Update app.py

app.py

@@ -13,13 +13,10 @@ 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":
-    uploaded_file = "STOCKS - Hoja 1.csv"  # Opcional: proporciona la ruta al archivo predeterminado
-    # Cargar y procesar el archivo predeterminado
+    uploaded_file = "STOCKS - Hoja 1.csv"  
     if uploaded_file is not None:
         if uploaded_file[-3:] == "csv":
             df = pd.read_csv(uploaded_file)
@@ -44,71 +41,86 @@ elif option == "Subir archivo propio":
             df = pd.read_csv(uploaded_file)
         else:
             df = pd.read_excel(uploaded_file)
-        # Resto del procesamiento del archivo ...
-
-# 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:
-        if uploaded_file.name[-3:] == "csv":
-            df = pd.read_csv(uploaded_file)
-        else:
-            df = pd.read_excel(uploaded_file)
+      
 
-    # 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}]")
+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)
+    
+    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}]")