    perf_data = summary_df.iloc[0].to_dict()
+    
+    # 2. Get Settings Data (From Session State/Params)
+    settings_data = {
+        "--- SETTINGS ---": "", # Separator
+        "Start Date": params.get('start_date'),
+        "End Date": params.get('end_date'),
+        "Primary Driver": params.get('primary_driver'),
+        "Confidence Threshold": params.get('confidence_threshold'),
+        "RSI Weight": params.get('rsi_w') if params.get('use_rsi') else "Off",
+        "Volatility Weight": params.get('vol_w') if params.get('use_volatility') else "Off",
+        "Trend Weight": params.get('trend_w') if params.get('use_trend') else "Off",
+        "Volume Weight": params.get('vol_w_val') if params.get('use_volume') else "Off",
+        "MACD Weight": params.get('macd_w') if params.get('use_macd') else "Off",
+        "MA Slope Weight": params.get('ma_slope_w') if params.get('use_ma_slope') else "Off",
+        "Markov Weight": params.get('markov_w') if params.get('use_markov') else "Off",
+        "MFI Weight": params.get('mfi_w') if params.get('use_mfi') else "Off",
+        "SuperTrend Weight": params.get('supertrend_w') if params.get('use_supertrend') else "Off",
+        "ADX Filter": f"On (< {params.get('adx_threshold')})" if params.get('use_adx_filter') else "Off",
+        "BB Period": params.get('bband_period'),
+        "BB Std Dev": params.get('bband_std_dev'),
+        "MA Period": params.get('large_ma_period'),
+        "Long Entry Thresh %": params.get('long_entry_threshold_pct'),
+        "Short Entry Thresh %": params.get('short_entry_threshold_pct'),
+        "Exit Logic": params.get('exit_logic_type'),
+        "Smart TSL %": params.get('smart_trailing_stop_pct')
+    }
+
+    # 3. Combine into one dictionary
+    combined_data = {**perf_data, **settings_data}
+    
+    # 4. Create DataFrame and convert to CSV
+    export_df = pd.DataFrame([combined_data])
+    return export_df.to_csv(index=False).encode('utf-8')
+
 def main():
+    if 'run_advanced_advisor' not in st.session_state: st.session_state.run_advanced_advisor = False
+    if 'run_user_advisor_setup' not in st.session_state: st.session_state.run_user_advisor_setup = False
+    
+    # 2. Define ALL Defaults (Prevents "AttributeError" crashes)
+    defaults = {
+        'start_date': date(2024, 1, 1), 'end_date': date.today(),
+        'bband_period': 20, 'bband_std_dev': 2.0,
+        'long_entry_threshold_pct': 0.0, 'short_entry_threshold_pct': 0.0,
+        'long_exit_ma_threshold_pct': 0.0, 'short_exit_ma_threshold_pct': 0.0,
+        'confidence_threshold': 50, 'max_long_duration': 60, 'max_short_duration': 60,
+        'long_trailing_stop_loss_pct': 0.0, 'short_trailing_stop_loss_pct': 0.0,
+        'primary_driver': 'Bollinger Bands',
+        'use_rsi': True, 'rsi_w': 0.5,
+        'use_volatility': True, 'vol_w': 0.5,
+        'use_trend': True, 'trend_w': 0.5,
+        'use_volume': True, 'vol_w_val': 0.5,
+        'use_macd': False, 'macd_w': 0.5,
+        'use_ma_slope': False, 'ma_slope_w': 0.5,
+        'use_markov': False, 'markov_w': 0.5,
+        # New Indicators
+        'use_mfi': False, 'mfi_w': 0.5,
+        'use_supertrend': False, 'supertrend_w': 0.5,
+        # Filters & Exits
+        'use_adx_filter': False, 'adx_threshold': 25, 'rsi_logic': 'Level',
+        'exit_logic_type': 'Standard (Price-Based)',
+        'smart_trailing_stop_pct': 0.05,
+        'smart_exit_atr_period': 14, 'smart_exit_atr_multiplier': 3.0,
+        'intelligent_tsl_pct': 1.0,
+        'use_ma_floor_filter': False,
+        'long_delay_days': 0, 'short_delay_days': 0,
+        'long_score_95_percentile': None, 'short_score_95_percentile': None,
+        'veto_setup_list': None,
+        'advisor_df': None, 'confidence_results_df': None, 
+        'single_ticker_results': None, 'summary_df': None, 
+        'open_trades_df': None, 'load_message': None,
+        'outlier_report': None,
+        'best_markov_setup': None,
+        # Optimization
+        'ma_period': 50, 'bb_period': 20, 'bb_std': 2.0, 
+        'confidence_slider': 50, 'long_entry': 0.0, 'long_exit': 0.0,
+        'long_sl': 0.0, 'long_delay': 0, 'short_entry': 0.0, 
+        'short_exit': 0.0, 'short_sl': 0.0, 'short_delay': 0, 'max_duration': 60,
+        'volume_w': 0.5 # fallback for vol_w_val naming differences
+    }
+    
+    # 3. Apply Defaults if Missing
+    for key, val in defaults.items():
+        if key not in st.session_state: st.session_state[key] = val
+        
+    # 4. Helper to update state from widgets
+    def update_state():
+        for key in defaults.keys():
+            widget_key = f"widget_{key}"
+            if widget_key in st.session_state: st.session_state[key] = st.session_state[widget_key]
+    
     st.set_page_config(page_title="Dave's Quant System", page_icon="🔴", layout="wide")
 
     # --- [FIX 1: Robust Initialization] ---
@@ -3305,7 +3403,7 @@ def main():
                 st.session_state.ticker_select = st.session_state.ticker_list[0]
             st.session_state.load_message = load_message
             st.session_state.outlier_report = outlier_report
-
+            
     master_df = st.session_state.master_df
     ticker_list = st.session_state.ticker_list
 
@@ -3354,20 +3452,7 @@ def main():
         div[data-testid="stSidebar"] button[kind="primary"] { width: 100%; }
         div[data-testid="stSidebar"] div[data-testid="stButton"] button { width: 100%; }
         </style>""", unsafe_allow_html=True)
-
-    # --- DISPLAY LOAD MESSAGES (Moved here to check Production Mode) ---
-    if 'load_message' in st.session_state:
-        with main_content_placeholder.container():
-            # Always show the main load success/fail message
-            st.info(st.session_state.load_message)
-            
-            # Only show Data Cleaning details if NOT in Production Mode
-            if st.session_state.outlier_report and not production_mode:
-                st.info(f"Data Cleaning: Found and removed price spikes >100% in {len(st.session_state.outlier_report)} tickers.")
-        
-        del st.session_state.load_message
-        if 'outlier_report' in st.session_state: del st.session_state.outlier_report
-
+   
 
     # --- SIDEBAR SECTION 1: Select Test Mode & Dates (Common to both) ---
     st.sidebar.header("1. Select Test Mode & Dates")
@@ -4334,112 +4419,170 @@ def main():
             else: st.info("No chart data available.")
 
         # 13. Display Full Analysis Summary
-        elif st.session_state.get('summary_df') is not None and not st.session_state.summary_df.empty:
-            # 1. Top Cards
-            display_summary_analytics(st.session_state.summary_df)
-            
-            # 2. Histogram 1: Profit Distribution
-            st.markdown("---")
-            try:
-                if 'generate_profit_distribution_chart' in globals():
-                    dist_fig = generate_profit_distribution_chart(st.session_state.summary_df)
-                    if dist_fig: st.plotly_chart(dist_fig, use_container_width=True)
-            except Exception: pass
+    if st.session_state.get('summary_df') is not None and not st.session_state.summary_df.empty:
+        # 1. Top Cards
+        display_summary_analytics(st.session_state.summary_df)
+        
+        # 2. Histogram 1: Profit Distribution
+        st.markdown("---")
+        try:
+            if 'generate_profit_distribution_chart' in globals():
+                dist_fig = generate_profit_distribution_chart(st.session_state.summary_df)
+                if dist_fig: st.plotly_chart(dist_fig, use_container_width=True)
+        except Exception: pass
 
-            # 3. Histogram 2: Trades Over Time (NEW)
-            try:
-                if 'generate_trades_timeline_histogram' in globals() and st.session_state.get('open_trades_df') is not None:
-                    timeline_fig = generate_trades_timeline_histogram(
-                        st.session_state.open_trades_df, 
-                        st.session_state.start_date, 
-                        st.session_state.end_date
-                    )
-                    if timeline_fig: st.plotly_chart(timeline_fig, use_container_width=True)
-            except Exception as e: st.error(f"Chart Error: {e}")
-            st.markdown("---")
+        # 3. Histogram 2: Trades Over Time (NEW)
+        try:
+            if 'generate_trades_timeline_histogram' in globals() and st.session_state.get('open_trades_df') is not None:
+                timeline_fig = generate_trades_timeline_histogram(
+                    st.session_state.open_trades_df, 
+                    st.session_state.start_date, 
+                    st.session_state.end_date
+                )
+                if timeline_fig: st.plotly_chart(timeline_fig, use_container_width=True)
+        except Exception as e: st.error(f"Chart Error: {e}")
+        st.markdown("---")
+
+        # 4. Results Per Ticker Table
+        st.subheader("Results per Ticker")
+        if st.checkbox("Only show tickers with trades", value=True):
+            df_to_display = st.session_state.summary_df[(st.session_state.summary_df['Num Long Trades'] > 0) | (st.session_state.summary_df['Num Short Trades'] > 0)].copy()
+        else:
+            df_to_display = st.session_state.summary_df.copy()
 
-            # 4. Results Per Ticker Table
-            st.subheader("Results per Ticker")
-            if st.checkbox("Only show tickers with trades", value=True):
-                df_to_display = st.session_state.summary_df[(st.session_state.summary_df['Num Long Trades'] > 0) | (st.session_state.summary_df['Num Short Trades'] > 0)].copy()
-            else:
-                df_to_display = st.session_state.summary_df.copy()
+        date_cols = ["First Long Entry", "Last Long Exit", "First Short Entry", "Last Short Exit"]
+        for col in date_cols:
+            if col in df_to_display.columns: df_to_display[col] = pd.to_datetime(df_to_display[col], errors='coerce').dt.strftime('%Y-%m-%d')
+        df_to_display.fillna('-', inplace=True)
+        st.dataframe(df_to_display.style.format({ "Cumulative Long P&L": "{:.2%}", "Avg Long Profit per Trade": "{:.2%}", "Avg Long Duration (Days)": "{:.1f}", "Cumulative Short P&L": "{:.2%}", "Avg Short Profit per Trade": "{:.2%}", "Avg Short Duration (Days)": "{:.1f}", "Avg Long Confidence": "{:.0f}%", "Avg Short Confidence": "{:.0f}%" }, na_rep='-'))
+        
+        if not production_mode:
+            if st.button("💾 Add these settings to User-Defined List", key="save_setup_from_analysis", on_click=add_setup_to_user_list): pass
 
-            date_cols = ["First Long Entry", "Last Long Exit", "First Short Entry", "Last Short Exit"]
-            for col in date_cols:
-                if col in df_to_display.columns: df_to_display[col] = pd.to_datetime(df_to_display[col], errors='coerce').dt.strftime('%Y-%m-%d')
-            df_to_display.fillna('-', inplace=True)
-            st.dataframe(df_to_display.style.format({ "Cumulative Long P&L": "{:.2%}", "Avg Long Profit per Trade": "{:.2%}", "Avg Long Duration (Days)": "{:.1f}", "Cumulative Short P&L": "{:.2%}", "Avg Short Profit per Trade": "{:.2%}", "Avg Short Duration (Days)": "{:.1f}", "Avg Long Confidence": "{:.0f}%", "Avg Short Confidence": "{:.0f}%" }, na_rep='-'))
+        # 5. Open Positions Table (With Filter)
+        st.subheader("👨🏽‍💼 Open Positions & Recently Closed", 
+                     help="This table displays all currently ACTIVE trades, plus any trades that closed within the last 30 days.")
+        
+        if st.session_state.get('open_trades_df') is not None and not st.session_state.open_trades_df.empty:
+            full_df = st.session_state.open_trades_df.copy()
             
-            if not production_mode:
-                if st.button("💾 Add these settings to User-Defined List", key="save_setup_from_analysis", on_click=add_setup_to_user_list): pass
-
-            # 5. Open Positions Table (With Filter)
-            st.subheader("👨🏻‍💼 Open Positions & Recently Closed", 
-                         help="This table displays all currently ACTIVE trades, plus any trades that closed within the last 30 days.")
+            # --- FILTER: Show 'Open' OR 'Closed in last 30 days' ---
+            cutoff = pd.Timestamp.now() - pd.Timedelta(days=30)
+            full_df['Date Closed'] = pd.to_datetime(full_df['Date Closed'], errors='coerce')
             
-            if st.session_state.get('open_trades_df') is not None and not st.session_state.open_trades_df.empty:
-                full_df = st.session_state.open_trades_df.copy()
-                
-                # --- FILTER: Show 'Open' OR 'Closed in last 30 days' ---
-                cutoff = pd.Timestamp.now() - pd.Timedelta(days=30)
-                # Ensure date column is datetime
-                full_df['Date Closed'] = pd.to_datetime(full_df['Date Closed'], errors='coerce')
-                
-                mask_open = (full_df['Status'] == 'Open')
-                mask_recent = (full_df['Status'] == 'Closed') & (full_df['Date Closed'] >= cutoff)
-                
-                display_open_df = full_df[mask_open | mask_recent].copy()
-                # -------------------------------------------------------
+            mask_open = (full_df['Status'] == 'Open')
+            mask_recent = (full_df['Status'] == 'Closed') & (full_df['Date Closed'] >= cutoff)
+            
+            display_open_df = full_df[mask_open | mask_recent].copy()
+            # -------------------------------------------------------
 
-                display_open_df.sort_values(by=['Status', 'Date Open'], ascending=[True, False], inplace=True)
-                
-                cols_order_manual = ['Ticker', 'Status', 'Final % P/L', 'Side', 'Date Open', 'Date Closed', 'Start Confidence']
-                existing_cols_open = [col for col in cols_order_manual if col in display_open_df.columns]
-                
-                if existing_cols_open and not display_open_df.empty:
-                     st.dataframe(display_open_df[existing_cols_open].style.format({
-                        "Final % P/L": lambda x: f"{x:.2%}" if pd.notna(x) else '-',
-                        "Date Open": lambda x: x.strftime('%Y-%m-%d') if pd.notna(x) else '-',
-                        "Date Closed": lambda x: x.strftime('%Y-%m-%d') if pd.notna(x) else '-',
-                        "Start Confidence": lambda x: f"{x:.0f}%" if pd.notna(x) else '-'
-                    }, na_rep='-'))
-                else:
-                    st.info("No Open or Recent trades found (older trades are hidden).")
+            display_open_df.sort_values(by=['Status', 'Date Open'], ascending=[True, False], inplace=True)
+            
+            cols_order_manual = ['Ticker', 'Status', 'Final % P/L', 'Side', 'Date Open', 'Date Closed', 'Start Confidence']
+            existing_cols_open = [col for col in cols_order_manual if col in display_open_df.columns]
+            
+            if existing_cols_open and not display_open_df.empty:
+                 st.dataframe(display_open_df[existing_cols_open].style.format({
+                    "Final % P/L": lambda x: f"{x:.2%}" if pd.notna(x) else '-',
+                    "Date Open": lambda x: x.strftime('%Y-%m-%d') if pd.notna(x) else '-',
+                    "Date Closed": lambda x: x.strftime('%Y-%m-%d') if pd.notna(x) else '-',
+                    "Start Confidence": lambda x: f"{x:.0f}%" if pd.notna(x) else '-'
+                }, na_rep='-'))
             else:
-                st.info("No trades found.")
-
-        # 14. Default Message
+                st.info("No Open or Recent trades found (older trades are hidden).")
         else:
-            if not any([st.session_state.get(k) for k in ['run_advanced_advisor','run_user_advisor_setup','advisor_df','confidence_results_df','single_ticker_results','summary_df','load_message']]): 
-                st.info("Click a 'Run' button in the sidebar to start.")
+            st.info("No trades found.")
 
-        # 15. Footer Buttons (Hidden in Production)
-        if not production_mode:
-            st.markdown("---")
-            col_load1, col_load2, col_load3 = st.columns(3)
-            with col_load1:
-                if st.session_state.get('best_params'): st.button("⬇️ Load Optimal Parameters", on_click=apply_best_params_to_widgets, use_container_width=True)
-            with col_load2:
-                if 'apply_best_weights_to_widgets' in locals() and st.session_state.get('best_weights'): st.button("⬇️ Load Optimal Weights", on_click=apply_best_weights_to_widgets, use_container_width=True)
-            with col_load3:
-                if st.session_state.get('worst_confidence_setups_list'):
-                    if st.button("Apply Top Worst Setups as Veto Filter", use_container_width=True):
-                        st.session_state.veto_setup_list = st.session_state.worst_confidence_setups_list
-                        st.session_state.worst_confidence_setups_list = None
-                        st.sidebar.info(f"Applying {len(st.session_state.veto_setup_list)} Veto filters.")
-                        st.rerun()
-
-            col_load4 = st.columns(1)[0]
-            with col_load4:
-                 is_markov_relevant = (st.session_state.primary_driver == 'Markov State') or st.session_state.use_markov
-                 if st.session_state.get('best_markov_setup') and is_markov_relevant:
-                    if st.button("⬇️ Load Best Markov Setup", on_click=None, use_container_width=True):
-                        st.session_state.primary_driver = "Markov State"
-                        st.session_state.use_markov = True 
-                        st.sidebar.success("Best Markov setup loaded!")
-                        st.rerun()
+        # --- NEW: SAVE RESULTS BUTTON ---
+        st.markdown("---")
+        st.subheader("💾 Save Analysis")
+        
+        current_config = {
+            'start_date': st.session_state.start_date, 'end_date': st.session_state.end_date,
+            'primary_driver': st.session_state.primary_driver, 'confidence_threshold': st.session_state.confidence_threshold,
+            'use_rsi': st.session_state.use_rsi, 'rsi_w': st.session_state.rsi_w,
+            'use_volatility': st.session_state.use_volatility, 'vol_w': st.session_state.vol_w,
+            'use_trend': st.session_state.use_trend, 'trend_w': st.session_state.trend_w,
+            'use_volume': st.session_state.use_volume, 'vol_w_val': st.session_state.vol_w_val,
+            'use_macd': st.session_state.use_macd, 'macd_w': st.session_state.macd_w,
+            'use_ma_slope': st.session_state.use_ma_slope, 'ma_slope_w': st.session_state.ma_slope_w,
+            'use_markov': st.session_state.use_markov, 'markov_w': st.session_state.markov_w,
+            'use_mfi': st.session_state.use_mfi, 'mfi_w': st.session_state.mfi_w,
+            'use_supertrend': st.session_state.use_supertrend, 'supertrend_w': st.session_state.supertrend_w,
+            'use_adx_filter': st.session_state.use_adx_filter, 'adx_threshold': st.session_state.adx_threshold,
+            'bband_period': st.session_state.bband_period, 'bband_std_dev': st.session_state.bband_std_dev,
+            'large_ma_period': st.session_state.get('large_ma_period', 50),
+            'long_entry_threshold_pct': st.session_state.long_entry_threshold_pct,
+            'short_entry_threshold_pct': st.session_state.short_entry_threshold_pct,
+            'exit_logic_type': st.session_state.exit_logic_type,
+            'smart_trailing_stop_pct': st.session_state.smart_trailing_stop_pct
+        }
+
+        # Generate CSV
+        if 'convert_results_to_csv' in globals():
+            csv_data = convert_results_to_csv(st.session_state.summary_df, current_config)
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M")
+            file_name = f"Backtest_Results_{timestamp}.csv"
+
+            st.download_button(
+                label="⬇️ Download Results & Settings as CSV",
+                data=csv_data,
+                file_name=file_name,
+                mime='text/csv',
+                use_container_width=True
+            )
+
+    # 14. Default Message
+    else:
+        if not any([st.session_state.get(k) for k in ['run_advanced_advisor','run_user_advisor_setup','advisor_df','confidence_results_df','single_ticker_results','summary_df','load_message']]): 
+            st.info("Click a 'Run' button in the sidebar to start.")
+
+    # 15. Footer Buttons (Hidden in Production)
+    if not production_mode:
+        st.markdown("---")
+        col_load1, col_load2, col_load3 = st.columns(3)
+        with col_load1:
+            if 'apply_best_params_to_widgets' in locals() and st.session_state.get('best_params'): 
+                st.button("⬇️ Load Optimal Parameters", on_click=apply_best_params_to_widgets, use_container_width=True)
+        with col_load2:
+            if 'apply_best_weights_to_widgets' in locals() and st.session_state.get('best_weights'): 
+                st.button("⬇️ Load Optimal Weights", on_click=apply_best_weights_to_widgets, use_container_width=True)
+        with col_load3:
+            if st.session_state.get('worst_confidence_setups_list'):
+                if st.button("Apply Top Worst Setups as Veto Filter", use_container_width=True):
+                    st.session_state.veto_setup_list = st.session_state.worst_confidence_setups_list
+                    st.session_state.worst_confidence_setups_list = None
+                    st.sidebar.info(f"Applying {len(st.session_state.veto_setup_list)} Veto filters.")
+                    st.rerun()
+
+        col_load4 = st.columns(1)[0]
+        with col_load4:
+             is_markov_relevant = (st.session_state.primary_driver == 'Markov State') or st.session_state.use_markov
+             if st.session_state.get('best_markov_setup') and is_markov_relevant:
+                if st.button("⬇️ Load Best Markov Setup", on_click=None, use_container_width=True):
+                    st.session_state.primary_driver = "Markov State"
+                    st.session_state.use_markov = True 
+                    st.sidebar.success("Best Markov setup loaded!")
+                    st.rerun()
+
+    # --- LOGIC HANDLERS (Hidden execution) ---
+    if st.session_state.run_advanced_advisor:
+        with st.spinner("Running Optimization..."):
+            run_optimization()
+            st.session_state.run_advanced_advisor = False
+
+    if st.session_state.run_user_advisor_setup:
+        with st.spinner("Running User Setup..."):
+            current_params = {k: st.session_state[k] for k in defaults.keys() if k in st.session_state}
+            current_params['use_mfi'] = st.session_state.use_mfi
+            current_params['mfi_w'] = st.session_state.mfi_w
+            current_params['use_supertrend'] = st.session_state.use_supertrend
+            current_params['supertrend_w'] = st.session_state.supertrend_w
+            
+            run_user_defined_setup(current_params)
+            st.session_state.run_user_advisor_setup = False
 
+# --- CHART FUNCTION (Must be outside main) ---
 def generate_trades_timeline_histogram(trades_df, start_date, end_date):
     """
     Creates a stacked histogram showing trade results over time.