Update src/streamlit_app.py

src/streamlit_app.py

@@ -258,45 +258,52 @@ if st.button("Run Honest Backtest"):
             
             # --- STRATEGY LOGIC (Same as before) ---
             
-            test_df['Signal'] = np.where(test_df['EMA_Short'] > test_df['EMA_Long'], 1, -1)
-            
+            test_df['Signal'] = np.where(test_df['EMA_Short'] > test_df['EMA_Long'], 1, 0)
             avg_train_vol = train_df['Volatility'].mean()
             test_df['Risk_Ratio'] = test_df['Predicted_Vol'] / avg_train_vol
             
-            # Initialize Position Column
-            test_df['Target_Position'] = 0.0
-
-            # --- CONDITIONS ---
-            # BULLISH Conditions
-            cond_bull_trend = (test_df['Signal'] == 1)
-            cond_safe_regime = (test_df['Regime'] == 0)   # HMM says "Calm"
-            cond_low_risk = (test_df['Risk_Ratio'] < risk_threshold) # SVR says "Low Vol"
-
-            # BEARISH Conditions (The "Destined to go down" setup)
-            cond_bear_trend = (test_df['Signal'] == -1)
-            cond_crash_regime = (test_df['Regime'] == (n_states - 1)) # HMM says "Crash"
-            # We use Risk Ratio > 1.0 to confirm high volatility momentum (Panic Selling)
-            cond_high_vol_momentum = (test_df['Risk_Ratio'] > 1.0) 
-
-            # --- LOGIC EXECUTION ---
+            test_df['Position_Size'] = 1.0
             
-            # 1. LONG LOGIC (Sniper Longs)
-            # If Bull Trend + Safe + Low Risk -> Boost Leverage
-            test_df.loc[cond_bull_trend & cond_safe_regime & cond_low_risk, 'Target_Position'] = leverage_mult
-            # If Bull Trend but Risk is normal -> 1x Long (Base case)
-            test_df.loc[cond_bull_trend & (~cond_safe_regime | ~cond_low_risk), 'Target_Position'] = 1.0
+            # Logic
+            cond_safe = (test_df['Regime'] == 0)
+            cond_low_risk = (test_df['Risk_Ratio'] < risk_threshold)
+            cond_crash = (test_df['Regime'] == (n_states - 1))
             
-            # 2. SHORT LOGIC (Sniper Shorts)
-            # ONLY Short if: Trend Down + HMM Crash Detected + Volatility is Expanding
-            test_df.loc[cond_bear_trend & cond_crash_regime & cond_high_vol_momentum, 'Target_Position'] = -1.0
+            # Boost
+            test_df['Position_Size'] = np.where(cond_safe & cond_low_risk, leverage_mult, test_df['Position_Size'])
+            # Cut
+            test_df['Position_Size'] = np.where(cond_crash, 0.0, test_df['Position_Size'])
             
-            # Note: If Bear Trend exists but HMM says "Calm" (State 0) or Vol is low, 
-            # we stay in CASH (0.0) because it might just be a sideways chop, not a crash.
-
-            # 3. Calculate Returns (Shift Logic)
-            # We apply the position chosen 'Today' to 'Tomorrow's' returns
-            test_df['Final_Position'] = test_df['Target_Position'].shift(1)
+            # Calculate Returns
+            test_df['Final_Position'] = (test_df['Signal'] * test_df['Position_Size']).shift(1)
             test_df['Simple_Returns'] = test_df['Close'].pct_change()
+            test_df['Strategy_Returns'] = test_df['Final_Position'] * test_df['Simple_Returns']
+            
+            # Metrics & Plots
+            test_df['Strategy_Value'] = (1 + test_df['Strategy_Returns'].fillna(0)).cumprod()
+            test_df['Buy_Hold_Value'] = (1 + test_df['Simple_Returns'].fillna(0)).cumprod()
+            test_df.dropna(inplace=True)
+            
+            metrics_df = calculate_metrics(test_df)
+            st.subheader("Performance vs Benchmark")
+            st.table(metrics_df)
+            
+            st.subheader("Equity Curve")
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=test_df.index, y=test_df['Buy_Hold_Value'], name='Buy & Hold', line=dict(color='gray', dash='dot')))
+            fig.add_trace(go.Scatter(x=test_df.index, y=test_df['Strategy_Value'], name='Smart Leverage', line=dict(color='#00CC96', width=2)))
+            st.plotly_chart(fig, use_container_width=True)
+            
+            st.subheader("Leverage Deployment")
+            fig_lev = go.Figure()
+            fig_lev.add_trace(go.Scatter(x=test_df.index, y=test_df['Position_Size'], mode='lines', fill='tozeroy', name='Lev', line=dict(color='#636EFA')))
+            st.plotly_chart(fig_lev, use_container_width=True)
             
-            # Math: If Final_Position is -1.0 and Simple_Returns is -5%, Strategy makes +5%
-            test_df['Strategy_Returns'] = test_df['Final_Position'] * test_df['Simple_Returns']
+            trade_log = generate_trade_log(test_df)
+            st.subheader("📝 Trade Log")
+            if not trade_log.empty:
+                display_log = trade_log.copy()
+                display_log['Trade PnL'] = display_log['Trade PnL'].map('{:.2%}'.format)
+                st.dataframe(display_log, use_container_width=True)
+            else:
+                st.write("No trades generated.")