Update app.py

app.py

@@ -99,9 +99,8 @@ if "tickers" in st.session_state:
 
     st.markdown(f"**Suma actual:** {total_weight:.2f}%")
 
-    # ✅ Botón para ajustar automáticamente
     if st.button("Ajustar automáticamente"):
-        fixed_index = 0  # asumimos que el primero se mantiene y el resto se ajusta
+        fixed_index = 0
         fixed_weight = weight_inputs[fixed_index]
         remaining_weight = 100.0 - fixed_weight
         rest = tickers[1:]
@@ -109,7 +108,6 @@ if "tickers" in st.session_state:
             st.session_state[f"weight_{t}"] = round(remaining_weight / (len(tickers) - 1), 2)
         st.experimental_rerun()
 
-    # Validación de suma
     if abs(total_weight - 100.0) > 0.01:
         st.warning("⚠️ La suma de los pesos debe ser exactamente 100%.")
     else:
@@ -118,32 +116,40 @@ if "tickers" in st.session_state:
             start_date = fecha_inicio.strftime("%Y-%m-%d")
             end_date = datetime.datetime.today().strftime("%Y-%m-%d")
 
+            # 🛡️ Validar datos antes de continuar
             data = yf.download(tickers, start=start_date, end=end_date)["Close"]
-            data = data.dropna()
-            returns = data.pct_change().dropna()
-            portfolio_returns = returns.dot(weights)
-
-            tail_prob = 1 - confidence_level
-            historical_VaR = np.percentile(portfolio_returns, tail_prob * 100)
-            mean_ret = portfolio_returns.mean()
-            std_ret = portfolio_returns.std()
-            z_score = norm.ppf(tail_prob)
-            parametric_VaR = mean_ret + z_score * std_ret
-            simulated_returns = np.random.normal(mean_ret, std_ret, 10000)
-            mc_VaR = np.percentile(simulated_returns, tail_prob * 100)
-            historical_CVaR = portfolio_returns[portfolio_returns <= historical_VaR].mean()
-
-            st.subheader("Resultados del Portafolio:")
-            st.markdown(f"**Historical VaR:** {historical_VaR:.4%}")
-            st.markdown(f"**Parametric VaR:** {parametric_VaR:.4%}")
-            st.markdown(f"**Monte Carlo VaR:** {mc_VaR:.4%}")
-            st.markdown(f"**Historical CVaR (Expected Shortfall):** {historical_CVaR:.4%}")
-
-            fig1, ax1 = plt.subplots(figsize=(10, 6))
-            ax1.hist(portfolio_returns, bins=50, density=True, alpha=0.5)
-            ax1.axvline(historical_VaR, color="red", linestyle="--", label="Historical VaR")
-            ax1.axvline(parametric_VaR, color="blue", linestyle="--", label="Parametric VaR")
-            ax1.axvline(mc_VaR, color="green", linestyle="--", label="Monte Carlo VaR")
-            ax1.set_title("Distribución de Retornos del Portafolio")
-            ax1.legend()
-            st.pyplot(fig1)
+
+            if data.empty or data.isnull().all().all():
+                st.error("No se encontraron datos históricos para la fecha seleccionada. Intenta con otra fecha.")
+            else:
+                data = data.dropna()
+                returns = data.pct_change().dropna()
+                portfolio_returns = returns.dot(weights)
+
+                if portfolio_returns.empty:
+                    st.error("No se pudieron calcular retornos del portafolio. Revisa los datos descargados.")
+                else:
+                    tail_prob = 1 - confidence_level
+                    historical_VaR = np.percentile(portfolio_returns, tail_prob * 100)
+                    mean_ret = portfolio_returns.mean()
+                    std_ret = portfolio_returns.std()
+                    z_score = norm.ppf(tail_prob)
+                    parametric_VaR = mean_ret + z_score * std_ret
+                    simulated_returns = np.random.normal(mean_ret, std_ret, 10000)
+                    mc_VaR = np.percentile(simulated_returns, tail_prob * 100)
+                    historical_CVaR = portfolio_returns[portfolio_returns <= historical_VaR].mean()
+
+                    st.subheader("Resultados del Portafolio:")
+                    st.markdown(f"**Historical VaR:** {historical_VaR:.4%}")
+                    st.markdown(f"**Parametric VaR:** {parametric_VaR:.4%}")
+                    st.markdown(f"**Monte Carlo VaR:** {mc_VaR:.4%}")
+                    st.markdown(f"**Historical CVaR (Expected Shortfall):** {historical_CVaR:.4%}")
+
+                    fig1, ax1 = plt.subplots(figsize=(10, 6))
+                    ax1.hist(portfolio_returns, bins=50, density=True, alpha=0.5)
+                    ax1.axvline(historical_VaR, color="red", linestyle="--", label="Historical VaR")
+                    ax1.axvline(parametric_VaR, color="blue", linestyle="--", label="Parametric VaR")
+                    ax1.axvline(mc_VaR, color="green", linestyle="--", label="Monte Carlo VaR")
+                    ax1.set_title("Distribución de Retornos del Portafolio")
+                    ax1.legend()
+                    st.pyplot(fig1)