Update app.py

app.py

@@ -64,7 +64,7 @@ if uploaded_file is not None:
         
     st.write(df[["Date"] + stocks_rets])
     
-    fig = px.line(df, x=df.Date, y=stocks, labels={'value': 'Value', 'variable': 'Series'}, title='Time Series Plot')
+    fig = px.line(df, x=df.Date, y=stocks, labels={'value': 'Value', 'variable': 'Series'}, title='Serie de tiempo')
     fig.update_layout(xaxis_title='Date', yaxis_title='Value')
     
     st.plotly_chart(fig)
@@ -82,10 +82,10 @@ if uploaded_file is not None:
     
     if optim_choice == "max returns":
         def objective(trial):
-            w1 = trial.suggest_uniform('w1', 0, 1)
-            w2 = trial.suggest_uniform('w2', 0, 1)
-            w3 = 1 - w1 - w2
-            weights = np.array([w1, w2, w3]).reshape(-1, 1)
+            a1 = trial.suggest_uniform('a1', 0, 1)
+            a2 = trial.suggest_uniform('a2', 0, 1)
+            a3 = 1 - a1 - a2
+            weights = np.array([a1, a2, a3]).reshape(-1, 1)
             return np.dot(weights.T, ret_list)
     
         study = optuna.create_study(direction="maximize")
@@ -93,10 +93,10 @@ if uploaded_file is not None:
     
     elif optim_choice == "min variance":
         def objective(trial):
-            w1 = trial.suggest_uniform('w1', 0, 1)
-            w2 = trial.suggest_uniform('w2', 0, 1)
-            w3 = 1 - w1 - w2
-            weights = np.array([w1, w2, w3]).reshape(-1, 1)
+            a1 = trial.suggest_uniform('a1', 0, 1)
+            a2 = trial.suggest_uniform('a2', 0, 1)
+            a3 = 1 - a1 - a2
+            weights = np.array([a1, a2, a3]).reshape(-1, 1)
             return np.dot(weights.T, np.dot(cov_matrix, weights))
     
         study = optuna.create_study(direction="minimize")
@@ -104,24 +104,24 @@ if uploaded_file is not None:
     
     else:
         def objective(trial):
-            w1 = trial.suggest_uniform('w1', 0, 1)
-            w2 = trial.suggest_uniform('w2', 0, 1)
-            w3 = 1 - w1 - w2
+            a1 = trial.suggest_uniform('a1', 0, 1)
+            a2 = trial.suggest_uniform('a2', 0, 1)
+            a3 = 1 - a1 - a2
             weights = np.array([w1, w2, w3]).reshape(-1, 1)
             return np.dot(weights.T, ret_list) - np.dot(weights.T, np.dot(cov_matrix, weights))
     
         study = optuna.create_study(direction="maximize")
         study.optimize(objective, n_trials=100, show_progress_bar=True)
     
-    w1 = study.best_params['w1']
-    w2 = study.best_params['w2']
-    w3 = 1- w1 - w2
+    a1 = study.best_params['a1']
+    a2 = study.best_params['a2']
+    a3 = 1- a1 - a2
     
-    weights = np.array([w1, w2, w3]).reshape(-1, 1)
+    weights = np.array([a1, a2, a3]).reshape(-1, 1)
     
     yearly_returns = (1 + np.dot(weights.T, ret_list)[0, 0]) ** 252 - 1
     yearly_variance = np.dot(weights.T, np.dot(cov_matrix, weights))[0, 0] * 252
         
-    st.write(f"Los pesos son: :green[{stocks[0]} -> {w1:,.4f}], :green[{stocks[1]} -> {w2:,.4f}], :green[{stocks[2]} -> {w3:,.4f}]")
+    st.write(f"Los pesos son: :green[{stocks[0]} -> {a1:,.4f}], :green[{stocks[1]} -> {a2:,.4f}], :green[{stocks[2]} -> {a3:,.4f}]")
     st.write(f"El retorno anualizado del portafolio es: :green[{yearly_returns:,.4f}]")
     st.write(f"La varianza anualizado del portafolio es: :green[{yearly_variance:,.4f}]")