Update app.py

app.py

@@ -13,6 +13,8 @@ help_string = "NOTA: Para su información los datos utilizados se extrajeron de
 api_url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&apikey=QVQGE7YPO68S403J&datatype=csv"
 symbols = ['META', 'AAPL', 'GOOG']
 
+uploaded_file = None  # Definir uploaded_file con un valor predeterminado
+
 option = st.selectbox("Selecciona la fuente de datos:", ("Usar archivo predeterminado", "API Alpha Vantage", "Subir archivo propio"))
 
 if option == "Usar archivo predeterminado":
@@ -44,84 +46,70 @@ elif option == "Subir archivo propio":
             df = pd.read_excel(uploaded_file)
         # Resto del procesamiento del archivo ...
 
-if uploaded_file is not None:
-    if uploaded_file[-3:] == "csv":
-        df = pd.read_csv(uploaded_file)
-    else:
-        df = pd.read_excel(uploaded_file)
-    df = df.drop(0, axis=0)
-    df = df.drop("Unnamed: 2", axis=1).drop("Unnamed: 4", axis=1).rename({"Unnamed: 0": "Date"}, axis=1) 
-    
-    df['Date'] = pd.to_datetime(df['Date'], format="%d/%m/%Y %H:%M:%S")
-    
-    stocks = list(df.columns)[-3:]
-    stocks_rets = []
-    
-    for i in stocks:
-        stocks_rets.append(i+"_ret")
-        df[i] = df[i].astype(float)
-        df[i+"_ret"] = (df[i] - df[i].shift(1)) / df[i].shift(1)
-        
-    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.update_layout(xaxis_title='Date', yaxis_title='Value')
-    
-    st.plotly_chart(fig)
-    
-    ret_list = df[stocks_rets].mean().to_numpy().reshape(-1, 1)
-    cov_matrix = df[stocks_rets].cov().to_numpy()
-    
-    optim_choice = st.selectbox("Elige la forma de optomizar :", ("max returns", "min variance", "max returns - variance"))
-    
-    def portfolio_variance(weights, covariance_matrix):
-        return np.dot(weights.T, np.dot(covariance_matrix, weights))
-    
-    def portfolio_returns(weights, expected_returns):
-        return np.dot(weights.T, expected_returns)
-    
-    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)
-            return np.dot(weights.T, ret_list)
-    
-        study = optuna.create_study(direction="maximize")
-        study.optimize(objective, n_trials=100, show_progress_bar=True)
-    
-    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)
-            return np.dot(weights.T, np.dot(cov_matrix, weights))
-    
-        study = optuna.create_study(direction="minimize")
-        study.optimize(objective, n_trials=100, show_progress_bar=True)
-    
+# Bloque de código para procesar y mostrar gráficas y varianzas
+if uploaded_file is not None or option == "API Alpha Vantage":
+    if uploaded_file is not None:
+        # Resto del código de procesamiento del archivo subido ...
+        pass
     else:
-        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)
-            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
-    
-    weights = np.array([w1, w2, w3]).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"El retorno anualizado del portafolio es: :green[{yearly_returns:,.4f}]")
-    st.write(f"La varianza anualizado del portafolio es: :green[{yearly_variance:,.4f}]")
+        # Resto del código de procesamiento de los datos del API ...
+        pass
+
+    # Código para mostrar gráficas y varianzas ...
+    if 'df' in locals():
+        df = df.drop(0, axis=0)
+        df = df.drop("Unnamed: 2", axis=1).drop("Unnamed: 4", axis=1).rename({"Unnamed: 0": "Date"}, axis=1) 
+        df['Date'] = pd.to_datetime(df['Date'], format="%d/%m/%Y %H:%M:%S")
+        stocks = list(df.columns)[-3:]
+        stocks_rets = []
+        for i in stocks:
+            stocks_rets.append(i+"_ret")
+            df[i] = df[i].astype(float)
+            df[i+"_ret"] = (df[i] - df[i].shift(1)) / df[i].shift(1)
+        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.update_layout(xaxis_title='Date', yaxis_title='Value')
+        st.plotly_chart(fig)
+        ret_list = df[stocks_rets].mean().to_numpy().reshape(-1, 1)
+        cov_matrix = df[stocks_rets].cov().to_numpy()
+        optim_choice = st.selectbox("Elige la forma de optimizar:", ("max returns", "min variance", "max returns - variance"))
+        def portfolio_variance(weights, covariance_matrix):
+            return np.dot(weights.T, np.dot(covariance_matrix, weights))
+        def portfolio_returns(weights, expected_returns):
+            return np.dot(weights.T, expected_returns)
+        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)
+                return np.dot(weights.T, ret_list)
+            study = optuna.create_study(direction="maximize")
+            study.optimize(objective, n_trials=100, show_progress_bar=True)
+        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)
+                return np.dot(weights.T, np.dot(cov_matrix, weights))
+            study = optuna.create_study(direction="minimize")
+            study.optimize(objective, n_trials=100, show_progress_bar=True)
+        else:
+            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)
+                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
+        weights = np.array([w1, w2, w3]).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"El retorno anualizado del portafolio es: :green[{yearly_returns:,.4f}]")
+        st.write(f"La varianza anualizada del portafolio es: :green[{yearly_variance:,.4f}]")